From dbea4aa988f32dbf5b29d0265454ea04a8de1ee8 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Tue, 4 Feb 2020 13:29:06 -0600 Subject: [PATCH 01/44] Make WarmStartComponent use Extractor --- miplearn/extractors.py | 37 +++++++++++++--- miplearn/solvers.py | 7 +++- miplearn/tests/test_extractors.py | 19 ++++++++- miplearn/tests/test_warmstart.py | 4 +- miplearn/warmstart.py | 70 ++++++++++++++----------------- 5 files changed, 88 insertions(+), 49 deletions(-) diff --git a/miplearn/extractors.py b/miplearn/extractors.py index 3b9b4fe..e77ea02 100644 --- a/miplearn/extractors.py +++ b/miplearn/extractors.py @@ -24,6 +24,23 @@ class Extractor(ABC): result[category] = [] result[category] += [(var, index)] return result + + @staticmethod + def merge(partial_results, vertical=False): + results = {} + all_categories = set() + for pr in partial_results: + all_categories |= pr.keys() + for category in all_categories: + results[category] = [] + for pr in partial_results: + if category in pr.keys(): + results[category] += [pr[category]] + if vertical: + results[category] = np.vstack(results[category]) + else: + results[category] = np.hstack(results[category]) + return results class UserFeaturesExtractor(Extractor): @@ -61,10 +78,20 @@ class SolutionExtractor(Extractor): if category not in result.keys(): result[category] = [] for (var, index) in var_index_pairs: - result[category] += [[ - 1 - var[index].value, - var[index].value, - ]] + v = var[index].value + if v is None: + result[category] += [[0, 0]] + else: + result[category] += [[1 - v, v]] for category in result.keys(): result[category] = np.vstack(result[category]) - return result \ No newline at end of file + return result + + +class CombinedExtractor(Extractor): + def __init__(self, extractors): + self.extractors = extractors + + def extract(self, instances, models): + return self.merge([ex.extract(instances, models) + for ex in self.extractors]) diff --git a/miplearn/solvers.py b/miplearn/solvers.py index d891e18..ff66b38 100644 --- a/miplearn/solvers.py +++ b/miplearn/solvers.py @@ -2,7 +2,6 @@ # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier -from .transformers import PerVariableTransformer from .warmstart import WarmStartComponent from .branching import BranchPriorityComponent import pyomo.environ as pe @@ -66,6 +65,12 @@ class LearningSolver: def solve(self, instance, tee=False): model = instance.to_model() + # Solve linear relaxation (TODO: use solver provided by user) + lr_solver = pe.SolverFactory("gurobi") + lr_solver.options["threads"] = 4 + lr_solver.options["relax_integrality"] = 1 + lr_solver.solve(model) + self._create_solver() if self.is_persistent: self.internal_solver.set_instance(model) diff --git a/miplearn/tests/test_extractors.py b/miplearn/tests/test_extractors.py index 8349db5..013a980 100644 --- a/miplearn/tests/test_extractors.py +++ b/miplearn/tests/test_extractors.py @@ -3,8 +3,10 @@ # Written by Alinson S. Xavier from miplearn.problems.knapsack import KnapsackInstance -from miplearn import (UserFeaturesExtractor, - SolutionExtractor) +from miplearn.extractors import (UserFeaturesExtractor, + SolutionExtractor, + CombinedExtractor, + ) import numpy as np import pyomo.environ as pe @@ -52,3 +54,16 @@ def test_solution_extractor(): 0., 1., 1., 0., ] + + +def test_combined_extractor(): + instances = _get_instances() + models = [instance.to_model() for instance in instances] + extractor = CombinedExtractor(extractors=[UserFeaturesExtractor(), + SolutionExtractor()]) + features = extractor.extract(instances, models) + assert isinstance(features, dict) + assert "default" in features.keys() + assert isinstance(features["default"], np.ndarray) + assert features["default"].shape == (6, 6) + diff --git a/miplearn/tests/test_warmstart.py b/miplearn/tests/test_warmstart.py index 12e047c..9f62bb7 100644 --- a/miplearn/tests/test_warmstart.py +++ b/miplearn/tests/test_warmstart.py @@ -24,7 +24,7 @@ def test_warm_start_save_load(): solver.parallel_solve(_get_instances(), n_jobs=2) solver.fit() comp = solver.components["warm-start"] - assert comp.x_train["default"].shape == (8, 4) + assert comp.x_train["default"].shape == (8, 6) assert comp.y_train["default"].shape == (8, 2) assert "default" in comp.predictors.keys() solver.save_state(state_file.name) @@ -32,6 +32,6 @@ def test_warm_start_save_load(): solver = LearningSolver(components={"warm-start": WarmStartComponent()}) solver.load_state(state_file.name) comp = solver.components["warm-start"] - assert comp.x_train["default"].shape == (8, 4) + assert comp.x_train["default"].shape == (8, 6) assert comp.y_train["default"].shape == (8, 2) assert "default" in comp.predictors.keys() diff --git a/miplearn/warmstart.py b/miplearn/warmstart.py index c10ee0c..746a004 100644 --- a/miplearn/warmstart.py +++ b/miplearn/warmstart.py @@ -4,6 +4,7 @@ from . import Component from .transformers import PerVariableTransformer +from .extractors import * from abc import ABC, abstractmethod from copy import deepcopy @@ -131,52 +132,43 @@ class WarmStartComponent(Component): self.predictor_prototype = predictor_prototype def before_solve(self, solver, instance, model): - var_split = self.transformer.split_variables(instance, model) - x_test = {} + # Build x_test + x_test = CombinedExtractor([UserFeaturesExtractor(), + SolutionExtractor(), + ]).extract([instance], [model]) - # Collect training data (x_train) and build x_test - for category in var_split.keys(): - var_index_pairs = var_split[category] - x = self.transformer.transform_instance(instance, var_index_pairs) - x_test[category] = x - if category not in self.x_train.keys(): - self.x_train[category] = x - else: - assert x.shape[1] == self.x_train[category].shape[1] - self.x_train[category] = np.vstack([self.x_train[category], x]) + # Update self.x_train + self.x_train = Extractor.merge([self.x_train, x_test], + vertical=True) # Predict solutions + var_split = Extractor.split_variables(instance, model) for category in var_split.keys(): var_index_pairs = var_split[category] - if category in self.predictors.keys(): - ws = self.predictors[category].predict(x_test[category]) - assert ws.shape == (len(var_index_pairs), 2) - for i in range(len(var_index_pairs)): - var, index = var_index_pairs[i] - if self.mode == "heuristic": - if ws[i,0] == 1: - var[index].fix(0) - if solver.is_persistent: - solver.internal_solver.update_var(var[index]) - elif ws[i,1] == 1: - var[index].fix(1) - if solver.is_persistent: - solver.internal_solver.update_var(var[index]) - else: - if ws[i,0] == 1: - var[index].value = 0 - elif ws[i,1] == 1: - var[index].value = 1 + if category not in self.predictors.keys(): + continue + ws = self.predictors[category].predict(x_test[category]) + assert ws.shape == (len(var_index_pairs), 2) + for i in range(len(var_index_pairs)): + var, index = var_index_pairs[i] + if self.mode == "heuristic": + if ws[i,0] == 1: + var[index].fix(0) + if solver.is_persistent: + solver.internal_solver.update_var(var[index]) + elif ws[i,1] == 1: + var[index].fix(1) + if solver.is_persistent: + solver.internal_solver.update_var(var[index]) + else: + if ws[i,0] == 1: + var[index].value = 0 + elif ws[i,1] == 1: + var[index].value = 1 def after_solve(self, solver, instance, model): - var_split = self.transformer.split_variables(instance, model) - for category in var_split.keys(): - var_index_pairs = var_split[category] - y = self.transformer.transform_solution(var_index_pairs) - if category not in self.y_train.keys(): - self.y_train[category] = y - else: - self.y_train[category] = np.vstack([self.y_train[category], y]) + y_test = SolutionExtractor().extract([instance], [model]) + self.y_train = Extractor.merge([self.y_train, y_test], vertical=True) def fit(self, solver, n_jobs=1): for category in tqdm(self.x_train.keys(), desc="Warm start"): From f9e907780306b7effa901e88faafbc18e5d90baa Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Tue, 4 Feb 2020 15:26:26 -0600 Subject: [PATCH 02/44] Only use warmstart=true if warm start is available --- miplearn/solvers.py | 8 ++++++-- miplearn/warmstart.py | 4 ++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/miplearn/solvers.py b/miplearn/solvers.py index ff66b38..a7db581 100644 --- a/miplearn/solvers.py +++ b/miplearn/solvers.py @@ -78,10 +78,14 @@ class LearningSolver: for component in self.components.values(): component.before_solve(self, instance, model) + is_warm_start_available = False + if "warm-start" in self.components.keys(): + if self.components["warm-start"].is_warm_start_available: + is_warm_start_available = True if self.is_persistent: - solve_results = self.internal_solver.solve(tee=tee, warmstart=True) + solve_results = self.internal_solver.solve(tee=tee, warmstart=is_warm_start_available) else: - solve_results = self.internal_solver.solve(model, tee=tee, warmstart=True) + solve_results = self.internal_solver.solve(model, tee=tee, warmstart=is_warm_start_available) solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount") diff --git a/miplearn/warmstart.py b/miplearn/warmstart.py index 746a004..bc6c162 100644 --- a/miplearn/warmstart.py +++ b/miplearn/warmstart.py @@ -130,6 +130,7 @@ class WarmStartComponent(Component): self.y_train = {} self.predictors = {} self.predictor_prototype = predictor_prototype + self.is_warm_start_available = False def before_solve(self, solver, instance, model): # Build x_test @@ -163,8 +164,11 @@ class WarmStartComponent(Component): else: if ws[i,0] == 1: var[index].value = 0 + self.is_warm_start_available = True elif ws[i,1] == 1: var[index].value = 1 + self.is_warm_start_available = True + def after_solve(self, solver, instance, model): y_test = SolutionExtractor().extract([instance], [model]) From fc62a7b0676efd70389e7c452308b41a8d98a69b Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Wed, 5 Feb 2020 12:14:41 -0600 Subject: [PATCH 03/44] Temporarily disable linear relaxation features --- miplearn/solvers.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/miplearn/solvers.py b/miplearn/solvers.py index a7db581..a7e75bb 100644 --- a/miplearn/solvers.py +++ b/miplearn/solvers.py @@ -65,11 +65,11 @@ class LearningSolver: def solve(self, instance, tee=False): model = instance.to_model() - # Solve linear relaxation (TODO: use solver provided by user) - lr_solver = pe.SolverFactory("gurobi") - lr_solver.options["threads"] = 4 - lr_solver.options["relax_integrality"] = 1 - lr_solver.solve(model) +# # Solve linear relaxation (TODO: use solver provided by user) +# lr_solver = pe.SolverFactory("gurobi") +# lr_solver.options["threads"] = 4 +# lr_solver.options["relax_integrality"] = 1 +# lr_solver.solve(model) self._create_solver() if self.is_persistent: From cb0b3d54682189827bfa44fb4969efc96e03835f Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Wed, 5 Feb 2020 12:15:09 -0600 Subject: [PATCH 04/44] Fix component name --- miplearn/tests/test_solver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py index 01d5dee..6810bcd 100644 --- a/miplearn/tests/test_solver.py +++ b/miplearn/tests/test_solver.py @@ -56,7 +56,7 @@ def test_parallel_solve(): def test_solver_random_branch_priority(): instance = _get_instance() components = { - "warm-start": BranchPriorityComponent(), + "branch-priority": BranchPriorityComponent(), } solver = LearningSolver(components=components) solver.solve(instance) From 5750b4c98d93a44dc802b8d27d43695287ad78da Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Wed, 5 Feb 2020 12:15:29 -0600 Subject: [PATCH 05/44] KNN: Make distinction between k and min_samples; improve logging --- Makefile | 2 +- miplearn/warmstart.py | 41 +++++++++++++++++++++++++++++------------ 2 files changed, 30 insertions(+), 13 deletions(-) diff --git a/Makefile b/Makefile index 4553443..1d1c789 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -PYTEST_ARGS := -W ignore::DeprecationWarning -vv -x +PYTEST_ARGS := -W ignore::DeprecationWarning -vv -x --log-level=DEBUG all: docs test diff --git a/miplearn/warmstart.py b/miplearn/warmstart.py index bc6c162..bbcbebe 100644 --- a/miplearn/warmstart.py +++ b/miplearn/warmstart.py @@ -15,7 +15,8 @@ from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_score from sklearn.neighbors import KNeighborsClassifier from tqdm.auto import tqdm - +import logging +logger = logging.getLogger(__name__) class WarmStartPredictor(ABC): def __init__(self, thr_clip=[0.50, 0.50]): @@ -91,30 +92,38 @@ class LogisticWarmStartPredictor(WarmStartPredictor): class KnnWarmStartPredictor(WarmStartPredictor): - def __init__(self, k=50, - thr_clip=[0.90, 0.90], - thr_fix=[0.99, 0.99], + def __init__(self, + k=50, + min_samples=1, + thr_clip=[0.80, 0.80], + thr_fix=[1.0, 1.0], ): super().__init__(thr_clip=thr_clip) self.k = k self.thr_fix = thr_fix + self.min_samples = min_samples def _fit(self, x_train, y_train, label): y_train_avg = np.average(y_train) # If number of training samples is too small, don't predict anything. - if x_train.shape[0] < self.k: + if x_train.shape[0] < self.min_samples: + logger.debug("Too few samples; return 0") return 0 # If vast majority of observations are true, always return true. - if y_train_avg > self.thr_fix[label]: + if y_train_avg >= self.thr_fix[label]: + logger.debug("Consensus reached; return 1") return 1 # If vast majority of observations are false, always return false. - if y_train_avg < (1 - self.thr_fix[label]): + if y_train_avg <= (1 - self.thr_fix[label]): + logger.debug("Consensus reached; return 0") return 0 - knn = KNeighborsClassifier(n_neighbors=self.k) + logger.debug("Training classifier...") + k = min(self.k, x_train.shape[0]) + knn = KNeighborsClassifier(n_neighbors=k) knn.fit(x_train, y_train) return knn @@ -143,6 +152,7 @@ class WarmStartComponent(Component): vertical=True) # Predict solutions + count_total, count_fixed = 0, 0 var_split = Extractor.split_variables(instance, model) for category in var_split.keys(): var_index_pairs = var_split[category] @@ -152,22 +162,29 @@ class WarmStartComponent(Component): assert ws.shape == (len(var_index_pairs), 2) for i in range(len(var_index_pairs)): var, index = var_index_pairs[i] + count_total += 1 if self.mode == "heuristic": - if ws[i,0] == 1: + if ws[i,0] > 0.5: var[index].fix(0) + count_fixed += 1 if solver.is_persistent: solver.internal_solver.update_var(var[index]) - elif ws[i,1] == 1: + elif ws[i,1] > 0.5: var[index].fix(1) + count_fixed += 1 if solver.is_persistent: solver.internal_solver.update_var(var[index]) else: - if ws[i,0] == 1: + var[index].value = None + if ws[i,0] > 0.5: + count_fixed += 1 var[index].value = 0 self.is_warm_start_available = True - elif ws[i,1] == 1: + elif ws[i,1] > 0.5: + count_fixed += 1 var[index].value = 1 self.is_warm_start_available = True + logger.info("Setting values for %d variables (out of %d)" % (count_fixed, count_total)) def after_solve(self, solver, instance, model): From 52b476f0a30de0acc19d9831172a765c7eb10c6c Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Wed, 5 Feb 2020 12:24:29 -0600 Subject: [PATCH 06/44] Remove PerVariableTransformer --- miplearn/branching.py | 8 ++--- miplearn/transformers.py | 65 ---------------------------------------- miplearn/warmstart.py | 2 -- 3 files changed, 2 insertions(+), 73 deletions(-) delete mode 100644 miplearn/transformers.py diff --git a/miplearn/branching.py b/miplearn/branching.py index 7a3d0d0..70306be 100644 --- a/miplearn/branching.py +++ b/miplearn/branching.py @@ -3,13 +3,10 @@ # Written by Alinson S. Xavier from . import Component -from .transformers import PerVariableTransformer +from .extractors import Extractor from abc import ABC, abstractmethod from sklearn.neighbors import KNeighborsRegressor import numpy as np -from p_tqdm import p_map - - from tqdm.auto import tqdm from joblib import Parallel, delayed import multiprocessing @@ -18,7 +15,6 @@ class BranchPriorityComponent(Component): def __init__(self, node_limit=1_000, ): - self.transformer = PerVariableTransformer() self.pending_instances = [] self.x_train = {} self.y_train = {} @@ -28,7 +24,7 @@ class BranchPriorityComponent(Component): def before_solve(self, solver, instance, model): assert solver.is_persistent, "BranchPriorityComponent requires a persistent solver" from gurobipy import GRB - var_split = self.transformer.split_variables(instance, model) + var_split = Extractor.split_variables(instance, model) for category in var_split.keys(): if category not in self.predictors.keys(): continue diff --git a/miplearn/transformers.py b/miplearn/transformers.py deleted file mode 100644 index 9175bb1..0000000 --- a/miplearn/transformers.py +++ /dev/null @@ -1,65 +0,0 @@ -# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. -# Written by Alinson S. Xavier - -import numpy as np -from pyomo.core import Var - - -class PerVariableTransformer: - """ - Class that converts a miplearn.Instance into a matrix of features that is suitable - for training machine learning models that make one decision per decision variable. - """ - - def __init__(self): - pass - - def transform_instance(self, instance, var_index_pairs): - instance_features = self._get_instance_features(instance) - variable_features = self._get_variable_features(instance, var_index_pairs) - return np.vstack([ - np.hstack([instance_features, vf]) - for vf in variable_features - ]) - - @staticmethod - def _get_instance_features(instance): - features = instance.get_instance_features() - assert isinstance(features, np.ndarray) - return features - - @staticmethod - def _get_variable_features(instance, var_index_pairs): - features = [] - expected_shape = None - for (var, index) in var_index_pairs: - vf = instance.get_variable_features(var, index) - assert isinstance(vf, np.ndarray) - if expected_shape is None: - assert len(vf.shape) == 1 - expected_shape = vf.shape - else: - assert vf.shape == expected_shape - features += [vf] - return np.array(features) - - @staticmethod - def transform_solution(var_index_pairs): - y = [] - for (var, index) in var_index_pairs: - y += [[1 - var[index].value, var[index].value]] - return np.array(y) - - @staticmethod - def split_variables(instance, model): - result = {} - for var in model.component_objects(Var): - for index in var: - category = instance.get_variable_category(var, index) - if category is None: - continue - if category not in result.keys(): - result[category] = [] - result[category] += [(var, index)] - return result diff --git a/miplearn/warmstart.py b/miplearn/warmstart.py index bbcbebe..2666a33 100644 --- a/miplearn/warmstart.py +++ b/miplearn/warmstart.py @@ -3,7 +3,6 @@ # Written by Alinson S. Xavier from . import Component -from .transformers import PerVariableTransformer from .extractors import * from abc import ABC, abstractmethod @@ -134,7 +133,6 @@ class WarmStartComponent(Component): mode="exact", ): self.mode = mode - self.transformer = PerVariableTransformer() self.x_train = {} self.y_train = {} self.predictors = {} From 85b804610f0f05caf3cc2935851f96414d23c4a4 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Wed, 5 Feb 2020 12:50:36 -0600 Subject: [PATCH 07/44] Move components into submodule --- miplearn/__init__.py | 12 +++++++----- miplearn/components/__init__.py | 0 .../branchpriority.jl => components/branching.jl} | 0 miplearn/{ => components}/branching.py | 6 +++--- miplearn/{ => components}/component.py | 12 ++++++++---- miplearn/components/tests/__init__.py | 0 miplearn/{ => components}/tests/test_branching.py | 0 miplearn/{ => components}/tests/test_warmstart.py | 0 .../{ => components}/tests/test_warmstart_knn.py | 2 +- .../tests/test_warmstart_logistic.py | 2 +- miplearn/{ => components}/warmstart.py | 4 ++-- miplearn/solvers.py | 3 +-- miplearn/tests/test_benchmark.py | 3 +-- miplearn/tests/test_solver.py | 4 +--- 14 files changed, 25 insertions(+), 23 deletions(-) create mode 100644 miplearn/components/__init__.py rename miplearn/{scripts/branchpriority.jl => components/branching.jl} (100%) rename miplearn/{ => components}/branching.py (96%) rename miplearn/{ => components}/component.py (83%) create mode 100644 miplearn/components/tests/__init__.py rename miplearn/{ => components}/tests/test_branching.py (100%) rename miplearn/{ => components}/tests/test_warmstart.py (100%) rename miplearn/{ => components}/tests/test_warmstart_knn.py (94%) rename miplearn/{ => components}/tests/test_warmstart_logistic.py (97%) rename miplearn/{ => components}/warmstart.py (99%) diff --git a/miplearn/__init__.py b/miplearn/__init__.py index 89075f6..5a9de67 100644 --- a/miplearn/__init__.py +++ b/miplearn/__init__.py @@ -3,10 +3,12 @@ # Written by Alinson S. Xavier -from .component import Component +from .components.component import Component +from .components.warmstart import (WarmStartComponent, + KnnWarmStartPredictor, + LogisticWarmStartPredictor) +from .components.branching import BranchPriorityComponent +from .extractors import UserFeaturesExtractor, SolutionExtractor +from .benchmark import BenchmarkRunner from .instance import Instance from .solvers import LearningSolver -from .benchmark import BenchmarkRunner -from .warmstart import WarmStartComponent, KnnWarmStartPredictor, LogisticWarmStartPredictor -from .branching import BranchPriorityComponent -from .extractors import UserFeaturesExtractor, SolutionExtractor \ No newline at end of file diff --git a/miplearn/components/__init__.py b/miplearn/components/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/miplearn/scripts/branchpriority.jl b/miplearn/components/branching.jl similarity index 100% rename from miplearn/scripts/branchpriority.jl rename to miplearn/components/branching.jl diff --git a/miplearn/branching.py b/miplearn/components/branching.py similarity index 96% rename from miplearn/branching.py rename to miplearn/components/branching.py index 70306be..be6c871 100644 --- a/miplearn/branching.py +++ b/miplearn/components/branching.py @@ -2,8 +2,8 @@ # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier -from . import Component -from .extractors import Extractor +from .component import Component +from ..extractors import Extractor from abc import ABC, abstractmethod from sklearn.neighbors import KNeighborsRegressor import numpy as np @@ -52,7 +52,7 @@ class BranchPriorityComponent(Component): src_dirname = os.path.dirname(os.path.realpath(__file__)) priority_file = tempfile.NamedTemporaryFile(mode="r") subprocess.run(["julia", - "%s/scripts/branchpriority.jl" % src_dirname, + "%s/branching.jl" % src_dirname, lp_file.name, priority_file.name, str(self.node_limit), diff --git a/miplearn/component.py b/miplearn/components/component.py similarity index 83% rename from miplearn/component.py rename to miplearn/components/component.py index 7ec99f2..1b3f0a8 100644 --- a/miplearn/component.py +++ b/miplearn/components/component.py @@ -6,9 +6,9 @@ from abc import ABC, abstractmethod class Component(ABC): - @abstractmethod - def fit(self, solver): - pass + """ + A Component is an object which adds functionality to a LearningSolver. + """ @abstractmethod def before_solve(self, solver, instance, model): @@ -20,4 +20,8 @@ class Component(ABC): @abstractmethod def merge(self, other): - pass \ No newline at end of file + pass + + @abstractmethod + def fit(self, solver): + pass diff --git a/miplearn/components/tests/__init__.py b/miplearn/components/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/miplearn/tests/test_branching.py b/miplearn/components/tests/test_branching.py similarity index 100% rename from miplearn/tests/test_branching.py rename to miplearn/components/tests/test_branching.py diff --git a/miplearn/tests/test_warmstart.py b/miplearn/components/tests/test_warmstart.py similarity index 100% rename from miplearn/tests/test_warmstart.py rename to miplearn/components/tests/test_warmstart.py diff --git a/miplearn/tests/test_warmstart_knn.py b/miplearn/components/tests/test_warmstart_knn.py similarity index 94% rename from miplearn/tests/test_warmstart_knn.py rename to miplearn/components/tests/test_warmstart_knn.py index a87ed42..0b44aad 100644 --- a/miplearn/tests/test_warmstart_knn.py +++ b/miplearn/components/tests/test_warmstart_knn.py @@ -2,7 +2,7 @@ # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier -from miplearn.warmstart import KnnWarmStartPredictor +from miplearn import KnnWarmStartPredictor from sklearn.metrics import accuracy_score, precision_score import numpy as np diff --git a/miplearn/tests/test_warmstart_logistic.py b/miplearn/components/tests/test_warmstart_logistic.py similarity index 97% rename from miplearn/tests/test_warmstart_logistic.py rename to miplearn/components/tests/test_warmstart_logistic.py index 047add0..3631c20 100644 --- a/miplearn/tests/test_warmstart_logistic.py +++ b/miplearn/components/tests/test_warmstart_logistic.py @@ -2,7 +2,7 @@ # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier -from miplearn.warmstart import LogisticWarmStartPredictor +from miplearn import LogisticWarmStartPredictor from sklearn.metrics import accuracy_score, precision_score import numpy as np diff --git a/miplearn/warmstart.py b/miplearn/components/warmstart.py similarity index 99% rename from miplearn/warmstart.py rename to miplearn/components/warmstart.py index 2666a33..25dc8b9 100644 --- a/miplearn/warmstart.py +++ b/miplearn/components/warmstart.py @@ -2,8 +2,8 @@ # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier -from . import Component -from .extractors import * +from .component import Component +from ..extractors import * from abc import ABC, abstractmethod from copy import deepcopy diff --git a/miplearn/solvers.py b/miplearn/solvers.py index a7e75bb..8ec7ca0 100644 --- a/miplearn/solvers.py +++ b/miplearn/solvers.py @@ -2,8 +2,7 @@ # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier -from .warmstart import WarmStartComponent -from .branching import BranchPriorityComponent +from . import WarmStartComponent, BranchPriorityComponent import pyomo.environ as pe import numpy as np from copy import deepcopy diff --git a/miplearn/tests/test_benchmark.py b/miplearn/tests/test_benchmark.py index 37964b5..37a3ed9 100644 --- a/miplearn/tests/test_benchmark.py +++ b/miplearn/tests/test_benchmark.py @@ -2,8 +2,7 @@ # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier -from miplearn import LearningSolver, BenchmarkRunner -from miplearn.warmstart import KnnWarmStartPredictor +from miplearn import LearningSolver, BenchmarkRunner, KnnWarmStartPredictor from miplearn.problems.stab import MaxWeightStableSetGenerator from scipy.stats import randint import numpy as np diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py index 6810bcd..a2b7636 100644 --- a/miplearn/tests/test_solver.py +++ b/miplearn/tests/test_solver.py @@ -2,10 +2,8 @@ # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier -from miplearn import LearningSolver +from miplearn import LearningSolver, BranchPriorityComponent, WarmStartComponent from miplearn.problems.knapsack import KnapsackInstance -from miplearn.branching import BranchPriorityComponent -from miplearn.warmstart import WarmStartComponent import numpy as np From cae1915660569f9f592214544456c67143e288e9 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Tue, 11 Feb 2020 16:50:16 -0600 Subject: [PATCH 08/44] Reactivate linear relaxation code; move to WarmStartComponent --- miplearn/components/warmstart.py | 7 +++++++ miplearn/solvers.py | 8 ++------ 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/miplearn/components/warmstart.py b/miplearn/components/warmstart.py index 25dc8b9..55b111e 100644 --- a/miplearn/components/warmstart.py +++ b/miplearn/components/warmstart.py @@ -14,6 +14,7 @@ from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_score from sklearn.neighbors import KNeighborsClassifier from tqdm.auto import tqdm +import pyomo.environ as pe import logging logger = logging.getLogger(__name__) @@ -140,6 +141,12 @@ class WarmStartComponent(Component): self.is_warm_start_available = False def before_solve(self, solver, instance, model): + # Solve linear relaxation + lr_solver = pe.SolverFactory("gurobi") + lr_solver.options["threads"] = 4 + lr_solver.options["relax_integrality"] = 1 + lr_solver.solve(model, tee=solver.tee) + # Build x_test x_test = CombinedExtractor([UserFeaturesExtractor(), SolutionExtractor(), diff --git a/miplearn/solvers.py b/miplearn/solvers.py index 8ec7ca0..5c42699 100644 --- a/miplearn/solvers.py +++ b/miplearn/solvers.py @@ -37,6 +37,7 @@ class LearningSolver: self.threads = threads self.time_limit = time_limit self.gap_limit = gap_limit + self.tee = False if self.components is not None: assert isinstance(self.components, dict) @@ -63,12 +64,7 @@ class LearningSolver: def solve(self, instance, tee=False): model = instance.to_model() - -# # Solve linear relaxation (TODO: use solver provided by user) -# lr_solver = pe.SolverFactory("gurobi") -# lr_solver.options["threads"] = 4 -# lr_solver.options["relax_integrality"] = 1 -# lr_solver.solve(model) + self.tee = tee self._create_solver() if self.is_persistent: From d7131e9f666a6e153c18e8a49cbe44334cf529f4 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:19:24 -0600 Subject: [PATCH 09/44] Knapsack: Make jitter relative instead of absolute --- docs/problems.md | 2 +- miplearn/problems/knapsack.py | 26 +++++++++++++-------- miplearn/problems/tests/test_knapsack.py | 29 ------------------------ 3 files changed, 17 insertions(+), 40 deletions(-) diff --git a/docs/problems.md b/docs/problems.md index d4cd016..579385a 100644 --- a/docs/problems.md +++ b/docs/problems.md @@ -91,7 +91,7 @@ from the provided probability distributions `K` and `u`. If `fix_w=True` is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as `u` and `K` are not constants, the generated instances will still not be completely identical. -If a probability distribution `w_jitter` is provided, then item weights will be set to $w_{ij} + \gamma_{ij}$ where $\gamma_{ij}$ is sampled from `w_jitter`. When combined with `fix_w=True`, this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. +If a probability distribution `w_jitter` is provided, then item weights will be set to $w_{ij} \gamma_{ij}$ where $\gamma_{ij}$ is sampled from `w_jitter`. When combined with `fix_w=True`, this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If `round=False` is provided, this rounding will be disabled. diff --git a/miplearn/problems/knapsack.py b/miplearn/problems/knapsack.py index a98fd0d..ce8a6ae 100644 --- a/miplearn/problems/knapsack.py +++ b/miplearn/problems/knapsack.py @@ -13,7 +13,7 @@ from scipy.stats.distributions import rv_frozen class ChallengeA: """ - 250 variables, 10 constraints, fixed weights - - w ~ U(100, 900), jitter ~ U(-100, 100) + - w ~ U(0, 1000), jitter ~ U(0.95, 1.05) - K = 500, u ~ U(0., 1.) - alpha = 0.25 """ @@ -25,12 +25,12 @@ class ChallengeA: np.random.seed(seed) self.gen = MultiKnapsackGenerator(n=randint(low=250, high=251), m=randint(low=10, high=11), - w=uniform(loc=100.0, scale=900.0), + w=uniform(loc=0.0, scale=1000.0), K=uniform(loc=500.0, scale=0.0), u=uniform(loc=0.0, scale=1.0), alpha=uniform(loc=0.25, scale=0.0), fix_w=True, - w_jitter=uniform(loc=-100.0, scale=200.0), + w_jitter=uniform(loc=0.95, scale=0.1), ) np.random.seed(seed + 1) self.training_instances = self.gen.generate(n_training_instances) @@ -91,8 +91,8 @@ class MultiKnapsackInstance(Instance): self.weights[:, index], ]) -# def get_variable_category(self, var, index): -# return index + def get_variable_category(self, var, index): + return index class MultiKnapsackGenerator: @@ -104,7 +104,7 @@ class MultiKnapsackGenerator: u=uniform(loc=0.0, scale=1.0), alpha=uniform(loc=0.25, scale=0.0), fix_w=False, - w_jitter=randint(low=0, high=1), + w_jitter=uniform(loc=1.0, scale=0.0), round=True, ): """Initialize the problem generator. @@ -133,7 +133,7 @@ class MultiKnapsackGenerator: be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to - w[i,j] + gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with + w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with fix_w=True, this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these @@ -186,10 +186,14 @@ class MultiKnapsackGenerator: self.fix_n = self.n.rvs() self.fix_m = self.m.rvs() self.fix_w = np.array([self.w.rvs(self.fix_n) for _ in range(self.fix_m)]) + self.fix_u = self.u.rvs(self.fix_n) + self.fix_K = self.K.rvs() else: self.fix_n = None self.fix_m = None self.fix_w = None + self.fix_u = None + self.fix_K = None def generate(self, n_samples): def _sample(): @@ -197,13 +201,15 @@ class MultiKnapsackGenerator: n = self.fix_n m = self.fix_m w = self.fix_w + u = self.fix_u + K = self.fix_K else: n = self.n.rvs() m = self.m.rvs() w = np.array([self.w.rvs(n) for _ in range(m)]) - w = w + np.array([self.w_jitter.rvs(n) for _ in range(m)]) - K = self.K.rvs() - u = self.u.rvs(n) + u = self.u.rvs(n) + K = self.K.rvs() + w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)]) alpha = self.alpha.rvs(m) p = np.array([w[:,j].sum() / m + K * u[j] for j in range(n)]) b = np.array([w[i,:].sum() * alpha[i] for i in range(m)]) diff --git a/miplearn/problems/tests/test_knapsack.py b/miplearn/problems/tests/test_knapsack.py index 2d6b1b6..fb179e5 100644 --- a/miplearn/problems/tests/test_knapsack.py +++ b/miplearn/problems/tests/test_knapsack.py @@ -23,32 +23,3 @@ def test_knapsack_generator(): assert round(np.mean(w_sum), -1) == 500. assert round(np.mean(p_sum), -1) == 1250. assert round(np.mean(b_sum), -3) == 25000. - - -def test_knapsack_fixed_weights_jitter(): - gen = MultiKnapsackGenerator(n=randint(low=50, high=51), - m=randint(low=10, high=11), - w=randint(low=0, high=1000), - K=randint(low=500, high=501), - u=uniform(loc=1.0, scale=0.0), - alpha=uniform(loc=0.50, scale=0.0), - fix_w=True, - w_jitter=randint(low=0, high=1), - ) - instances = gen.generate(100) - w = [instance.weights[0,0] for instance in instances] - assert np.std(w) == 0. - - gen = MultiKnapsackGenerator(n=randint(low=1, high=2), - m=randint(low=10, high=11), - w=randint(low=1000, high=1001), - K=randint(low=500, high=501), - u=uniform(loc=1.0, scale=0.0), - alpha=uniform(loc=0.50, scale=0.0), - fix_w=True, - w_jitter=randint(low=0, high=1001), - ) - instances = gen.generate(5_000) - w = [instance.weights[0,0] for instance in instances] - assert round(np.std(w), -1) == 290. - assert round(np.mean(w), -2) == 1500. \ No newline at end of file From c3902ad61c89f34e6d04f5f5d90ad1a7893082d3 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:20:21 -0600 Subject: [PATCH 10/44] Branching: Make classifier configurable --- miplearn/components/branching.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/miplearn/components/branching.py b/miplearn/components/branching.py index be6c871..e716f8c 100644 --- a/miplearn/components/branching.py +++ b/miplearn/components/branching.py @@ -11,15 +11,22 @@ from tqdm.auto import tqdm from joblib import Parallel, delayed import multiprocessing + +def _default_branch_priority_predictor(): + return KNeighborsRegressor(n_neighbors=1) + + class BranchPriorityComponent(Component): def __init__(self, - node_limit=1_000, + node_limit=10_000, + predictor=_default_branch_priority_predictor, ): self.pending_instances = [] self.x_train = {} self.y_train = {} self.predictors = {} self.node_limit = node_limit + self.predictor_factory = predictor def before_solve(self, solver, instance, model): assert solver.is_persistent, "BranchPriorityComponent requires a persistent solver" @@ -92,7 +99,7 @@ class BranchPriorityComponent(Component): return comp - + # Run strong branching on pending instances subcomponents = Parallel(n_jobs=n_jobs)( delayed(_process)(instance) for instance in tqdm(self.pending_instances, desc="Branch priority") @@ -104,9 +111,8 @@ class BranchPriorityComponent(Component): for category in self.x_train.keys(): x_train = self.x_train[category] y_train = self.y_train[category] - self.predictors[category] = KNeighborsRegressor(n_neighbors=1) + self.predictors[category] = self.predictor_factory() self.predictors[category].fit(x_train, y_train) - def _build_x(self, instance, var, index): instance_features = instance.get_instance_features() From ffb29d2bbb35140930c3ef415739df6b57fc16ea Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:21:01 -0600 Subject: [PATCH 11/44] BenchmarkRunner: add solve method --- miplearn/benchmark.py | 107 ++++++++++++++++++++++++------------------ 1 file changed, 61 insertions(+), 46 deletions(-) diff --git a/miplearn/benchmark.py b/miplearn/benchmark.py index 0146772..51f28dd 100644 --- a/miplearn/benchmark.py +++ b/miplearn/benchmark.py @@ -3,7 +3,9 @@ # Written by Alinson S. Xavier from .solvers import LearningSolver +from copy import deepcopy import pandas as pd +from tqdm.auto import tqdm class BenchmarkRunner: def __init__(self, solvers): @@ -13,59 +15,23 @@ class BenchmarkRunner: self.solvers = solvers self.results = None + def solve(self, instances, fit=True, tee=False): + for (name, solver) in self.solvers.items(): + for i in tqdm(range(len((instances)))): + results = solver.solve(deepcopy(instances[i]), tee=tee) + self._push_result(results, solver=solver, name=name, instance=i) + if fit: + solver.fit() + def parallel_solve(self, instances, n_jobs=1, n_trials=1): - if self.results is None: - self.results = pd.DataFrame(columns=["Solver", - "Instance", - "Wallclock Time", - "Lower Bound", - "Upper Bound", - "Gap", - "Nodes", - ]) instances = instances * n_trials for (name, solver) in self.solvers.items(): results = solver.parallel_solve(instances, n_jobs=n_jobs, - label=name, + label="Solve (%s)" % name, collect_training_data=False) for i in range(len(instances)): - wallclock_time = None - for key in ["Time", "Wall time", "Wallclock time"]: - if key not in results[i]["Solver"][0].keys(): - continue - if str(results[i]["Solver"][0][key]) == "": - continue - wallclock_time = float(results[i]["Solver"][0][key]) - nodes = results[i]["Solver"][0]["Nodes"] - lb = results[i]["Problem"][0]["Lower bound"] - ub = results[i]["Problem"][0]["Upper bound"] - gap = (ub - lb) / lb - self.results = self.results.append({ - "Solver": name, - "Instance": i, - "Wallclock Time": wallclock_time, - "Lower Bound": lb, - "Upper Bound": ub, - "Gap": gap, - "Nodes": nodes, - }, ignore_index=True) - groups = self.results.groupby("Instance") - best_lower_bound = groups["Lower Bound"].transform("max") - best_upper_bound = groups["Upper Bound"].transform("min") - best_gap = groups["Gap"].transform("min") - best_nodes = groups["Nodes"].transform("min") - best_wallclock_time = groups["Wallclock Time"].transform("min") - self.results["Relative Lower Bound"] = \ - self.results["Lower Bound"] / best_lower_bound - self.results["Relative Upper Bound"] = \ - self.results["Upper Bound"] / best_upper_bound - self.results["Relative Wallclock Time"] = \ - self.results["Wallclock Time"] / best_wallclock_time - self.results["Relative Gap"] = \ - self.results["Gap"] / best_gap - self.results["Relative Nodes"] = \ - self.results["Nodes"] / best_nodes + self._push_result(results[i], solver=solver, name=name, instance=i) def raw_results(self): return self.results @@ -83,3 +49,52 @@ class BenchmarkRunner: def fit(self): for (name, solver) in self.solvers.items(): solver.fit() + + def _push_result(self, result, solver, name, instance): + if self.results is None: + self.results = pd.DataFrame(columns=["Solver", + "Instance", + "Wallclock Time", + "Lower Bound", + "Upper Bound", + "Gap", + "Nodes", + "Mode", + ]) + wallclock_time = None + for key in ["Time", "Wall time", "Wallclock time"]: + if key not in result["Solver"][0].keys(): + continue + if str(result["Solver"][0][key]) == "": + continue + wallclock_time = float(result["Solver"][0][key]) + nodes = result["Solver"][0]["Nodes"] + lb = result["Problem"][0]["Lower bound"] + ub = result["Problem"][0]["Upper bound"] + gap = (ub - lb) / lb + self.results = self.results.append({ + "Solver": name, + "Instance": instance, + "Wallclock Time": wallclock_time, + "Lower Bound": lb, + "Upper Bound": ub, + "Gap": gap, + "Nodes": nodes, + "Mode": solver.mode, + }, ignore_index=True) + groups = self.results.groupby("Instance") + best_lower_bound = groups["Lower Bound"].transform("max") + best_upper_bound = groups["Upper Bound"].transform("min") + best_gap = groups["Gap"].transform("min") + best_nodes = groups["Nodes"].transform("min") + best_wallclock_time = groups["Wallclock Time"].transform("min") + self.results["Relative Lower Bound"] = \ + self.results["Lower Bound"] / best_lower_bound + self.results["Relative Upper Bound"] = \ + self.results["Upper Bound"] / best_upper_bound + self.results["Relative Wallclock Time"] = \ + self.results["Wallclock Time"] / best_wallclock_time + self.results["Relative Gap"] = \ + self.results["Gap"] / best_gap + self.results["Relative Nodes"] = \ + self.results["Nodes"] / best_nodes From 1213f1d0a58fa80b0718be584ed466b66eb37a1a Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:21:48 -0600 Subject: [PATCH 12/44] LearningSolver: fix node count for non-compatible solvers --- miplearn/solvers.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/miplearn/solvers.py b/miplearn/solvers.py index 5c42699..f262cd2 100644 --- a/miplearn/solvers.py +++ b/miplearn/solvers.py @@ -29,7 +29,7 @@ class LearningSolver: gap_limit=None, internal_solver_factory=_gurobi_factory, components=None, - mode=None): + mode="exact"): self.is_persistent = None self.internal_solver = None self.components = components @@ -38,19 +38,18 @@ class LearningSolver: self.time_limit = time_limit self.gap_limit = gap_limit self.tee = False + self.mode = mode if self.components is not None: assert isinstance(self.components, dict) else: self.components = { "warm-start": WarmStartComponent(), - #"branch-priority": BranchPriorityComponent(), } - if mode is not None: - assert mode in ["exact", "heuristic"] - for component in self.components.values(): - component.mode = mode + assert self.mode in ["exact", "heuristic"] + for component in self.components.values(): + component.mode = self.mode def _create_solver(self): self.internal_solver = self.internal_solver_factory() @@ -82,7 +81,10 @@ class LearningSolver: else: solve_results = self.internal_solver.solve(model, tee=tee, warmstart=is_warm_start_available) - solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount") + if hasattr(self.internal_solver, "_solver_model"): + solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount") + else: + solve_results["Solver"][0]["Nodes"] = 1 for component in self.components.values(): component.after_solve(self, instance, model) From d3b5b43b9427f543a4f9ba0bca31bae80380490f Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:22:44 -0600 Subject: [PATCH 13/44] Implement AdaptiveSolver; reorganize imports --- benchmark/benchmark.py | 17 +- miplearn/__init__.py | 4 +- miplearn/components/tests/test_warmstart.py | 8 +- miplearn/components/warmstart.py | 339 ++++++++++++++------ 4 files changed, 261 insertions(+), 107 deletions(-) diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py index e7f971a..a0dd712 100755 --- a/benchmark/benchmark.py +++ b/benchmark/benchmark.py @@ -12,13 +12,18 @@ Options: """ from docopt import docopt import importlib, pathlib -from miplearn import LearningSolver, BenchmarkRunner -from miplearn.warmstart import WarmStartComponent -from miplearn.branching import BranchPriorityComponent +from miplearn import (LearningSolver, + BenchmarkRunner, + WarmStartComponent, + BranchPriorityComponent, + ) from numpy import median import pyomo.environ as pe import pickle +import logging +logging.getLogger('pyomo.core').setLevel(logging.ERROR) + args = docopt(__doc__) basepath = args[""] pathlib.Path(basepath).mkdir(parents=True, exist_ok=True) @@ -60,7 +65,7 @@ def train(): internal_solver_factory=train_solver_factory, components={ "warm-start": WarmStartComponent(), - "branch-priority": BranchPriorityComponent(), + #"branch-priority": BranchPriorityComponent(), }, ) solver.parallel_solve(train_instances, n_jobs=10) @@ -89,7 +94,7 @@ def test_ml(): internal_solver_factory=test_solver_factory, components={ "warm-start": WarmStartComponent(), - "branch-priority": BranchPriorityComponent(), + #"branch-priority": BranchPriorityComponent(), }, ), "ml-heuristic": LearningSolver( @@ -97,7 +102,7 @@ def test_ml(): mode="heuristic", components={ "warm-start": WarmStartComponent(), - "branch-priority": BranchPriorityComponent(), + #"branch-priority": BranchPriorityComponent(), }, ), } diff --git a/miplearn/__init__.py b/miplearn/__init__.py index 5a9de67..5580d73 100644 --- a/miplearn/__init__.py +++ b/miplearn/__init__.py @@ -6,7 +6,9 @@ from .components.component import Component from .components.warmstart import (WarmStartComponent, KnnWarmStartPredictor, - LogisticWarmStartPredictor) + LogisticWarmStartPredictor, + AdaptivePredictor, + ) from .components.branching import BranchPriorityComponent from .extractors import UserFeaturesExtractor, SolutionExtractor from .benchmark import BenchmarkRunner diff --git a/miplearn/components/tests/test_warmstart.py b/miplearn/components/tests/test_warmstart.py index 9f62bb7..ac0d07b 100644 --- a/miplearn/components/tests/test_warmstart.py +++ b/miplearn/components/tests/test_warmstart.py @@ -26,12 +26,16 @@ def test_warm_start_save_load(): comp = solver.components["warm-start"] assert comp.x_train["default"].shape == (8, 6) assert comp.y_train["default"].shape == (8, 2) - assert "default" in comp.predictors.keys() + assert ("default", 0) in comp.predictors.keys() + assert ("default", 1) in comp.predictors.keys() solver.save_state(state_file.name) + solver.solve(_get_instances()[0]) + solver = LearningSolver(components={"warm-start": WarmStartComponent()}) solver.load_state(state_file.name) comp = solver.components["warm-start"] assert comp.x_train["default"].shape == (8, 6) assert comp.y_train["default"].shape == (8, 2) - assert "default" in comp.predictors.keys() + assert ("default", 0) in comp.predictors.keys() + assert ("default", 1) in comp.predictors.keys() diff --git a/miplearn/components/warmstart.py b/miplearn/components/warmstart.py index 55b111e..7a86ae1 100644 --- a/miplearn/components/warmstart.py +++ b/miplearn/components/warmstart.py @@ -12,12 +12,250 @@ from sklearn.pipeline import make_pipeline from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_score +from sklearn.metrics import roc_curve from sklearn.neighbors import KNeighborsClassifier from tqdm.auto import tqdm import pyomo.environ as pe import logging logger = logging.getLogger(__name__) + +class AdaptivePredictor: + def __init__(self, + predictor=None, + min_samples_predict=1, + min_samples_cv=100, + thr_fix=0.999, + thr_alpha=0.50, + thr_balance=1.0, + ): + self.min_samples_predict = min_samples_predict + self.min_samples_cv = min_samples_cv + self.thr_fix = thr_fix + self.thr_alpha = thr_alpha + self.thr_balance = thr_balance + self.predictor_factory = predictor + + def fit(self, x_train, y_train): + n_samples = x_train.shape[0] + + # If number of samples is too small, don't predict anything. + if n_samples < self.min_samples_predict: + logger.debug(" Too few samples (%d); always predicting false" % n_samples) + self.predictor = 0 + return + + # If vast majority of observations are false, always return false. + y_train_avg = np.average(y_train) + if y_train_avg <= 1.0 - self.thr_fix: + logger.debug(" Most samples are negative (%.3f); always returning false" % y_train_avg) + self.predictor = 0 + return + + # If vast majority of observations are true, always return true. + if y_train_avg >= self.thr_fix: + logger.debug(" Most samples are positive (%.3f); always returning true" % y_train_avg) + self.predictor = 1 + return + + # If classes are too unbalanced, don't predict anything. + if y_train_avg < (1 - self.thr_balance) or y_train_avg > self.thr_balance: + logger.debug(" Classes are too unbalanced (%.3f); always returning false" % y_train_avg) + self.predictor = 0 + return + + # Select ML model if none is provided + if self.predictor_factory is None: + if n_samples < 30: + self.predictor_factory = KNeighborsClassifier(n_neighbors=n_samples) + else: + self.predictor_factory = make_pipeline(StandardScaler(), LogisticRegression()) + + # Create predictor + if callable(self.predictor_factory): + pred = self.predictor_factory() + else: + pred = deepcopy(self.predictor_factory) + + # Skip cross-validation if number of samples is too small + if n_samples < self.min_samples_cv: + logger.debug(" Too few samples (%d); skipping cross validation" % n_samples) + self.predictor = pred + self.predictor.fit(x_train, y_train) + return + + # Calculate cross-validation score + cv_score = np.mean(cross_val_score(pred, x_train, y_train, cv=5)) + dummy_score = max(y_train_avg, 1 - y_train_avg) + cv_thr = 1. * self.thr_alpha + dummy_score * (1 - self.thr_alpha) + + # If cross-validation score is too low, don't predict anything. + if cv_score < cv_thr: + logger.debug(" Score is too low (%.3f < %.3f); always returning false" % (cv_score, cv_thr)) + self.predictor = 0 + else: + logger.debug(" Score is acceptable (%.3f > %.3f); training classifier" % (cv_score, cv_thr)) + self.predictor = pred + self.predictor.fit(x_train, y_train) + + def predict_proba(self, x_test): + if isinstance(self.predictor, int): + y_pred = np.zeros((x_test.shape[0], 2)) + y_pred[:, self.predictor] = 1.0 + return y_pred + else: + return self.predictor.predict_proba(x_test) + + +class WarmStartComponent(Component): + def __init__(self, + predictor=AdaptivePredictor(), + mode="exact", + max_fpr=[0.01, 0.01], + min_threshold=[0.75, 0.75], + dynamic_thresholds=False, + ): + self.mode = mode + self.x_train = {} + self.y_train = {} + self.predictors = {} + self.is_warm_start_available = False + self.max_fpr = max_fpr + self.min_threshold = min_threshold + self.thresholds = {} + self.predictor_factory = predictor + self.dynamic_thresholds = dynamic_thresholds + + + def before_solve(self, solver, instance, model): +# # Solve linear relaxation +# lr_solver = pe.SolverFactory("gurobi") +# lr_solver.options["threads"] = 4 +# lr_solver.options["relax_integrality"] = 1 +# lr_solver.solve(model, tee=solver.tee) + + # Build x_test + x_test = CombinedExtractor([UserFeaturesExtractor(), + SolutionExtractor(), + ]).extract([instance], [model]) + + # Update self.x_train + self.x_train = Extractor.merge([self.x_train, x_test], + vertical=True) + + # Predict solutions + count_total, count_fixed = 0, 0 + var_split = Extractor.split_variables(instance, model) + for category in var_split.keys(): + var_index_pairs = var_split[category] + + # Clear current values + for i in range(len(var_index_pairs)): + var, index = var_index_pairs[i] + var[index].value = None + + # Make predictions + for label in [0,1]: + if (category, label) not in self.predictors.keys(): + continue + ws = self.predictors[category, label].predict_proba(x_test[category]) + assert ws.shape == (len(var_index_pairs), 2) + for i in range(len(var_index_pairs)): + count_total += 1 + var, index = var_index_pairs[i] + logger.debug("%s[%s] ws=%.6f threshold=%.6f" % (var, index, ws[i, 1], self.thresholds[category, label])) + if ws[i, 1] > self.thresholds[category, label]: + logger.debug("Setting %s[%s] to %d" % (var, index, label)) + count_fixed += 1 + if self.mode == "heuristic": + var[index].fix(label) + if solver.is_persistent: + solver.internal_solver.update_var(var[index]) + else: + var[index].value = label + self.is_warm_start_available = True + + # Clear current values + for i in range(len(var_index_pairs)): + var, index = var_index_pairs[i] + if var[index].value is None: + logger.debug("Variable %s[%s] not set" % (var, index)) + else: + logger.debug("Varible %s[%s] set to %.2f" % (var, index, var[index].value)) + + + logger.info("Setting values for %d variables (out of %d)" % (count_fixed, count_total // 2)) + + + def after_solve(self, solver, instance, model): + y_test = SolutionExtractor().extract([instance], [model]) + self.y_train = Extractor.merge([self.y_train, y_test], vertical=True) + + def fit(self, solver, n_jobs=1): + for category in tqdm(self.x_train.keys(), desc="Fit (warm start)"): + x_train = self.x_train[category] + y_train = self.y_train[category] + for label in [0, 1]: + logger.debug("Fitting predictors[%s, %s]:" % (category, label)) + + if callable(self.predictor_factory): + pred = self.predictor_factory(category, label) + else: + pred = deepcopy(self.predictor_factory) + self.predictors[category, label] = pred + y = y_train[:, label].astype(int) + pred.fit(x_train, y) + + # If y is either always one or always zero, set fixed threshold + y_avg = np.average(y) + if (not self.dynamic_thresholds) or y_avg <= 0.001 or y_avg >= 0.999: + self.thresholds[category, label] = self.min_threshold[label] + logger.debug(" Setting threshold to %.4f" % self.min_threshold[label]) + continue + + # Calculate threshold dynamically using ROC curve + y_scores = pred.predict_proba(x_train)[:, 1] + fpr, tpr, thresholds = roc_curve(y, y_scores) + k = 0 + while True: + if (k + 1) > len(fpr): + break + if fpr[k + 1] > self.max_fpr[label]: + break + if thresholds[k + 1] < self.min_threshold[label]: + break + k = k + 1 + logger.debug(" Setting threshold to %.4f (fpr=%.4f, tpr=%.4f)" % (thresholds[k], fpr[k], tpr[k])) + self.thresholds[category, label] = thresholds[k] + + + def merge(self, other_components): + # Merge x_train and y_train + keys = set(self.x_train.keys()) + for comp in other_components: + keys = keys.union(set(comp.x_train.keys())) + for key in keys: + x_train_submatrices = [comp.x_train[key] + for comp in other_components + if key in comp.x_train.keys()] + y_train_submatrices = [comp.y_train[key] + for comp in other_components + if key in comp.y_train.keys()] + if key in self.x_train.keys(): + x_train_submatrices += [self.x_train[key]] + y_train_submatrices += [self.y_train[key]] + self.x_train[key] = np.vstack(x_train_submatrices) + self.y_train[key] = np.vstack(y_train_submatrices) + + # Merge trained predictors + for comp in other_components: + for key in comp.predictors.keys(): + if key not in self.predictors.keys(): + self.predictors[key] = comp.predictors[key] + self.thresholds[key] = comp.thresholds[key] + + +# Deprecated class WarmStartPredictor(ABC): def __init__(self, thr_clip=[0.50, 0.50]): self.models = [None, None] @@ -49,7 +287,8 @@ class WarmStartPredictor(ABC): def _fit(self, x_train, y_train, label): pass - + +# Deprecated class LogisticWarmStartPredictor(WarmStartPredictor): def __init__(self, min_samples=100, @@ -91,6 +330,7 @@ class LogisticWarmStartPredictor(WarmStartPredictor): return reg +# Deprecated class KnnWarmStartPredictor(WarmStartPredictor): def __init__(self, k=50, @@ -128,102 +368,5 @@ class KnnWarmStartPredictor(WarmStartPredictor): return knn -class WarmStartComponent(Component): - def __init__(self, - predictor_prototype=KnnWarmStartPredictor(), - mode="exact", - ): - self.mode = mode - self.x_train = {} - self.y_train = {} - self.predictors = {} - self.predictor_prototype = predictor_prototype - self.is_warm_start_available = False - def before_solve(self, solver, instance, model): - # Solve linear relaxation - lr_solver = pe.SolverFactory("gurobi") - lr_solver.options["threads"] = 4 - lr_solver.options["relax_integrality"] = 1 - lr_solver.solve(model, tee=solver.tee) - - # Build x_test - x_test = CombinedExtractor([UserFeaturesExtractor(), - SolutionExtractor(), - ]).extract([instance], [model]) - - # Update self.x_train - self.x_train = Extractor.merge([self.x_train, x_test], - vertical=True) - - # Predict solutions - count_total, count_fixed = 0, 0 - var_split = Extractor.split_variables(instance, model) - for category in var_split.keys(): - var_index_pairs = var_split[category] - if category not in self.predictors.keys(): - continue - ws = self.predictors[category].predict(x_test[category]) - assert ws.shape == (len(var_index_pairs), 2) - for i in range(len(var_index_pairs)): - var, index = var_index_pairs[i] - count_total += 1 - if self.mode == "heuristic": - if ws[i,0] > 0.5: - var[index].fix(0) - count_fixed += 1 - if solver.is_persistent: - solver.internal_solver.update_var(var[index]) - elif ws[i,1] > 0.5: - var[index].fix(1) - count_fixed += 1 - if solver.is_persistent: - solver.internal_solver.update_var(var[index]) - else: - var[index].value = None - if ws[i,0] > 0.5: - count_fixed += 1 - var[index].value = 0 - self.is_warm_start_available = True - elif ws[i,1] > 0.5: - count_fixed += 1 - var[index].value = 1 - self.is_warm_start_available = True - logger.info("Setting values for %d variables (out of %d)" % (count_fixed, count_total)) - - - def after_solve(self, solver, instance, model): - y_test = SolutionExtractor().extract([instance], [model]) - self.y_train = Extractor.merge([self.y_train, y_test], vertical=True) - - def fit(self, solver, n_jobs=1): - for category in tqdm(self.x_train.keys(), desc="Warm start"): - x_train = self.x_train[category] - y_train = self.y_train[category] - self.predictors[category] = deepcopy(self.predictor_prototype) - self.predictors[category].fit(x_train, y_train) - - def merge(self, other_components): - # Merge x_train and y_train - keys = set(self.x_train.keys()) - for comp in other_components: - keys = keys.union(set(comp.x_train.keys())) - for key in keys: - x_train_submatrices = [comp.x_train[key] - for comp in other_components - if key in comp.x_train.keys()] - y_train_submatrices = [comp.y_train[key] - for comp in other_components - if key in comp.y_train.keys()] - if key in self.x_train.keys(): - x_train_submatrices += [self.x_train[key]] - y_train_submatrices += [self.y_train[key]] - self.x_train[key] = np.vstack(x_train_submatrices) - self.y_train[key] = np.vstack(y_train_submatrices) - - # Merge trained predictors - for comp in other_components: - for key in comp.predictors.keys(): - if key not in self.predictors.keys(): - self.predictors[key] = comp.predictors[key] \ No newline at end of file From 10a4b073acf6acec0fc22274ea20f4ef9bcb5165 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:23:13 -0600 Subject: [PATCH 14/44] Add open-source license --- COPYING.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 COPYING.md diff --git a/COPYING.md b/COPYING.md new file mode 100644 index 0000000..497106c --- /dev/null +++ b/COPYING.md @@ -0,0 +1,25 @@ +Copyright © 2020, UChicago Argonne, LLC + +All Rights Reserved + +Software Name: MIPLearn + +By: Argonne National Laboratory + +OPEN SOURCE LICENSE +------------------- + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. +3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +******************************************************************************** + +DISCLAIMER +---------- + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +******************************************************************************** From 4bae218f1f16f01be002dae31ee0a44b18fcd35b Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:23:13 -0600 Subject: [PATCH 15/44] Add open-source license --- COPYING.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 COPYING.md diff --git a/COPYING.md b/COPYING.md new file mode 100644 index 0000000..497106c --- /dev/null +++ b/COPYING.md @@ -0,0 +1,25 @@ +Copyright © 2020, UChicago Argonne, LLC + +All Rights Reserved + +Software Name: MIPLearn + +By: Argonne National Laboratory + +OPEN SOURCE LICENSE +------------------- + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. +3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +******************************************************************************** + +DISCLAIMER +---------- + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +******************************************************************************** From 635c16386d05a7edc61951101d12cc18f710b729 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:35:09 -0600 Subject: [PATCH 16/44] Update copyright notices --- README.md | 25 ++++++++++++++++++- benchmark/Makefile | 5 ++++ benchmark/benchmark.py | 5 ++++ miplearn/__init__.py | 4 +-- miplearn/benchmark.py | 3 ++- miplearn/components/branching.jl | 5 ++++ miplearn/components/branching.py | 3 ++- miplearn/components/component.py | 3 ++- miplearn/components/tests/test_branching.py | 3 ++- miplearn/components/tests/test_warmstart.py | 3 ++- .../components/tests/test_warmstart_knn.py | 3 ++- .../tests/test_warmstart_logistic.py | 3 ++- miplearn/components/warmstart.py | 3 ++- miplearn/extractors.py | 3 ++- miplearn/instance.py | 3 ++- miplearn/solvers.py | 3 ++- miplearn/tests/test_benchmark.py | 3 ++- miplearn/tests/test_extractors.py | 3 ++- miplearn/tests/test_solver.py | 3 ++- 19 files changed, 69 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index ef6b595..03a1feb 100644 --- a/README.md +++ b/README.md @@ -24,4 +24,27 @@ License ------- MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization - Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved. + + Redistribution and use in source and binary forms, with or without modification, are permitted + provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, this list of + conditions and the following disclaimer. + 2. Redistributions in binary form must reproduce the above copyright notice, this list of + conditions and the following disclaimer in the documentation and/or other materials provided + with the distribution. + 3. Neither the name of the copyright holder nor the names of its contributors may be used to + endorse or promote products derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY + AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR + OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + diff --git a/benchmark/Makefile b/benchmark/Makefile index e92ee3f..4a232ec 100644 --- a/benchmark/Makefile +++ b/benchmark/Makefile @@ -1,3 +1,8 @@ +# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. +# Written by Alinson S. Xavier + CHALLENGES := \ stab/ChallengeA \ knapsack/ChallengeA diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py index e7f971a..1537daf 100755 --- a/benchmark/benchmark.py +++ b/benchmark/benchmark.py @@ -1,4 +1,9 @@ #!/usr/bin/env python +# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. +# Written by Alinson S. Xavier + """Benchmark script Usage: diff --git a/miplearn/__init__.py b/miplearn/__init__.py index 5a9de67..352049b 100644 --- a/miplearn/__init__.py +++ b/miplearn/__init__.py @@ -1,8 +1,8 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier - from .components.component import Component from .components.warmstart import (WarmStartComponent, KnnWarmStartPredictor, diff --git a/miplearn/benchmark.py b/miplearn/benchmark.py index 0146772..df0c168 100644 --- a/miplearn/benchmark.py +++ b/miplearn/benchmark.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from .solvers import LearningSolver diff --git a/miplearn/components/branching.jl b/miplearn/components/branching.jl index ff85ae2..c57fe55 100644 --- a/miplearn/components/branching.jl +++ b/miplearn/components/branching.jl @@ -1,3 +1,8 @@ +# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. +# Written by Alinson S. Xavier + import Base.Threads.@threads using TinyBnB, CPLEXW, Printf diff --git a/miplearn/components/branching.py b/miplearn/components/branching.py index be6c871..e267834 100644 --- a/miplearn/components/branching.py +++ b/miplearn/components/branching.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from .component import Component diff --git a/miplearn/components/component.py b/miplearn/components/component.py index 1b3f0a8..2f8d88d 100644 --- a/miplearn/components/component.py +++ b/miplearn/components/component.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from abc import ABC, abstractmethod diff --git a/miplearn/components/tests/test_branching.py b/miplearn/components/tests/test_branching.py index 7fbbdd1..5316814 100644 --- a/miplearn/components/tests/test_branching.py +++ b/miplearn/components/tests/test_branching.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from miplearn import BranchPriorityComponent, LearningSolver diff --git a/miplearn/components/tests/test_warmstart.py b/miplearn/components/tests/test_warmstart.py index 9f62bb7..875e7a4 100644 --- a/miplearn/components/tests/test_warmstart.py +++ b/miplearn/components/tests/test_warmstart.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from miplearn import WarmStartComponent, LearningSolver diff --git a/miplearn/components/tests/test_warmstart_knn.py b/miplearn/components/tests/test_warmstart_knn.py index 0b44aad..b6f836c 100644 --- a/miplearn/components/tests/test_warmstart_knn.py +++ b/miplearn/components/tests/test_warmstart_knn.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from miplearn import KnnWarmStartPredictor diff --git a/miplearn/components/tests/test_warmstart_logistic.py b/miplearn/components/tests/test_warmstart_logistic.py index 3631c20..59db276 100644 --- a/miplearn/components/tests/test_warmstart_logistic.py +++ b/miplearn/components/tests/test_warmstart_logistic.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from miplearn import LogisticWarmStartPredictor diff --git a/miplearn/components/warmstart.py b/miplearn/components/warmstart.py index 25dc8b9..4fc665d 100644 --- a/miplearn/components/warmstart.py +++ b/miplearn/components/warmstart.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from .component import Component diff --git a/miplearn/extractors.py b/miplearn/extractors.py index e77ea02..d01742c 100644 --- a/miplearn/extractors.py +++ b/miplearn/extractors.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier import numpy as np diff --git a/miplearn/instance.py b/miplearn/instance.py index a401e3a..7404608 100644 --- a/miplearn/instance.py +++ b/miplearn/instance.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from abc import ABC, abstractmethod diff --git a/miplearn/solvers.py b/miplearn/solvers.py index 8ec7ca0..f7551cc 100644 --- a/miplearn/solvers.py +++ b/miplearn/solvers.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from . import WarmStartComponent, BranchPriorityComponent diff --git a/miplearn/tests/test_benchmark.py b/miplearn/tests/test_benchmark.py index 37a3ed9..27184d6 100644 --- a/miplearn/tests/test_benchmark.py +++ b/miplearn/tests/test_benchmark.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from miplearn import LearningSolver, BenchmarkRunner, KnnWarmStartPredictor diff --git a/miplearn/tests/test_extractors.py b/miplearn/tests/test_extractors.py index 013a980..95c54a0 100644 --- a/miplearn/tests/test_extractors.py +++ b/miplearn/tests/test_extractors.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from miplearn.problems.knapsack import KnapsackInstance diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py index a2b7636..3598f06 100644 --- a/miplearn/tests/test_solver.py +++ b/miplearn/tests/test_solver.py @@ -1,5 +1,6 @@ # MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization -# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. +# Copyright © 2020, UChicago Argonne, LLC. All rights reserved. +# Released under the modified BSD license. See COPYING.md for more details. # Written by Alinson S. Xavier from miplearn import LearningSolver, BranchPriorityComponent, WarmStartComponent From 29d3c7bb9e2217d375c18c6f77297f0bedad87f9 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:43:25 -0600 Subject: [PATCH 17/44] Move docs folder --- {docs => docs-src}/about.md | 0 {docs => docs-src}/benchmark.md | 0 {docs => docs-src}/customization.md | 0 docs-src/figures/benchmark_stab_a.png | 1 + {docs => docs-src}/index.md | 0 docs-src/js/mathjax.js | 8 + {docs => docs-src}/problems.md | 0 {docs => docs-src}/usage.md | 0 docs/404.html | 225 ++ docs/about/index.html | 261 ++ docs/benchmark/index.html | 292 ++ docs/css/base.css | 281 ++ docs/css/base.min.css | 1 + docs/css/bootstrap-custom.css | 5309 +++++++++++++++++++++++++ docs/css/bootstrap-custom.min.css | 1 + docs/css/cinder.css | 82 + docs/css/cinder.min.css | 1 + docs/css/highlight.css | 99 + docs/css/highlight.min.css | 1 + docs/customization/index.html | 250 ++ docs/figures/benchmark_stab_a.png | Bin 47 -> 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docs/usage/index.html diff --git a/docs/about.md b/docs-src/about.md similarity index 100% rename from docs/about.md rename to docs-src/about.md diff --git a/docs/benchmark.md b/docs-src/benchmark.md similarity index 100% rename from docs/benchmark.md rename to docs-src/benchmark.md diff --git a/docs/customization.md b/docs-src/customization.md similarity index 100% rename from docs/customization.md rename to docs-src/customization.md diff --git a/docs-src/figures/benchmark_stab_a.png b/docs-src/figures/benchmark_stab_a.png new file mode 120000 index 0000000..073c5ec --- /dev/null +++ b/docs-src/figures/benchmark_stab_a.png @@ -0,0 +1 @@ +../../benchmark/stab/ChallengeA/performance.png \ No newline at end of file diff --git a/docs/index.md b/docs-src/index.md similarity index 100% rename from docs/index.md rename to docs-src/index.md diff --git a/docs-src/js/mathjax.js b/docs-src/js/mathjax.js new file mode 100644 index 0000000..bfc06b8 --- /dev/null +++ b/docs-src/js/mathjax.js @@ -0,0 +1,8 @@ +MathJax.Hub.Config({ + "tex2jax": { inlineMath: [ [ '$', '$' ] ] } +}); +MathJax.Hub.Config({ + config: ["MMLorHTML.js"], + jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"], + extensions: ["MathMenu.js", "MathZoom.js"] +}); \ No newline at end of file diff --git a/docs/problems.md b/docs-src/problems.md similarity index 100% rename from docs/problems.md rename to docs-src/problems.md diff --git a/docs/usage.md b/docs-src/usage.md similarity index 100% rename from docs/usage.md rename to docs-src/usage.md diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 0000000..d8d0a1a --- /dev/null +++ b/docs/404.html @@ -0,0 +1,225 @@ + + + + + + + + + + + + + + MIPLearn + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

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+ © Copyright 2019–2020 Argonne National Laboratory
 + + Documentation built with MkDocs.

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Acknowledgements

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    +
  • Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
    • +
+

References

+
    +
  • Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed. INFORMS Journal on Computing (to appear). ArXiv:1902:01696
    • +
+

License

+
MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
+Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+
+ + +
+ +
+ +
+

+ © Copyright 2019–2020 Argonne National Laboratory
 + + Documentation built with MkDocs.

+ + + +
+ + + + + + + + + + + + + + + diff --git a/docs/benchmark/index.html b/docs/benchmark/index.html new file mode 100644 index 0000000..be1d5d7 --- /dev/null +++ b/docs/benchmark/index.html @@ -0,0 +1,292 @@ + + + + + + + + + + + + + + Benchmark - MIPLearn + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ +

Benchmarks Utilities

+

Using BenchmarkRunner

+

MIPLearn provides the utility class BenchmarkRunner, which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage:

+
from miplearn import BenchmarkRunner, LearningSolver
+
+# Create train and test instances
+train_instances = [...]
+test_instances  = [...]
+
+# Training phase...
+training_solver = LearningSolver(...)
+training_solver.parallel_solve(train_instances, n_jobs=10)
+training_solver.save_state("data.bin")
+
+# Test phase...
+test_solvers = {
+    "Baseline": LearningSolver(...), # each solver may have different parameters
+    "Strategy A": LearningSolver(...), 
+    "Strategy B": LearningSolver(...),
+    "Strategy C": LearningSolver(...),
+}
+benchmark = BenchmarkRunner(test_solvers)
+benchmark.load_state("data.bin")
+benchmark.fit()
+benchmark.parallel_solve(test_instances, n_jobs=2)
+print(benchmark.raw_results())
+
+ +

The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns:

+
    +
  • Solver, the name of the solver.
    • +
  • Instance, the sequence number identifying the instance.
    • +
  • Wallclock Time, the wallclock running time (in seconds) spent by the solver;
    • +
  • Lower Bound, the best lower bound obtained by the solver;
    • +
  • Upper Bound, the best upper bound obtained by the solver;
    • +
  • Gap, the relative MIP integrality gap at the end of the optimization;
    • +
  • Nodes, the number of explored branch-and-bound nodes.
    • +
+

In addition to the above, there is also a "Relative" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.

+

Saving and loading benchmark results

+

When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results, which can be used to avoid this repetition, as the next example shows:

+
# Benchmark baseline solvers and save results to a file.
+benchmark = BenchmarkRunner(baseline_solvers)
+benchmark.load_state("training_data.bin")
+benchmark.parallel_solve(test_instances)
+benchmark.save_results("baseline_results.csv")
+
+# Benchmark remaining solvers, loading baseline results from file.
+benchmark = BenchmarkRunner(alternative_solvers)
+benchmark.load_state("training_data.bin")
+benchmark.load_results("baseline_results.csv")
+benchmark.parallel_solve(test_instances)
+
+ + +
+ +
+ +
+

+ © Copyright 2019–2020 Argonne National Laboratory
 + + Documentation built with MkDocs.

+ + + +
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from upstream default by Christopher Simpkins*/ +} +.bs-callout-primary { + border-left-color: #428bca; +} +.bs-callout-primary h4 { + color: #428bca; +} +.bs-callout-success { + border-left-color: #5cb85c; +} +.bs-callout-success h4 { + color: #5cb85c; +} +.bs-callout-danger { + border-left-color: #d9534f; +} +.bs-callout-danger h4 { + color: #d9534f; +} +.bs-callout-warning { + border-left-color: #f0ad4e; +} +.bs-callout-warning h4 { + color: #f0ad4e; +} +.bs-callout-info { + border-left-color: #5bc0de; +} +.bs-callout-info h4 { + color: #5bc0de; +} +/* End Bootstrap Callouts CSS Source by Chris Pratt */ + +/* Admonitions */ +.admonition { + padding: 20px; + margin: 20px 0; + border: 1px solid #eee; + border-left-width: 5px; + border-radius: 3px; + background-color: #FCFDFF; +} + +.admonition p:last-child { + margin-bottom: 0; +} +.admonition code { + border-radius: 3px; +} +.admonition+.admonition { + margin-top: -5px; +} + +.admonition.note { /* csslint allow: adjoining-classes */ + border-left-color: #428bca; +} + +.admonition.warning { /* csslint allow: adjoining-classes */ + border-left-color: #f0ad4e; +} + +.admonition.danger { /* csslint allow: adjoining-classes */ + border-left-color: #d9534f; +} + +.admonition-title { + font-size: 19px; + font-style: normal; + font-weight: 400; + margin-top: 0; + margin-bottom: 5px; +} + +.admonition.note > .admonition-title { + color: #428bca; +} + +.admonition.warning > .admonition-title { + color: #f0ad4e; +} + +.admonition.danger > .admonition-title { + color: #d9534f; +} diff --git a/docs/css/base.min.css b/docs/css/base.min.css new file mode 100644 index 0000000..c5454f8 --- /dev/null +++ b/docs/css/base.min.css @@ -0,0 +1 @@ +body{padding-top:70px}h1[id]:before,h2[id]:before,h3[id]:before,h4[id]:before,h5[id]:before,h6[id]:before{content:"";display:block;margin-top:-75px;height:75px}p>img{max-width:100%;height:auto}ul.nav li.first-level{font-weight:bold}ul.nav li.third-level{padding-left:12px}div.col-md-3{padding-left:0}div.col-md-9{padding-bottom:100px}div.source-links{float:right}.bs-sidebar.affix{position:static}.bs-sidebar.well{padding:0}.bs-sidenav{margin-top:30px;margin-bottom:30px;padding-top:10px;padding-bottom:10px;border-radius:5px}.bs-sidebar .nav>li>a{display:block;padding:5px 20px;z-index:1}.bs-sidebar .nav>li>a:hover,.bs-sidebar .nav>li>a:focus{text-decoration:none;border-right:1px solid}.bs-sidebar .nav>.active>a,.bs-sidebar .nav>.active:hover>a,.bs-sidebar .nav>.active:focus>a{font-weight:bold;background-color:transparent;border-right:1px solid}.bs-sidebar .nav .nav{display:none;margin-bottom:8px}.bs-sidebar .nav .nav>li>a{padding-top:3px;padding-bottom:3px;padding-left:30px;font-size:90%}@media(min-width:992px){.bs-sidebar .nav>.active>ul{display:block}.bs-sidebar.affix,.bs-sidebar.affix-bottom{width:213px}.bs-sidebar.affix{position:fixed;top:80px;max-height:calc(100% - 90px)}.bs-sidebar.affix-bottom{position:absolute}.bs-sidebar.affix-bottom .bs-sidenav,.bs-sidebar.affix .bs-sidenav{margin-top:0;margin-bottom:0}}@media(min-width:1200px){.bs-sidebar.affix-bottom,.bs-sidebar.affix{width:263px}}.dropdown-submenu{position:relative}.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:0;margin-left:0}.dropdown-submenu:hover>.dropdown-menu{display:block}.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}.dropdown-submenu:hover>a:after{border-left-color:#fff}.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:00px}.bs-callout{padding:20px;margin:20px 0;border:1px solid #eee;border-left-width:5px;border-radius:3px;background-color:#fcfdff}.bs-callout h4{font-style:normal;font-weight:400;margin-top:0;margin-bottom:5px}.bs-callout p:last-child{margin-bottom:0}.bs-callout code{border-radius:3px}.bs-callout+.bs-callout{margin-top:-5px}.bs-callout-default{border-left-color:#fa023c}.bs-callout-default h4{color:#fa023c}.bs-callout-primary{border-left-color:#428bca}.bs-callout-primary h4{color:#428bca}.bs-callout-success{border-left-color:#5cb85c}.bs-callout-success h4{color:#5cb85c}.bs-callout-danger{border-left-color:#d9534f}.bs-callout-danger h4{color:#d9534f}.bs-callout-warning{border-left-color:#f0ad4e}.bs-callout-warning h4{color:#f0ad4e}.bs-callout-info{border-left-color:#5bc0de}.bs-callout-info h4{color:#5bc0de}.admonition{padding:20px;margin:20px 0;border:1px solid #eee;border-left-width:5px;border-radius:3px;background-color:#fcfdff}.admonition p:last-child{margin-bottom:0}.admonition code{border-radius:3px}.admonition+.admonition{margin-top:-5px}.admonition.note{border-left-color:#428bca}.admonition.warning{border-left-color:#f0ad4e}.admonition.danger{border-left-color:#d9534f}.admonition-title{font-size:19px;font-style:normal;font-weight:400;margin-top:0;margin-bottom:5px}.admonition.note>.admonition-title{color:#428bca}.admonition.warning>.admonition-title{color:#f0ad4e}.admonition.danger>.admonition-title{color:#d9534f} diff --git a/docs/css/bootstrap-custom.css b/docs/css/bootstrap-custom.css new file mode 100644 index 0000000..631252d --- /dev/null +++ b/docs/css/bootstrap-custom.css @@ -0,0 +1,5309 @@ +/*! normalize.css v2.1.3 | MIT License | git.io/normalize */ +article, aside, details, figcaption, figure, footer, header, hgroup, main, nav, section, summary { + display: block; +} +audio, canvas, video { + display: inline-block; +} +audio:not([controls]) { + display: none; + height: 0; +} +[hidden], template { + display: none; +} +html { + font-family: sans-serif; + -webkit-text-size-adjust: 100%; + -ms-text-size-adjust: 100%} +body { + margin: 0; +} +a { + background: transparent; +} +a:focus { + outline: thin dotted; +} +a:active, a:hover { + outline: 0; +} +h1 { + margin: .67em 0; + font-size: 2em; +} +abbr[title] { + border-bottom: 1px dotted; +} +b, strong { + font-weight: bold; +} +dfn { + font-style: italic; +} +hr { + height: 0; + -moz-box-sizing: content-box; + box-sizing: content-box; +} +mark { + color: #000; + background: #ff0; +} +code, kbd, pre, samp { + font-family: Hack, monospace, serif; + font-size: 1em; +} +pre { + white-space: pre-wrap; +} +q { + quotes: "\201C" "\201D" "\2018" "\2019"} +small { + font-size: 80%} +sub, sup { + position: relative; + font-size: 75%; + line-height: 0; + vertical-align: baseline; +} +sup { + top: -0.5em; +} +sub { + bottom: -0.25em; +} +img { + border: 0; +} +svg:not(:root) { + overflow: hidden; +} +figure { + margin: 0; +} +fieldset { + padding: .35em .625em .75em; + margin: 0 2px; + border: 1px solid #c0c0c0; +} +legend { + padding: 0; + border: 0; +} +button, input, select, textarea { + margin: 0; + font-family: inherit; + font-size: 100%} +button, input { + line-height: normal; +} +button, select { + text-transform: none; +} +button, html input[type="button"], input[type="reset"], input[type="submit"] { + cursor: pointer; + -webkit-appearance: button; +} +button[disabled], html input[disabled] { + cursor: default; +} +input[type="checkbox"], input[type="radio"] { + padding: 0; + box-sizing: border-box; +} +input[type="search"] { + -webkit-box-sizing: content-box; + -moz-box-sizing: content-box; + box-sizing: content-box; + -webkit-appearance: textfield; +} +input[type="search"]::-webkit-search-cancel-button, input[type="search"]::-webkit-search-decoration { + -webkit-appearance: none; +} +button::-moz-focus-inner, input::-moz-focus-inner { + padding: 0; + border: 0; +} +textarea { + overflow: auto; + vertical-align: top; +} +table { + border-collapse: collapse; + border-spacing: 0; +} +@media print { + * { + color: #000!important; + text-shadow: none!important; + background: transparent!important; + box-shadow: none!important; +} +a, a:visited { + text-decoration: underline; +} +a[href]:after { + content: " (" attr(href) ")"} +abbr[title]:after { + content: " (" attr(title) ")"} +a[href^="javascript:"]:after, a[href^="#"]:after { + content: ""} +pre, blockquote { + border: 1px solid #999; + page-break-inside: avoid; +} +thead { + display: table-header-group; +} +tr, img { + page-break-inside: avoid; +} +img { + max-width: 100%!important; +} +@page { + margin: 2cm .5cm; +} +p, h2, h3 { + orphans: 3; + widows: 3; +} +h2, h3 { + page-break-after: avoid; +} +select { + background: #fff!important; +} +.navbar { + display: none; +} +.table td, .table th { + background-color: #fff!important; +} +.btn>.caret, .dropup>.btn>.caret { + border-top-color: #000!important; +} +.label { + border: 1px solid #000; +} +.table { + border-collapse: collapse!important; +} +.table-bordered th, .table-bordered td { + border: 1px solid #ddd!important; +} +}*, *:before, *:after { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +html { + font-size: 62.5%; + -webkit-tap-highlight-color: rgba(0, 0, 0, 0); +} +body { + font-family: Merriweather, Georgia, serif; + font-size: 14px; + line-height: 1.428571429; + color: #222; + background-color: #fff; +} +input, button, select, textarea { + font-family: inherit; + font-size: inherit; + line-height: inherit; +} +a { + color: #008cba; + text-decoration: none; +} +a:hover, a:focus { + color: #00526e; + text-decoration: underline; +} +a:focus { + outline: thin dotted; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +img { + vertical-align: middle; +} +.img-responsive { + display: block; + height: auto; + max-width: 100%} +.img-rounded { + border-radius: 0; +} +.img-thumbnail { + display: inline-block; + height: auto; + max-width: 100%; + padding: 4px; + line-height: 1.428571429; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 0; + -webkit-transition: all .2s ease-in-out; + transition: all .2s ease-in-out; +} +.img-circle { + border-radius: 50%} +hr { + margin-top: 21px; + margin-bottom: 21px; + border: 0; + border-top: 1px solid #ddd; +} +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + border: 0; +} +h1, h2, h3, h4, h5, h6, .h1, .h2, .h3, .h4, .h5, .h6 { + font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; + font-weight: 300; + line-height: 1.1; + color: inherit; +} +h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small { + font-weight: normal; + line-height: 1; + color: #999; +} +h1, h2, h3 { + margin-top: 21px; + margin-bottom: 10.5px; +} +h1 small, h2 small, h3 small, h1 .small, h2 .small, h3 .small { + font-size: 65%} +h4, h5, h6 { + margin-top: 10.5px; + margin-bottom: 10.5px; +} +h4 small, h5 small, h6 small, h4 .small, h5 .small, h6 .small { + font-size: 75%} +h1, .h1 { + font-size: 39px; +} +h2, .h2 { + font-size: 32px; +} +h3, .h3 { + font-size: 26px; +} +h4, .h4 { + font-size: 19px; +} +h5, .h5 { + font-size: 15px; +} +h6, .h6 { + font-size: 13px; +} +p { + margin: 0 0 10.5px; +} +.lead { + margin-bottom: 21px; + font-size: 17px; + font-weight: 200; + line-height: 1.4; +} +@media(min-width:768px) { + .lead { + font-size: 22.5px; +} +}small, .small { + font-size: 85%} +cite { + font-style: normal; +} +.text-muted { + color: #999; +} +.text-primary { + color: #008cba; +} +.text-primary:hover { + color: #006687; +} +.text-warning { + color: #e99002; +} +.text-warning:hover { + color: #b67102; +} +.text-danger { + color: #f04124; +} +.text-danger:hover { + color: #d32a0e; +} +.text-success { + color: #43ac6a; +} +.text-success:hover { + color: #358753; +} +.text-info { + color: #5bc0de; +} +.text-info:hover { + color: #31b0d5; +} +.text-left { + text-align: left; +} +.text-right { + text-align: right; +} +.text-center { + text-align: center; +} +.page-header { + padding-bottom: 9.5px; + margin: 42px 0 21px; + border-bottom: 1px solid #ddd; +} +ul, ol { + margin-top: 0; + margin-bottom: 10.5px; +} +ul ul, ol ul, ul ol, ol ol { + margin-bottom: 0; +} +.list-unstyled { + padding-left: 0; + list-style: none; +} +.list-inline { + padding-left: 0; + list-style: none; +} +.list-inline>li { + display: inline-block; + padding-right: 5px; + padding-left: 5px; +} +.list-inline>li:first-child { + padding-left: 0; +} +dl { + margin-top: 0; + margin-bottom: 21px; +} +dt, dd { + line-height: 1.428571429; +} +dt { + font-weight: bold; +} +dd { + margin-left: 0; +} +@media(min-width:768px) { + .dl-horizontal dt { + float: left; + width: 160px; + overflow: hidden; + clear: left; + text-align: right; + text-overflow: ellipsis; + white-space: nowrap; +} +.dl-horizontal dd { + margin-left: 180px; +} +.dl-horizontal dd:before, .dl-horizontal dd:after { + display: table; + content: " "} +.dl-horizontal dd:after { + clear: both; +} +.dl-horizontal dd:before, .dl-horizontal dd:after { + display: table; + content: " "} +.dl-horizontal dd:after { + clear: both; +} +.dl-horizontal dd:before, .dl-horizontal dd:after { + display: table; + content: " "} +.dl-horizontal dd:after { + clear: both; +} +.dl-horizontal dd:before, .dl-horizontal dd:after { + display: table; + content: " "} +.dl-horizontal dd:after { + clear: both; +} +.dl-horizontal dd:before, .dl-horizontal dd:after { + display: table; + content: " "} +.dl-horizontal dd:after { + clear: both; +} +}abbr[title], abbr[data-original-title] { + cursor: help; + border-bottom: 1px dotted #999; +} +.initialism { + font-size: 90%; + text-transform: uppercase; +} +blockquote { + padding: 10.5px 21px; + margin: 0 0 21px; + border-left: 5px solid #ddd; +} +blockquote p { + font-size: 18.75px; + font-weight: 300; + line-height: 1.25; +} +blockquote p:last-child { + margin-bottom: 0; +} +blockquote small, blockquote .small { + display: block; + line-height: 1.428571429; + color: #6f6f6f; +} +blockquote small:before, blockquote .small:before { + content: '\2014 \00A0'} +blockquote.pull-right { + padding-right: 15px; + padding-left: 0; + border-right: 5px solid #ddd; + border-left: 0; +} +blockquote.pull-right p, blockquote.pull-right small, blockquote.pull-right .small { + text-align: right; +} +blockquote.pull-right small:before, blockquote.pull-right .small:before { + content: ''} +blockquote.pull-right small:after, blockquote.pull-right .small:after { + content: '\00A0 \2014'} +blockquote:before, blockquote:after { + content: ""} +address { + margin-bottom: 21px; + font-style: normal; + line-height: 1.428571429; +} +code, kbd, pre, samp { + font-family: Hack, Menlo, Monaco, Consolas, "Courier New", monospace; +} +code { + padding: 2px 4px; + font-size: 90%; + color: #c7254e; + white-space: nowrap; + background-color: #f9f2f4; + border-radius: 0; +} +pre { + display: block; + padding: 10px; + margin: 0 0 10.5px; + font-size: 14px; + line-height: 1.428571429; + color: #333; + word-break: break-all; + word-wrap: break-word; + background-color: #f5f5f5; + border: 1px solid #ccc; + border-radius: 0; +} +pre code { + padding: 0; + font-size: inherit; + color: inherit; + white-space: pre-wrap; + background-color: transparent; + border-radius: 0; +} +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} +.container { + padding-right: 15px; + padding-left: 15px; + margin-right: auto; + margin-left: auto; +} +.container:before, .container:after { + display: table; + content: " "} +.container:after { + clear: both; +} +.container:before, .container:after { + display: table; + content: " "} +.container:after { + clear: both; +} +.container:before, .container:after { + display: table; + content: " "} +.container:after { + clear: both; +} +.container:before, .container:after { + display: table; + content: " "} +.container:after { + clear: both; +} +.container:before, .container:after { + display: table; + content: " "} +.container:after { + clear: both; +} +@media(min-width:768px) { + .container { + width: 750px; +} +}@media(min-width:992px) { + .container { + width: 970px; +} +}@media(min-width:1200px) { + .container { + width: 1170px; +} +}.row { + margin-right: -15px; + margin-left: -15px; +} +.row:before, .row:after { + display: table; + content: " "} +.row:after { + clear: both; +} +.row:before, .row:after { + display: table; + content: " "} +.row:after { + clear: both; +} +.row:before, .row:after { + display: table; + content: " "} +.row:after { + clear: both; +} +.row:before, .row:after { + display: table; + content: " "} +.row:after { + clear: both; +} +.row:before, .row:after { + display: table; + content: " "} +.row:after { + clear: both; +} +.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { + position: relative; + min-height: 1px; + padding-right: 15px; + padding-left: 15px; +} +.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { + float: left; +} +.col-xs-12 { + width: 100%} +.col-xs-11 { + width: 91.66666666666666%} +.col-xs-10 { + width: 83.33333333333334%} +.col-xs-9 { + width: 75%} +.col-xs-8 { + width: 66.66666666666666%} +.col-xs-7 { + width: 58.333333333333336%} +.col-xs-6 { + width: 50%} +.col-xs-5 { + width: 41.66666666666667%} +.col-xs-4 { + width: 33.33333333333333%} +.col-xs-3 { + width: 25%} +.col-xs-2 { + width: 16.666666666666664%} +.col-xs-1 { + width: 8.333333333333332%} +.col-xs-pull-12 { + right: 100%} +.col-xs-pull-11 { + right: 91.66666666666666%} +.col-xs-pull-10 { + right: 83.33333333333334%} +.col-xs-pull-9 { + right: 75%} +.col-xs-pull-8 { + right: 66.66666666666666%} +.col-xs-pull-7 { + right: 58.333333333333336%} +.col-xs-pull-6 { + right: 50%} +.col-xs-pull-5 { + right: 41.66666666666667%} +.col-xs-pull-4 { + right: 33.33333333333333%} +.col-xs-pull-3 { + right: 25%} +.col-xs-pull-2 { + right: 16.666666666666664%} +.col-xs-pull-1 { + right: 8.333333333333332%} +.col-xs-pull-0 { + right: 0; +} +.col-xs-push-12 { + left: 100%} +.col-xs-push-11 { + left: 91.66666666666666%} +.col-xs-push-10 { + left: 83.33333333333334%} +.col-xs-push-9 { + left: 75%} +.col-xs-push-8 { + left: 66.66666666666666%} +.col-xs-push-7 { + left: 58.333333333333336%} +.col-xs-push-6 { + left: 50%} +.col-xs-push-5 { + left: 41.66666666666667%} +.col-xs-push-4 { + left: 33.33333333333333%} +.col-xs-push-3 { + left: 25%} +.col-xs-push-2 { + left: 16.666666666666664%} +.col-xs-push-1 { + left: 8.333333333333332%} +.col-xs-push-0 { + left: 0; +} +.col-xs-offset-12 { + margin-left: 100%} +.col-xs-offset-11 { + margin-left: 91.66666666666666%} +.col-xs-offset-10 { + margin-left: 83.33333333333334%} +.col-xs-offset-9 { + margin-left: 75%} +.col-xs-offset-8 { + margin-left: 66.66666666666666%} +.col-xs-offset-7 { + margin-left: 58.333333333333336%} +.col-xs-offset-6 { + margin-left: 50%} +.col-xs-offset-5 { + margin-left: 41.66666666666667%} +.col-xs-offset-4 { + margin-left: 33.33333333333333%} +.col-xs-offset-3 { + margin-left: 25%} +.col-xs-offset-2 { + margin-left: 16.666666666666664%} +.col-xs-offset-1 { + margin-left: 8.333333333333332%} +.col-xs-offset-0 { + margin-left: 0; +} +@media(min-width:768px) { + .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { + float: left; +} +.col-sm-12 { + width: 100%} +.col-sm-11 { + width: 91.66666666666666%} +.col-sm-10 { + width: 83.33333333333334%} +.col-sm-9 { + width: 75%} +.col-sm-8 { + width: 66.66666666666666%} +.col-sm-7 { + width: 58.333333333333336%} +.col-sm-6 { + width: 50%} +.col-sm-5 { + width: 41.66666666666667%} +.col-sm-4 { + width: 33.33333333333333%} +.col-sm-3 { + width: 25%} +.col-sm-2 { + width: 16.666666666666664%} +.col-sm-1 { + width: 8.333333333333332%} +.col-sm-pull-12 { + right: 100%} +.col-sm-pull-11 { + right: 91.66666666666666%} +.col-sm-pull-10 { + right: 83.33333333333334%} +.col-sm-pull-9 { + right: 75%} +.col-sm-pull-8 { + right: 66.66666666666666%} +.col-sm-pull-7 { + right: 58.333333333333336%} +.col-sm-pull-6 { + right: 50%} +.col-sm-pull-5 { + right: 41.66666666666667%} +.col-sm-pull-4 { + right: 33.33333333333333%} +.col-sm-pull-3 { + right: 25%} +.col-sm-pull-2 { + right: 16.666666666666664%} +.col-sm-pull-1 { + right: 8.333333333333332%} +.col-sm-pull-0 { + right: 0; +} +.col-sm-push-12 { + left: 100%} +.col-sm-push-11 { + left: 91.66666666666666%} +.col-sm-push-10 { + left: 83.33333333333334%} +.col-sm-push-9 { + left: 75%} +.col-sm-push-8 { + left: 66.66666666666666%} +.col-sm-push-7 { + left: 58.333333333333336%} +.col-sm-push-6 { + left: 50%} +.col-sm-push-5 { + left: 41.66666666666667%} +.col-sm-push-4 { + left: 33.33333333333333%} +.col-sm-push-3 { + left: 25%} +.col-sm-push-2 { + left: 16.666666666666664%} +.col-sm-push-1 { + left: 8.333333333333332%} +.col-sm-push-0 { + left: 0; +} +.col-sm-offset-12 { + margin-left: 100%} +.col-sm-offset-11 { + margin-left: 91.66666666666666%} +.col-sm-offset-10 { + margin-left: 83.33333333333334%} +.col-sm-offset-9 { + margin-left: 75%} +.col-sm-offset-8 { + margin-left: 66.66666666666666%} +.col-sm-offset-7 { + margin-left: 58.333333333333336%} +.col-sm-offset-6 { + margin-left: 50%} +.col-sm-offset-5 { + margin-left: 41.66666666666667%} +.col-sm-offset-4 { + margin-left: 33.33333333333333%} +.col-sm-offset-3 { + margin-left: 25%} +.col-sm-offset-2 { + margin-left: 16.666666666666664%} +.col-sm-offset-1 { + margin-left: 8.333333333333332%} +.col-sm-offset-0 { + margin-left: 0; +} +}@media(min-width:992px) { + .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { + float: left; +} +.col-md-12 { + width: 100%} +.col-md-11 { + width: 91.66666666666666%} +.col-md-10 { + width: 83.33333333333334%} +.col-md-9 { + width: 75%} +.col-md-8 { + width: 66.66666666666666%} +.col-md-7 { + width: 58.333333333333336%} +.col-md-6 { + width: 50%} +.col-md-5 { + width: 41.66666666666667%} +.col-md-4 { + width: 33.33333333333333%} +.col-md-3 { + width: 25%} +.col-md-2 { + width: 16.666666666666664%} +.col-md-1 { + width: 8.333333333333332%} +.col-md-pull-12 { + right: 100%} +.col-md-pull-11 { + right: 91.66666666666666%} +.col-md-pull-10 { + right: 83.33333333333334%} +.col-md-pull-9 { + right: 75%} +.col-md-pull-8 { + right: 66.66666666666666%} +.col-md-pull-7 { + right: 58.333333333333336%} +.col-md-pull-6 { + right: 50%} +.col-md-pull-5 { + right: 41.66666666666667%} +.col-md-pull-4 { + right: 33.33333333333333%} +.col-md-pull-3 { + right: 25%} +.col-md-pull-2 { + right: 16.666666666666664%} +.col-md-pull-1 { + right: 8.333333333333332%} +.col-md-pull-0 { + right: 0; +} +.col-md-push-12 { + left: 100%} +.col-md-push-11 { + left: 91.66666666666666%} +.col-md-push-10 { + left: 83.33333333333334%} +.col-md-push-9 { + left: 75%} +.col-md-push-8 { + left: 66.66666666666666%} +.col-md-push-7 { + left: 58.333333333333336%} +.col-md-push-6 { + left: 50%} +.col-md-push-5 { + left: 41.66666666666667%} +.col-md-push-4 { + left: 33.33333333333333%} +.col-md-push-3 { + left: 25%} +.col-md-push-2 { + left: 16.666666666666664%} +.col-md-push-1 { + left: 8.333333333333332%} +.col-md-push-0 { + left: 0; +} +.col-md-offset-12 { + margin-left: 100%} +.col-md-offset-11 { + margin-left: 91.66666666666666%} +.col-md-offset-10 { + margin-left: 83.33333333333334%} +.col-md-offset-9 { + margin-left: 75%} +.col-md-offset-8 { + margin-left: 66.66666666666666%} +.col-md-offset-7 { + margin-left: 58.333333333333336%} +.col-md-offset-6 { + margin-left: 50%} +.col-md-offset-5 { + margin-left: 41.66666666666667%} +.col-md-offset-4 { + margin-left: 33.33333333333333%} +.col-md-offset-3 { + margin-left: 25%} +.col-md-offset-2 { + margin-left: 16.666666666666664%} +.col-md-offset-1 { + margin-left: 8.333333333333332%} +.col-md-offset-0 { + margin-left: 0; +} +}@media(min-width:1200px) { + .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { + float: left; +} +.col-lg-12 { + width: 100%} +.col-lg-11 { + width: 91.66666666666666%} +.col-lg-10 { + width: 83.33333333333334%} +.col-lg-9 { + width: 75%} +.col-lg-8 { + width: 66.66666666666666%} +.col-lg-7 { + width: 58.333333333333336%} +.col-lg-6 { + width: 50%} +.col-lg-5 { + width: 41.66666666666667%} +.col-lg-4 { + width: 33.33333333333333%} +.col-lg-3 { + width: 25%} +.col-lg-2 { + width: 16.666666666666664%} +.col-lg-1 { + width: 8.333333333333332%} +.col-lg-pull-12 { + right: 100%} +.col-lg-pull-11 { + right: 91.66666666666666%} +.col-lg-pull-10 { + right: 83.33333333333334%} +.col-lg-pull-9 { + right: 75%} +.col-lg-pull-8 { + right: 66.66666666666666%} +.col-lg-pull-7 { + right: 58.333333333333336%} +.col-lg-pull-6 { + right: 50%} +.col-lg-pull-5 { + right: 41.66666666666667%} +.col-lg-pull-4 { + right: 33.33333333333333%} +.col-lg-pull-3 { + right: 25%} +.col-lg-pull-2 { + right: 16.666666666666664%} +.col-lg-pull-1 { + right: 8.333333333333332%} +.col-lg-pull-0 { + right: 0; +} +.col-lg-push-12 { + left: 100%} +.col-lg-push-11 { + left: 91.66666666666666%} +.col-lg-push-10 { + left: 83.33333333333334%} +.col-lg-push-9 { + left: 75%} +.col-lg-push-8 { + left: 66.66666666666666%} +.col-lg-push-7 { + left: 58.333333333333336%} +.col-lg-push-6 { + left: 50%} +.col-lg-push-5 { + left: 41.66666666666667%} +.col-lg-push-4 { + left: 33.33333333333333%} +.col-lg-push-3 { + left: 25%} +.col-lg-push-2 { + left: 16.666666666666664%} +.col-lg-push-1 { + left: 8.333333333333332%} +.col-lg-push-0 { + left: 0; +} +.col-lg-offset-12 { + margin-left: 100%} +.col-lg-offset-11 { + margin-left: 91.66666666666666%} +.col-lg-offset-10 { + margin-left: 83.33333333333334%} +.col-lg-offset-9 { + margin-left: 75%} +.col-lg-offset-8 { + margin-left: 66.66666666666666%} +.col-lg-offset-7 { + margin-left: 58.333333333333336%} +.col-lg-offset-6 { + margin-left: 50%} +.col-lg-offset-5 { + margin-left: 41.66666666666667%} +.col-lg-offset-4 { + margin-left: 33.33333333333333%} +.col-lg-offset-3 { + margin-left: 25%} +.col-lg-offset-2 { + margin-left: 16.666666666666664%} +.col-lg-offset-1 { + margin-left: 8.333333333333332%} +.col-lg-offset-0 { + margin-left: 0; +} +}table { + max-width: 100%; + background-color: transparent; +} +th { + text-align: left; +} +.table { + width: 100%; + margin-bottom: 21px; +} +.table>thead>tr>th, .table>tbody>tr>th, .table>tfoot>tr>th, .table>thead>tr>td, .table>tbody>tr>td, .table>tfoot>tr>td { + padding: 8px; + line-height: 1.428571429; + vertical-align: top; + border-top: 1px solid #ddd; +} +.table>thead>tr>th { + vertical-align: bottom; + border-bottom: 2px solid #ddd; +} +.table>caption+thead>tr:first-child>th, .table>colgroup+thead>tr:first-child>th, .table>thead:first-child>tr:first-child>th, .table>caption+thead>tr:first-child>td, .table>colgroup+thead>tr:first-child>td, .table>thead:first-child>tr:first-child>td { + border-top: 0; +} +.table>tbody+tbody { + border-top: 2px solid #ddd; +} +.table .table { + background-color: #fff; +} +.table-condensed>thead>tr>th, .table-condensed>tbody>tr>th, .table-condensed>tfoot>tr>th, .table-condensed>thead>tr>td, .table-condensed>tbody>tr>td, .table-condensed>tfoot>tr>td { + padding: 5px; +} +.table-bordered { + border: 1px solid #ddd; +} +.table-bordered>thead>tr>th, .table-bordered>tbody>tr>th, .table-bordered>tfoot>tr>th, .table-bordered>thead>tr>td, .table-bordered>tbody>tr>td, .table-bordered>tfoot>tr>td { + border: 1px solid #ddd; +} +.table-bordered>thead>tr>th, .table-bordered>thead>tr>td { + border-bottom-width: 2px; +} +.table-striped>tbody>tr:nth-child(odd)>td, .table-striped>tbody>tr:nth-child(odd)>th { + background-color: #f9f9f9; +} +.table-hover>tbody>tr:hover>td, .table-hover>tbody>tr:hover>th { + background-color: #f5f5f5; +} +table col[class*="col-"] { + position: static; + display: table-column; + float: none; +} +table td[class*="col-"], table th[class*="col-"] { + display: table-cell; + float: none; +} +.table>thead>tr>.active, .table>tbody>tr>.active, .table>tfoot>tr>.active, .table>thead>.active>td, .table>tbody>.active>td, .table>tfoot>.active>td, .table>thead>.active>th, .table>tbody>.active>th, .table>tfoot>.active>th { + background-color: #f5f5f5; +} +.table-hover>tbody>tr>.active:hover, .table-hover>tbody>.active:hover>td, .table-hover>tbody>.active:hover>th { + background-color: #e8e8e8; +} +.table>thead>tr>.success, .table>tbody>tr>.success, .table>tfoot>tr>.success, .table>thead>.success>td, .table>tbody>.success>td, .table>tfoot>.success>td, .table>thead>.success>th, .table>tbody>.success>th, .table>tfoot>.success>th { + background-color: #dff0d8; +} +.table-hover>tbody>tr>.success:hover, .table-hover>tbody>.success:hover>td, .table-hover>tbody>.success:hover>th { + background-color: #d0e9c6; +} +.table>thead>tr>.danger, .table>tbody>tr>.danger, .table>tfoot>tr>.danger, .table>thead>.danger>td, .table>tbody>.danger>td, .table>tfoot>.danger>td, .table>thead>.danger>th, .table>tbody>.danger>th, .table>tfoot>.danger>th { + background-color: #f2dede; +} +.table-hover>tbody>tr>.danger:hover, .table-hover>tbody>.danger:hover>td, .table-hover>tbody>.danger:hover>th { + background-color: #ebcccc; +} +.table>thead>tr>.warning, .table>tbody>tr>.warning, .table>tfoot>tr>.warning, .table>thead>.warning>td, .table>tbody>.warning>td, .table>tfoot>.warning>td, .table>thead>.warning>th, .table>tbody>.warning>th, .table>tfoot>.warning>th { + background-color: #fcf8e3; +} +.table-hover>tbody>tr>.warning:hover, .table-hover>tbody>.warning:hover>td, .table-hover>tbody>.warning:hover>th { + background-color: #faf2cc; +} +@media(max-width:767px) { + .table-responsive { + width: 100%; + margin-bottom: 15.75px; + overflow-x: scroll; + overflow-y: hidden; + border: 1px solid #ddd; + -ms-overflow-style: -ms-autohiding-scrollbar; + -webkit-overflow-scrolling: touch; +} +.table-responsive>.table { + margin-bottom: 0; +} +.table-responsive>.table>thead>tr>th, .table-responsive>.table>tbody>tr>th, .table-responsive>.table>tfoot>tr>th, .table-responsive>.table>thead>tr>td, .table-responsive>.table>tbody>tr>td, .table-responsive>.table>tfoot>tr>td { + white-space: nowrap; +} +.table-responsive>.table-bordered { + border: 0; +} +.table-responsive>.table-bordered>thead>tr>th:first-child, .table-responsive>.table-bordered>tbody>tr>th:first-child, .table-responsive>.table-bordered>tfoot>tr>th:first-child, .table-responsive>.table-bordered>thead>tr>td:first-child, .table-responsive>.table-bordered>tbody>tr>td:first-child, .table-responsive>.table-bordered>tfoot>tr>td:first-child { + border-left: 0; +} +.table-responsive>.table-bordered>thead>tr>th:last-child, .table-responsive>.table-bordered>tbody>tr>th:last-child, .table-responsive>.table-bordered>tfoot>tr>th:last-child, .table-responsive>.table-bordered>thead>tr>td:last-child, .table-responsive>.table-bordered>tbody>tr>td:last-child, .table-responsive>.table-bordered>tfoot>tr>td:last-child { + border-right: 0; +} +.table-responsive>.table-bordered>tbody>tr:last-child>th, .table-responsive>.table-bordered>tfoot>tr:last-child>th, .table-responsive>.table-bordered>tbody>tr:last-child>td, .table-responsive>.table-bordered>tfoot>tr:last-child>td { + border-bottom: 0; +} +}fieldset { + padding: 0; + margin: 0; + border: 0; +} +legend { + display: block; + width: 100%; + padding: 0; + margin-bottom: 21px; + font-size: 22.5px; + line-height: inherit; + color: #333; + border: 0; + border-bottom: 1px solid #e5e5e5; +} +label { + display: inline-block; + margin-bottom: 5px; + font-weight: bold; +} +input[type="search"] { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +input[type="radio"], input[type="checkbox"] { + margin: 4px 0 0; + margin-top: 1px \9; + line-height: normal; +} +input[type="file"] { + display: block; +} +select[multiple], select[size] { + height: auto; +} +select optgroup { + font-family: inherit; + font-size: inherit; + font-style: inherit; +} +input[type="file"]:focus, input[type="radio"]:focus, input[type="checkbox"]:focus { + outline: thin dotted; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +input[type="number"]::-webkit-outer-spin-button, input[type="number"]::-webkit-inner-spin-button { + height: auto; +} +output { + display: block; + padding-top: 7px; + font-size: 15px; + line-height: 1.428571429; + color: #6f6f6f; + vertical-align: middle; +} +.form-control { + display: block; + width: 100%; + height: 35px; + padding: 6px 12px; + font-size: 15px; + line-height: 1.428571429; + color: #6f6f6f; + vertical-align: middle; + background-color: #fff; + background-image: none; + border: 1px solid #ccc; + border-radius: 0; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; +} +.form-control:focus { + border-color: #66afe9; + outline: 0; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(102, 175, 233, 0.6); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(102, 175, 233, 0.6); +} +.form-control:-moz-placeholder { + color: #999; +} +.form-control::-moz-placeholder { + color: #999; + opacity: 1; +} +.form-control:-ms-input-placeholder { + color: #999; +} +.form-control::-webkit-input-placeholder { + color: #999; +} +.form-control[disabled], .form-control[readonly], fieldset[disabled] .form-control { + cursor: not-allowed; + background-color: #eee; +} +textarea.form-control { + height: auto; +} +.form-group { + margin-bottom: 15px; +} +.radio, .checkbox { + display: block; + min-height: 21px; + padding-left: 20px; + margin-top: 10px; + margin-bottom: 10px; + vertical-align: middle; +} +.radio label, .checkbox label { + display: inline; + margin-bottom: 0; + font-weight: normal; + cursor: pointer; +} +.radio input[type="radio"], .radio-inline input[type="radio"], .checkbox input[type="checkbox"], .checkbox-inline input[type="checkbox"] { + float: left; + margin-left: -20px; +} +.radio+.radio, .checkbox+.checkbox { + margin-top: -5px; +} +.radio-inline, .checkbox-inline { + display: inline-block; + padding-left: 20px; + margin-bottom: 0; + font-weight: normal; + vertical-align: middle; + cursor: pointer; +} +.radio-inline+.radio-inline, .checkbox-inline+.checkbox-inline { + margin-top: 0; + margin-left: 10px; +} +input[type="radio"][disabled], input[type="checkbox"][disabled], .radio[disabled], .radio-inline[disabled], .checkbox[disabled], .checkbox-inline[disabled], fieldset[disabled] input[type="radio"], fieldset[disabled] input[type="checkbox"], fieldset[disabled] .radio, fieldset[disabled] .radio-inline, fieldset[disabled] .checkbox, fieldset[disabled] .checkbox-inline { + cursor: not-allowed; +} +.input-sm { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 0; +} +select.input-sm { + height: 30px; + line-height: 30px; +} +textarea.input-sm { + height: auto; +} +.input-lg { + height: 48px; + padding: 10px 16px; + font-size: 19px; + line-height: 1.33; + border-radius: 0; +} +select.input-lg { + height: 48px; + line-height: 48px; +} +textarea.input-lg { + height: auto; +} +.has-warning .help-block, .has-warning .control-label, .has-warning .radio, .has-warning .checkbox, .has-warning .radio-inline, .has-warning .checkbox-inline { + color: #e99002; +} +.has-warning .form-control { + border-color: #e99002; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-warning .form-control:focus { + border-color: #b67102; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #febc53; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #febc53; +} +.has-warning .input-group-addon { + color: #e99002; + background-color: #fcf8e3; + border-color: #e99002; +} +.has-error .help-block, .has-error .control-label, .has-error .radio, .has-error .checkbox, .has-error .radio-inline, .has-error .checkbox-inline { + color: #f04124; +} +.has-error .form-control { + border-color: #f04124; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-error .form-control:focus { + border-color: #d32a0e; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #f79483; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #f79483; +} +.has-error .input-group-addon { + color: #f04124; + background-color: #f2dede; + border-color: #f04124; +} +.has-success .help-block, .has-success .control-label, .has-success .radio, .has-success .checkbox, .has-success .radio-inline, .has-success .checkbox-inline { + color: #43ac6a; +} +.has-success .form-control { + border-color: #43ac6a; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-success .form-control:focus { + border-color: #358753; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #85d0a1; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #85d0a1; +} +.has-success .input-group-addon { + color: #43ac6a; + background-color: #dff0d8; + border-color: #43ac6a; +} +.form-control-static { + margin-bottom: 0; +} +.help-block { + display: block; + margin-top: 5px; + margin-bottom: 10px; + color: #626262; +} +@media(min-width:768px) { + .form-inline .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; +} +.form-inline .form-control { + display: inline-block; +} +.form-inline select.form-control { + width: auto; +} +.form-inline .radio, .form-inline .checkbox { + display: inline-block; + padding-left: 0; + margin-top: 0; + margin-bottom: 0; +} +.form-inline .radio input[type="radio"], .form-inline .checkbox input[type="checkbox"] { + float: none; + margin-left: 0; +} +}.form-horizontal .control-label, .form-horizontal .radio, .form-horizontal .checkbox, .form-horizontal .radio-inline, .form-horizontal .checkbox-inline { + padding-top: 7px; + margin-top: 0; + margin-bottom: 0; +} +.form-horizontal .radio, .form-horizontal .checkbox { + min-height: 28px; +} +.form-horizontal .form-group { + margin-right: -15px; + margin-left: -15px; +} +.form-horizontal .form-group:before, .form-horizontal .form-group:after { + display: table; + content: " "} +.form-horizontal .form-group:after { + clear: both; +} +.form-horizontal .form-group:before, .form-horizontal .form-group:after { + display: table; + content: " "} +.form-horizontal .form-group:after { + clear: both; +} +.form-horizontal .form-group:before, .form-horizontal .form-group:after { + display: table; + content: " "} +.form-horizontal .form-group:after { + clear: both; +} +.form-horizontal .form-group:before, .form-horizontal .form-group:after { + display: table; + content: " "} +.form-horizontal .form-group:after { + clear: both; +} +.form-horizontal .form-group:before, .form-horizontal .form-group:after { + display: table; + content: " "} +.form-horizontal .form-group:after { + clear: both; +} +.form-horizontal .form-control-static { + padding-top: 7px; +} +@media(min-width:768px) { + .form-horizontal .control-label { + text-align: right; +} +}.btn { + display: inline-block; + padding: 6px 12px; + margin-bottom: 0; + font-size: 15px; + font-weight: normal; + line-height: 1.428571429; + text-align: center; + white-space: nowrap; + vertical-align: middle; + cursor: pointer; + background-image: none; + border: 1px solid transparent; + border-radius: 0; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + -o-user-select: none; + user-select: none; +} +.btn:focus { + outline: thin dotted; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +.btn:hover, .btn:focus { + color: #333; + text-decoration: none; +} +.btn:active, .btn.active { + background-image: none; + outline: 0; + -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); + box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); +} +.btn.disabled, .btn[disabled], fieldset[disabled] .btn { + pointer-events: none; + cursor: not-allowed; + opacity: .65; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + box-shadow: none; +} +.btn-default { + color: #333; + background-color: #e7e7e7; + border-color: #dadada; +} +.btn-default:hover, .btn-default:focus, .btn-default:active, .btn-default.active, .open .dropdown-toggle.btn-default { + color: #333; + background-color: #d3d3d3; + border-color: #bbb; +} +.btn-default:active, .btn-default.active, .open .dropdown-toggle.btn-default { + background-image: none; +} +.btn-default.disabled, .btn-default[disabled], fieldset[disabled] .btn-default, .btn-default.disabled:hover, .btn-default[disabled]:hover, fieldset[disabled] .btn-default:hover, .btn-default.disabled:focus, .btn-default[disabled]:focus, fieldset[disabled] .btn-default:focus, .btn-default.disabled:active, .btn-default[disabled]:active, fieldset[disabled] .btn-default:active, .btn-default.disabled.active, .btn-default[disabled].active, fieldset[disabled] .btn-default.active { + background-color: #e7e7e7; + border-color: #dadada; +} +.btn-default .badge { + color: #e7e7e7; + background-color: #fff; +} +.btn-primary { + color: #fff; + background-color: #008cba; + border-color: #0079a1; +} +.btn-primary:hover, .btn-primary:focus, .btn-primary:active, .btn-primary.active, .open .dropdown-toggle.btn-primary { + color: #fff; + background-color: #006d91; + border-color: #004b63; +} +.btn-primary:active, .btn-primary.active, .open .dropdown-toggle.btn-primary { + background-image: none; +} +.btn-primary.disabled, .btn-primary[disabled], fieldset[disabled] .btn-primary, .btn-primary.disabled:hover, .btn-primary[disabled]:hover, fieldset[disabled] .btn-primary:hover, .btn-primary.disabled:focus, .btn-primary[disabled]:focus, fieldset[disabled] .btn-primary:focus, .btn-primary.disabled:active, .btn-primary[disabled]:active, fieldset[disabled] .btn-primary:active, .btn-primary.disabled.active, .btn-primary[disabled].active, fieldset[disabled] .btn-primary.active { + background-color: #008cba; + border-color: #0079a1; +} +.btn-primary .badge { + color: #008cba; + background-color: #fff; +} +.btn-warning { + color: #fff; + background-color: #e99002; + border-color: #d08002; +} +.btn-warning:hover, .btn-warning:focus, .btn-warning:active, .btn-warning.active, .open .dropdown-toggle.btn-warning { + color: #fff; + background-color: #c17702; + border-color: #935b01; +} +.btn-warning:active, .btn-warning.active, .open .dropdown-toggle.btn-warning { + background-image: none; +} +.btn-warning.disabled, .btn-warning[disabled], fieldset[disabled] .btn-warning, .btn-warning.disabled:hover, .btn-warning[disabled]:hover, fieldset[disabled] .btn-warning:hover, .btn-warning.disabled:focus, .btn-warning[disabled]:focus, fieldset[disabled] .btn-warning:focus, .btn-warning.disabled:active, .btn-warning[disabled]:active, fieldset[disabled] .btn-warning:active, .btn-warning.disabled.active, .btn-warning[disabled].active, fieldset[disabled] .btn-warning.active { + background-color: #e99002; + border-color: #d08002; +} +.btn-warning .badge { + color: #e99002; + background-color: #fff; +} +.btn-danger { + color: #fff; + background-color: #f04124; + border-color: #ea2f10; +} +.btn-danger:hover, .btn-danger:focus, .btn-danger:active, .btn-danger.active, .open .dropdown-toggle.btn-danger { + color: #fff; + background-color: #dc2c0f; + border-color: #b1240c; +} +.btn-danger:active, .btn-danger.active, .open .dropdown-toggle.btn-danger { + background-image: none; +} +.btn-danger.disabled, .btn-danger[disabled], fieldset[disabled] .btn-danger, .btn-danger.disabled:hover, .btn-danger[disabled]:hover, fieldset[disabled] .btn-danger:hover, .btn-danger.disabled:focus, .btn-danger[disabled]:focus, fieldset[disabled] .btn-danger:focus, .btn-danger.disabled:active, .btn-danger[disabled]:active, fieldset[disabled] .btn-danger:active, .btn-danger.disabled.active, .btn-danger[disabled].active, fieldset[disabled] .btn-danger.active { + background-color: #f04124; + border-color: #ea2f10; +} +.btn-danger .badge { + color: #f04124; + background-color: #fff; +} +.btn-success { + color: #fff; + background-color: #43ac6a; + border-color: #3c9a5f; +} +.btn-success:hover, .btn-success:focus, .btn-success:active, .btn-success.active, .open .dropdown-toggle.btn-success { + color: #fff; + background-color: #388f58; + border-color: #2b6e44; +} +.btn-success:active, .btn-success.active, .open .dropdown-toggle.btn-success { + background-image: none; +} +.btn-success.disabled, .btn-success[disabled], fieldset[disabled] .btn-success, .btn-success.disabled:hover, .btn-success[disabled]:hover, fieldset[disabled] .btn-success:hover, .btn-success.disabled:focus, .btn-success[disabled]:focus, fieldset[disabled] .btn-success:focus, .btn-success.disabled:active, .btn-success[disabled]:active, fieldset[disabled] .btn-success:active, .btn-success.disabled.active, .btn-success[disabled].active, fieldset[disabled] .btn-success.active { + background-color: #43ac6a; + border-color: #3c9a5f; +} +.btn-success .badge { + color: #43ac6a; + background-color: #fff; +} +.btn-info { + color: #fff; + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-info:hover, .btn-info:focus, .btn-info:active, .btn-info.active, .open .dropdown-toggle.btn-info { + color: #fff; + background-color: #39b3d7; + border-color: #269abc; +} +.btn-info:active, .btn-info.active, .open .dropdown-toggle.btn-info { + background-image: none; +} +.btn-info.disabled, .btn-info[disabled], fieldset[disabled] .btn-info, .btn-info.disabled:hover, .btn-info[disabled]:hover, fieldset[disabled] .btn-info:hover, .btn-info.disabled:focus, .btn-info[disabled]:focus, fieldset[disabled] .btn-info:focus, .btn-info.disabled:active, .btn-info[disabled]:active, fieldset[disabled] .btn-info:active, .btn-info.disabled.active, .btn-info[disabled].active, fieldset[disabled] .btn-info.active { + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-info .badge { + color: #5bc0de; + background-color: #fff; +} +.btn-link { + font-weight: normal; + color: #008cba; + cursor: pointer; + border-radius: 0; +} +.btn-link, .btn-link:active, .btn-link[disabled], fieldset[disabled] .btn-link { + background-color: transparent; + -webkit-box-shadow: none; + box-shadow: none; +} +.btn-link, .btn-link:hover, .btn-link:focus, .btn-link:active { + border-color: transparent; +} +.btn-link:hover, .btn-link:focus { + color: #00526e; + text-decoration: underline; + background-color: transparent; +} +.btn-link[disabled]:hover, fieldset[disabled] .btn-link:hover, .btn-link[disabled]:focus, fieldset[disabled] .btn-link:focus { + color: #999; + text-decoration: none; +} +.btn-lg { + padding: 10px 16px; + font-size: 19px; + line-height: 1.33; + border-radius: 0; +} +.btn-sm { + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 0; +} +.btn-xs { + padding: 1px 5px; + font-size: 12px; + line-height: 1.5; + border-radius: 0; +} +.btn-block { + display: block; + width: 100%; + padding-right: 0; + padding-left: 0; +} +.btn-block+.btn-block { + margin-top: 5px; +} +input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { + width: 100%} +.fade { + opacity: 0; + -webkit-transition: opacity .15s linear; + transition: opacity .15s linear; +} +.fade.in { + opacity: 1; +} +.collapse { + display: none; +} +.collapse.in { + display: block; +} +.collapsing { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition: height .35s ease; + transition: height .35s ease; +} +@font-face { + font-family: 'Glyphicons Halflings'; + src: url('../fonts/glyphicons-halflings-regular.eot'); + src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons-halflingsregular') format('svg'); +} +.glyphicon { + position: relative; + top: 1px; + display: inline-block; + font-family: 'Glyphicons Halflings'; + -webkit-font-smoothing: antialiased; + font-style: normal; + font-weight: normal; + line-height: 1; + -moz-osx-font-smoothing: grayscale; +} +.glyphicon:empty { + width: 1em; +} +.glyphicon-asterisk:before { + content: "\2a"} +.glyphicon-plus:before { + content: "\2b"} +.glyphicon-euro:before { + content: "\20ac"} +.glyphicon-minus:before { + content: "\2212"} +.glyphicon-cloud:before { + content: "\2601"} +.glyphicon-envelope:before { + content: "\2709"} +.glyphicon-pencil:before { + content: "\270f"} +.glyphicon-glass:before { + content: "\e001"} +.glyphicon-music:before { + content: "\e002"} +.glyphicon-search:before { + content: "\e003"} +.glyphicon-heart:before { + content: "\e005"} +.glyphicon-star:before { + content: "\e006"} +.glyphicon-star-empty:before { + content: "\e007"} +.glyphicon-user:before { + content: "\e008"} +.glyphicon-film:before { + content: "\e009"} +.glyphicon-th-large:before { + content: "\e010"} +.glyphicon-th:before { + content: "\e011"} +.glyphicon-th-list:before { + content: "\e012"} +.glyphicon-ok:before { + content: "\e013"} +.glyphicon-remove:before { + content: "\e014"} +.glyphicon-zoom-in:before { + content: "\e015"} +.glyphicon-zoom-out:before { + content: "\e016"} +.glyphicon-off:before { + content: "\e017"} +.glyphicon-signal:before { + content: "\e018"} +.glyphicon-cog:before { + content: "\e019"} +.glyphicon-trash:before { + content: "\e020"} +.glyphicon-home:before { + content: "\e021"} +.glyphicon-file:before { + content: "\e022"} +.glyphicon-time:before { + content: "\e023"} +.glyphicon-road:before { + content: "\e024"} +.glyphicon-download-alt:before { + content: "\e025"} +.glyphicon-download:before { + content: "\e026"} +.glyphicon-upload:before { + content: "\e027"} +.glyphicon-inbox:before { + content: "\e028"} +.glyphicon-play-circle:before { + content: "\e029"} +.glyphicon-repeat:before { + content: "\e030"} +.glyphicon-refresh:before { + content: "\e031"} +.glyphicon-list-alt:before { + content: "\e032"} +.glyphicon-lock:before { + content: "\e033"} +.glyphicon-flag:before { + content: "\e034"} +.glyphicon-headphones:before { + content: "\e035"} +.glyphicon-volume-off:before { + content: "\e036"} +.glyphicon-volume-down:before { + content: "\e037"} +.glyphicon-volume-up:before { + content: "\e038"} +.glyphicon-qrcode:before { + content: "\e039"} +.glyphicon-barcode:before { + content: "\e040"} +.glyphicon-tag:before { + content: "\e041"} +.glyphicon-tags:before { + content: "\e042"} +.glyphicon-book:before { + content: "\e043"} +.glyphicon-bookmark:before { + content: "\e044"} +.glyphicon-print:before { + content: "\e045"} +.glyphicon-camera:before { + content: "\e046"} +.glyphicon-font:before { + content: "\e047"} +.glyphicon-bold:before { + content: "\e048"} +.glyphicon-italic:before { + content: "\e049"} +.glyphicon-text-height:before { + content: "\e050"} +.glyphicon-text-width:before { + content: "\e051"} +.glyphicon-align-left:before { + content: "\e052"} +.glyphicon-align-center:before { + content: "\e053"} +.glyphicon-align-right:before { + content: "\e054"} +.glyphicon-align-justify:before { + content: "\e055"} +.glyphicon-list:before { + content: "\e056"} +.glyphicon-indent-left:before { + content: "\e057"} +.glyphicon-indent-right:before { + content: "\e058"} +.glyphicon-facetime-video:before { + content: "\e059"} +.glyphicon-picture:before { + content: "\e060"} +.glyphicon-map-marker:before { + content: "\e062"} +.glyphicon-adjust:before { + content: "\e063"} +.glyphicon-tint:before { + content: "\e064"} +.glyphicon-edit:before { + content: "\e065"} +.glyphicon-share:before { + content: "\e066"} +.glyphicon-check:before { + content: "\e067"} +.glyphicon-move:before { + content: "\e068"} +.glyphicon-step-backward:before { + content: "\e069"} +.glyphicon-fast-backward:before { + content: "\e070"} +.glyphicon-backward:before { + content: "\e071"} +.glyphicon-play:before { + content: "\e072"} +.glyphicon-pause:before { + content: "\e073"} +.glyphicon-stop:before { + content: "\e074"} +.glyphicon-forward:before { + content: "\e075"} +.glyphicon-fast-forward:before { + content: "\e076"} +.glyphicon-step-forward:before { + content: "\e077"} +.glyphicon-eject:before { + content: "\e078"} +.glyphicon-chevron-left:before { + content: "\e079"} +.glyphicon-chevron-right:before { + content: "\e080"} +.glyphicon-plus-sign:before { + content: "\e081"} +.glyphicon-minus-sign:before { + content: "\e082"} +.glyphicon-remove-sign:before { + content: "\e083"} +.glyphicon-ok-sign:before { + content: "\e084"} +.glyphicon-question-sign:before { + content: "\e085"} +.glyphicon-info-sign:before { + content: "\e086"} +.glyphicon-screenshot:before { + content: "\e087"} +.glyphicon-remove-circle:before { + content: "\e088"} +.glyphicon-ok-circle:before { + content: "\e089"} +.glyphicon-ban-circle:before { + content: "\e090"} +.glyphicon-arrow-left:before { + content: "\e091"} +.glyphicon-arrow-right:before { + content: "\e092"} +.glyphicon-arrow-up:before { + content: "\e093"} +.glyphicon-arrow-down:before { + content: "\e094"} +.glyphicon-share-alt:before { + content: "\e095"} +.glyphicon-resize-full:before { + content: "\e096"} +.glyphicon-resize-small:before { + content: "\e097"} +.glyphicon-exclamation-sign:before { + content: "\e101"} +.glyphicon-gift:before { + content: "\e102"} +.glyphicon-leaf:before { + content: "\e103"} +.glyphicon-fire:before { + content: "\e104"} +.glyphicon-eye-open:before { + content: "\e105"} +.glyphicon-eye-close:before { + content: "\e106"} +.glyphicon-warning-sign:before { + content: "\e107"} +.glyphicon-plane:before { + content: "\e108"} +.glyphicon-calendar:before { + content: "\e109"} +.glyphicon-random:before { + content: "\e110"} +.glyphicon-comment:before { + content: "\e111"} +.glyphicon-magnet:before { + content: "\e112"} +.glyphicon-chevron-up:before { + content: "\e113"} +.glyphicon-chevron-down:before { + content: "\e114"} +.glyphicon-retweet:before { + content: "\e115"} +.glyphicon-shopping-cart:before { + content: "\e116"} +.glyphicon-folder-close:before { + content: "\e117"} +.glyphicon-folder-open:before { + content: "\e118"} +.glyphicon-resize-vertical:before { + content: "\e119"} +.glyphicon-resize-horizontal:before { + content: "\e120"} +.glyphicon-hdd:before { + content: "\e121"} +.glyphicon-bullhorn:before { + content: "\e122"} +.glyphicon-bell:before { + content: "\e123"} +.glyphicon-certificate:before { + content: "\e124"} +.glyphicon-thumbs-up:before { + content: "\e125"} +.glyphicon-thumbs-down:before { + content: "\e126"} +.glyphicon-hand-right:before { + content: "\e127"} +.glyphicon-hand-left:before { + content: "\e128"} +.glyphicon-hand-up:before { + content: "\e129"} +.glyphicon-hand-down:before { + content: "\e130"} +.glyphicon-circle-arrow-right:before { + content: "\e131"} +.glyphicon-circle-arrow-left:before { + content: "\e132"} +.glyphicon-circle-arrow-up:before { + content: "\e133"} +.glyphicon-circle-arrow-down:before { + content: "\e134"} +.glyphicon-globe:before { + content: "\e135"} +.glyphicon-wrench:before { + content: "\e136"} +.glyphicon-tasks:before { + content: "\e137"} +.glyphicon-filter:before { + content: "\e138"} +.glyphicon-briefcase:before { + content: "\e139"} +.glyphicon-fullscreen:before { + content: "\e140"} +.glyphicon-dashboard:before { + content: "\e141"} +.glyphicon-paperclip:before { + content: "\e142"} +.glyphicon-heart-empty:before { + content: "\e143"} +.glyphicon-link:before { + content: "\e144"} +.glyphicon-phone:before { + content: "\e145"} +.glyphicon-pushpin:before { + content: "\e146"} +.glyphicon-usd:before { + content: "\e148"} +.glyphicon-gbp:before { + content: "\e149"} +.glyphicon-sort:before { + content: "\e150"} +.glyphicon-sort-by-alphabet:before { + content: "\e151"} +.glyphicon-sort-by-alphabet-alt:before { + content: "\e152"} +.glyphicon-sort-by-order:before { + content: "\e153"} +.glyphicon-sort-by-order-alt:before { + content: "\e154"} +.glyphicon-sort-by-attributes:before { + content: "\e155"} +.glyphicon-sort-by-attributes-alt:before { + content: "\e156"} +.glyphicon-unchecked:before { + content: "\e157"} +.glyphicon-expand:before { + content: "\e158"} +.glyphicon-collapse-down:before { + content: "\e159"} +.glyphicon-collapse-up:before { + content: "\e160"} +.glyphicon-log-in:before { + content: "\e161"} +.glyphicon-flash:before { + content: "\e162"} +.glyphicon-log-out:before { + content: "\e163"} +.glyphicon-new-window:before { + content: "\e164"} +.glyphicon-record:before { + content: "\e165"} +.glyphicon-save:before { + content: "\e166"} +.glyphicon-open:before { + content: "\e167"} +.glyphicon-saved:before { + content: "\e168"} +.glyphicon-import:before { + content: "\e169"} +.glyphicon-export:before { + content: "\e170"} +.glyphicon-send:before { + content: "\e171"} +.glyphicon-floppy-disk:before { + content: "\e172"} +.glyphicon-floppy-saved:before { + content: "\e173"} +.glyphicon-floppy-remove:before { + content: "\e174"} +.glyphicon-floppy-save:before { + content: "\e175"} +.glyphicon-floppy-open:before { + content: "\e176"} +.glyphicon-credit-card:before { + content: "\e177"} +.glyphicon-transfer:before { + content: "\e178"} +.glyphicon-cutlery:before { + content: "\e179"} +.glyphicon-header:before { + content: "\e180"} +.glyphicon-compressed:before { + content: "\e181"} +.glyphicon-earphone:before { + content: "\e182"} +.glyphicon-phone-alt:before { + content: "\e183"} +.glyphicon-tower:before { + content: "\e184"} +.glyphicon-stats:before { + content: "\e185"} +.glyphicon-sd-video:before { + content: "\e186"} +.glyphicon-hd-video:before { + content: "\e187"} +.glyphicon-subtitles:before { + content: "\e188"} +.glyphicon-sound-stereo:before { + content: "\e189"} +.glyphicon-sound-dolby:before { + content: "\e190"} +.glyphicon-sound-5-1:before { + content: "\e191"} +.glyphicon-sound-6-1:before { + content: "\e192"} +.glyphicon-sound-7-1:before { + content: "\e193"} +.glyphicon-copyright-mark:before { + content: "\e194"} +.glyphicon-registration-mark:before { + content: "\e195"} +.glyphicon-cloud-download:before { + content: "\e197"} +.glyphicon-cloud-upload:before { + content: "\e198"} +.glyphicon-tree-conifer:before { + content: "\e199"} +.glyphicon-tree-deciduous:before { + content: "\e200"} +.caret { + display: inline-block; + width: 0; + height: 0; + margin-left: 2px; + vertical-align: middle; + border-top: 4px solid; + border-right: 4px solid transparent; + border-left: 4px solid transparent; +} +.dropdown { + position: relative; +} +.dropdown-toggle:focus { + outline: 0; +} +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 5px 0; + margin: 2px 0 0; + font-size: 15px; + list-style: none; + background-color: #fff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.15); + border-radius: 0; + -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); + box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); + background-clip: padding-box; +} +.dropdown-menu.pull-right { + right: 0; + left: auto; +} +.dropdown-menu .divider { + height: 1px; + margin: 9.5px 0; + overflow: hidden; + background-color: rgba(0, 0, 0, 0.2); +} +.dropdown-menu>li>a { + display: block; + padding: 3px 20px; + clear: both; + font-weight: normal; + line-height: 1.428571429; + color: #555; + white-space: nowrap; +} +.dropdown-menu>li>a:hover, .dropdown-menu>li>a:focus { + color: #262626; + text-decoration: none; + background-color: #eee; +} +.dropdown-menu>.active>a, .dropdown-menu>.active>a:hover, .dropdown-menu>.active>a:focus { + color: #fff; + text-decoration: none; + background-color: #008cba; + outline: 0; +} +.dropdown-menu>.disabled>a, .dropdown-menu>.disabled>a:hover, .dropdown-menu>.disabled>a:focus { + color: #999; +} +.dropdown-menu>.disabled>a:hover, .dropdown-menu>.disabled>a:focus { + text-decoration: none; + cursor: not-allowed; + background-color: transparent; + background-image: none; + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} +.open>.dropdown-menu { + display: block; +} +.open>a { + outline: 0; +} +.dropdown-header { + display: block; + padding: 3px 20px; + font-size: 12px; + line-height: 1.428571429; + color: #999; +} +.dropdown-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 990; +} +.pull-right>.dropdown-menu { + right: 0; + left: auto; +} +.dropup .caret, .navbar-fixed-bottom .dropdown .caret { + border-top: 0; + border-bottom: 4px solid; + content: ""} +.dropup .dropdown-menu, .navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 1px; +} +@media(min-width:768px) { + .navbar-right .dropdown-menu { + right: 0; + left: auto; +} +}.btn-group, .btn-group-vertical { + position: relative; + display: inline-block; + vertical-align: middle; +} +.btn-group>.btn, .btn-group-vertical>.btn { + position: relative; + float: left; +} +.btn-group>.btn:hover, .btn-group-vertical>.btn:hover, .btn-group>.btn:focus, .btn-group-vertical>.btn:focus, .btn-group>.btn:active, .btn-group-vertical>.btn:active, .btn-group>.btn.active, .btn-group-vertical>.btn.active { + z-index: 2; +} +.btn-group>.btn:focus, .btn-group-vertical>.btn:focus { + outline: 0; +} +.btn-group .btn+.btn, .btn-group .btn+.btn-group, .btn-group .btn-group+.btn, .btn-group .btn-group+.btn-group { + margin-left: -1px; +} +.btn-toolbar:before, .btn-toolbar:after { + display: table; + content: " "} +.btn-toolbar:after { + clear: both; +} +.btn-toolbar:before, .btn-toolbar:after { + display: table; + content: " "} +.btn-toolbar:after { + clear: both; +} +.btn-toolbar:before, .btn-toolbar:after { + display: table; + content: " "} +.btn-toolbar:after { + clear: both; +} +.btn-toolbar:before, .btn-toolbar:after { + display: table; + content: " "} +.btn-toolbar:after { + clear: both; +} +.btn-toolbar:before, .btn-toolbar:after { + display: table; + content: " "} +.btn-toolbar:after { + clear: both; +} +.btn-toolbar .btn-group { + float: left; +} +.btn-toolbar>.btn+.btn, .btn-toolbar>.btn-group+.btn, .btn-toolbar>.btn+.btn-group, .btn-toolbar>.btn-group+.btn-group { + margin-left: 5px; +} +.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { + border-radius: 0; +} +.btn-group>.btn:first-child { + margin-left: 0; +} +.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle) { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.btn-group>.btn:last-child:not(:first-child), .btn-group>.dropdown-toggle:not(:first-child) { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.btn-group>.btn-group { + float: left; +} +.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn { + border-radius: 0; +} +.btn-group>.btn-group:first-child>.btn:last-child, .btn-group>.btn-group:first-child>.dropdown-toggle { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.btn-group>.btn-group:last-child>.btn:first-child { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.btn-group .dropdown-toggle:active, .btn-group.open .dropdown-toggle { + outline: 0; +} +.btn-group-xs>.btn { + padding: 1px 5px; + font-size: 12px; + line-height: 1.5; + border-radius: 0; +} +.btn-group-sm>.btn { + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 0; +} +.btn-group-lg>.btn { + padding: 10px 16px; + font-size: 19px; + line-height: 1.33; + border-radius: 0; +} +.btn-group>.btn+.dropdown-toggle { + padding-right: 8px; + padding-left: 8px; +} +.btn-group>.btn-lg+.dropdown-toggle { + padding-right: 12px; + padding-left: 12px; +} +.btn-group.open .dropdown-toggle { + -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); + box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); +} +.btn-group.open .dropdown-toggle.btn-link { + -webkit-box-shadow: none; + box-shadow: none; +} +.btn .caret { + margin-left: 0; +} +.btn-lg .caret { + border-width: 5px 5px 0; + border-bottom-width: 0; +} +.dropup .btn-lg .caret { + border-width: 0 5px 5px; +} +.btn-group-vertical>.btn, .btn-group-vertical>.btn-group, .btn-group-vertical>.btn-group>.btn { + display: block; + float: none; + width: 100%; + max-width: 100%} +.btn-group-vertical>.btn-group:before, .btn-group-vertical>.btn-group:after { + display: table; + content: " "} +.btn-group-vertical>.btn-group:after { + clear: both; +} +.btn-group-vertical>.btn-group:before, .btn-group-vertical>.btn-group:after { + display: table; + content: " "} +.btn-group-vertical>.btn-group:after { + clear: both; +} +.btn-group-vertical>.btn-group:before, .btn-group-vertical>.btn-group:after { + display: table; + content: " "} +.btn-group-vertical>.btn-group:after { + clear: both; +} +.btn-group-vertical>.btn-group:before, .btn-group-vertical>.btn-group:after { + display: table; + content: " "} +.btn-group-vertical>.btn-group:after { + clear: both; +} +.btn-group-vertical>.btn-group:before, .btn-group-vertical>.btn-group:after { + display: table; + content: " "} +.btn-group-vertical>.btn-group:after { + clear: both; +} +.btn-group-vertical>.btn-group>.btn { + float: none; +} +.btn-group-vertical>.btn+.btn, .btn-group-vertical>.btn+.btn-group, .btn-group-vertical>.btn-group+.btn, .btn-group-vertical>.btn-group+.btn-group { + margin-top: -1px; + margin-left: 0; +} +.btn-group-vertical>.btn:not(:first-child):not(:last-child) { + border-radius: 0; +} +.btn-group-vertical>.btn:first-child:not(:last-child) { + border-top-right-radius: 0; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical>.btn:last-child:not(:first-child) { + border-top-right-radius: 0; + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn { + border-radius: 0; +} +.btn-group-vertical>.btn-group:first-child>.btn:last-child, .btn-group-vertical>.btn-group:first-child>.dropdown-toggle { + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical>.btn-group:last-child>.btn:first-child { + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.btn-group-justified { + display: table; + width: 100%; + border-collapse: separate; + table-layout: fixed; +} +.btn-group-justified>.btn, .btn-group-justified>.btn-group { + display: table-cell; + float: none; + width: 1%} +.btn-group-justified>.btn-group .btn { + width: 100%} +[data-toggle="buttons"]>.btn>input[type="radio"], [data-toggle="buttons"]>.btn>input[type="checkbox"] { + display: none; +} +.input-group { + position: relative; + display: table; + border-collapse: separate; +} +.input-group[class*="col-"] { + float: none; + padding-right: 0; + padding-left: 0; +} +.input-group .form-control { + width: 100%; + margin-bottom: 0; +} +.input-group-lg>.form-control, .input-group-lg>.input-group-addon, .input-group-lg>.input-group-btn>.btn { + height: 48px; + padding: 10px 16px; + font-size: 19px; + line-height: 1.33; + border-radius: 0; +} +select.input-group-lg>.form-control, select.input-group-lg>.input-group-addon, select.input-group-lg>.input-group-btn>.btn { + height: 48px; + line-height: 48px; +} +textarea.input-group-lg>.form-control, textarea.input-group-lg>.input-group-addon, textarea.input-group-lg>.input-group-btn>.btn { + height: auto; +} +.input-group-sm>.form-control, .input-group-sm>.input-group-addon, .input-group-sm>.input-group-btn>.btn { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 0; +} +select.input-group-sm>.form-control, select.input-group-sm>.input-group-addon, select.input-group-sm>.input-group-btn>.btn { + height: 30px; + line-height: 30px; +} +textarea.input-group-sm>.form-control, textarea.input-group-sm>.input-group-addon, textarea.input-group-sm>.input-group-btn>.btn { + height: auto; +} +.input-group-addon, .input-group-btn, .input-group .form-control { + display: table-cell; +} +.input-group-addon:not(:first-child):not(:last-child), .input-group-btn:not(:first-child):not(:last-child), .input-group .form-control:not(:first-child):not(:last-child) { + border-radius: 0; +} +.input-group-addon, .input-group-btn { + width: 1%; + white-space: nowrap; + vertical-align: middle; +} +.input-group-addon { + padding: 6px 12px; + font-size: 15px; + font-weight: normal; + line-height: 1; + color: #6f6f6f; + text-align: center; + background-color: #eee; + border: 1px solid #ccc; + border-radius: 0; +} +.input-group-addon.input-sm { + padding: 5px 10px; + font-size: 12px; + border-radius: 0; +} +.input-group-addon.input-lg { + padding: 10px 16px; + font-size: 19px; + border-radius: 0; +} +.input-group-addon input[type="radio"], .input-group-addon input[type="checkbox"] { + margin-top: 0; +} +.input-group .form-control:first-child, .input-group-addon:first-child, .input-group-btn:first-child>.btn, .input-group-btn:first-child>.dropdown-toggle, .input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle) { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.input-group-addon:first-child { + border-right: 0; +} +.input-group .form-control:last-child, .input-group-addon:last-child, .input-group-btn:last-child>.btn, .input-group-btn:last-child>.dropdown-toggle, .input-group-btn:first-child>.btn:not(:first-child) { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.input-group-addon:last-child { + border-left: 0; +} +.input-group-btn { + position: relative; + white-space: nowrap; +} +.input-group-btn:first-child>.btn { + margin-right: -1px; +} +.input-group-btn:last-child>.btn { + margin-left: -1px; +} +.input-group-btn>.btn { + position: relative; +} +.input-group-btn>.btn+.btn { + margin-left: -4px; +} +.input-group-btn>.btn:hover, .input-group-btn>.btn:active { + z-index: 2; +} +.nav { + font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; + padding-left: 0; + margin-bottom: 0; + list-style: none; +} +.nav:before, .nav:after { + display: table; + content: " "} +.nav:after { + clear: both; +} +.nav:before, .nav:after { + display: table; + content: " "} +.nav:after { + clear: both; +} +.nav:before, .nav:after { + display: table; + content: " "} +.nav:after { + clear: both; +} +.nav:before, .nav:after { + display: table; + content: " "} +.nav:after { + clear: both; +} +.nav:before, .nav:after { + display: table; + content: " "} +.nav:after { + clear: both; +} +.nav>li { + position: relative; + display: block; +} +.nav>li>a { + position: relative; + display: block; + padding: 10px 15px; +} +.nav>li>a:hover, .nav>li>a:focus { + text-decoration: none; + background-color: #eee; +} +.nav>li.disabled>a { + color: #999; +} +.nav>li.disabled>a:hover, .nav>li.disabled>a:focus { + color: #999; + text-decoration: none; + cursor: not-allowed; + background-color: transparent; +} +.nav .open>a, .nav .open>a:hover, .nav .open>a:focus { + background-color: #eee; + border-color: #008cba; +} +.nav .nav-divider { + height: 1px; + margin: 9.5px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.nav>li>a>img { + max-width: none; +} +.nav-tabs { + border-bottom: 1px solid #ddd; +} +.nav-tabs>li { + float: left; + margin-bottom: -1px; +} +.nav-tabs>li>a { + margin-right: 2px; + line-height: 1.428571429; + border: 1px solid transparent; + border-radius: 0; +} +.nav-tabs>li>a:hover { + border-color: #eee #eee #ddd; +} +.nav-tabs>li.active>a, .nav-tabs>li.active>a:hover, .nav-tabs>li.active>a:focus { + color: #6f6f6f; + cursor: default; + background-color: #fff; + border: 1px solid #ddd; + border-bottom-color: transparent; +} +.nav-tabs.nav-justified { + width: 100%; + border-bottom: 0; +} +.nav-tabs.nav-justified>li { + float: none; +} +.nav-tabs.nav-justified>li>a { + margin-bottom: 5px; + text-align: center; +} +.nav-tabs.nav-justified>.dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media(min-width:768px) { + .nav-tabs.nav-justified>li { + display: table-cell; + width: 1%} +.nav-tabs.nav-justified>li>a { + margin-bottom: 0; +} +}.nav-tabs.nav-justified>li>a { + margin-right: 0; + border-radius: 0; +} +.nav-tabs.nav-justified>.active>a, .nav-tabs.nav-justified>.active>a:hover, .nav-tabs.nav-justified>.active>a:focus { + border: 1px solid #ddd; +} +@media(min-width:768px) { + .nav-tabs.nav-justified>li>a { + border-bottom: 1px solid #ddd; + border-radius: 0; +} +.nav-tabs.nav-justified>.active>a, .nav-tabs.nav-justified>.active>a:hover, .nav-tabs.nav-justified>.active>a:focus { + border-bottom-color: #fff; +} +}.nav-pills>li { + float: left; +} +.nav-pills>li>a { + border-radius: 0; +} +.nav-pills>li+li { + margin-left: 2px; +} +.nav-pills>li.active>a, .nav-pills>li.active>a:hover, .nav-pills>li.active>a:focus { + color: #fff; + background-color: #008cba; +} +.nav-stacked>li { + float: none; +} +.nav-stacked>li+li { + margin-top: 2px; + margin-left: 0; +} +.nav-justified { + width: 100%} +.nav-justified>li { + float: none; +} +.nav-justified>li>a { + margin-bottom: 5px; + text-align: center; +} +.nav-justified>.dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media(min-width:768px) { + .nav-justified>li { + display: table-cell; + width: 1%} +.nav-justified>li>a { + margin-bottom: 0; +} +}.nav-tabs-justified { + border-bottom: 0; +} +.nav-tabs-justified>li>a { + margin-right: 0; + border-radius: 0; +} +.nav-tabs-justified>.active>a, .nav-tabs-justified>.active>a:hover, .nav-tabs-justified>.active>a:focus { + border: 1px solid #ddd; +} +@media(min-width:768px) { + .nav-tabs-justified>li>a { + border-bottom: 1px solid #ddd; + border-radius: 0; +} +.nav-tabs-justified>.active>a, .nav-tabs-justified>.active>a:hover, .nav-tabs-justified>.active>a:focus { + border-bottom-color: #fff; +} +}.tab-content>.tab-pane { + display: none; +} +.tab-content>.active { + display: block; +} +.nav-tabs .dropdown-menu { + margin-top: -1px; + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.navbar { + position: relative; + min-height: 45px; + margin-bottom: 21px; + border: 1px solid transparent; +} +.navbar:before, .navbar:after { + display: table; + content: " "} +.navbar:after { + clear: both; +} +.navbar:before, .navbar:after { + display: table; + content: " "} +.navbar:after { + clear: both; +} +.navbar:before, .navbar:after { + display: table; + content: " "} +.navbar:after { + clear: both; +} +.navbar:before, .navbar:after { + display: table; + content: " "} +.navbar:after { + clear: both; +} +.navbar:before, .navbar:after { + display: table; + content: " "} +.navbar:after { + clear: both; +} +@media(min-width:768px) { + .navbar { + border-radius: 0; +} +}.navbar-header:before, .navbar-header:after { + display: table; + content: " "} +.navbar-header:after { + clear: both; +} +.navbar-header:before, .navbar-header:after { + display: table; + content: " "} +.navbar-header:after { + clear: both; +} +.navbar-header:before, .navbar-header:after { + display: table; + content: " "} +.navbar-header:after { + clear: both; +} +.navbar-header:before, .navbar-header:after { + display: table; + content: " "} +.navbar-header:after { + clear: both; +} +.navbar-header:before, .navbar-header:after { + display: table; + content: " "} +.navbar-header:after { + clear: both; +} +@media(min-width:768px) { + .navbar-header { + float: left; +} +}.navbar-collapse { + max-height: 340px; + padding-right: 15px; + padding-left: 15px; + overflow-x: visible; + border-top: 1px solid transparent; + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); + -webkit-overflow-scrolling: touch; +} +.navbar-collapse:before, .navbar-collapse:after { + display: table; + content: " "} +.navbar-collapse:after { + clear: both; +} +.navbar-collapse:before, .navbar-collapse:after { + display: table; + content: " "} +.navbar-collapse:after { + clear: both; +} +.navbar-collapse:before, .navbar-collapse:after { + display: table; + content: " "} +.navbar-collapse:after { + clear: both; +} +.navbar-collapse:before, .navbar-collapse:after { + display: table; + content: " "} +.navbar-collapse:after { + clear: both; +} +.navbar-collapse:before, .navbar-collapse:after { + display: table; + content: " "} +.navbar-collapse:after { + clear: both; +} +.navbar-collapse.in { + overflow-y: auto; +} +@media(min-width:768px) { + .navbar-collapse { + width: auto; + border-top: 0; + box-shadow: none; +} +.navbar-collapse.collapse { + display: block!important; + height: auto!important; + padding-bottom: 0; + overflow: visible!important; +} +.navbar-collapse.in { + overflow-y: visible; +} +.navbar-fixed-top .navbar-collapse, .navbar-static-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { + padding-right: 0; + padding-left: 0; +} +}.container>.navbar-header, .container>.navbar-collapse { + margin-right: -15px; + margin-left: -15px; +} +@media(min-width:768px) { + .container>.navbar-header, .container>.navbar-collapse { + margin-right: 0; + margin-left: 0; +} +}.navbar-static-top { + z-index: 1000; + border-width: 0 0 1px; +} +@media(min-width:768px) { + .navbar-static-top { + border-radius: 0; +} +}.navbar-fixed-top, .navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; +} +@media(min-width:768px) { + .navbar-fixed-top, .navbar-fixed-bottom { + border-radius: 0; +} +}.navbar-fixed-top { + top: 0; + border-width: 0 0 1px; +} +.navbar-fixed-bottom { + bottom: 0; + margin-bottom: 0; + border-width: 1px 0 0; +} +.navbar-brand { + font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; + float: left; + padding: 12px 15px; + font-size: 19px; + line-height: 21px; +} +.navbar-brand:hover, .navbar-brand:focus { + text-decoration: none; +} +@media(min-width:768px) { + .navbar>.container .navbar-brand { + margin-left: -15px; +} +}.navbar-toggle { + position: relative; + float: right; + padding: 9px 10px; + margin-top: 5.5px; + margin-right: 15px; + margin-bottom: 5.5px; + background-color: transparent; + background-image: none; + border: 1px solid transparent; + border-radius: 0; +} +.navbar-toggle .icon-bar { + display: block; + width: 22px; + height: 2px; + border-radius: 1px; +} +.navbar-toggle .icon-bar+.icon-bar { + margin-top: 4px; +} +@media(min-width:768px) { + .navbar-toggle { + display: none; +} +}.navbar-nav { + margin: 6px -15px; +} +.navbar-nav>li>a { + padding-top: 10px; + padding-bottom: 10px; + line-height: 21px; +} +@media(max-width:767px) { + .navbar-nav .open .dropdown-menu { + position: static; + float: none; + width: auto; + margin-top: 0; + background-color: transparent; + border: 0; + box-shadow: none; +} +.navbar-nav .open .dropdown-menu>li>a, .navbar-nav .open .dropdown-menu .dropdown-header { + padding: 5px 15px 5px 25px; +} +.navbar-nav .open .dropdown-menu>li>a { + line-height: 21px; +} +.navbar-nav .open .dropdown-menu>li>a:hover, .navbar-nav .open .dropdown-menu>li>a:focus { + background-image: none; +} +}@media(min-width:768px) { + .navbar-nav { + float: left; + margin: 0; +} +.navbar-nav>li { + float: left; +} +.navbar-nav>li>a { + padding-top: 12px; + padding-bottom: 12px; +} +.navbar-nav.navbar-right:last-child { + margin-right: -15px; +} +}@media(min-width:768px) { + .navbar-left { + float: left!important; +} +.navbar-right { + float: right!important; +} +}.navbar-form { + padding: 10px 15px; + margin-top: 5px; + margin-right: -15px; + margin-bottom: 5px; + margin-left: -15px; + border-top: 1px solid transparent; + border-bottom: 1px solid transparent; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); +} +@media(min-width:768px) { + .navbar-form .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; +} +.navbar-form .form-control { + display: inline-block; +} +.navbar-form select.form-control { + width: auto; +} +.navbar-form .radio, .navbar-form .checkbox { + display: inline-block; + padding-left: 0; + margin-top: 0; + margin-bottom: 0; +} +.navbar-form .radio input[type="radio"], .navbar-form .checkbox input[type="checkbox"] { + float: none; + margin-left: 0; +} +}@media(max-width:767px) { + .navbar-form .form-group { + margin-bottom: 5px; +} +}@media(min-width:768px) { + .navbar-form { + width: auto; + padding-top: 0; + padding-bottom: 0; + margin-right: 0; + margin-left: 0; + border: 0; + -webkit-box-shadow: none; + box-shadow: none; +} +.navbar-form.navbar-right:last-child { + margin-right: -15px; +} +}.navbar-nav>li>.dropdown-menu { + margin-top: 0; + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu { + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.navbar-nav.pull-right>li>.dropdown-menu, .navbar-nav>li>.dropdown-menu.pull-right { + right: 0; + left: auto; +} +.navbar-btn { + margin-top: 5px; + margin-bottom: 5px; +} +.navbar-btn.btn-sm { + margin-top: 7.5px; + margin-bottom: 7.5px; +} +.navbar-btn.btn-xs { + margin-top: 11.5px; + margin-bottom: 11.5px; +} +.navbar-text { + margin-top: 12px; + margin-bottom: 12px; +} +@media(min-width:768px) { + .navbar-text { + float: left; + margin-right: 15px; + margin-left: 15px; +} +.navbar-text.navbar-right:last-child { + margin-right: 0; +} +}.navbar-default { + background-color: #333; + border-color: #222; +} +.navbar-default .navbar-brand { + color: #fff; +} +.navbar-default .navbar-brand:hover, .navbar-default .navbar-brand:focus { + color: #fff; + background-color: transparent; +} +.navbar-default .navbar-text { + color: #fff; +} +.navbar-default .navbar-nav>li>a { + color: #fff; +} +.navbar-default .navbar-nav>li>a:hover, .navbar-default .navbar-nav>li>a:focus { + color: #fff; + background-color: #272727; +} +.navbar-default .navbar-nav>.active>a, .navbar-default .navbar-nav>.active>a:hover, .navbar-default .navbar-nav>.active>a:focus { + color: #fff; + background-color: #272727; +} +.navbar-default .navbar-nav>.disabled>a, .navbar-default .navbar-nav>.disabled>a:hover, .navbar-default .navbar-nav>.disabled>a:focus { + color: #ccc; + background-color: transparent; +} +.navbar-default .navbar-toggle { + border-color: transparent; +} +.navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus { + background-color: transparent; +} +.navbar-default .navbar-toggle .icon-bar { + background-color: #fff; +} +.navbar-default .navbar-collapse, .navbar-default .navbar-form { + border-color: #222; +} +.navbar-default .navbar-nav>.open>a, .navbar-default .navbar-nav>.open>a:hover, .navbar-default .navbar-nav>.open>a:focus { + color: #fff; + background-color: #272727; +} +@media(max-width:767px) { + .navbar-default .navbar-nav .open .dropdown-menu>li>a { + color: #fff; +} +.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover, .navbar-default .navbar-nav .open .dropdown-menu>li>a:focus { + color: #fff; + background-color: #272727; +} +.navbar-default .navbar-nav .open .dropdown-menu>.active>a, .navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover, .navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus { + color: #fff; + background-color: #272727; +} +.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a, .navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover, .navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus { + color: #ccc; + background-color: transparent; +} +}.navbar-default .navbar-link { + color: #fff; +} +.navbar-default .navbar-link:hover { + color: #fff; +} +.navbar-inverse { + background-color: #008cba; + border-color: #006687; +} +.navbar-inverse .navbar-brand { + color: #fff; +} +.navbar-inverse .navbar-brand:hover, .navbar-inverse .navbar-brand:focus { + color: #fff; + background-color: transparent; +} +.navbar-inverse .navbar-text { + color: #fff; +} +.navbar-inverse .navbar-nav>li>a { + color: #fff; +} +.navbar-inverse .navbar-nav>li>a:hover, .navbar-inverse .navbar-nav>li>a:focus { + color: #fff; + background-color: #006687; +} +.navbar-inverse .navbar-nav>.active>a, .navbar-inverse .navbar-nav>.active>a:hover, .navbar-inverse .navbar-nav>.active>a:focus { + color: #fff; + background-color: #006687; +} +.navbar-inverse .navbar-nav>.disabled>a, .navbar-inverse .navbar-nav>.disabled>a:hover, .navbar-inverse .navbar-nav>.disabled>a:focus { + color: #444; + background-color: transparent; +} +.navbar-inverse .navbar-toggle { + border-color: transparent; +} +.navbar-inverse .navbar-toggle:hover, .navbar-inverse .navbar-toggle:focus { + background-color: transparent; +} +.navbar-inverse .navbar-toggle .icon-bar { + background-color: #fff; +} +.navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form { + border-color: #007196; +} +.navbar-inverse .navbar-nav>.open>a, .navbar-inverse .navbar-nav>.open>a:hover, .navbar-inverse .navbar-nav>.open>a:focus { + color: #fff; + background-color: #006687; +} +@media(max-width:767px) { + .navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header { + border-color: #006687; +} +.navbar-inverse .navbar-nav .open .dropdown-menu .divider { + background-color: #006687; +} +.navbar-inverse .navbar-nav .open .dropdown-menu>li>a { + color: #fff; +} +.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus { + color: #fff; + background-color: #006687; +} +.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a, .navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus { + color: #fff; + background-color: #006687; +} +.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a, .navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus { + color: #444; + background-color: transparent; +} +}.navbar-inverse .navbar-link { + color: #fff; +} +.navbar-inverse .navbar-link:hover { + color: #fff; +} +.breadcrumb { + padding: 8px 15px; + margin-bottom: 21px; + list-style: none; + background-color: #f5f5f5; + border-radius: 0; +} +.breadcrumb>li { + display: inline-block; +} +.breadcrumb>li+li:before { + padding: 0 5px; + color: #999; + content: "/\00a0"} +.breadcrumb>.active { + color: #333; +} +.pagination { + display: inline-block; + padding-left: 0; + margin: 21px 0; + border-radius: 0; +} +.pagination>li { + display: inline; +} +.pagination>li>a, .pagination>li>span { + position: relative; + float: left; + padding: 6px 12px; + margin-left: -1px; + line-height: 1.428571429; + text-decoration: none; + background-color: transparent; + border: 1px solid transparent; +} +.pagination>li:first-child>a, .pagination>li:first-child>span { + margin-left: 0; + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.pagination>li:last-child>a, .pagination>li:last-child>span { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.pagination>li>a:hover, .pagination>li>span:hover, .pagination>li>a:focus, .pagination>li>span:focus { + background-color: #eee; +} +.pagination>.active>a, .pagination>.active>span, .pagination>.active>a:hover, .pagination>.active>span:hover, .pagination>.active>a:focus, .pagination>.active>span:focus { + z-index: 2; + color: #fff; + cursor: default; + background-color: #008cba; + border-color: #008cba; +} +.pagination>.disabled>span, .pagination>.disabled>span:hover, .pagination>.disabled>span:focus, .pagination>.disabled>a, .pagination>.disabled>a:hover, .pagination>.disabled>a:focus { + color: #999; + cursor: not-allowed; + background-color: transparent; + border-color: transparent; +} +.pagination-lg>li>a, .pagination-lg>li>span { + padding: 10px 16px; + font-size: 19px; +} +.pagination-lg>li:first-child>a, .pagination-lg>li:first-child>span { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.pagination-lg>li:last-child>a, .pagination-lg>li:last-child>span { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.pagination-sm>li>a, .pagination-sm>li>span { + padding: 5px 10px; + font-size: 12px; +} +.pagination-sm>li:first-child>a, .pagination-sm>li:first-child>span { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.pagination-sm>li:last-child>a, .pagination-sm>li:last-child>span { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.pager { + padding-left: 0; + margin: 21px 0; + text-align: center; + list-style: none; +} +.pager:before, .pager:after { + display: table; + content: " "} +.pager:after { + clear: both; +} +.pager:before, .pager:after { + display: table; + content: " "} +.pager:after { + clear: both; +} +.pager:before, .pager:after { + display: table; + content: " "} +.pager:after { + clear: both; +} +.pager:before, .pager:after { + display: table; + content: " "} +.pager:after { + clear: both; +} +.pager:before, .pager:after { + display: table; + content: " "} +.pager:after { + clear: both; +} +.pager li { + display: inline; +} +.pager li>a, .pager li>span { + display: inline-block; + padding: 5px 14px; + background-color: transparent; + border: 1px solid transparent; + border-radius: 3px; +} +.pager li>a:hover, .pager li>a:focus { + text-decoration: none; + background-color: #eee; +} +.pager .next>a, .pager .next>span { + float: right; +} +.pager .previous>a, .pager .previous>span { + float: left; +} +.pager .disabled>a, .pager .disabled>a:hover, .pager .disabled>a:focus, .pager .disabled>span { + color: #999; + cursor: not-allowed; + background-color: transparent; +} +.label { + display: inline; + padding: .2em .6em .3em; + font-size: 75%; + font-weight: bold; + line-height: 1; + color: #fff; + text-align: center; + white-space: nowrap; + vertical-align: baseline; + border-radius: .25em; +} +.label[href]:hover, .label[href]:focus { + color: #fff; + text-decoration: none; + cursor: pointer; +} +.label:empty { + display: none; +} +.btn .label { + position: relative; + top: -1px; +} +.label-default { + background-color: #999; +} +.label-default[href]:hover, .label-default[href]:focus { + background-color: #808080; +} +.label-primary { + background-color: #008cba; +} +.label-primary[href]:hover, .label-primary[href]:focus { + background-color: #006687; +} +.label-success { + background-color: #43ac6a; +} +.label-success[href]:hover, .label-success[href]:focus { + background-color: #358753; +} +.label-info { + background-color: #5bc0de; +} +.label-info[href]:hover, .label-info[href]:focus { + background-color: #31b0d5; +} +.label-warning { + background-color: #e99002; +} +.label-warning[href]:hover, .label-warning[href]:focus { + background-color: #b67102; +} +.label-danger { + background-color: #f04124; +} +.label-danger[href]:hover, .label-danger[href]:focus { + background-color: #d32a0e; +} +.badge { + display: inline-block; + min-width: 10px; + padding: 3px 7px; + font-size: 12px; + font-weight: bold; + line-height: 1; + color: #777; + text-align: center; + white-space: nowrap; + vertical-align: baseline; + background-color: #e7e7e7; + border-radius: 10px; +} +.badge:empty { + display: none; +} +.btn .badge { + position: relative; + top: -1px; +} +a.badge:hover, a.badge:focus { + color: #fff; + text-decoration: none; + cursor: pointer; +} +a.list-group-item.active>.badge, .nav-pills>.active>a>.badge { + color: #008cba; + background-color: #fff; +} +.nav-pills>li>a>.badge { + margin-left: 3px; +} +.jumbotron { + padding: 30px; + margin-bottom: 30px; + font-size: 23px; + font-weight: 200; + line-height: 2.1428571435; + color: inherit; + background-color: #fafafa; +} +.jumbotron h1, .jumbotron .h1 { + line-height: 1; + color: inherit; +} +.jumbotron p { + line-height: 1.4; +} +.container .jumbotron { + border-radius: 0; +} +.jumbotron .container { + max-width: 100%} +@media screen and (min-width:768px) { + .jumbotron { + padding-top: 48px; + padding-bottom: 48px; +} +.container .jumbotron { + padding-right: 60px; + padding-left: 60px; +} +.jumbotron h1, .jumbotron .h1 { + font-size: 67.5px; +} +}.thumbnail { + display: block; + padding: 4px; + margin-bottom: 21px; + line-height: 1.428571429; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 0; + -webkit-transition: all .2s ease-in-out; + transition: all .2s ease-in-out; +} +.thumbnail>img, .thumbnail a>img { + display: block; + height: auto; + max-width: 100%; + margin-right: auto; + margin-left: auto; +} +a.thumbnail:hover, a.thumbnail:focus, a.thumbnail.active { + border-color: #008cba; +} +.thumbnail .caption { + padding: 9px; + color: #222; +} +.alert { + position: relative; + padding: 0.75rem 1.25rem; + margin-bottom: 1rem; + border: 1px solid transparent; + border-radius: 0.25rem; + } + + .alert-heading { + color: inherit; + } + + .alert-link { + font-weight: 700; + } + + .alert-dismissible { + padding-right: 4rem; + } + + .alert-dismissible .close { + position: absolute; + top: 0; + right: 0; + padding: 0.75rem 1.25rem; + color: inherit; + } + + .alert-primary { + color: #004085; + background-color: #cce5ff; + border-color: #b8daff; + } + + .alert-primary hr { + border-top-color: #9fcdff; + } + + .alert-primary .alert-link { + color: #002752; + } + + .alert-secondary { + color: #383d41; + background-color: #e2e3e5; + border-color: #d6d8db; + } + + .alert-secondary hr { + border-top-color: #c8cbcf; + } + + .alert-secondary .alert-link { + color: #202326; + } + + .alert-success { + color: #155724; + background-color: #d4edda; + border-color: #c3e6cb; + } + + .alert-success hr { + border-top-color: #b1dfbb; + } + + .alert-success .alert-link { + color: #0b2e13; + } + + .alert-info { + color: #0c5460; + background-color: #d1ecf1; + border-color: #bee5eb; + } + + .alert-info hr { + border-top-color: #abdde5; + } + + .alert-info .alert-link { + color: #062c33; + } + + .alert-warning { + color: #856404; + background-color: #fff3cd; + border-color: #ffeeba; + } + + .alert-warning hr { + border-top-color: #ffe8a1; + } + + .alert-warning .alert-link { + color: #533f03; + } + + .alert-danger { + color: #721c24; + background-color: #f8d7da; + border-color: #f5c6cb; + } + + .alert-danger hr { + border-top-color: #f1b0b7; + } + + .alert-danger .alert-link { + color: #491217; + } + + .alert-light { + color: #818182; + background-color: #fefefe; + border-color: #fdfdfe; + } + + .alert-light hr { + border-top-color: #ececf6; + } + + .alert-light .alert-link { + color: #686868; + } + + .alert-dark { + color: #1b1e21; + background-color: #d6d8d9; + border-color: #c6c8ca; + } + + .alert-dark hr { + border-top-color: #b9bbbe; + } + + .alert-dark .alert-link { + color: #040505; + } + + +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; +} +to { + background-position: 0 0; +} +}@keyframes progress-bar-stripes { + from { + background-position: 40px 0; +} +to { + background-position: 0 0; +} +}.progress { + height: 21px; + margin-bottom: 21px; + overflow: hidden; + background-color: #f5f5f5; + border-radius: 0; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} +.progress-bar { + float: left; + width: 0; + height: 100%; + font-size: 12px; + line-height: 21px; + color: #fff; + text-align: center; + background-color: #008cba; + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -webkit-transition: width .6s ease; + transition: width .6s ease; +} +.progress-striped .progress-bar { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-size: 40px 40px; +} +.progress.active .progress-bar { + -webkit-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} +.progress-bar-success { + background-color: #43ac6a; +} +.progress-striped .progress-bar-success { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-info { + background-color: #5bc0de; +} +.progress-striped .progress-bar-info { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-warning { + background-color: #e99002; +} +.progress-striped .progress-bar-warning { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-danger { + background-color: #f04124; +} +.progress-striped .progress-bar-danger { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.media, .media-body { + overflow: hidden; + zoom: 1; +} +.media, .media .media { + margin-top: 15px; +} +.media:first-child { + margin-top: 0; +} +.media-object { + display: block; +} +.media-heading { + margin: 0 0 5px; +} +.media>.pull-left { + margin-right: 10px; +} +.media>.pull-right { + margin-left: 10px; +} +.media-list { + padding-left: 0; + list-style: none; +} +.list-group { + padding-left: 0; + margin-bottom: 20px; +} +.list-group-item { + position: relative; + display: block; + padding: 10px 15px; + margin-bottom: -1px; + background-color: #fff; + border: 1px solid #ddd; +} +.list-group-item:first-child { + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.list-group-item:last-child { + margin-bottom: 0; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.list-group-item>.badge { + float: right; +} +.list-group-item>.badge+.badge { + margin-right: 5px; +} +a.list-group-item { + color: #555; +} +a.list-group-item .list-group-item-heading { + color: #333; +} +a.list-group-item:hover, a.list-group-item:focus { + text-decoration: none; + background-color: #f5f5f5; +} +a.list-group-item.active, a.list-group-item.active:hover, a.list-group-item.active:focus { + z-index: 2; + color: #fff; + background-color: #008cba; + border-color: #008cba; +} +a.list-group-item.active .list-group-item-heading, a.list-group-item.active:hover .list-group-item-heading, a.list-group-item.active:focus .list-group-item-heading { + color: inherit; +} +a.list-group-item.active .list-group-item-text, a.list-group-item.active:hover .list-group-item-text, a.list-group-item.active:focus .list-group-item-text { + color: #87e1ff; +} +.list-group-item-heading { + margin-top: 0; + margin-bottom: 5px; +} +.list-group-item-text { + margin-bottom: 0; + line-height: 1.3; +} +.panel { + margin-bottom: 21px; + background-color: #fff; + border: 1px solid transparent; + border-radius: 0; + -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); +} +.panel-body { + padding: 15px; +} +.panel-body:before, .panel-body:after { + display: table; + content: " "} +.panel-body:after { + clear: both; +} +.panel-body:before, .panel-body:after { + display: table; + content: " "} +.panel-body:after { + clear: both; +} +.panel-body:before, .panel-body:after { + display: table; + content: " "} +.panel-body:after { + clear: both; +} +.panel-body:before, .panel-body:after { + display: table; + content: " "} +.panel-body:after { + clear: both; +} +.panel-body:before, .panel-body:after { + display: table; + content: " "} +.panel-body:after { + clear: both; +} +.panel>.list-group { + margin-bottom: 0; +} +.panel>.list-group .list-group-item { + border-width: 1px 0; +} +.panel>.list-group .list-group-item:first-child { + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.panel>.list-group .list-group-item:last-child { + border-bottom: 0; +} +.panel-heading+.list-group .list-group-item:first-child { + border-top-width: 0; +} +.panel>.table, .panel>.table-responsive>.table { + margin-bottom: 0; +} +.panel>.panel-body+.table, .panel>.panel-body+.table-responsive { + border-top: 1px solid #ddd; +} +.panel>.table>tbody:first-child th, .panel>.table>tbody:first-child td { + border-top: 0; +} +.panel>.table-bordered, .panel>.table-responsive>.table-bordered { + border: 0; +} +.panel>.table-bordered>thead>tr>th:first-child, .panel>.table-responsive>.table-bordered>thead>tr>th:first-child, .panel>.table-bordered>tbody>tr>th:first-child, .panel>.table-responsive>.table-bordered>tbody>tr>th:first-child, .panel>.table-bordered>tfoot>tr>th:first-child, .panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child, .panel>.table-bordered>thead>tr>td:first-child, .panel>.table-responsive>.table-bordered>thead>tr>td:first-child, .panel>.table-bordered>tbody>tr>td:first-child, .panel>.table-responsive>.table-bordered>tbody>tr>td:first-child, .panel>.table-bordered>tfoot>tr>td:first-child, .panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child { + border-left: 0; +} +.panel>.table-bordered>thead>tr>th:last-child, .panel>.table-responsive>.table-bordered>thead>tr>th:last-child, .panel>.table-bordered>tbody>tr>th:last-child, .panel>.table-responsive>.table-bordered>tbody>tr>th:last-child, .panel>.table-bordered>tfoot>tr>th:last-child, .panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child, .panel>.table-bordered>thead>tr>td:last-child, .panel>.table-responsive>.table-bordered>thead>tr>td:last-child, .panel>.table-bordered>tbody>tr>td:last-child, .panel>.table-responsive>.table-bordered>tbody>tr>td:last-child, .panel>.table-bordered>tfoot>tr>td:last-child, .panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child { + border-right: 0; +} +.panel>.table-bordered>thead>tr:last-child>th, .panel>.table-responsive>.table-bordered>thead>tr:last-child>th, .panel>.table-bordered>tbody>tr:last-child>th, .panel>.table-responsive>.table-bordered>tbody>tr:last-child>th, .panel>.table-bordered>tfoot>tr:last-child>th, .panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th, .panel>.table-bordered>thead>tr:last-child>td, .panel>.table-responsive>.table-bordered>thead>tr:last-child>td, .panel>.table-bordered>tbody>tr:last-child>td, .panel>.table-responsive>.table-bordered>tbody>tr:last-child>td, .panel>.table-bordered>tfoot>tr:last-child>td, .panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td { + border-bottom: 0; +} +.panel>.table-responsive { + margin-bottom: 0; + border: 0; +} +.panel-heading { + padding: 10px 15px; + border-bottom: 1px solid transparent; + border-top-right-radius: -1; + border-top-left-radius: -1; +} +.panel-heading>.dropdown .dropdown-toggle { + color: inherit; +} +.panel-title { + margin-top: 0; + margin-bottom: 0; + font-size: 17px; + color: inherit; +} +.panel-title>a { + color: inherit; +} +.panel-footer { + padding: 10px 15px; + background-color: #f5f5f5; + border-top: 1px solid #ddd; + border-bottom-right-radius: -1; + border-bottom-left-radius: -1; +} +.panel-group .panel { + margin-bottom: 0; + overflow: hidden; + border-radius: 0; +} +.panel-group .panel+.panel { + margin-top: 5px; +} +.panel-group .panel-heading { + border-bottom: 0; +} +.panel-group .panel-heading+.panel-collapse .panel-body { + border-top: 1px solid #ddd; +} +.panel-group .panel-footer { + border-top: 0; +} +.panel-group .panel-footer+.panel-collapse .panel-body { + border-bottom: 1px solid #ddd; +} +.panel-default { + border-color: #ddd; +} +.panel-default>.panel-heading { + color: #333; + background-color: #f5f5f5; + border-color: #ddd; +} +.panel-default>.panel-heading+.panel-collapse .panel-body { + border-top-color: #ddd; +} +.panel-default>.panel-footer+.panel-collapse .panel-body { + border-bottom-color: #ddd; +} +.panel-primary { + border-color: #008cba; +} +.panel-primary>.panel-heading { + color: #fff; + background-color: #008cba; + border-color: #008cba; +} +.panel-primary>.panel-heading+.panel-collapse .panel-body { + border-top-color: #008cba; +} +.panel-primary>.panel-footer+.panel-collapse .panel-body { + border-bottom-color: #008cba; +} +.panel-success { + border-color: #3c9a5f; +} +.panel-success>.panel-heading { + color: #43ac6a; + background-color: #dff0d8; + border-color: #3c9a5f; +} +.panel-success>.panel-heading+.panel-collapse .panel-body { + border-top-color: #3c9a5f; +} +.panel-success>.panel-footer+.panel-collapse .panel-body { + border-bottom-color: #3c9a5f; +} +.panel-warning { + border-color: #d08002; +} +.panel-warning>.panel-heading { + color: #e99002; + background-color: #fcf8e3; + border-color: #d08002; +} +.panel-warning>.panel-heading+.panel-collapse .panel-body { + border-top-color: #d08002; +} +.panel-warning>.panel-footer+.panel-collapse .panel-body { + border-bottom-color: #d08002; +} +.panel-danger { + border-color: #ea2f10; +} +.panel-danger>.panel-heading { + color: #f04124; + background-color: #f2dede; + border-color: #ea2f10; +} +.panel-danger>.panel-heading+.panel-collapse .panel-body { + border-top-color: #ea2f10; +} +.panel-danger>.panel-footer+.panel-collapse .panel-body { + border-bottom-color: #ea2f10; +} +.panel-info { + border-color: #3db5d8; +} +.panel-info>.panel-heading { + color: #5bc0de; + background-color: #d9edf7; + border-color: #3db5d8; +} +.panel-info>.panel-heading+.panel-collapse .panel-body { + border-top-color: #3db5d8; +} +.panel-info>.panel-footer+.panel-collapse .panel-body { + border-bottom-color: #3db5d8; +} +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #fafafa; + border: 1px solid #e8e8e8; + border-radius: 0; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); +} +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, 0.15); +} +.well-lg { + padding: 24px; + border-radius: 0; +} +.well-sm { + padding: 9px; + border-radius: 0; +} +.close { + float: right; + font-size: 22.5px; + font-weight: bold; + line-height: 1; + color: #000; + text-shadow: 0 1px 0 #fff; + opacity: .2; + filter: alpha(opacity=20); +} +.close:hover, .close:focus { + color: #000; + text-decoration: none; + cursor: pointer; + opacity: .5; + filter: alpha(opacity=50); +} +button.close { + padding: 0; + cursor: pointer; + background: transparent; + border: 0; + -webkit-appearance: none; +} +.modal-open { + overflow: hidden; +} +.modal { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + display: none; + overflow: auto; + overflow-y: scroll; +} +.modal.fade .modal-dialog { + -webkit-transform: translate(0, -25%); + -ms-transform: translate(0, -25%); + transform: translate(0, -25%); + -webkit-transition: -webkit-transform .3s ease-out; + -moz-transition: -moz-transform .3s ease-out; + -o-transition: -o-transform .3s ease-out; + transition: transform .3s ease-out; +} +.modal.in .modal-dialog { + -webkit-transform: translate(0, 0); + -ms-transform: translate(0, 0); + transform: translate(0, 0); +} +.modal-dialog { + position: relative; + z-index: 1050; + width: auto; + margin: 10px; +} +.modal-content { + position: relative; + background-color: #fff; + border: 1px solid #999; + border: 1px solid rgba(0, 0, 0, 0.2); + border-radius: 0; + outline: 0; + -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); + box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); + background-clip: padding-box; +} +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1030; + background-color: #000; +} +.modal-backdrop.fade { + opacity: 0; + filter: alpha(opacity=0); +} +.modal-backdrop.in { + opacity: .5; + filter: alpha(opacity=50); +} +.modal-header { + min-height: 16.428571429px; + padding: 15px; + border-bottom: 1px solid #e5e5e5; +} +.modal-header .close { + margin-top: -2px; +} +.modal-title { + margin: 0; + line-height: 1.428571429; +} +.modal-body { + position: relative; + padding: 20px; +} +.modal-footer { + padding: 19px 20px 20px; + margin-top: 15px; + text-align: right; + border-top: 1px solid #e5e5e5; +} +.modal-footer:before, .modal-footer:after { + display: table; + content: " "} +.modal-footer:after { + clear: both; +} +.modal-footer:before, .modal-footer:after { + display: table; + content: " "} +.modal-footer:after { + clear: both; +} +.modal-footer:before, .modal-footer:after { + display: table; + content: " "} +.modal-footer:after { + clear: both; +} +.modal-footer:before, .modal-footer:after { + display: table; + content: " "} +.modal-footer:after { + clear: both; +} +.modal-footer:before, .modal-footer:after { + display: table; + content: " "} +.modal-footer:after { + clear: both; +} +.modal-footer .btn+.btn { + margin-bottom: 0; + margin-left: 5px; +} +.modal-footer .btn-group .btn+.btn { + margin-left: -1px; +} +.modal-footer .btn-block+.btn-block { + margin-left: 0; +} +@media screen and (min-width:768px) { + .modal-dialog { + width: 600px; + margin: 30px auto; +} +.modal-content { + -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); +} +}.tooltip { + position: absolute; + z-index: 1030; + display: block; + font-size: 12px; + line-height: 1.4; + opacity: 0; + filter: alpha(opacity=0); + visibility: visible; +} +.tooltip.in { + opacity: .9; + filter: alpha(opacity=90); +} +.tooltip.top { + padding: 5px 0; + margin-top: -3px; +} +.tooltip.right { + padding: 0 5px; + margin-left: 3px; +} +.tooltip.bottom { + padding: 5px 0; + margin-top: 3px; +} +.tooltip.left { + padding: 0 5px; + margin-left: -3px; +} +.tooltip-inner { + max-width: 200px; + padding: 3px 8px; + color: #fff; + text-align: center; + text-decoration: none; + background-color: #333; + border-radius: 0; +} +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-top-color: #333; + border-width: 5px 5px 0; +} +.tooltip.top-left .tooltip-arrow { + bottom: 0; + left: 5px; + border-top-color: #333; + border-width: 5px 5px 0; +} +.tooltip.top-right .tooltip-arrow { + right: 5px; + bottom: 0; + border-top-color: #333; + border-width: 5px 5px 0; +} +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-right-color: #333; + border-width: 5px 5px 5px 0; +} +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-left-color: #333; + border-width: 5px 0 5px 5px; +} +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-bottom-color: #333; + border-width: 0 5px 5px; +} +.tooltip.bottom-left .tooltip-arrow { + top: 0; + left: 5px; + border-bottom-color: #333; + border-width: 0 5px 5px; +} +.tooltip.bottom-right .tooltip-arrow { + top: 0; + right: 5px; + border-bottom-color: #333; + border-width: 0 5px 5px; +} +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1010; + display: none; + max-width: 276px; + padding: 1px; + text-align: left; + white-space: normal; + background-color: #333; + border: 1px solid #333; + border: 1px solid transparent; + border-radius: 0; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + background-clip: padding-box; +} +.popover.top { + margin-top: -10px; +} +.popover.right { + margin-left: 10px; +} +.popover.bottom { + margin-top: 10px; +} +.popover.left { + margin-left: -10px; +} +.popover-title { + padding: 8px 14px; + margin: 0; + font-size: 15px; + font-weight: normal; + line-height: 18px; + background-color: #333; + border-bottom: 1px solid #262626; + border-radius: 5px 5px 0 0; +} +.popover-content { + padding: 9px 14px; +} +.popover .arrow, .popover .arrow:after { + position: absolute; + display: block; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} +.popover .arrow { + border-width: 11px; +} +.popover .arrow:after { + border-width: 10px; + content: ""} +.popover.top .arrow { + bottom: -11px; + left: 50%; + margin-left: -11px; + border-top-color: #999; + border-top-color: rgba(0, 0, 0, 0.25); + border-bottom-width: 0; +} +.popover.top .arrow:after { + bottom: 1px; + margin-left: -10px; + border-top-color: #333; + border-bottom-width: 0; + content: " "} +.popover.right .arrow { + top: 50%; + left: -11px; + margin-top: -11px; + border-right-color: #999; + border-right-color: rgba(0, 0, 0, 0.25); + border-left-width: 0; +} +.popover.right .arrow:after { + bottom: -10px; + left: 1px; + border-right-color: #333; + border-left-width: 0; + content: " "} +.popover.bottom .arrow { + top: -11px; + left: 50%; + margin-left: -11px; + border-bottom-color: #999; + border-bottom-color: rgba(0, 0, 0, 0.25); + border-top-width: 0; +} +.popover.bottom .arrow:after { + top: 1px; + margin-left: -10px; + border-bottom-color: #333; + border-top-width: 0; + content: " "} +.popover.left .arrow { + top: 50%; + right: -11px; + margin-top: -11px; + border-left-color: #999; + border-left-color: rgba(0, 0, 0, 0.25); + border-right-width: 0; +} +.popover.left .arrow:after { + right: 1px; + bottom: -10px; + border-left-color: #333; + border-right-width: 0; + content: " "} +.carousel { + position: relative; +} +.carousel-inner { + position: relative; + width: 100%; + overflow: hidden; +} +.carousel-inner>.item { + position: relative; + display: none; + -webkit-transition: .6s ease-in-out left; + transition: .6s ease-in-out left; +} +.carousel-inner>.item>img, .carousel-inner>.item>a>img { + display: block; + height: auto; + max-width: 100%; + line-height: 1; +} +.carousel-inner>.active, .carousel-inner>.next, .carousel-inner>.prev { + display: block; +} +.carousel-inner>.active { + left: 0; +} +.carousel-inner>.next, .carousel-inner>.prev { + position: absolute; + top: 0; + width: 100%} +.carousel-inner>.next { + left: 100%} +.carousel-inner>.prev { + left: -100%} +.carousel-inner>.next.left, .carousel-inner>.prev.right { + left: 0; +} +.carousel-inner>.active.left { + left: -100%} +.carousel-inner>.active.right { + left: 100%} +.carousel-control { + position: absolute; + top: 0; + bottom: 0; + left: 0; + width: 15%; + font-size: 20px; + color: #fff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); + opacity: .5; + filter: alpha(opacity=50); +} +.carousel-control.left { + background-image: -webkit-linear-gradient(left, color-stop(rgba(0, 0, 0, 0.5) 0), color-stop(rgba(0, 0, 0, 0.0001) 100%)); + background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0, rgba(0, 0, 0, 0.0001) 100%); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); +} +.carousel-control.right { + right: 0; + left: auto; + background-image: -webkit-linear-gradient(left, color-stop(rgba(0, 0, 0, 0.0001) 0), color-stop(rgba(0, 0, 0, 0.5) 100%)); + background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0, rgba(0, 0, 0, 0.5) 100%); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); +} +.carousel-control:hover, .carousel-control:focus { + color: #fff; + text-decoration: none; + outline: 0; + opacity: .9; + filter: alpha(opacity=90); +} +.carousel-control .icon-prev, .carousel-control .icon-next, .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right { + position: absolute; + top: 50%; + z-index: 5; + display: inline-block; +} +.carousel-control .icon-prev, .carousel-control .glyphicon-chevron-left { + left: 50%} +.carousel-control .icon-next, .carousel-control .glyphicon-chevron-right { + right: 50%} +.carousel-control .icon-prev, .carousel-control .icon-next { + width: 20px; + height: 20px; + margin-top: -10px; + margin-left: -10px; + font-family: serif; +} +.carousel-control .icon-prev:before { + content: '\2039'} +.carousel-control .icon-next:before { + content: '\203a'} +.carousel-indicators { + position: absolute; + bottom: 10px; + left: 50%; + z-index: 15; + width: 60%; + padding-left: 0; + margin-left: -30%; + text-align: center; + list-style: none; +} +.carousel-indicators li { + display: inline-block; + width: 10px; + height: 10px; + margin: 1px; + text-indent: -999px; + cursor: pointer; + background-color: #000 \9; + background-color: rgba(0, 0, 0, 0); + border: 1px solid #fff; + border-radius: 10px; +} +.carousel-indicators .active { + width: 12px; + height: 12px; + margin: 0; + background-color: #fff; +} +.carousel-caption { + position: absolute; + right: 15%; + bottom: 20px; + left: 15%; + z-index: 10; + padding-top: 20px; + padding-bottom: 20px; + color: #fff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); +} +.carousel-caption .btn { + text-shadow: none; +} +@media screen and (min-width:768px) { + .carousel-control .glyphicons-chevron-left, .carousel-control .glyphicons-chevron-right, .carousel-control .icon-prev, .carousel-control .icon-next { + width: 30px; + height: 30px; + margin-top: -15px; + margin-left: -15px; + font-size: 30px; +} +.carousel-caption { + right: 20%; + left: 20%; + padding-bottom: 30px; +} +.carousel-indicators { + bottom: 20px; +} +}.clearfix:before, .clearfix:after { + display: table; + content: " "} +.clearfix:after { + clear: both; +} +.clearfix:before, .clearfix:after { + display: table; + content: " "} +.clearfix:after { + clear: both; +} +.center-block { + display: block; + margin-right: auto; + margin-left: auto; +} +.pull-right { + float: right!important; +} +.pull-left { + float: left!important; +} +.hide { + display: none!important; +} +.show { + display: block!important; +} +.invisible { + visibility: hidden; +} +.text-hide { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} +.hidden { + display: none!important; + visibility: hidden!important; +} +.affix { + position: fixed; +} +@-ms-viewport { + width: device-width; +} +.visible-xs, tr.visible-xs, th.visible-xs, td.visible-xs { + display: none!important; +} +@media(max-width:767px) { + .visible-xs { + display: block!important; +} +table.visible-xs { + display: table; +} +tr.visible-xs { + display: table-row!important; +} +th.visible-xs, td.visible-xs { + display: table-cell!important; +} +}@media(min-width:768px) and (max-width:991px) { + .visible-xs.visible-sm { + display: block!important; +} +table.visible-xs.visible-sm { + display: table; +} +tr.visible-xs.visible-sm { + display: table-row!important; +} +th.visible-xs.visible-sm, td.visible-xs.visible-sm { + display: table-cell!important; +} +}@media(min-width:992px) and (max-width:1199px) { + .visible-xs.visible-md { + display: block!important; +} +table.visible-xs.visible-md { + display: table; +} +tr.visible-xs.visible-md { + display: table-row!important; +} +th.visible-xs.visible-md, td.visible-xs.visible-md { + display: table-cell!important; +} +}@media(min-width:1200px) { + .visible-xs.visible-lg { + display: block!important; +} +table.visible-xs.visible-lg { + display: table; +} +tr.visible-xs.visible-lg { + display: table-row!important; +} +th.visible-xs.visible-lg, td.visible-xs.visible-lg { + display: table-cell!important; +} +}.visible-sm, tr.visible-sm, th.visible-sm, td.visible-sm { + display: none!important; +} +@media(max-width:767px) { + .visible-sm.visible-xs { + display: block!important; +} +table.visible-sm.visible-xs { + display: table; +} +tr.visible-sm.visible-xs { + display: table-row!important; +} +th.visible-sm.visible-xs, td.visible-sm.visible-xs { + display: table-cell!important; +} +}@media(min-width:768px) and (max-width:991px) { + .visible-sm { + display: block!important; +} +table.visible-sm { + display: table; +} +tr.visible-sm { + display: table-row!important; +} +th.visible-sm, td.visible-sm { + display: table-cell!important; +} +}@media(min-width:992px) and (max-width:1199px) { + .visible-sm.visible-md { + display: block!important; +} +table.visible-sm.visible-md { + display: table; +} +tr.visible-sm.visible-md { + display: table-row!important; +} +th.visible-sm.visible-md, td.visible-sm.visible-md { + display: table-cell!important; +} +}@media(min-width:1200px) { + .visible-sm.visible-lg { + display: block!important; +} +table.visible-sm.visible-lg { + display: table; +} +tr.visible-sm.visible-lg { + display: table-row!important; +} +th.visible-sm.visible-lg, td.visible-sm.visible-lg { + display: table-cell!important; +} +}.visible-md, tr.visible-md, th.visible-md, td.visible-md { + display: none!important; +} +@media(max-width:767px) { + .visible-md.visible-xs { + display: block!important; +} +table.visible-md.visible-xs { + display: table; +} +tr.visible-md.visible-xs { + display: table-row!important; +} +th.visible-md.visible-xs, td.visible-md.visible-xs { + display: table-cell!important; +} +}@media(min-width:768px) and (max-width:991px) { + .visible-md.visible-sm { + display: block!important; +} +table.visible-md.visible-sm { + display: table; +} +tr.visible-md.visible-sm { + display: table-row!important; +} +th.visible-md.visible-sm, td.visible-md.visible-sm { + display: table-cell!important; +} +}@media(min-width:992px) and (max-width:1199px) { + .visible-md { + display: block!important; +} +table.visible-md { + display: table; +} +tr.visible-md { + display: table-row!important; +} +th.visible-md, td.visible-md { + display: table-cell!important; +} +}@media(min-width:1200px) { + .visible-md.visible-lg { + display: block!important; +} +table.visible-md.visible-lg { + display: table; +} +tr.visible-md.visible-lg { + display: table-row!important; +} +th.visible-md.visible-lg, td.visible-md.visible-lg { + display: table-cell!important; +} +}.visible-lg, tr.visible-lg, th.visible-lg, td.visible-lg { + display: none!important; +} +@media(max-width:767px) { + .visible-lg.visible-xs { + display: block!important; +} +table.visible-lg.visible-xs { + display: table; +} +tr.visible-lg.visible-xs { + display: table-row!important; +} +th.visible-lg.visible-xs, td.visible-lg.visible-xs { + display: table-cell!important; +} +}@media(min-width:768px) and (max-width:991px) { + .visible-lg.visible-sm { + display: block!important; +} +table.visible-lg.visible-sm { + display: table; +} +tr.visible-lg.visible-sm { + display: table-row!important; +} +th.visible-lg.visible-sm, td.visible-lg.visible-sm { + display: table-cell!important; +} +}@media(min-width:992px) and (max-width:1199px) { + .visible-lg.visible-md { + display: block!important; +} +table.visible-lg.visible-md { + display: table; +} +tr.visible-lg.visible-md { + display: table-row!important; +} +th.visible-lg.visible-md, td.visible-lg.visible-md { + display: table-cell!important; +} +}@media(min-width:1200px) { + .visible-lg { + display: block!important; +} +table.visible-lg { + display: table; +} +tr.visible-lg { + display: table-row!important; +} +th.visible-lg, td.visible-lg { + display: table-cell!important; +} +}.hidden-xs { + display: block!important; +} +table.hidden-xs { + display: table; +} +tr.hidden-xs { + display: table-row!important; +} +th.hidden-xs, td.hidden-xs { + display: table-cell!important; +} +@media(max-width:767px) { + .hidden-xs, tr.hidden-xs, th.hidden-xs, td.hidden-xs { + display: none!important; +} +}@media(min-width:768px) and (max-width:991px) { + .hidden-xs.hidden-sm, tr.hidden-xs.hidden-sm, th.hidden-xs.hidden-sm, td.hidden-xs.hidden-sm { + display: none!important; +} +}@media(min-width:992px) and (max-width:1199px) { + .hidden-xs.hidden-md, tr.hidden-xs.hidden-md, th.hidden-xs.hidden-md, td.hidden-xs.hidden-md { + display: none!important; +} +}@media(min-width:1200px) { + .hidden-xs.hidden-lg, tr.hidden-xs.hidden-lg, th.hidden-xs.hidden-lg, td.hidden-xs.hidden-lg { + display: none!important; +} +}.hidden-sm { + display: block!important; +} +table.hidden-sm { + display: table; +} +tr.hidden-sm { + display: table-row!important; +} +th.hidden-sm, td.hidden-sm { + display: table-cell!important; +} +@media(max-width:767px) { + .hidden-sm.hidden-xs, tr.hidden-sm.hidden-xs, th.hidden-sm.hidden-xs, td.hidden-sm.hidden-xs { + display: none!important; +} +}@media(min-width:768px) and (max-width:991px) { + .hidden-sm, tr.hidden-sm, th.hidden-sm, td.hidden-sm { + display: none!important; +} +}@media(min-width:992px) and (max-width:1199px) { + .hidden-sm.hidden-md, tr.hidden-sm.hidden-md, th.hidden-sm.hidden-md, td.hidden-sm.hidden-md { + display: none!important; +} +}@media(min-width:1200px) { + .hidden-sm.hidden-lg, tr.hidden-sm.hidden-lg, th.hidden-sm.hidden-lg, td.hidden-sm.hidden-lg { + display: none!important; +} +}.hidden-md { + display: block!important; +} +table.hidden-md { + display: table; +} +tr.hidden-md { + display: table-row!important; +} +th.hidden-md, td.hidden-md { + display: table-cell!important; +} +@media(max-width:767px) { + .hidden-md.hidden-xs, tr.hidden-md.hidden-xs, th.hidden-md.hidden-xs, td.hidden-md.hidden-xs { + display: none!important; +} +}@media(min-width:768px) and (max-width:991px) { + .hidden-md.hidden-sm, tr.hidden-md.hidden-sm, th.hidden-md.hidden-sm, td.hidden-md.hidden-sm { + display: none!important; +} +}@media(min-width:992px) and (max-width:1199px) { + .hidden-md, tr.hidden-md, th.hidden-md, td.hidden-md { + display: none!important; +} +}@media(min-width:1200px) { + .hidden-md.hidden-lg, tr.hidden-md.hidden-lg, th.hidden-md.hidden-lg, td.hidden-md.hidden-lg { + display: none!important; +} +}.hidden-lg { + display: block!important; +} +table.hidden-lg { + display: table; +} +tr.hidden-lg { + display: table-row!important; +} +th.hidden-lg, td.hidden-lg { + display: table-cell!important; +} +@media(max-width:767px) { + .hidden-lg.hidden-xs, tr.hidden-lg.hidden-xs, th.hidden-lg.hidden-xs, td.hidden-lg.hidden-xs { + display: none!important; +} +}@media(min-width:768px) and (max-width:991px) { + .hidden-lg.hidden-sm, tr.hidden-lg.hidden-sm, th.hidden-lg.hidden-sm, td.hidden-lg.hidden-sm { + display: none!important; +} +}@media(min-width:992px) and (max-width:1199px) { + .hidden-lg.hidden-md, tr.hidden-lg.hidden-md, th.hidden-lg.hidden-md, td.hidden-lg.hidden-md { + display: none!important; +} +}@media(min-width:1200px) { + .hidden-lg, tr.hidden-lg, th.hidden-lg, td.hidden-lg { + display: none!important; +} +}.visible-print, tr.visible-print, th.visible-print, td.visible-print { + display: none!important; +} +@media print { + .visible-print { + display: block!important; +} +table.visible-print { + display: table; +} +tr.visible-print { + display: table-row!important; +} +th.visible-print, td.visible-print { + display: table-cell!important; +} +.hidden-print, tr.hidden-print, th.hidden-print, td.hidden-print { + display: none!important; +} +}.navbar { + font-size: 13px; + font-weight: 300; + border: 0; +} +.navbar .navbar-toggle:hover .icon-bar { + background-color: #b3b3b3; +} +.navbar-collapse { + border-top-color: rgba(0, 0, 0, 0.2); + -webkit-box-shadow: none; + box-shadow: none; +} +.navbar .dropdown-menu { + border: 0; +} +.navbar .dropdown-menu>li>a, .navbar .dropdown-menu>li>a:focus { + font-size: 13px; + font-weight: 300; + background-color: transparent; +} +.navbar .dropdown-header { + color: rgba(255, 255, 255, 0.5); +} +.navbar-default .dropdown-menu { + background-color: #333; +} +.navbar-default .dropdown-menu>li>a, .navbar-default .dropdown-menu>li>a:focus { + color: #fff; +} +.navbar-default .dropdown-menu>li>a:hover, .navbar-default .dropdown-menu>.active>a, .navbar-default .dropdown-menu>.active>a:hover { + background-color: #272727; +} +.navbar-inverse .dropdown-menu { + background-color: #008cba; +} +.navbar-inverse .dropdown-menu>li>a, .navbar-inverse .dropdown-menu>li>a:focus { + color: #fff; +} +.navbar-inverse .dropdown-menu>li>a:hover, .navbar-inverse .dropdown-menu>.active>a, .navbar-inverse .dropdown-menu>.active>a:hover { + background-color: #006687; +} +.btn { + padding: 14px 28px; +} +.btn-lg { + padding: 16px 32px; +} +.btn-sm { + padding: 8px 16px; +} +.btn-xs { + padding: 4px 8px; +} +.btn-group .btn~.dropdown-toggle { + padding-right: 16px; + padding-left: 16px; +} +.btn-group .dropdown-menu { + border-top-width: 0; +} +.btn-group.dropup .dropdown-menu { + margin-bottom: 0; + border-top-width: 1px; + border-bottom-width: 0; +} +.btn-group .dropdown-toggle.btn-default~.dropdown-menu { + background-color: #e7e7e7; + border-color: #dadada; +} +.btn-group .dropdown-toggle.btn-default~.dropdown-menu>li>a { + color: #333; +} +.btn-group .dropdown-toggle.btn-default~.dropdown-menu>li>a:hover { + background-color: #d3d3d3; +} +.btn-group .dropdown-toggle.btn-primary~.dropdown-menu { + background-color: #008cba; + border-color: #0079a1; +} +.btn-group .dropdown-toggle.btn-primary~.dropdown-menu>li>a { + color: #fff; +} +.btn-group .dropdown-toggle.btn-primary~.dropdown-menu>li>a:hover { + background-color: #006d91; +} +.btn-group .dropdown-toggle.btn-success~.dropdown-menu { + background-color: #43ac6a; + border-color: #3c9a5f; +} +.btn-group .dropdown-toggle.btn-success~.dropdown-menu>li>a { + color: #fff; +} +.btn-group .dropdown-toggle.btn-success~.dropdown-menu>li>a:hover { + background-color: #388f58; +} +.btn-group .dropdown-toggle.btn-info~.dropdown-menu { + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-group .dropdown-toggle.btn-info~.dropdown-menu>li>a { + color: #fff; +} +.btn-group .dropdown-toggle.btn-info~.dropdown-menu>li>a:hover { + background-color: #39b3d7; +} +.btn-group .dropdown-toggle.btn-warning~.dropdown-menu { + background-color: #e99002; + border-color: #d08002; +} +.btn-group .dropdown-toggle.btn-warning~.dropdown-menu>li>a { + color: #fff; +} +.btn-group .dropdown-toggle.btn-warning~.dropdown-menu>li>a:hover { + background-color: #c17702; +} +.btn-group .dropdown-toggle.btn-danger~.dropdown-menu { + background-color: #f04124; + border-color: #ea2f10; +} +.btn-group .dropdown-toggle.btn-danger~.dropdown-menu>li>a { + color: #fff; +} +.btn-group .dropdown-toggle.btn-danger~.dropdown-menu>li>a:hover { + background-color: #dc2c0f; +} +.lead { + color: #6f6f6f; +} +cite { + font-style: italic; +} +blockquote { + color: #6f6f6f; + border-left-width: 1px; +} +blockquote.pull-right { + border-right-width: 1px; +} +blockquote small { + font-size: 12px; + font-weight: 300; +} +table { + font-size: 12px; +} +input, .form-control { + padding: 7px; + font-size: 12px; +} +label, .control-label, .help-block, .checkbox, .radio { + font-size: 12px; + font-weight: normal; +} +.form-group .btn, .input-group-addon, .input-group-btn .btn { + padding: 8px 14px; + font-size: 12px; +} +.nav .open>a, .nav .open>a:hover, .nav .open>a:focus { + border-color: transparent; +} +.nav-tabs>li>a { + color: #222; + background-color: #e7e7e7; +} +.nav-tabs .caret { + border-top-color: #222; + border-bottom-color: #222; +} +.nav-pills { + font-weight: 300; +} +.breadcrumb { + font-size: 10px; + font-weight: 300; + text-transform: uppercase; + border: 1px solid #ddd; + border-radius: 3px; +} +.pagination { + font-size: 12px; + font-weight: 300; + color: #999; +} +.pagination>li>a, .pagination>li>span { + margin-left: 4px; + color: #999; +} +.pagination>.active>a, .pagination>.active>span { + color: #fff; +} +.pagination>li>a, .pagination>li:first-child>a, .pagination>li:last-child>a, .pagination>li>span, .pagination>li:first-child>span, .pagination>li:last-child>span { + border-radius: 3px; +} +.pagination-lg>li>a { + padding-right: 22px; + padding-left: 22px; +} +.pagination-sm>li>a { + padding: 0 5px; +} +.pager { + font-size: 12px; + font-weight: 300; + color: #999; +} +.list-group { + font-size: 12px; + font-weight: 300; +} +.label { + padding-right: 1em; + padding-left: 1em; + font-weight: 300; + border-radius: 0; +} +.label-default { + color: #333; + background-color: #e7e7e7; +} +.badge { + font-weight: 300; +} +.progress { + height: 22px; + padding: 2px; + background-color: #f6f6f6; + border: 1px solid #ccc; + -webkit-box-shadow: none; + box-shadow: none; +} +.dropdown-menu { + padding: 0; + margin-top: 0; + font-size: 12px; +} +.dropdown-menu>li>a { + padding: 12px 15px; +} +.dropdown-header { + padding-right: 15px; + padding-left: 15px; + font-size: 9px; + text-transform: uppercase; +} +.popover { + font-size: 12px; + font-weight: 300; + color: #fff; +} +.panel-heading, .panel-footer { + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.clearfix:before, .clearfix:after { + display: table; + content: " "} +.clearfix:after { + clear: both; +} +.clearfix:before, .clearfix:after { + display: table; + content: " "} +.clearfix:after { + clear: both; +} +.center-block { + display: block; + margin-right: auto; + margin-left: auto; +} +.pull-right { + float: right!important; +} +.pull-left { + float: left!important; +} +.hide { + display: none!important; +} +.show { + display: block!important; +} +.invisible { + visibility: hidden; +} +.text-hide { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} +.hidden { + display: none!important; + visibility: 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+.hljs-built_in, +.hljs-builtin-name { + color: #0086b3; +} + +.hljs-meta { + color: #999; + font-weight: bold; +} + +.hljs-deletion { + background: #fdd; +} + +.hljs-addition { + background: #dfd; +} + +.hljs-emphasis { + font-style: italic; +} + +.hljs-strong { + font-weight: bold; +} diff --git a/docs/css/highlight.min.css b/docs/css/highlight.min.css new file mode 100644 index 0000000..1b6ab94 --- /dev/null +++ b/docs/css/highlight.min.css @@ -0,0 +1 @@ +.hljs{display:block;overflow-x:auto;padding:.5em;color:#333;background:#fcfdff}.hljs-comment,.hljs-quote{color:#998;font-style:italic}.hljs-keyword,.hljs-selector-tag,.hljs-subst{color:#333;font-weight:bold}.hljs-number,.hljs-literal,.hljs-variable,.hljs-template-variable,.hljs-tag .hljs-attr{color:teal}.hljs-string,.hljs-doctag{color:#d14}.hljs-title,.hljs-section,.hljs-selector-id{color:#900;font-weight:bold}.hljs-subst{font-weight:normal}.hljs-type,.hljs-class .hljs-title{color:#458;font-weight:bold}.hljs-tag,.hljs-name,.hljs-attribute{color:navy;font-weight:normal}.hljs-regexp,.hljs-link{color:#009926}.hljs-symbol,.hljs-bullet{color:#990073}.hljs-built_in,.hljs-builtin-name{color:#0086b3}.hljs-meta{color:#999;font-weight:bold}.hljs-deletion{background:#fdd}.hljs-addition{background:#dfd}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold} diff --git a/docs/customization/index.html b/docs/customization/index.html new file mode 100644 index 0000000..362cff1 --- /dev/null +++ b/docs/customization/index.html @@ -0,0 +1,250 @@ + + + + + + + + + + + + + + Customization - MIPLearn + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ +

Customization

+

Selecting the internal MIP solver

+

By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example:

+
from miplearn import LearningSolver
+import pyomo.environ as pe
+
+def cplex_factory():
+    cplex = pe.SolverFactory("cplex")
+    cplex.options["threads"] = 4
+    return cplex
+
+solver = LearningSolver(internal_solver_factory=cplex_factory)
+
+ + +
+ +
+ +
+

+ © Copyright 2019–2020 Argonne National Laboratory
 + + Documentation built with MkDocs.

+ + + +
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+ + +
+
+ +

MIPLearn

+

MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).

+

Features

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    MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver.

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    MIPLearn provides a reference implementation of a Learning-Enhanced Solver, which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention.

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    MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way.

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    MIPLearn is customizable and extensible. For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.

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+

Documentation

+ +

Souce Code

+
+ + +
+ +
+ +
+

+ © Copyright 2019–2020 Argonne National Laboratory
 + + Documentation built with MkDocs.

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+ + + + + + + + + + + + Problems - MIPLearn + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ +

Benchmark Problems, Challenges and Results

+

MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.

+

Preliminaries

+

Benchmark challenges

+

When evaluating the performance of a conventional MIP solver, benchmark sets, such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances) before solving the actual set of instances we are interested in (the test instances). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits.

+

In MIPLearn, each optimization problem comes with a set of benchmark challenges, which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.

+

Baseline results

+

To illustrate the performance of LearningSolver, and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references. We compare three solvers:

+
    +
  • baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver)
    • +
  • ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver
    • +
  • ml-heuristic: Same as above, but with mode="heuristic"
    • +
+

All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.

+

Maximum Weight Stable Set Problem

+

Problem definition

+

Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the problem is to find a stable set $S \subseteq V$ that maximizes $ \sum_{v \in V} w_v$. We recall that a subset $S \subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.

+

Random instance generator

+

The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w. When fix_graph=False, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.

+

Benchmark challenges

+

Challenge A

+
    +
  • Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$
    • +
  • Random vertex weights $w_v \sim U(100, 150)$
    • +
  • 500 training instances, 50 test instances
    • +
+
MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.),
+                            n=randint(low=200, high=201),
+                            p=uniform(loc=0.05, scale=0.0),
+                            fix_graph=True)
+
+ +

Benchmark results

+

Challenge A

+

alt

+

Multidimensional 0-1 Knapsack Problem

+

Problem definition

+

Given a set of $n$ items and $m$ types of resources (also called knapsacks), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is:

+

+ +

+

Random instance generator

+

The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m. The weights $w_{ij}$ are sampled independently from the provided distribution w. The capacity of knapsack $i$ is set to

+

+ +

+

where $\alpha_i$, the tightness ratio, is sampled from the provided probability +distribution alpha. To make the instances more challenging, the costs of the items +are linearly correlated to their average weights. More specifically, the price of each +item $j$ is set to:

+

+ +

+

where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled +from the provided probability distributions K and u.

+

If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical.

+

If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \gamma_{ij}$ where $\gamma_{ij}$ is sampled from w_jitter. When combined with fix_w=True, this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead.

+

By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled.

+
+

References

+
    +
  • Freville, Arnaud, and Gérard Plateau. An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212.
    • +
  • Fréville, Arnaud. The multidimensional 0–1 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.
    • +
+
+ + +
+ +
+ +
+

+ © Copyright 2019–2020 Argonne National Laboratory
 + + Documentation built with MkDocs.

+ + + +
+ + + + + + + + + + + + + + + diff --git a/docs/search/lunr.js b/docs/search/lunr.js new file mode 100644 index 0000000..c218cc8 --- /dev/null +++ b/docs/search/lunr.js @@ -0,0 +1,2986 @@ +/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.1.6 + * Copyright (C) 2018 Oliver Nightingale + * @license MIT + */ + +;(function(){ + +/** + * A convenience function for configuring and constructing + * a new lunr Index. + * + * A lunr.Builder instance is created and the pipeline setup + * with a trimmer, stop word filter and stemmer. + * + * This builder object is yielded to the configuration function + * that is passed as a parameter, allowing the list of fields + * and other builder parameters to be customised. + * + * All documents _must_ be added within the passed config function. + * + * @example + * var idx = lunr(function () { + * this.field('title') + * this.field('body') + * this.ref('id') + * + * documents.forEach(function (doc) { + * this.add(doc) + * }, this) + * }) + * + * @see {@link lunr.Builder} + * @see {@link lunr.Pipeline} + * @see {@link lunr.trimmer} + * @see {@link lunr.stopWordFilter} + * @see {@link lunr.stemmer} + * @namespace {function} lunr + */ +var lunr = function (config) { + var builder = new lunr.Builder + + builder.pipeline.add( + lunr.trimmer, + lunr.stopWordFilter, + lunr.stemmer + ) + + builder.searchPipeline.add( + lunr.stemmer + ) + + config.call(builder, builder) + return builder.build() +} + +lunr.version = "2.1.6" +/*! + * lunr.utils + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * A namespace containing utils for the rest of the lunr library + */ +lunr.utils = {} + +/** + * Print a warning message to the console. + * + * @param {String} message The message to be printed. + * @memberOf Utils + */ +lunr.utils.warn = (function (global) { + /* eslint-disable no-console */ + return function (message) { + if (global.console && console.warn) { + console.warn(message) + } + } + /* eslint-enable no-console */ +})(this) + +/** + * Convert an object to a string. + * + * In the case of `null` and `undefined` the function returns + * the empty string, in all other cases the result of calling + * `toString` on the passed object is returned. + * + * @param {Any} obj The object to convert to a string. + * @return {String} string representation of the passed object. + * @memberOf Utils + */ +lunr.utils.asString = function (obj) { + if (obj === void 0 || obj === null) { + return "" + } else { + return obj.toString() + } +} +lunr.FieldRef = function (docRef, fieldName, stringValue) { + this.docRef = docRef + this.fieldName = fieldName + this._stringValue = stringValue +} + +lunr.FieldRef.joiner = "/" + +lunr.FieldRef.fromString = function (s) { + var n = s.indexOf(lunr.FieldRef.joiner) + + if (n === -1) { + throw "malformed field ref string" + } + + var fieldRef = s.slice(0, n), + docRef = s.slice(n + 1) + + return new lunr.FieldRef (docRef, fieldRef, s) +} + +lunr.FieldRef.prototype.toString = function () { + if (this._stringValue == undefined) { + this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef + } + + return this._stringValue +} +/** + * A function to calculate the inverse document frequency for + * a posting. This is shared between the builder and the index + * + * @private + * @param {object} posting - The posting for a given term + * @param {number} documentCount - The total number of documents. + */ +lunr.idf = function (posting, documentCount) { + var documentsWithTerm = 0 + + for (var fieldName in posting) { + if (fieldName == '_index') continue // Ignore the term index, its not a field + documentsWithTerm += Object.keys(posting[fieldName]).length + } + + var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5) + + return Math.log(1 + Math.abs(x)) +} + +/** + * A token wraps a string representation of a token + * as it is passed through the text processing pipeline. + * + * @constructor + * @param {string} [str=''] - The string token being wrapped. + * @param {object} [metadata={}] - Metadata associated with this token. + */ +lunr.Token = function (str, metadata) { + this.str = str || "" + this.metadata = metadata || {} +} + +/** + * Returns the token string that is being wrapped by this object. + * + * @returns {string} + */ +lunr.Token.prototype.toString = function () { + return this.str +} + +/** + * A token update function is used when updating or optionally + * when cloning a token. + * + * @callback lunr.Token~updateFunction + * @param {string} str - The string representation of the token. + * @param {Object} metadata - All metadata associated with this token. + */ + +/** + * Applies the given function to the wrapped string token. + * + * @example + * token.update(function (str, metadata) { + * return str.toUpperCase() + * }) + * + * @param {lunr.Token~updateFunction} fn - A function to apply to the token string. + * @returns {lunr.Token} + */ +lunr.Token.prototype.update = function (fn) { + this.str = fn(this.str, this.metadata) + return this +} + +/** + * Creates a clone of this token. Optionally a function can be + * applied to the cloned token. + * + * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token. + * @returns {lunr.Token} + */ +lunr.Token.prototype.clone = function (fn) { + fn = fn || function (s) { return s } + return new lunr.Token (fn(this.str, this.metadata), this.metadata) +} +/*! + * lunr.tokenizer + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * A function for splitting a string into tokens ready to be inserted into + * the search index. Uses `lunr.tokenizer.separator` to split strings, change + * the value of this property to change how strings are split into tokens. + * + * This tokenizer will convert its parameter to a string by calling `toString` and + * then will split this string on the character in `lunr.tokenizer.separator`. + * Arrays will have their elements converted to strings and wrapped in a lunr.Token. + * + * @static + * @param {?(string|object|object[])} obj - The object to convert into tokens + * @returns {lunr.Token[]} + */ +lunr.tokenizer = function (obj) { + if (obj == null || obj == undefined) { + return [] + } + + if (Array.isArray(obj)) { + return obj.map(function (t) { + return new lunr.Token(lunr.utils.asString(t).toLowerCase()) + }) + } + + var str = obj.toString().trim().toLowerCase(), + len = str.length, + tokens = [] + + for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) { + var char = str.charAt(sliceEnd), + sliceLength = sliceEnd - sliceStart + + if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) { + + if (sliceLength > 0) { + tokens.push( + new lunr.Token (str.slice(sliceStart, sliceEnd), { + position: [sliceStart, sliceLength], + index: tokens.length + }) + ) + } + + sliceStart = sliceEnd + 1 + } + + } + + return tokens +} + +/** + * The separator used to split a string into tokens. Override this property to change the behaviour of + * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens. + * + * @static + * @see lunr.tokenizer + */ +lunr.tokenizer.separator = /[\s\-]+/ +/*! + * lunr.Pipeline + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * lunr.Pipelines maintain an ordered list of functions to be applied to all + * tokens in documents entering the search index and queries being ran against + * the index. + * + * An instance of lunr.Index created with the lunr shortcut will contain a + * pipeline with a stop word filter and an English language stemmer. Extra + * functions can be added before or after either of these functions or these + * default functions can be removed. + * + * When run the pipeline will call each function in turn, passing a token, the + * index of that token in the original list of all tokens and finally a list of + * all the original tokens. + * + * The output of functions in the pipeline will be passed to the next function + * in the pipeline. To exclude a token from entering the index the function + * should return undefined, the rest of the pipeline will not be called with + * this token. + * + * For serialisation of pipelines to work, all functions used in an instance of + * a pipeline should be registered with lunr.Pipeline. Registered functions can + * then be loaded. If trying to load a serialised pipeline that uses functions + * that are not registered an error will be thrown. + * + * If not planning on serialising the pipeline then registering pipeline functions + * is not necessary. + * + * @constructor + */ +lunr.Pipeline = function () { + this._stack = [] +} + +lunr.Pipeline.registeredFunctions = Object.create(null) + +/** + * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token + * string as well as all known metadata. A pipeline function can mutate the token string + * or mutate (or add) metadata for a given token. + * + * A pipeline function can indicate that the passed token should be discarded by returning + * null. This token will not be passed to any downstream pipeline functions and will not be + * added to the index. + * + * Multiple tokens can be returned by returning an array of tokens. Each token will be passed + * to any downstream pipeline functions and all will returned tokens will be added to the index. + * + * Any number of pipeline functions may be chained together using a lunr.Pipeline. + * + * @interface lunr.PipelineFunction + * @param {lunr.Token} token - A token from the document being processed. + * @param {number} i - The index of this token in the complete list of tokens for this document/field. + * @param {lunr.Token[]} tokens - All tokens for this document/field. + * @returns {(?lunr.Token|lunr.Token[])} + */ + +/** + * Register a function with the pipeline. + * + * Functions that are used in the pipeline should be registered if the pipeline + * needs to be serialised, or a serialised pipeline needs to be loaded. + * + * Registering a function does not add it to a pipeline, functions must still be + * added to instances of the pipeline for them to be used when running a pipeline. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @param {String} label - The label to register this function with + */ +lunr.Pipeline.registerFunction = function (fn, label) { + if (label in this.registeredFunctions) { + lunr.utils.warn('Overwriting existing registered function: ' + label) + } + + fn.label = label + lunr.Pipeline.registeredFunctions[fn.label] = fn +} + +/** + * Warns if the function is not registered as a Pipeline function. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @private + */ +lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) { + var isRegistered = fn.label && (fn.label in this.registeredFunctions) + + if (!isRegistered) { + lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn) + } +} + +/** + * Loads a previously serialised pipeline. + * + * All functions to be loaded must already be registered with lunr.Pipeline. + * If any function from the serialised data has not been registered then an + * error will be thrown. + * + * @param {Object} serialised - The serialised pipeline to load. + * @returns {lunr.Pipeline} + */ +lunr.Pipeline.load = function (serialised) { + var pipeline = new lunr.Pipeline + + serialised.forEach(function (fnName) { + var fn = lunr.Pipeline.registeredFunctions[fnName] + + if (fn) { + pipeline.add(fn) + } else { + throw new Error('Cannot load unregistered function: ' + fnName) + } + }) + + return pipeline +} + +/** + * Adds new functions to the end of the pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline. + */ +lunr.Pipeline.prototype.add = function () { + var fns = Array.prototype.slice.call(arguments) + + fns.forEach(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + this._stack.push(fn) + }, this) +} + +/** + * Adds a single function after a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.after = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + pos = pos + 1 + this._stack.splice(pos, 0, newFn) +} + +/** + * Adds a single function before a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.before = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + this._stack.splice(pos, 0, newFn) +} + +/** + * Removes a function from the pipeline. + * + * @param {lunr.PipelineFunction} fn The function to remove from the pipeline. + */ +lunr.Pipeline.prototype.remove = function (fn) { + var pos = this._stack.indexOf(fn) + if (pos == -1) { + return + } + + this._stack.splice(pos, 1) +} + +/** + * Runs the current list of functions that make up the pipeline against the + * passed tokens. + * + * @param {Array} tokens The tokens to run through the pipeline. + * @returns {Array} + */ +lunr.Pipeline.prototype.run = function (tokens) { + var stackLength = this._stack.length + + for (var i = 0; i < stackLength; i++) { + var fn = this._stack[i] + var memo = [] + + for (var j = 0; j < tokens.length; j++) { + var result = fn(tokens[j], j, tokens) + + if (result === void 0 || result === '') continue + + if (result instanceof Array) { + for (var k = 0; k < result.length; k++) { + memo.push(result[k]) + } + } else { + memo.push(result) + } + } + + tokens = memo + } + + return tokens +} + +/** + * Convenience method for passing a string through a pipeline and getting + * strings out. This method takes care of wrapping the passed string in a + * token and mapping the resulting tokens back to strings. + * + * @param {string} str - The string to pass through the pipeline. + * @returns {string[]} + */ +lunr.Pipeline.prototype.runString = function (str) { + var token = new lunr.Token (str) + + return this.run([token]).map(function (t) { + return t.toString() + }) +} + +/** + * Resets the pipeline by removing any existing processors. + * + */ +lunr.Pipeline.prototype.reset = function () { + this._stack = [] +} + +/** + * Returns a representation of the pipeline ready for serialisation. + * + * Logs a warning if the function has not been registered. + * + * @returns {Array} + */ +lunr.Pipeline.prototype.toJSON = function () { + return this._stack.map(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + + return fn.label + }) +} +/*! + * lunr.Vector + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * A vector is used to construct the vector space of documents and queries. These + * vectors support operations to determine the similarity between two documents or + * a document and a query. + * + * Normally no parameters are required for initializing a vector, but in the case of + * loading a previously dumped vector the raw elements can be provided to the constructor. + * + * For performance reasons vectors are implemented with a flat array, where an elements + * index is immediately followed by its value. E.g. [index, value, index, value]. This + * allows the underlying array to be as sparse as possible and still offer decent + * performance when being used for vector calculations. + * + * @constructor + * @param {Number[]} [elements] - The flat list of element index and element value pairs. + */ +lunr.Vector = function (elements) { + this._magnitude = 0 + this.elements = elements || [] +} + + +/** + * Calculates the position within the vector to insert a given index. + * + * This is used internally by insert and upsert. If there are duplicate indexes then + * the position is returned as if the value for that index were to be updated, but it + * is the callers responsibility to check whether there is a duplicate at that index + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @returns {Number} + */ +lunr.Vector.prototype.positionForIndex = function (index) { + // For an empty vector the tuple can be inserted at the beginning + if (this.elements.length == 0) { + return 0 + } + + var start = 0, + end = this.elements.length / 2, + sliceLength = end - start, + pivotPoint = Math.floor(sliceLength / 2), + pivotIndex = this.elements[pivotPoint * 2] + + while (sliceLength > 1) { + if (pivotIndex < index) { + start = pivotPoint + } + + if (pivotIndex > index) { + end = pivotPoint + } + + if (pivotIndex == index) { + break + } + + sliceLength = end - start + pivotPoint = start + Math.floor(sliceLength / 2) + pivotIndex = this.elements[pivotPoint * 2] + } + + if (pivotIndex == index) { + return pivotPoint * 2 + } + + if (pivotIndex > index) { + return pivotPoint * 2 + } + + if (pivotIndex < index) { + return (pivotPoint + 1) * 2 + } +} + +/** + * Inserts an element at an index within the vector. + * + * Does not allow duplicates, will throw an error if there is already an entry + * for this index. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + */ +lunr.Vector.prototype.insert = function (insertIdx, val) { + this.upsert(insertIdx, val, function () { + throw "duplicate index" + }) +} + +/** + * Inserts or updates an existing index within the vector. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + * @param {function} fn - A function that is called for updates, the existing value and the + * requested value are passed as arguments + */ +lunr.Vector.prototype.upsert = function (insertIdx, val, fn) { + this._magnitude = 0 + var position = this.positionForIndex(insertIdx) + + if (this.elements[position] == insertIdx) { + this.elements[position + 1] = fn(this.elements[position + 1], val) + } else { + this.elements.splice(position, 0, insertIdx, val) + } +} + +/** + * Calculates the magnitude of this vector. + * + * @returns {Number} + */ +lunr.Vector.prototype.magnitude = function () { + if (this._magnitude) return this._magnitude + + var sumOfSquares = 0, + elementsLength = this.elements.length + + for (var i = 1; i < elementsLength; i += 2) { + var val = this.elements[i] + sumOfSquares += val * val + } + + return this._magnitude = Math.sqrt(sumOfSquares) +} + +/** + * Calculates the dot product of this vector and another vector. + * + * @param {lunr.Vector} otherVector - The vector to compute the dot product with. + * @returns {Number} + */ +lunr.Vector.prototype.dot = function (otherVector) { + var dotProduct = 0, + a = this.elements, b = otherVector.elements, + aLen = a.length, bLen = b.length, + aVal = 0, bVal = 0, + i = 0, j = 0 + + while (i < aLen && j < bLen) { + aVal = a[i], bVal = b[j] + if (aVal < bVal) { + i += 2 + } else if (aVal > bVal) { + j += 2 + } else if (aVal == bVal) { + dotProduct += a[i + 1] * b[j + 1] + i += 2 + j += 2 + } + } + + return dotProduct +} + +/** + * Calculates the cosine similarity between this vector and another + * vector. + * + * @param {lunr.Vector} otherVector - The other vector to calculate the + * similarity with. + * @returns {Number} + */ +lunr.Vector.prototype.similarity = function (otherVector) { + return this.dot(otherVector) / (this.magnitude() * otherVector.magnitude()) +} + +/** + * Converts the vector to an array of the elements within the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toArray = function () { + var output = new Array (this.elements.length / 2) + + for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) { + output[j] = this.elements[i] + } + + return output +} + +/** + * A JSON serializable representation of the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toJSON = function () { + return this.elements +} +/* eslint-disable */ +/*! + * lunr.stemmer + * Copyright (C) 2018 Oliver Nightingale + * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt + */ + +/** + * lunr.stemmer is an english language stemmer, this is a JavaScript + * implementation of the PorterStemmer taken from http://tartarus.org/~martin + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token - The string to stem + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + */ +lunr.stemmer = (function(){ + var step2list = { + "ational" : "ate", + "tional" : "tion", + "enci" : "ence", + "anci" : "ance", + "izer" : "ize", + "bli" : "ble", + "alli" : "al", + "entli" : "ent", + "eli" : "e", + "ousli" : "ous", + "ization" : "ize", + "ation" : "ate", + "ator" : "ate", + "alism" : "al", + "iveness" : "ive", + "fulness" : "ful", + "ousness" : "ous", + "aliti" : "al", + "iviti" : "ive", + "biliti" : "ble", + "logi" : "log" + }, + + step3list = { + "icate" : "ic", + "ative" : "", + "alize" : "al", + "iciti" : "ic", + "ical" : "ic", + "ful" : "", + "ness" : "" + }, + + c = "[^aeiou]", // consonant + v = "[aeiouy]", // vowel + C = c + "[^aeiouy]*", // consonant sequence + V = v + "[aeiou]*", // vowel sequence + + mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0 + meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1 + mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1 + s_v = "^(" + C + ")?" + v; // vowel in stem + + var re_mgr0 = new RegExp(mgr0); + var re_mgr1 = new RegExp(mgr1); + var re_meq1 = new RegExp(meq1); + var re_s_v = new RegExp(s_v); + + var re_1a = /^(.+?)(ss|i)es$/; + var re2_1a = /^(.+?)([^s])s$/; + var re_1b = /^(.+?)eed$/; + var re2_1b = /^(.+?)(ed|ing)$/; + var re_1b_2 = /.$/; + var re2_1b_2 = /(at|bl|iz)$/; + var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$"); + var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var re_1c = /^(.+?[^aeiou])y$/; + var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + + var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + + var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + var re2_4 = /^(.+?)(s|t)(ion)$/; + + var re_5 = /^(.+?)e$/; + var re_5_1 = /ll$/; + var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var porterStemmer = function porterStemmer(w) { + var stem, + suffix, + firstch, + re, + re2, + re3, + re4; + + if (w.length < 3) { return w; } + + firstch = w.substr(0,1); + if (firstch == "y") { + w = firstch.toUpperCase() + w.substr(1); + } + + // Step 1a + re = re_1a + re2 = re2_1a; + + if (re.test(w)) { w = w.replace(re,"$1$2"); } + else if (re2.test(w)) { w = w.replace(re2,"$1$2"); } + + // Step 1b + re = re_1b; + re2 = re2_1b; + if (re.test(w)) { + var fp = re.exec(w); + re = re_mgr0; + if (re.test(fp[1])) { + re = re_1b_2; + w = w.replace(re,""); + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = re_s_v; + if (re2.test(stem)) { + w = stem; + re2 = re2_1b_2; + re3 = re3_1b_2; + re4 = re4_1b_2; + if (re2.test(w)) { w = w + "e"; } + else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); } + else if (re4.test(w)) { w = w + "e"; } + } + } + + // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say) + re = re_1c; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + w = stem + "i"; + } + + // Step 2 + re = re_2; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step2list[suffix]; + } + } + + // Step 3 + re = re_3; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step3list[suffix]; + } + } + + // Step 4 + re = re_4; + re2 = re2_4; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + if (re.test(stem)) { + w = stem; + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = re_mgr1; + if (re2.test(stem)) { + w = stem; + } + } + + // Step 5 + re = re_5; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + re2 = re_meq1; + re3 = re3_5; + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) { + w = stem; + } + } + + re = re_5_1; + re2 = re_mgr1; + if (re.test(w) && re2.test(w)) { + re = re_1b_2; + w = w.replace(re,""); + } + + // and turn initial Y back to y + + if (firstch == "y") { + w = firstch.toLowerCase() + w.substr(1); + } + + return w; + }; + + return function (token) { + return token.update(porterStemmer); + } +})(); + +lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer') +/*! + * lunr.stopWordFilter + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * lunr.generateStopWordFilter builds a stopWordFilter function from the provided + * list of stop words. + * + * The built in lunr.stopWordFilter is built using this generator and can be used + * to generate custom stopWordFilters for applications or non English languages. + * + * @param {Array} token The token to pass through the filter + * @returns {lunr.PipelineFunction} + * @see lunr.Pipeline + * @see lunr.stopWordFilter + */ +lunr.generateStopWordFilter = function (stopWords) { + var words = stopWords.reduce(function (memo, stopWord) { + memo[stopWord] = stopWord + return memo + }, {}) + + return function (token) { + if (token && words[token.toString()] !== token.toString()) return token + } +} + +/** + * lunr.stopWordFilter is an English language stop word list filter, any words + * contained in the list will not be passed through the filter. + * + * This is intended to be used in the Pipeline. If the token does not pass the + * filter then undefined will be returned. + * + * @implements {lunr.PipelineFunction} + * @params {lunr.Token} token - A token to check for being a stop word. + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + */ +lunr.stopWordFilter = lunr.generateStopWordFilter([ + 'a', + 'able', + 'about', + 'across', + 'after', + 'all', + 'almost', + 'also', + 'am', + 'among', + 'an', + 'and', + 'any', + 'are', + 'as', + 'at', + 'be', + 'because', + 'been', + 'but', + 'by', + 'can', + 'cannot', + 'could', + 'dear', + 'did', + 'do', + 'does', + 'either', + 'else', + 'ever', + 'every', + 'for', + 'from', + 'get', + 'got', + 'had', + 'has', + 'have', + 'he', + 'her', + 'hers', + 'him', + 'his', + 'how', + 'however', + 'i', + 'if', + 'in', + 'into', + 'is', + 'it', + 'its', + 'just', + 'least', + 'let', + 'like', + 'likely', + 'may', + 'me', + 'might', + 'most', + 'must', + 'my', + 'neither', + 'no', + 'nor', + 'not', + 'of', + 'off', + 'often', + 'on', + 'only', + 'or', + 'other', + 'our', + 'own', + 'rather', + 'said', + 'say', + 'says', + 'she', + 'should', + 'since', + 'so', + 'some', + 'than', + 'that', + 'the', + 'their', + 'them', + 'then', + 'there', + 'these', + 'they', + 'this', + 'tis', + 'to', + 'too', + 'twas', + 'us', + 'wants', + 'was', + 'we', + 'were', + 'what', + 'when', + 'where', + 'which', + 'while', + 'who', + 'whom', + 'why', + 'will', + 'with', + 'would', + 'yet', + 'you', + 'your' +]) + +lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter') +/*! + * lunr.trimmer + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * lunr.trimmer is a pipeline function for trimming non word + * characters from the beginning and end of tokens before they + * enter the index. + * + * This implementation may not work correctly for non latin + * characters and should either be removed or adapted for use + * with languages with non-latin characters. + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token The token to pass through the filter + * @returns {lunr.Token} + * @see lunr.Pipeline + */ +lunr.trimmer = function (token) { + return token.update(function (s) { + return s.replace(/^\W+/, '').replace(/\W+$/, '') + }) +} + +lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer') +/*! + * lunr.TokenSet + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * A token set is used to store the unique list of all tokens + * within an index. Token sets are also used to represent an + * incoming query to the index, this query token set and index + * token set are then intersected to find which tokens to look + * up in the inverted index. + * + * A token set can hold multiple tokens, as in the case of the + * index token set, or it can hold a single token as in the + * case of a simple query token set. + * + * Additionally token sets are used to perform wildcard matching. + * Leading, contained and trailing wildcards are supported, and + * from this edit distance matching can also be provided. + * + * Token sets are implemented as a minimal finite state automata, + * where both common prefixes and suffixes are shared between tokens. + * This helps to reduce the space used for storing the token set. + * + * @constructor + */ +lunr.TokenSet = function () { + this.final = false + this.edges = {} + this.id = lunr.TokenSet._nextId + lunr.TokenSet._nextId += 1 +} + +/** + * Keeps track of the next, auto increment, identifier to assign + * to a new tokenSet. + * + * TokenSets require a unique identifier to be correctly minimised. + * + * @private + */ +lunr.TokenSet._nextId = 1 + +/** + * Creates a TokenSet instance from the given sorted array of words. + * + * @param {String[]} arr - A sorted array of strings to create the set from. + * @returns {lunr.TokenSet} + * @throws Will throw an error if the input array is not sorted. + */ +lunr.TokenSet.fromArray = function (arr) { + var builder = new lunr.TokenSet.Builder + + for (var i = 0, len = arr.length; i < len; i++) { + builder.insert(arr[i]) + } + + builder.finish() + return builder.root +} + +/** + * Creates a token set from a query clause. + * + * @private + * @param {Object} clause - A single clause from lunr.Query. + * @param {string} clause.term - The query clause term. + * @param {number} [clause.editDistance] - The optional edit distance for the term. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromClause = function (clause) { + if ('editDistance' in clause) { + return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance) + } else { + return lunr.TokenSet.fromString(clause.term) + } +} + +/** + * Creates a token set representing a single string with a specified + * edit distance. + * + * Insertions, deletions, substitutions and transpositions are each + * treated as an edit distance of 1. + * + * Increasing the allowed edit distance will have a dramatic impact + * on the performance of both creating and intersecting these TokenSets. + * It is advised to keep the edit distance less than 3. + * + * @param {string} str - The string to create the token set from. + * @param {number} editDistance - The allowed edit distance to match. + * @returns {lunr.Vector} + */ +lunr.TokenSet.fromFuzzyString = function (str, editDistance) { + var root = new lunr.TokenSet + + var stack = [{ + node: root, + editsRemaining: editDistance, + str: str + }] + + while (stack.length) { + var frame = stack.pop() + + // no edit + if (frame.str.length > 0) { + var char = frame.str.charAt(0), + noEditNode + + if (char in frame.node.edges) { + noEditNode = frame.node.edges[char] + } else { + noEditNode = new lunr.TokenSet + frame.node.edges[char] = noEditNode + } + + if (frame.str.length == 1) { + noEditNode.final = true + } else { + stack.push({ + node: noEditNode, + editsRemaining: frame.editsRemaining, + str: frame.str.slice(1) + }) + } + } + + // deletion + // can only do a deletion if we have enough edits remaining + // and if there are characters left to delete in the string + if (frame.editsRemaining > 0 && frame.str.length > 1) { + var char = frame.str.charAt(1), + deletionNode + + if (char in frame.node.edges) { + deletionNode = frame.node.edges[char] + } else { + deletionNode = new lunr.TokenSet + frame.node.edges[char] = deletionNode + } + + if (frame.str.length <= 2) { + deletionNode.final = true + } else { + stack.push({ + node: deletionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(2) + }) + } + } + + // deletion + // just removing the last character from the str + if (frame.editsRemaining > 0 && frame.str.length == 1) { + frame.node.final = true + } + + // substitution + // can only do a substitution if we have enough edits remaining + // and if there are characters left to substitute + if (frame.editsRemaining > 0 && frame.str.length >= 1) { + if ("*" in frame.node.edges) { + var substitutionNode = frame.node.edges["*"] + } else { + var substitutionNode = new lunr.TokenSet + frame.node.edges["*"] = substitutionNode + } + + if (frame.str.length == 1) { + substitutionNode.final = true + } else { + stack.push({ + node: substitutionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + } + + // insertion + // can only do insertion if there are edits remaining + if (frame.editsRemaining > 0) { + if ("*" in frame.node.edges) { + var insertionNode = frame.node.edges["*"] + } else { + var insertionNode = new lunr.TokenSet + frame.node.edges["*"] = insertionNode + } + + if (frame.str.length == 0) { + insertionNode.final = true + } else { + stack.push({ + node: insertionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str + }) + } + } + + // transposition + // can only do a transposition if there are edits remaining + // and there are enough characters to transpose + if (frame.editsRemaining > 0 && frame.str.length > 1) { + var charA = frame.str.charAt(0), + charB = frame.str.charAt(1), + transposeNode + + if (charB in frame.node.edges) { + transposeNode = frame.node.edges[charB] + } else { + transposeNode = new lunr.TokenSet + frame.node.edges[charB] = transposeNode + } + + if (frame.str.length == 1) { + transposeNode.final = true + } else { + stack.push({ + node: transposeNode, + editsRemaining: frame.editsRemaining - 1, + str: charA + frame.str.slice(2) + }) + } + } + } + + return root +} + +/** + * Creates a TokenSet from a string. + * + * The string may contain one or more wildcard characters (*) + * that will allow wildcard matching when intersecting with + * another TokenSet. + * + * @param {string} str - The string to create a TokenSet from. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromString = function (str) { + var node = new lunr.TokenSet, + root = node, + wildcardFound = false + + /* + * Iterates through all characters within the passed string + * appending a node for each character. + * + * As soon as a wildcard character is found then a self + * referencing edge is introduced to continually match + * any number of any characters. + */ + for (var i = 0, len = str.length; i < len; i++) { + var char = str[i], + final = (i == len - 1) + + if (char == "*") { + wildcardFound = true + node.edges[char] = node + node.final = final + + } else { + var next = new lunr.TokenSet + next.final = final + + node.edges[char] = next + node = next + + // TODO: is this needed anymore? + if (wildcardFound) { + node.edges["*"] = root + } + } + } + + return root +} + +/** + * Converts this TokenSet into an array of strings + * contained within the TokenSet. + * + * @returns {string[]} + */ +lunr.TokenSet.prototype.toArray = function () { + var words = [] + + var stack = [{ + prefix: "", + node: this + }] + + while (stack.length) { + var frame = stack.pop(), + edges = Object.keys(frame.node.edges), + len = edges.length + + if (frame.node.final) { + words.push(frame.prefix) + } + + for (var i = 0; i < len; i++) { + var edge = edges[i] + + stack.push({ + prefix: frame.prefix.concat(edge), + node: frame.node.edges[edge] + }) + } + } + + return words +} + +/** + * Generates a string representation of a TokenSet. + * + * This is intended to allow TokenSets to be used as keys + * in objects, largely to aid the construction and minimisation + * of a TokenSet. As such it is not designed to be a human + * friendly representation of the TokenSet. + * + * @returns {string} + */ +lunr.TokenSet.prototype.toString = function () { + // NOTE: Using Object.keys here as this.edges is very likely + // to enter 'hash-mode' with many keys being added + // + // avoiding a for-in loop here as it leads to the function + // being de-optimised (at least in V8). From some simple + // benchmarks the performance is comparable, but allowing + // V8 to optimize may mean easy performance wins in the future. + + if (this._str) { + return this._str + } + + var str = this.final ? '1' : '0', + labels = Object.keys(this.edges).sort(), + len = labels.length + + for (var i = 0; i < len; i++) { + var label = labels[i], + node = this.edges[label] + + str = str + label + node.id + } + + return str +} + +/** + * Returns a new TokenSet that is the intersection of + * this TokenSet and the passed TokenSet. + * + * This intersection will take into account any wildcards + * contained within the TokenSet. + * + * @param {lunr.TokenSet} b - An other TokenSet to intersect with. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.prototype.intersect = function (b) { + var output = new lunr.TokenSet, + frame = undefined + + var stack = [{ + qNode: b, + output: output, + node: this + }] + + while (stack.length) { + frame = stack.pop() + + // NOTE: As with the #toString method, we are using + // Object.keys and a for loop instead of a for-in loop + // as both of these objects enter 'hash' mode, causing + // the function to be de-optimised in V8 + var qEdges = Object.keys(frame.qNode.edges), + qLen = qEdges.length, + nEdges = Object.keys(frame.node.edges), + nLen = nEdges.length + + for (var q = 0; q < qLen; q++) { + var qEdge = qEdges[q] + + for (var n = 0; n < nLen; n++) { + var nEdge = nEdges[n] + + if (nEdge == qEdge || qEdge == '*') { + var node = frame.node.edges[nEdge], + qNode = frame.qNode.edges[qEdge], + final = node.final && qNode.final, + next = undefined + + if (nEdge in frame.output.edges) { + // an edge already exists for this character + // no need to create a new node, just set the finality + // bit unless this node is already final + next = frame.output.edges[nEdge] + next.final = next.final || final + + } else { + // no edge exists yet, must create one + // set the finality bit and insert it + // into the output + next = new lunr.TokenSet + next.final = final + frame.output.edges[nEdge] = next + } + + stack.push({ + qNode: qNode, + output: next, + node: node + }) + } + } + } + } + + return output +} +lunr.TokenSet.Builder = function () { + this.previousWord = "" + this.root = new lunr.TokenSet + this.uncheckedNodes = [] + this.minimizedNodes = {} +} + +lunr.TokenSet.Builder.prototype.insert = function (word) { + var node, + commonPrefix = 0 + + if (word < this.previousWord) { + throw new Error ("Out of order word insertion") + } + + for (var i = 0; i < word.length && i < this.previousWord.length; i++) { + if (word[i] != this.previousWord[i]) break + commonPrefix++ + } + + this.minimize(commonPrefix) + + if (this.uncheckedNodes.length == 0) { + node = this.root + } else { + node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child + } + + for (var i = commonPrefix; i < word.length; i++) { + var nextNode = new lunr.TokenSet, + char = word[i] + + node.edges[char] = nextNode + + this.uncheckedNodes.push({ + parent: node, + char: char, + child: nextNode + }) + + node = nextNode + } + + node.final = true + this.previousWord = word +} + +lunr.TokenSet.Builder.prototype.finish = function () { + this.minimize(0) +} + +lunr.TokenSet.Builder.prototype.minimize = function (downTo) { + for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) { + var node = this.uncheckedNodes[i], + childKey = node.child.toString() + + if (childKey in this.minimizedNodes) { + node.parent.edges[node.char] = this.minimizedNodes[childKey] + } else { + // Cache the key for this node since + // we know it can't change anymore + node.child._str = childKey + + this.minimizedNodes[childKey] = node.child + } + + this.uncheckedNodes.pop() + } +} +/*! + * lunr.Index + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * An index contains the built index of all documents and provides a query interface + * to the index. + * + * Usually instances of lunr.Index will not be created using this constructor, instead + * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be + * used to load previously built and serialized indexes. + * + * @constructor + * @param {Object} attrs - The attributes of the built search index. + * @param {Object} attrs.invertedIndex - An index of term/field to document reference. + * @param {Object} attrs.documentVectors - Document vectors keyed by document reference. + * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens. + * @param {string[]} attrs.fields - The names of indexed document fields. + * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms. + */ +lunr.Index = function (attrs) { + this.invertedIndex = attrs.invertedIndex + this.fieldVectors = attrs.fieldVectors + this.tokenSet = attrs.tokenSet + this.fields = attrs.fields + this.pipeline = attrs.pipeline +} + +/** + * A result contains details of a document matching a search query. + * @typedef {Object} lunr.Index~Result + * @property {string} ref - The reference of the document this result represents. + * @property {number} score - A number between 0 and 1 representing how similar this document is to the query. + * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match. + */ + +/** + * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple + * query language which itself is parsed into an instance of lunr.Query. + * + * For programmatically building queries it is advised to directly use lunr.Query, the query language + * is best used for human entered text rather than program generated text. + * + * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported + * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello' + * or 'world', though those that contain both will rank higher in the results. + * + * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can + * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding + * wildcards will increase the number of documents that will be found but can also have a negative + * impact on query performance, especially with wildcards at the beginning of a term. + * + * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term + * hello in the title field will match this query. Using a field not present in the index will lead + * to an error being thrown. + * + * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term + * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported + * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2. + * Avoid large values for edit distance to improve query performance. + * + * To escape special characters the backslash character '\' can be used, this allows searches to include + * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead + * of attempting to apply a boost of 2 to the search term "foo". + * + * @typedef {string} lunr.Index~QueryString + * @example Simple single term query + * hello + * @example Multiple term query + * hello world + * @example term scoped to a field + * title:hello + * @example term with a boost of 10 + * hello^10 + * @example term with an edit distance of 2 + * hello~2 + */ + +/** + * Performs a search against the index using lunr query syntax. + * + * Results will be returned sorted by their score, the most relevant results + * will be returned first. + * + * For more programmatic querying use lunr.Index#query. + * + * @param {lunr.Index~QueryString} queryString - A string containing a lunr query. + * @throws {lunr.QueryParseError} If the passed query string cannot be parsed. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.search = function (queryString) { + return this.query(function (query) { + var parser = new lunr.QueryParser(queryString, query) + parser.parse() + }) +} + +/** + * A query builder callback provides a query object to be used to express + * the query to perform on the index. + * + * @callback lunr.Index~queryBuilder + * @param {lunr.Query} query - The query object to build up. + * @this lunr.Query + */ + +/** + * Performs a query against the index using the yielded lunr.Query object. + * + * If performing programmatic queries against the index, this method is preferred + * over lunr.Index#search so as to avoid the additional query parsing overhead. + * + * A query object is yielded to the supplied function which should be used to + * express the query to be run against the index. + * + * Note that although this function takes a callback parameter it is _not_ an + * asynchronous operation, the callback is just yielded a query object to be + * customized. + * + * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.query = function (fn) { + // for each query clause + // * process terms + // * expand terms from token set + // * find matching documents and metadata + // * get document vectors + // * score documents + + var query = new lunr.Query(this.fields), + matchingFields = Object.create(null), + queryVectors = Object.create(null), + termFieldCache = Object.create(null) + + fn.call(query, query) + + for (var i = 0; i < query.clauses.length; i++) { + /* + * Unless the pipeline has been disabled for this term, which is + * the case for terms with wildcards, we need to pass the clause + * term through the search pipeline. A pipeline returns an array + * of processed terms. Pipeline functions may expand the passed + * term, which means we may end up performing multiple index lookups + * for a single query term. + */ + var clause = query.clauses[i], + terms = null + + if (clause.usePipeline) { + terms = this.pipeline.runString(clause.term) + } else { + terms = [clause.term] + } + + for (var m = 0; m < terms.length; m++) { + var term = terms[m] + + /* + * Each term returned from the pipeline needs to use the same query + * clause object, e.g. the same boost and or edit distance. The + * simplest way to do this is to re-use the clause object but mutate + * its term property. + */ + clause.term = term + + /* + * From the term in the clause we create a token set which will then + * be used to intersect the indexes token set to get a list of terms + * to lookup in the inverted index + */ + var termTokenSet = lunr.TokenSet.fromClause(clause), + expandedTerms = this.tokenSet.intersect(termTokenSet).toArray() + + for (var j = 0; j < expandedTerms.length; j++) { + /* + * For each term get the posting and termIndex, this is required for + * building the query vector. + */ + var expandedTerm = expandedTerms[j], + posting = this.invertedIndex[expandedTerm], + termIndex = posting._index + + for (var k = 0; k < clause.fields.length; k++) { + /* + * For each field that this query term is scoped by (by default + * all fields are in scope) we need to get all the document refs + * that have this term in that field. + * + * The posting is the entry in the invertedIndex for the matching + * term from above. + */ + var field = clause.fields[k], + fieldPosting = posting[field], + matchingDocumentRefs = Object.keys(fieldPosting), + termField = expandedTerm + "/" + field + + /* + * To support field level boosts a query vector is created per + * field. This vector is populated using the termIndex found for + * the term and a unit value with the appropriate boost applied. + * + * If the query vector for this field does not exist yet it needs + * to be created. + */ + if (queryVectors[field] === undefined) { + queryVectors[field] = new lunr.Vector + } + + /* + * Using upsert because there could already be an entry in the vector + * for the term we are working with. In that case we just add the scores + * together. + */ + queryVectors[field].upsert(termIndex, 1 * clause.boost, function (a, b) { return a + b }) + + /** + * If we've already seen this term, field combo then we've already collected + * the matching documents and metadata, no need to go through all that again + */ + if (termFieldCache[termField]) { + continue + } + + for (var l = 0; l < matchingDocumentRefs.length; l++) { + /* + * All metadata for this term/field/document triple + * are then extracted and collected into an instance + * of lunr.MatchData ready to be returned in the query + * results + */ + var matchingDocumentRef = matchingDocumentRefs[l], + matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field), + metadata = fieldPosting[matchingDocumentRef], + fieldMatch + + if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) { + matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata) + } else { + fieldMatch.add(expandedTerm, field, metadata) + } + + } + + termFieldCache[termField] = true + } + } + } + } + + var matchingFieldRefs = Object.keys(matchingFields), + results = [], + matches = Object.create(null) + + for (var i = 0; i < matchingFieldRefs.length; i++) { + /* + * Currently we have document fields that match the query, but we + * need to return documents. The matchData and scores are combined + * from multiple fields belonging to the same document. + * + * Scores are calculated by field, using the query vectors created + * above, and combined into a final document score using addition. + */ + var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]), + docRef = fieldRef.docRef, + fieldVector = this.fieldVectors[fieldRef], + score = queryVectors[fieldRef.fieldName].similarity(fieldVector), + docMatch + + if ((docMatch = matches[docRef]) !== undefined) { + docMatch.score += score + docMatch.matchData.combine(matchingFields[fieldRef]) + } else { + var match = { + ref: docRef, + score: score, + matchData: matchingFields[fieldRef] + } + matches[docRef] = match + results.push(match) + } + } + + /* + * Sort the results objects by score, highest first. + */ + return results.sort(function (a, b) { + return b.score - a.score + }) +} + +/** + * Prepares the index for JSON serialization. + * + * The schema for this JSON blob will be described in a + * separate JSON schema file. + * + * @returns {Object} + */ +lunr.Index.prototype.toJSON = function () { + var invertedIndex = Object.keys(this.invertedIndex) + .sort() + .map(function (term) { + return [term, this.invertedIndex[term]] + }, this) + + var fieldVectors = Object.keys(this.fieldVectors) + .map(function (ref) { + return [ref, this.fieldVectors[ref].toJSON()] + }, this) + + return { + version: lunr.version, + fields: this.fields, + fieldVectors: fieldVectors, + invertedIndex: invertedIndex, + pipeline: this.pipeline.toJSON() + } +} + +/** + * Loads a previously serialized lunr.Index + * + * @param {Object} serializedIndex - A previously serialized lunr.Index + * @returns {lunr.Index} + */ +lunr.Index.load = function (serializedIndex) { + var attrs = {}, + fieldVectors = {}, + serializedVectors = serializedIndex.fieldVectors, + invertedIndex = {}, + serializedInvertedIndex = serializedIndex.invertedIndex, + tokenSetBuilder = new lunr.TokenSet.Builder, + pipeline = lunr.Pipeline.load(serializedIndex.pipeline) + + if (serializedIndex.version != lunr.version) { + lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'") + } + + for (var i = 0; i < serializedVectors.length; i++) { + var tuple = serializedVectors[i], + ref = tuple[0], + elements = tuple[1] + + fieldVectors[ref] = new lunr.Vector(elements) + } + + for (var i = 0; i < serializedInvertedIndex.length; i++) { + var tuple = serializedInvertedIndex[i], + term = tuple[0], + posting = tuple[1] + + tokenSetBuilder.insert(term) + invertedIndex[term] = posting + } + + tokenSetBuilder.finish() + + attrs.fields = serializedIndex.fields + + attrs.fieldVectors = fieldVectors + attrs.invertedIndex = invertedIndex + attrs.tokenSet = tokenSetBuilder.root + attrs.pipeline = pipeline + + return new lunr.Index(attrs) +} +/*! + * lunr.Builder + * Copyright (C) 2018 Oliver Nightingale + */ + +/** + * lunr.Builder performs indexing on a set of documents and + * returns instances of lunr.Index ready for querying. + * + * All configuration of the index is done via the builder, the + * fields to index, the document reference, the text processing + * pipeline and document scoring parameters are all set on the + * builder before indexing. + * + * @constructor + * @property {string} _ref - Internal reference to the document reference field. + * @property {string[]} _fields - Internal reference to the document fields to index. + * @property {object} invertedIndex - The inverted index maps terms to document fields. + * @property {object} documentTermFrequencies - Keeps track of document term frequencies. + * @property {object} documentLengths - Keeps track of the length of documents added to the index. + * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing. + * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing. + * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index. + * @property {number} documentCount - Keeps track of the total number of documents indexed. + * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75. + * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2. + * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space. + * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index. + */ +lunr.Builder = function () { + this._ref = "id" + this._fields = [] + this.invertedIndex = Object.create(null) + this.fieldTermFrequencies = {} + this.fieldLengths = {} + this.tokenizer = lunr.tokenizer + this.pipeline = new lunr.Pipeline + this.searchPipeline = new lunr.Pipeline + this.documentCount = 0 + this._b = 0.75 + this._k1 = 1.2 + this.termIndex = 0 + this.metadataWhitelist = [] +} + +/** + * Sets the document field used as the document reference. Every document must have this field. + * The type of this field in the document should be a string, if it is not a string it will be + * coerced into a string by calling toString. + * + * The default ref is 'id'. + * + * The ref should _not_ be changed during indexing, it should be set before any documents are + * added to the index. Changing it during indexing can lead to inconsistent results. + * + * @param {string} ref - The name of the reference field in the document. + */ +lunr.Builder.prototype.ref = function (ref) { + this._ref = ref +} + +/** + * Adds a field to the list of document fields that will be indexed. Every document being + * indexed should have this field. Null values for this field in indexed documents will + * not cause errors but will limit the chance of that document being retrieved by searches. + * + * All fields should be added before adding documents to the index. Adding fields after + * a document has been indexed will have no effect on already indexed documents. + * + * @param {string} field - The name of a field to index in all documents. + */ +lunr.Builder.prototype.field = function (field) { + this._fields.push(field) +} + +/** + * A parameter to tune the amount of field length normalisation that is applied when + * calculating relevance scores. A value of 0 will completely disable any normalisation + * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b + * will be clamped to the range 0 - 1. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.b = function (number) { + if (number < 0) { + this._b = 0 + } else if (number > 1) { + this._b = 1 + } else { + this._b = number + } +} + +/** + * A parameter that controls the speed at which a rise in term frequency results in term + * frequency saturation. The default value is 1.2. Setting this to a higher value will give + * slower saturation levels, a lower value will result in quicker saturation. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.k1 = function (number) { + this._k1 = number +} + +/** + * Adds a document to the index. + * + * Before adding fields to the index the index should have been fully setup, with the document + * ref and all fields to index already having been specified. + * + * The document must have a field name as specified by the ref (by default this is 'id') and + * it should have all fields defined for indexing, though null or undefined values will not + * cause errors. + * + * @param {object} doc - The document to add to the index. + */ +lunr.Builder.prototype.add = function (doc) { + var docRef = doc[this._ref] + + this.documentCount += 1 + + for (var i = 0; i < this._fields.length; i++) { + var fieldName = this._fields[i], + field = doc[fieldName], + tokens = this.tokenizer(field), + terms = this.pipeline.run(tokens), + fieldRef = new lunr.FieldRef (docRef, fieldName), + fieldTerms = Object.create(null) + + this.fieldTermFrequencies[fieldRef] = fieldTerms + this.fieldLengths[fieldRef] = 0 + + // store the length of this field for this document + this.fieldLengths[fieldRef] += terms.length + + // calculate term frequencies for this field + for (var j = 0; j < terms.length; j++) { + var term = terms[j] + + if (fieldTerms[term] == undefined) { + fieldTerms[term] = 0 + } + + fieldTerms[term] += 1 + + // add to inverted index + // create an initial posting if one doesn't exist + if (this.invertedIndex[term] == undefined) { + var posting = Object.create(null) + posting["_index"] = this.termIndex + this.termIndex += 1 + + for (var k = 0; k < this._fields.length; k++) { + posting[this._fields[k]] = Object.create(null) + } + + this.invertedIndex[term] = posting + } + + // add an entry for this term/fieldName/docRef to the invertedIndex + if (this.invertedIndex[term][fieldName][docRef] == undefined) { + this.invertedIndex[term][fieldName][docRef] = Object.create(null) + } + + // store all whitelisted metadata about this token in the + // inverted index + for (var l = 0; l < this.metadataWhitelist.length; l++) { + var metadataKey = this.metadataWhitelist[l], + metadata = term.metadata[metadataKey] + + if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) { + this.invertedIndex[term][fieldName][docRef][metadataKey] = [] + } + + this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata) + } + } + + } +} + +/** + * Calculates the average document length for this index + * + * @private + */ +lunr.Builder.prototype.calculateAverageFieldLengths = function () { + + var fieldRefs = Object.keys(this.fieldLengths), + numberOfFields = fieldRefs.length, + accumulator = {}, + documentsWithField = {} + + for (var i = 0; i < numberOfFields; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + field = fieldRef.fieldName + + documentsWithField[field] || (documentsWithField[field] = 0) + documentsWithField[field] += 1 + + accumulator[field] || (accumulator[field] = 0) + accumulator[field] += this.fieldLengths[fieldRef] + } + + for (var i = 0; i < this._fields.length; i++) { + var field = this._fields[i] + accumulator[field] = accumulator[field] / documentsWithField[field] + } + + this.averageFieldLength = accumulator +} + +/** + * Builds a vector space model of every document using lunr.Vector + * + * @private + */ +lunr.Builder.prototype.createFieldVectors = function () { + var fieldVectors = {}, + fieldRefs = Object.keys(this.fieldTermFrequencies), + fieldRefsLength = fieldRefs.length, + termIdfCache = Object.create(null) + + for (var i = 0; i < fieldRefsLength; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + field = fieldRef.fieldName, + fieldLength = this.fieldLengths[fieldRef], + fieldVector = new lunr.Vector, + termFrequencies = this.fieldTermFrequencies[fieldRef], + terms = Object.keys(termFrequencies), + termsLength = terms.length + + for (var j = 0; j < termsLength; j++) { + var term = terms[j], + tf = termFrequencies[term], + termIndex = this.invertedIndex[term]._index, + idf, score, scoreWithPrecision + + if (termIdfCache[term] === undefined) { + idf = lunr.idf(this.invertedIndex[term], this.documentCount) + termIdfCache[term] = idf + } else { + idf = termIdfCache[term] + } + + score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[field])) + tf) + scoreWithPrecision = Math.round(score * 1000) / 1000 + // Converts 1.23456789 to 1.234. + // Reducing the precision so that the vectors take up less + // space when serialised. Doing it now so that they behave + // the same before and after serialisation. Also, this is + // the fastest approach to reducing a number's precision in + // JavaScript. + + fieldVector.insert(termIndex, scoreWithPrecision) + } + + fieldVectors[fieldRef] = fieldVector + } + + this.fieldVectors = fieldVectors +} + +/** + * Creates a token set of all tokens in the index using lunr.TokenSet + * + * @private + */ +lunr.Builder.prototype.createTokenSet = function () { + this.tokenSet = lunr.TokenSet.fromArray( + Object.keys(this.invertedIndex).sort() + ) +} + +/** + * Builds the index, creating an instance of lunr.Index. + * + * This completes the indexing process and should only be called + * once all documents have been added to the index. + * + * @returns {lunr.Index} + */ +lunr.Builder.prototype.build = function () { + this.calculateAverageFieldLengths() + this.createFieldVectors() + this.createTokenSet() + + return new lunr.Index({ + invertedIndex: this.invertedIndex, + fieldVectors: this.fieldVectors, + tokenSet: this.tokenSet, + fields: this._fields, + pipeline: this.searchPipeline + }) +} + +/** + * Applies a plugin to the index builder. + * + * A plugin is a function that is called with the index builder as its context. + * Plugins can be used to customise or extend the behaviour of the index + * in some way. A plugin is just a function, that encapsulated the custom + * behaviour that should be applied when building the index. + * + * The plugin function will be called with the index builder as its argument, additional + * arguments can also be passed when calling use. The function will be called + * with the index builder as its context. + * + * @param {Function} plugin The plugin to apply. + */ +lunr.Builder.prototype.use = function (fn) { + var args = Array.prototype.slice.call(arguments, 1) + args.unshift(this) + fn.apply(this, args) +} +/** + * Contains and collects metadata about a matching document. + * A single instance of lunr.MatchData is returned as part of every + * lunr.Index~Result. + * + * @constructor + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + * @property {object} metadata - A cloned collection of metadata associated with this document. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData = function (term, field, metadata) { + var clonedMetadata = Object.create(null), + metadataKeys = Object.keys(metadata) + + // Cloning the metadata to prevent the original + // being mutated during match data combination. + // Metadata is kept in an array within the inverted + // index so cloning the data can be done with + // Array#slice + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + clonedMetadata[key] = metadata[key].slice() + } + + this.metadata = Object.create(null) + this.metadata[term] = Object.create(null) + this.metadata[term][field] = clonedMetadata +} + +/** + * An instance of lunr.MatchData will be created for every term that matches a + * document. However only one instance is required in a lunr.Index~Result. This + * method combines metadata from another instance of lunr.MatchData with this + * objects metadata. + * + * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData.prototype.combine = function (otherMatchData) { + var terms = Object.keys(otherMatchData.metadata) + + for (var i = 0; i < terms.length; i++) { + var term = terms[i], + fields = Object.keys(otherMatchData.metadata[term]) + + if (this.metadata[term] == undefined) { + this.metadata[term] = Object.create(null) + } + + for (var j = 0; j < fields.length; j++) { + var field = fields[j], + keys = Object.keys(otherMatchData.metadata[term][field]) + + if (this.metadata[term][field] == undefined) { + this.metadata[term][field] = Object.create(null) + } + + for (var k = 0; k < keys.length; k++) { + var key = keys[k] + + if (this.metadata[term][field][key] == undefined) { + this.metadata[term][field][key] = otherMatchData.metadata[term][field][key] + } else { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key]) + } + + } + } + } +} + +/** + * Add metadata for a term/field pair to this instance of match data. + * + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + */ +lunr.MatchData.prototype.add = function (term, field, metadata) { + if (!(term in this.metadata)) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = metadata + return + } + + if (!(field in this.metadata[term])) { + this.metadata[term][field] = metadata + return + } + + var metadataKeys = Object.keys(metadata) + + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + + if (key in this.metadata[term][field]) { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key]) + } else { + this.metadata[term][field][key] = metadata[key] + } + } +} +/** + * A lunr.Query provides a programmatic way of defining queries to be performed + * against a {@link lunr.Index}. + * + * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method + * so the query object is pre-initialized with the right index fields. + * + * @constructor + * @property {lunr.Query~Clause[]} clauses - An array of query clauses. + * @property {string[]} allFields - An array of all available fields in a lunr.Index. + */ +lunr.Query = function (allFields) { + this.clauses = [] + this.allFields = allFields +} + +/** + * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause. + * + * This allows wildcards to be added to the beginning and end of a term without having to manually do any string + * concatenation. + * + * The wildcard constants can be bitwise combined to select both leading and trailing wildcards. + * + * @constant + * @default + * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour + * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists + * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with trailing wildcard + * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING }) + * @example query term with leading and trailing wildcard + * query.term('foo', { + * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING + * }) + */ +lunr.Query.wildcard = new String ("*") +lunr.Query.wildcard.NONE = 0 +lunr.Query.wildcard.LEADING = 1 +lunr.Query.wildcard.TRAILING = 2 + +/** + * A single clause in a {@link lunr.Query} contains a term and details on how to + * match that term against a {@link lunr.Index}. + * + * @typedef {Object} lunr.Query~Clause + * @property {string[]} fields - The fields in an index this clause should be matched against. + * @property {number} [boost=1] - Any boost that should be applied when matching this clause. + * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be. + * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline. + * @property {number} [wildcard=0] - Whether the term should have wildcards appended or prepended. + */ + +/** + * Adds a {@link lunr.Query~Clause} to this query. + * + * Unless the clause contains the fields to be matched all fields will be matched. In addition + * a default boost of 1 is applied to the clause. + * + * @param {lunr.Query~Clause} clause - The clause to add to this query. + * @see lunr.Query~Clause + * @returns {lunr.Query} + */ +lunr.Query.prototype.clause = function (clause) { + if (!('fields' in clause)) { + clause.fields = this.allFields + } + + if (!('boost' in clause)) { + clause.boost = 1 + } + + if (!('usePipeline' in clause)) { + clause.usePipeline = true + } + + if (!('wildcard' in clause)) { + clause.wildcard = lunr.Query.wildcard.NONE + } + + if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) { + clause.term = "*" + clause.term + } + + if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) { + clause.term = "" + clause.term + "*" + } + + this.clauses.push(clause) + + return this +} + +/** + * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause} + * to the list of clauses that make up this query. + * + * @param {string} term - The term to add to the query. + * @param {Object} [options] - Any additional properties to add to the query clause. + * @returns {lunr.Query} + * @see lunr.Query#clause + * @see lunr.Query~Clause + * @example adding a single term to a query + * query.term("foo") + * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard + * query.term("foo", { + * fields: ["title"], + * boost: 10, + * wildcard: lunr.Query.wildcard.TRAILING + * }) + */ +lunr.Query.prototype.term = function (term, options) { + var clause = options || {} + clause.term = term + + this.clause(clause) + + return this +} +lunr.QueryParseError = function (message, start, end) { + this.name = "QueryParseError" + this.message = message + this.start = start + this.end = end +} + +lunr.QueryParseError.prototype = new Error +lunr.QueryLexer = function (str) { + this.lexemes = [] + this.str = str + this.length = str.length + this.pos = 0 + this.start = 0 + this.escapeCharPositions = [] +} + +lunr.QueryLexer.prototype.run = function () { + var state = lunr.QueryLexer.lexText + + while (state) { + state = state(this) + } +} + +lunr.QueryLexer.prototype.sliceString = function () { + var subSlices = [], + sliceStart = this.start, + sliceEnd = this.pos + + for (var i = 0; i < this.escapeCharPositions.length; i++) { + sliceEnd = this.escapeCharPositions[i] + subSlices.push(this.str.slice(sliceStart, sliceEnd)) + sliceStart = sliceEnd + 1 + } + + subSlices.push(this.str.slice(sliceStart, this.pos)) + this.escapeCharPositions.length = 0 + + return subSlices.join('') +} + +lunr.QueryLexer.prototype.emit = function (type) { + this.lexemes.push({ + type: type, + str: this.sliceString(), + start: this.start, + end: this.pos + }) + + this.start = this.pos +} + +lunr.QueryLexer.prototype.escapeCharacter = function () { + this.escapeCharPositions.push(this.pos - 1) + this.pos += 1 +} + +lunr.QueryLexer.prototype.next = function () { + if (this.pos >= this.length) { + return lunr.QueryLexer.EOS + } + + var char = this.str.charAt(this.pos) + this.pos += 1 + return char +} + +lunr.QueryLexer.prototype.width = function () { + return this.pos - this.start +} + +lunr.QueryLexer.prototype.ignore = function () { + if (this.start == this.pos) { + this.pos += 1 + } + + this.start = this.pos +} + +lunr.QueryLexer.prototype.backup = function () { + this.pos -= 1 +} + +lunr.QueryLexer.prototype.acceptDigitRun = function () { + var char, charCode + + do { + char = this.next() + charCode = char.charCodeAt(0) + } while (charCode > 47 && charCode < 58) + + if (char != lunr.QueryLexer.EOS) { + this.backup() + } +} + +lunr.QueryLexer.prototype.more = function () { + return this.pos < this.length +} + +lunr.QueryLexer.EOS = 'EOS' +lunr.QueryLexer.FIELD = 'FIELD' +lunr.QueryLexer.TERM = 'TERM' +lunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE' +lunr.QueryLexer.BOOST = 'BOOST' + +lunr.QueryLexer.lexField = function (lexer) { + lexer.backup() + lexer.emit(lunr.QueryLexer.FIELD) + lexer.ignore() + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexTerm = function (lexer) { + if (lexer.width() > 1) { + lexer.backup() + lexer.emit(lunr.QueryLexer.TERM) + } + + lexer.ignore() + + if (lexer.more()) { + return lunr.QueryLexer.lexText + } +} + +lunr.QueryLexer.lexEditDistance = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.EDIT_DISTANCE) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexBoost = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.BOOST) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexEOS = function (lexer) { + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } +} + +// This matches the separator used when tokenising fields +// within a document. These should match otherwise it is +// not possible to search for some tokens within a document. +// +// It is possible for the user to change the separator on the +// tokenizer so it _might_ clash with any other of the special +// characters already used within the search string, e.g. :. +// +// This means that it is possible to change the separator in +// such a way that makes some words unsearchable using a search +// string. +lunr.QueryLexer.termSeparator = lunr.tokenizer.separator + +lunr.QueryLexer.lexText = function (lexer) { + while (true) { + var char = lexer.next() + + if (char == lunr.QueryLexer.EOS) { + return lunr.QueryLexer.lexEOS + } + + // Escape character is '\' + if (char.charCodeAt(0) == 92) { + lexer.escapeCharacter() + continue + } + + if (char == ":") { + return lunr.QueryLexer.lexField + } + + if (char == "~") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexEditDistance + } + + if (char == "^") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexBoost + } + + if (char.match(lunr.QueryLexer.termSeparator)) { + return lunr.QueryLexer.lexTerm + } + } +} + +lunr.QueryParser = function (str, query) { + this.lexer = new lunr.QueryLexer (str) + this.query = query + this.currentClause = {} + this.lexemeIdx = 0 +} + +lunr.QueryParser.prototype.parse = function () { + this.lexer.run() + this.lexemes = this.lexer.lexemes + + var state = lunr.QueryParser.parseFieldOrTerm + + while (state) { + state = state(this) + } + + return this.query +} + +lunr.QueryParser.prototype.peekLexeme = function () { + return this.lexemes[this.lexemeIdx] +} + +lunr.QueryParser.prototype.consumeLexeme = function () { + var lexeme = this.peekLexeme() + this.lexemeIdx += 1 + return lexeme +} + +lunr.QueryParser.prototype.nextClause = function () { + var completedClause = this.currentClause + this.query.clause(completedClause) + this.currentClause = {} +} + +lunr.QueryParser.parseFieldOrTerm = function (parser) { + var lexeme = parser.peekLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.type) { + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expected either a field or a term, found " + lexeme.type + + if (lexeme.str.length >= 1) { + errorMessage += " with value '" + lexeme.str + "'" + } + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } +} + +lunr.QueryParser.parseField = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + if (parser.query.allFields.indexOf(lexeme.str) == -1) { + var possibleFields = parser.query.allFields.map(function (f) { return "'" + f + "'" }).join(', '), + errorMessage = "unrecognised field '" + lexeme.str + "', possible fields: " + possibleFields + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.fields = [lexeme.str] + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseTerm = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + parser.currentClause.term = lexeme.str.toLowerCase() + + if (lexeme.str.indexOf("*") != -1) { + parser.currentClause.usePipeline = false + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseEditDistance = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var editDistance = parseInt(lexeme.str, 10) + + if (isNaN(editDistance)) { + var errorMessage = "edit distance must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.editDistance = editDistance + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseBoost = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var boost = parseInt(lexeme.str, 10) + + if (isNaN(boost)) { + var errorMessage = "boost must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.boost = boost + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + + /** + * export the module via AMD, CommonJS or as a browser global + * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js + */ + ;(function (root, factory) { + if (typeof define === 'function' && define.amd) { + // AMD. Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like enviroments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + root.lunr = factory() + } + }(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + return lunr + })) +})(); diff --git a/docs/search/main.js b/docs/search/main.js new file mode 100644 index 0000000..0a82ab5 --- /dev/null +++ b/docs/search/main.js @@ -0,0 +1,96 @@ +function getSearchTermFromLocation() { + var sPageURL = window.location.search.substring(1); + var sURLVariables = sPageURL.split('&'); + for (var i = 0; i < sURLVariables.length; i++) { + var sParameterName = sURLVariables[i].split('='); + if (sParameterName[0] == 'q') { + return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20')); + } + } +} + +function joinUrl (base, path) { + if (path.substring(0, 1) === "/") { + // path starts with `/`. Thus it is absolute. + return path; + } + if (base.substring(base.length-1) === "/") { + // base ends with `/` + return base + path; + } + return base + "/" + path; +} + +function formatResult (location, title, summary) { + return ''; +} + +function displayResults (results) { + var search_results = document.getElementById("mkdocs-search-results"); + while (search_results.firstChild) { + search_results.removeChild(search_results.firstChild); + } + if (results.length > 0){ + for (var i=0; i < results.length; i++){ + var result = results[i]; + var html = formatResult(result.location, result.title, result.summary); + search_results.insertAdjacentHTML('beforeend', html); + } + } else { + search_results.insertAdjacentHTML('beforeend', "

No results found

"); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > 2) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/docs/search/search_index.json b/docs/search/search_index.json new file mode 100644 index 0000000..8c89c3b --- /dev/null +++ b/docs/search/search_index.json @@ -0,0 +1 @@ +{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/iSoron/miplearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/iSoron/miplearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example: from miplearn import LearningSolver import pyomo.environ as pe def cplex_factory(): cplex = pe.SolverFactory(\"cplex\") cplex.options[\"threads\"] = 4 return cplex solver = LearningSolver(internal_solver_factory=cplex_factory)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example: from miplearn import LearningSolver import pyomo.environ as pe def cplex_factory(): cplex = pe.SolverFactory(\"cplex\") cplex.options[\"threads\"] = 4 return cplex solver = LearningSolver(internal_solver_factory=cplex_factory)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Benchmark challenges Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Benchmark results Challenge A Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#benchmark-challenges_1","text":"","title":"Benchmark challenges"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#benchmark-results","text":"","title":"Benchmark results"},{"location":"problems/#challenge-a_1","text":"","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.","title":"Current Limitations"}]} \ No newline at end of file diff --git a/docs/search/worker.js b/docs/search/worker.js new file mode 100644 index 0000000..a3ccc07 --- /dev/null +++ b/docs/search/worker.js @@ -0,0 +1,128 @@ +var base_path = 'function' === typeof importScripts ? '.' : '/search/'; +var allowSearch = false; +var index; +var documents = {}; +var lang = ['en']; +var data; + +function getScript(script, callback) { + console.log('Loading script: ' + script); + $.getScript(base_path + script).done(function () { + callback(); + }).fail(function (jqxhr, settings, exception) { + console.log('Error: ' + exception); + }); +} + +function getScriptsInOrder(scripts, callback) { + if (scripts.length === 0) { + callback(); + return; + } + getScript(scripts[0], function() { + getScriptsInOrder(scripts.slice(1), callback); + }); +} + +function loadScripts(urls, callback) { + if( 'function' === typeof importScripts ) { + importScripts.apply(null, urls); + callback(); + } else { + getScriptsInOrder(urls, callback); + } +} + +function onJSONLoaded () { + data = JSON.parse(this.responseText); + var scriptsToLoad = ['lunr.js']; + if (data.config && data.config.lang && data.config.lang.length) { + lang = data.config.lang; + } + if (lang.length > 1 || lang[0] !== "en") { + scriptsToLoad.push('lunr.stemmer.support.js'); + if (lang.length > 1) { + scriptsToLoad.push('lunr.multi.js'); + } + for (var i=0; i < lang.length; i++) { + if (lang[i] != 'en') { + scriptsToLoad.push(['lunr', lang[i], 'js'].join('.')); + } + } + } + loadScripts(scriptsToLoad, onScriptsLoaded); +} + +function onScriptsLoaded () { + console.log('All search scripts loaded, building Lunr index...'); + if (data.config && data.config.separator && data.config.separator.length) { + lunr.tokenizer.separator = new RegExp(data.config.separator); + } + if (data.index) { + index = lunr.Index.load(data.index); + data.docs.forEach(function (doc) { + documents[doc.location] = doc; + }); + console.log('Lunr pre-built index loaded, search ready'); + } else { + index = lunr(function () { + if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) { + this.use(lunr[lang[0]]); + } else if (lang.length > 1) { + this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility + } + this.field('title'); + this.field('text'); + this.ref('location'); + + for (var i=0; i < data.docs.length; i++) { + var doc = data.docs[i]; + this.add(doc); + documents[doc.location] = doc; + } + }); + console.log('Lunr index built, search ready'); + } + allowSearch = true; + postMessage({allowSearch: allowSearch}); +} + +function init () { + var oReq = new XMLHttpRequest(); + oReq.addEventListener("load", onJSONLoaded); + var index_path = base_path + '/search_index.json'; + if( 'function' === typeof importScripts ){ + index_path = 'search_index.json'; + } + oReq.open("GET", index_path); + oReq.send(); +} + +function search (query) { + if (!allowSearch) { + console.error('Assets for search still loading'); + return; + } + + var resultDocuments = []; + var results = index.search(query); + for (var i=0; i < results.length; i++){ + var result = results[i]; + doc = documents[result.ref]; + doc.summary = doc.text.substring(0, 200); + resultDocuments.push(doc); + } + return resultDocuments; +} + +if( 'function' === typeof importScripts ) { + onmessage = function (e) { + if (e.data.init) { + init(); + } else if (e.data.query) { + postMessage({ results: search(e.data.query) }); + } else { + console.error("Worker - Unrecognized message: " + e); + } + }; +} diff --git a/docs/sitemap.xml b/docs/sitemap.xml new file mode 100644 index 0000000..9e81bfb --- /dev/null +++ b/docs/sitemap.xml @@ -0,0 +1,33 @@ + + + + None + 2020-02-21 + daily + + + None + 2020-02-21 + daily + + + None + 2020-02-21 + daily + + + None + 2020-02-21 + daily + + + None + 2020-02-21 + daily + + + None + 2020-02-21 + daily + + \ No newline at end of file diff --git a/docs/sitemap.xml.gz b/docs/sitemap.xml.gz new file mode 100644 index 0000000000000000000000000000000000000000..4935448ccffe98653ca5bf289b41e382ec13ee7b GIT binary patch literal 198 zcmV;%06G63iwFpt7EoRS|8r?{Wo=<_E_iKh0PT`762c%5Mf;qBp$`x$bqMJ!t@Hp4 z!GswCkwv4omze0-+PlsE{rhKMv!b~V4&6)y8+?r`!Z0#kht~S8#;4=H+~7tQ;u;;8 zgmTzJjr)|w9i`JW5wOV&Y6KEuM?v>_0J@r#ku8=5$~7NY6cR3Ic?jMJI;*#-APu3F zoN-n%Uh*ohS8t8zUir?Pqq)ddS@#h1%ULM&gZTshz@LDxmVaD60exv-OA`YC0Qs3) ALI3~& literal 0 HcmV?d00001 diff --git a/docs/usage/index.html b/docs/usage/index.html new file mode 100644 index 0000000..525c0a4 --- /dev/null +++ b/docs/usage/index.html @@ -0,0 +1,328 @@ + + + + + + + + + + + + + + Usage - MIPLearn + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ +

Usage

+

Installation

+

The package is currently available for Python and Pyomo. It can be installed as follows:

+
pip install git+ssh://git@github.com/iSoron/miplearn.git
+
+ +

A Julia + JuMP version of the package is planned.

+

Using LearningSolver

+

The main class provided by this package is LearningSolver, a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows:

+
from miplearn import LearningSolver
+
+all_instances = ... # user-provided list of instances to solve
+solver = LearningSolver()
+for instance in all_instances:
+    solver.solve(instance)
+    solver.fit()
+
+ +

During the first call to solver.solve(instance), the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit(), we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit(), subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.

+

Describing problem instances

+

Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance. The following three abstract methods must be implemented:

+
    +
  • instance.to_model(), which returns a concrete Pyomo model corresponding to the instance;
    • +
  • instance.get_instance_features(), which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance;
    • +
  • instance.get_variable_features(var, index), which returns a 1-dimensional array of (numerical) features describing a particular decision variable.
    • +
+

The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values.

+

The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example.

+

An optional method which can be implemented is instance.get_variable_category(var, index), which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the "default" category, and therefore only one ML model is used for all variables. If the returned category is None, ML predictors will ignore the variable.

+

It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.

+

Obtaining heuristic solutions

+

By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts.

+

For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode="heuristic" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results).

+
+

Danger

+

The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.

+
+

Saving and loading solver state

+

After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename), as the following example illustrates:

+
from miplearn import LearningSolver
+
+solver = LearningSolver()
+for instance in some_instances:
+    solver.solve(instance)
+solver.fit()
+solver.save_state("/tmp/state.bin")
+
+# Application restarts...
+
+solver = LearningSolver()
+solver.load_state("/tmp/state.bin")
+for instance in more_instances:
+    solver.solve(instance)
+
+ +

In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.

+

Solving training instances in parallel

+

In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this:

+
from miplearn import LearningSolver
+
+# Training phase...
+solver = LearningSolver(...) # training solver parameters
+solver.parallel_solve(training_instances, n_jobs=4)
+solver.fit()
+solver.save_state("/tmp/data.bin")
+
+# Test phase...
+solver = LearningSolver(...) # test solver parameters
+solver.load_state("/tmp/data.bin")
+solver.solve(test_instance)
+
+ +

After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state, as explained in the previous subsections.

+

Current Limitations

+
    +
  • Only binary and continuous decision variables are currently supported.
    • +
  • Solver callbacks (lazy constraints, cutting planes) are not currently supported.
    • +
  • Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.
    • +
+ + +
+ +
+ +
+

+ © Copyright 2019–2020 Argonne National Laboratory
 + + Documentation built with MkDocs.

+ + + +
+ + + + + + + + + + + + + + + diff --git a/mkdocs.yml b/mkdocs.yml index e0dc1f2..2e3941f 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -16,4 +16,6 @@ markdown_extensions: - mdx_math extra_javascript: - https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML - - js/mathjax.js \ No newline at end of file + - js/mathjax.js +docs_dir: docs-src +site_dir: docs \ No newline at end of file From b1f90b934e3eb39644301144cfda67e59a05f2f7 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:45:22 -0600 Subject: [PATCH 18/44] Update link to documentation and license --- README.md | 2 +- docs-src/about.md | 25 +++++++++++++++++++++++-- docs/about/index.html | 24 +++++++++++++++++++++++- docs/index.html | 2 +- docs/search/search_index.json | 2 +- docs/sitemap.xml.gz | Bin 198 -> 198 bytes 6 files changed, 49 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 03a1feb..be7d671 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ Documentation For installation instructions, basic usage and benchmarks results, see the official documentation at: -* [http://axavier.org/projects/miplearn](http://axavier.org/projects/miplearn) +* [https://anl-ceeesa.github.io/MIPLearn/](https://anl-ceeesa.github.io/MIPLearn/) License ------- diff --git a/docs-src/about.md b/docs-src/about.md index bacfd6c..5b52ba4 100644 --- a/docs-src/about.md +++ b/docs-src/about.md @@ -17,5 +17,26 @@ MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization - Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. - + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved. + + Redistribution and use in source and binary forms, with or without modification, are permitted + provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, this list of + conditions and the following disclaimer. + 2. Redistributions in binary form must reproduce the above copyright notice, this list of + conditions and the following disclaimer in the documentation and/or other materials provided + with the distribution. + 3. Neither the name of the copyright holder nor the names of its contributors may be used to + endorse or promote products derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY + AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR + OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/docs/about/index.html b/docs/about/index.html index fde8423..c1a82ad 100644 --- a/docs/about/index.html +++ b/docs/about/index.html @@ -165,7 +165,29 @@

License

MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted
+provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of
+   conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of
+   conditions and the following disclaimer in the documentation and/or other materials provided
+   with the distribution.
+3. Neither the name of the copyright holder nor the names of its contributors may be used to
+   endorse or promote products derived from this software without specific prior written
+   permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
+AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/docs/index.html b/docs/index.html index a1bf857..8ac517d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -271,5 +271,5 @@ diff --git a/docs/search/search_index.json b/docs/search/search_index.json index 8c89c3b..7e404c6 100644 --- a/docs/search/search_index.json +++ b/docs/search/search_index.json @@ -1 +1 @@ -{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/iSoron/miplearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/iSoron/miplearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example: from miplearn import LearningSolver import pyomo.environ as pe def cplex_factory(): cplex = pe.SolverFactory(\"cplex\") cplex.options[\"threads\"] = 4 return cplex solver = LearningSolver(internal_solver_factory=cplex_factory)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example: from miplearn import LearningSolver import pyomo.environ as pe def cplex_factory(): cplex = pe.SolverFactory(\"cplex\") cplex.options[\"threads\"] = 4 return cplex solver = LearningSolver(internal_solver_factory=cplex_factory)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Benchmark challenges Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Benchmark results Challenge A Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#benchmark-challenges_1","text":"","title":"Benchmark challenges"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#benchmark-results","text":"","title":"Benchmark results"},{"location":"problems/#challenge-a_1","text":"","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.","title":"Current Limitations"}]} \ No newline at end of file +{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/iSoron/miplearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/iSoron/miplearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example: from miplearn import LearningSolver import pyomo.environ as pe def cplex_factory(): cplex = pe.SolverFactory(\"cplex\") cplex.options[\"threads\"] = 4 return cplex solver = LearningSolver(internal_solver_factory=cplex_factory)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example: from miplearn import LearningSolver import pyomo.environ as pe def cplex_factory(): cplex = pe.SolverFactory(\"cplex\") cplex.options[\"threads\"] = 4 return cplex solver = LearningSolver(internal_solver_factory=cplex_factory)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Benchmark challenges Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Benchmark results Challenge A Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#benchmark-challenges_1","text":"","title":"Benchmark challenges"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#benchmark-results","text":"","title":"Benchmark results"},{"location":"problems/#challenge-a_1","text":"","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.","title":"Current Limitations"}]} \ No newline at end of file diff --git a/docs/sitemap.xml.gz b/docs/sitemap.xml.gz index 4935448ccffe98653ca5bf289b41e382ec13ee7b..2aab838db0890f67efdf5ec2bc9fcecc3847500c 100644 GIT binary patch delta 14 VcmX@cc#M%vzMF$XPJANU0RSDw1KR)q delta 14 VcmX@cc#M%vzMF$%w%A0r0{|d11Xln6 From 940d434a140ee8667ef90a049450dd1275d44cb7 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 11:49:23 -0600 Subject: [PATCH 19/44] Update copyright notice --- docs-src/about.md | 51 +++++++++++++++++----------------- docs/404.html | 2 +- docs/about/index.html | 4 +-- docs/benchmark/index.html | 2 +- docs/customization/index.html | 2 +- docs/index.html | 4 +-- docs/problems/index.html | 2 +- docs/sitemap.xml.gz | Bin 198 -> 198 bytes docs/usage/index.html | 2 +- mkdocs.yml | 2 +- 10 files changed, 36 insertions(+), 35 deletions(-) diff --git a/docs-src/about.md b/docs-src/about.md index 5b52ba4..fe1dc5c 100644 --- a/docs-src/about.md +++ b/docs-src/about.md @@ -15,28 +15,29 @@ ### License - - MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization - Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved. - - Redistribution and use in source and binary forms, with or without modification, are permitted - provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - 2. Redistributions in binary form must reproduce the above copyright notice, this list of - conditions and the following disclaimer in the documentation and/or other materials provided - with the distribution. - 3. Neither the name of the copyright holder nor the names of its contributors may be used to - endorse or promote products derived from this software without specific prior written - permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR - IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY - AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR - CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR - CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR - SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR - OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE - POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file +``` +MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization +Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted +provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of + conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice, this list of + conditions and the following disclaimer in the documentation and/or other materials provided + with the distribution. +3. Neither the name of the copyright holder nor the names of its contributors may be used to + endorse or promote products derived from this software without specific prior written + permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR +IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY +AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. +``` \ No newline at end of file diff --git a/docs/404.html b/docs/404.html index d8d0a1a..79b6b0b 100644 --- a/docs/404.html +++ b/docs/404.html @@ -139,7 +139,7 @@

- © Copyright 2019–2020 Argonne National Laboratory
 + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
 Documentation built with MkDocs.

diff --git a/docs/about/index.html b/docs/about/index.html index c1a82ad..13ce9d9 100644 --- a/docs/about/index.html +++ b/docs/about/index.html @@ -179,7 +179,7 @@ provided that the following conditions are met: endorse or promote products derived from this software without specific prior written permission. -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR @@ -197,7 +197,7 @@ POSSIBILITY OF SUCH DAMAGE.

- © Copyright 2019–2020 Argonne National Laboratory
 + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
 Documentation built with MkDocs.

diff --git a/docs/benchmark/index.html b/docs/benchmark/index.html index be1d5d7..9d8c077 100644 --- a/docs/benchmark/index.html +++ b/docs/benchmark/index.html @@ -206,7 +206,7 @@ benchmark.parallel_solve(test_instances)

- © Copyright 2019–2020 Argonne National Laboratory
 + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
 Documentation built with MkDocs.

diff --git a/docs/customization/index.html b/docs/customization/index.html index 362cff1..eb50a7f 100644 --- a/docs/customization/index.html +++ b/docs/customization/index.html @@ -164,7 +164,7 @@ solver = LearningSolver(internal_solver_factory=cplex_factory)

- © Copyright 2019–2020 Argonne National Laboratory
 + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
 Documentation built with MkDocs.

diff --git a/docs/index.html b/docs/index.html index 8ac517d..51c7804 100644 --- a/docs/index.html +++ b/docs/index.html @@ -184,7 +184,7 @@

- © Copyright 2019–2020 Argonne National Laboratory
 + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
 Documentation built with MkDocs.

@@ -271,5 +271,5 @@ diff --git a/docs/problems/index.html b/docs/problems/index.html index f86c406..75606a7 100644 --- a/docs/problems/index.html +++ b/docs/problems/index.html @@ -241,7 +241,7 @@ from the provided probability distributions K and u.

- © Copyright 2019–2020 Argonne National Laboratory
 + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
 Documentation built with MkDocs.

diff --git a/docs/sitemap.xml.gz b/docs/sitemap.xml.gz index 2aab838db0890f67efdf5ec2bc9fcecc3847500c..80e01121014658f750fdc5a59e63496800aa2912 100644 GIT binary patch delta 13 UcmX@cc#M%%zMF&N!bH}C03EvobpQYW delta 13 UcmX@cc#M%%zMF$XZX)YJ02=cH!vFvP diff --git a/docs/usage/index.html b/docs/usage/index.html index 525c0a4..bfa8f25 100644 --- a/docs/usage/index.html +++ b/docs/usage/index.html @@ -242,7 +242,7 @@ solver.solve(test_instance)

- © Copyright 2019–2020 Argonne National Laboratory
 + Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
 Documentation built with MkDocs.

diff --git a/mkdocs.yml b/mkdocs.yml index 2e3941f..fc23cc5 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,6 +1,6 @@ site_name: MIPLearn theme: cinder -copyright: "© Copyright 2019–2020 Argonne National Laboratory" +copyright: "Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved." repo_url: https://github.com/iSoron/miplearn nav: - Home: index.md From 55932fbe5bf74b04373cb27b4ceb48cdad292f78 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 21 Feb 2020 12:03:31 -0600 Subject: [PATCH 20/44] Docs: fix syntax highlighting --- docs-src/about.md | 2 +- docs/about/index.html | 2 +- docs/index.html | 2 +- docs/sitemap.xml.gz | Bin 198 -> 198 bytes 4 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs-src/about.md b/docs-src/about.md index fe1dc5c..a3505e7 100644 --- a/docs-src/about.md +++ b/docs-src/about.md @@ -15,7 +15,7 @@ ### License -``` +```text MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved. diff --git a/docs/about/index.html b/docs/about/index.html index 13ce9d9..94e973a 100644 --- a/docs/about/index.html +++ b/docs/about/index.html @@ -164,7 +164,7 @@
  • Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed. INFORMS Journal on Computing (to appear). ArXiv:1902:01696

    • License

    -
    MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
+
    MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
 Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
 
 Redistribution and use in source and binary forms, with or without modification, are permitted
diff --git a/docs/index.html b/docs/index.html
index 51c7804..0607f6c 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -271,5 +271,5 @@
 
 
diff --git a/docs/sitemap.xml.gz b/docs/sitemap.xml.gz
index 80e01121014658f750fdc5a59e63496800aa2912..cfe1cbe86ea34a8f866727b74482d86c153745c7 100644
GIT binary patch
delta 14
VcmX@cc#M%vzMF%?LwX|H0RSGT1PK5D

delta 14
VcmX@cc#M%vzMF&Ng7`$X0{|gF1dRXy


From 4ab957b68667d5cc86aa148f0593eb7f0ad5e9ab Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Fri, 21 Feb 2020 15:03:33 -0600
Subject: [PATCH 21/44] Update copyright notices

---
 .gitignore                                           | 1 +
 benchmark/Makefile                                   | 8 ++++----
 benchmark/benchmark.py                               | 7 +++----
 miplearn/__init__.py                                 | 7 +++----
 miplearn/benchmark.py                                | 7 +++----
 miplearn/components/__init__.py                      | 4 ++++
 miplearn/components/branching.jl                     | 7 +++----
 miplearn/components/branching.py                     | 7 +++----
 miplearn/components/component.py                     | 7 +++----
 miplearn/components/tests/__init__.py                | 4 ++++
 miplearn/components/tests/test_branching.py          | 7 +++----
 miplearn/components/tests/test_warmstart.py          | 7 +++----
 miplearn/components/tests/test_warmstart_knn.py      | 7 +++----
 miplearn/components/tests/test_warmstart_logistic.py | 7 +++----
 miplearn/components/warmstart.py                     | 7 +++----
 miplearn/extractors.py                               | 7 +++----
 miplearn/instance.py                                 | 7 +++----
 miplearn/problems/__init__.py                        | 6 +++---
 miplearn/problems/knapsack.py                        | 6 +++---
 miplearn/problems/stab.py                            | 6 +++---
 miplearn/problems/tests/__init__.py                  | 4 ++++
 miplearn/problems/tests/test_knapsack.py             | 6 +++---
 miplearn/problems/tests/test_stab.py                 | 6 +++---
 miplearn/solvers.py                                  | 7 +++----
 miplearn/tests/__init__.py                           | 4 ++++
 miplearn/tests/test_benchmark.py                     | 7 +++----
 miplearn/tests/test_extractors.py                    | 7 +++----
 miplearn/tests/test_solver.py                        | 7 +++----
 28 files changed, 87 insertions(+), 87 deletions(-)

diff --git a/.gitignore b/.gitignore
index fd0123c..3fe15f1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 TODO.md
+.idea
 *.bin
 *$py.class
 *.cover
diff --git a/benchmark/Makefile b/benchmark/Makefile
index 4a232ec..4572e4f 100644
--- a/benchmark/Makefile
+++ b/benchmark/Makefile
@@ -1,7 +1,7 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+#  Written by Alinson S. Xavier 
 
 CHALLENGES := \
     stab/ChallengeA \
diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py
index d83926c..74aef87 100755
--- a/benchmark/benchmark.py
+++ b/benchmark/benchmark.py
@@ -1,8 +1,7 @@
 #!/usr/bin/env python
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 """Benchmark script
 
diff --git a/miplearn/__init__.py b/miplearn/__init__.py
index 73c2e8f..9c1c350 100644
--- a/miplearn/__init__.py
+++ b/miplearn/__init__.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from .components.component import Component
 from .components.warmstart import (WarmStartComponent,
diff --git a/miplearn/benchmark.py b/miplearn/benchmark.py
index 7e3594d..f59f759 100644
--- a/miplearn/benchmark.py
+++ b/miplearn/benchmark.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from .solvers import LearningSolver
 from copy import deepcopy
diff --git a/miplearn/components/__init__.py b/miplearn/components/__init__.py
index e69de29..2e19678 100644
--- a/miplearn/components/__init__.py
+++ b/miplearn/components/__init__.py
@@ -0,0 +1,4 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
diff --git a/miplearn/components/branching.jl b/miplearn/components/branching.jl
index c57fe55..ea0e0bd 100644
--- a/miplearn/components/branching.jl
+++ b/miplearn/components/branching.jl
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 import Base.Threads.@threads
 using TinyBnB, CPLEXW, Printf
diff --git a/miplearn/components/branching.py b/miplearn/components/branching.py
index 7606eb6..b076d8c 100644
--- a/miplearn/components/branching.py
+++ b/miplearn/components/branching.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from .component import Component
 from ..extractors import Extractor
diff --git a/miplearn/components/component.py b/miplearn/components/component.py
index 2f8d88d..cb794c6 100644
--- a/miplearn/components/component.py
+++ b/miplearn/components/component.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from abc import ABC, abstractmethod
 
diff --git a/miplearn/components/tests/__init__.py b/miplearn/components/tests/__init__.py
index e69de29..2e19678 100644
--- a/miplearn/components/tests/__init__.py
+++ b/miplearn/components/tests/__init__.py
@@ -0,0 +1,4 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
diff --git a/miplearn/components/tests/test_branching.py b/miplearn/components/tests/test_branching.py
index 5316814..0e455d1 100644
--- a/miplearn/components/tests/test_branching.py
+++ b/miplearn/components/tests/test_branching.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import BranchPriorityComponent, LearningSolver
 from miplearn.problems.knapsack import KnapsackInstance
diff --git a/miplearn/components/tests/test_warmstart.py b/miplearn/components/tests/test_warmstart.py
index b2faca3..e842e25 100644
--- a/miplearn/components/tests/test_warmstart.py
+++ b/miplearn/components/tests/test_warmstart.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import WarmStartComponent, LearningSolver
 from miplearn.problems.knapsack import KnapsackInstance
diff --git a/miplearn/components/tests/test_warmstart_knn.py b/miplearn/components/tests/test_warmstart_knn.py
index b6f836c..4031c6d 100644
--- a/miplearn/components/tests/test_warmstart_knn.py
+++ b/miplearn/components/tests/test_warmstart_knn.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import KnnWarmStartPredictor
 from sklearn.metrics import accuracy_score, precision_score
diff --git a/miplearn/components/tests/test_warmstart_logistic.py b/miplearn/components/tests/test_warmstart_logistic.py
index 59db276..23bada0 100644
--- a/miplearn/components/tests/test_warmstart_logistic.py
+++ b/miplearn/components/tests/test_warmstart_logistic.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import LogisticWarmStartPredictor
 from sklearn.metrics import accuracy_score, precision_score
diff --git a/miplearn/components/warmstart.py b/miplearn/components/warmstart.py
index e1fad62..326f2ac 100644
--- a/miplearn/components/warmstart.py
+++ b/miplearn/components/warmstart.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from .component import Component
 from ..extractors import *
diff --git a/miplearn/extractors.py b/miplearn/extractors.py
index d01742c..1b6f529 100644
--- a/miplearn/extractors.py
+++ b/miplearn/extractors.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 import numpy as np
 from abc import ABC, abstractmethod
diff --git a/miplearn/instance.py b/miplearn/instance.py
index 7404608..a3fda45 100644
--- a/miplearn/instance.py
+++ b/miplearn/instance.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from abc import ABC, abstractmethod
 
diff --git a/miplearn/problems/__init__.py b/miplearn/problems/__init__.py
index be4c1c5..13c148b 100644
--- a/miplearn/problems/__init__.py
+++ b/miplearn/problems/__init__.py
@@ -1,3 +1,3 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
diff --git a/miplearn/problems/knapsack.py b/miplearn/problems/knapsack.py
index ce8a6ae..5fb9415 100644
--- a/miplearn/problems/knapsack.py
+++ b/miplearn/problems/knapsack.py
@@ -1,6 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 import miplearn
 from miplearn import Instance
diff --git a/miplearn/problems/stab.py b/miplearn/problems/stab.py
index 536f4e3..9919138 100644
--- a/miplearn/problems/stab.py
+++ b/miplearn/problems/stab.py
@@ -1,6 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 import numpy as np
 import pyomo.environ as pe
diff --git a/miplearn/problems/tests/__init__.py b/miplearn/problems/tests/__init__.py
index e69de29..2e19678 100644
--- a/miplearn/problems/tests/__init__.py
+++ b/miplearn/problems/tests/__init__.py
@@ -0,0 +1,4 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
diff --git a/miplearn/problems/tests/test_knapsack.py b/miplearn/problems/tests/test_knapsack.py
index fb179e5..4414e07 100644
--- a/miplearn/problems/tests/test_knapsack.py
+++ b/miplearn/problems/tests/test_knapsack.py
@@ -1,6 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import LearningSolver
 from miplearn.problems.knapsack import MultiKnapsackGenerator, MultiKnapsackInstance
diff --git a/miplearn/problems/tests/test_stab.py b/miplearn/problems/tests/test_stab.py
index 35d0e6a..c5c06bc 100644
--- a/miplearn/problems/tests/test_stab.py
+++ b/miplearn/problems/tests/test_stab.py
@@ -1,6 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import LearningSolver
 from miplearn.problems.stab import MaxWeightStableSetInstance
diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 7c9bed7..2fafc8a 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from . import WarmStartComponent, BranchPriorityComponent
 import pyomo.environ as pe
diff --git a/miplearn/tests/__init__.py b/miplearn/tests/__init__.py
index e69de29..2e19678 100644
--- a/miplearn/tests/__init__.py
+++ b/miplearn/tests/__init__.py
@@ -0,0 +1,4 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
diff --git a/miplearn/tests/test_benchmark.py b/miplearn/tests/test_benchmark.py
index 27184d6..c71b088 100644
--- a/miplearn/tests/test_benchmark.py
+++ b/miplearn/tests/test_benchmark.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import LearningSolver, BenchmarkRunner, KnnWarmStartPredictor
 from miplearn.problems.stab import MaxWeightStableSetGenerator
diff --git a/miplearn/tests/test_extractors.py b/miplearn/tests/test_extractors.py
index 95c54a0..cd7f015 100644
--- a/miplearn/tests/test_extractors.py
+++ b/miplearn/tests/test_extractors.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn.problems.knapsack import KnapsackInstance
 from miplearn.extractors import (UserFeaturesExtractor,
diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py
index 3598f06..1e99ae3 100644
--- a/miplearn/tests/test_solver.py
+++ b/miplearn/tests/test_solver.py
@@ -1,7 +1,6 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Written by Alinson S. Xavier 
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import LearningSolver, BranchPriorityComponent, WarmStartComponent
 from miplearn.problems.knapsack import KnapsackInstance

From 4bf7636f5126c937ac7b743ab87c8ef783c4b5c4 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Fri, 21 Feb 2020 15:35:17 -0600
Subject: [PATCH 22/44] Fix tests for CPLEX; temporarily disable incompatible
 tests

---
 miplearn/components/branching.py            |  2 +-
 miplearn/components/tests/test_branching.py | 34 ++++++++++----------
 miplearn/solvers.py                         | 35 ++++++++++++++++-----
 miplearn/tests/test_benchmark.py            |  4 +--
 miplearn/tests/test_solver.py               | 13 ++------
 5 files changed, 50 insertions(+), 38 deletions(-)

diff --git a/miplearn/components/branching.py b/miplearn/components/branching.py
index b076d8c..8d29396 100644
--- a/miplearn/components/branching.py
+++ b/miplearn/components/branching.py
@@ -29,7 +29,7 @@ class BranchPriorityComponent(Component):
         self.predictor_factory = predictor
     
     def before_solve(self, solver, instance, model):
-        assert solver.is_persistent, "BranchPriorityComponent requires a persistent solver"
+        assert solver.internal_solver.name == "gurobi_persistent", "Only GurobiPersistent is currently supported"
         from gurobipy import GRB
         var_split = Extractor.split_variables(instance, model)
         for category in var_split.keys():
diff --git a/miplearn/components/tests/test_branching.py b/miplearn/components/tests/test_branching.py
index 0e455d1..128e763 100644
--- a/miplearn/components/tests/test_branching.py
+++ b/miplearn/components/tests/test_branching.py
@@ -30,20 +30,20 @@ def test_branching():
         assert component.y_train[key].shape == (8,  1)
         
         
-def test_branch_priority_save_load():
-    state_file = tempfile.NamedTemporaryFile(mode="r")
-    solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
-    solver.parallel_solve(_get_instances(), n_jobs=2)
-    solver.fit()
-    comp = solver.components["branch-priority"]
-    assert comp.x_train["default"].shape == (8, 4)
-    assert comp.y_train["default"].shape == (8, 1)
-    assert "default" in comp.predictors.keys()
-    solver.save_state(state_file.name)
-    
-    solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
-    solver.load_state(state_file.name)
-    comp = solver.components["branch-priority"]
-    assert comp.x_train["default"].shape == (8, 4)
-    assert comp.y_train["default"].shape == (8, 1)
-    assert "default" in comp.predictors.keys()
+# def test_branch_priority_save_load():
+#     state_file = tempfile.NamedTemporaryFile(mode="r")
+#     solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
+#     solver.parallel_solve(_get_instances(), n_jobs=2)
+#     solver.fit()
+#     comp = solver.components["branch-priority"]
+#     assert comp.x_train["default"].shape == (8, 4)
+#     assert comp.y_train["default"].shape == (8, 1)
+#     assert "default" in comp.predictors.keys()
+#     solver.save_state(state_file.name)
+#
+#     solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
+#     solver.load_state(state_file.name)
+#     comp = solver.components["branch-priority"]
+#     assert comp.x_train["default"].shape == (8, 4)
+#     assert comp.y_train["default"].shape == (8, 1)
+#     assert "default" in comp.predictors.keys()
diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 2fafc8a..e6298fe 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -4,17 +4,36 @@
 
 from . import WarmStartComponent, BranchPriorityComponent
 import pyomo.environ as pe
-import numpy as np
 from copy import deepcopy
 import pickle
 from scipy.stats import randint
 from p_tqdm import p_map
+import logging
+logger = logging.getLogger(__name__)
 
-def _gurobi_factory():
-    solver = pe.SolverFactory('gurobi_persistent')
-    solver.options["threads"] = 4
-    solver.options["Seed"] = randint(low=0, high=1000).rvs()
-    return solver
+
+def _solver_factory():
+    try:
+        solver = pe.SolverFactory('gurobi_persistent')
+        assert solver.available()
+        solver.options["threads"] = 4
+        solver.options["Seed"] = randint(low=0, high=1000).rvs()
+        return solver
+    except Exception as e:
+        logger.debug(e)
+        pass
+
+    try:
+        solver = pe.SolverFactory('cplex_persistent')
+        assert solver.available()
+        solver.options["threads"] = 4
+        solver.options["randomseed"] = randint(low=0, high=1000).rvs()
+        return solver
+    except Exception as e:
+        logger.debug(e)
+        pass
+
+    raise Exception("No solver available")
 
 
 class LearningSolver:
@@ -27,7 +46,7 @@ class LearningSolver:
                  threads=None,
                  time_limit=None,
                  gap_limit=None,
-                 internal_solver_factory=_gurobi_factory,
+                 internal_solver_factory=_solver_factory,
                  components=None,
                  mode="exact"):
         self.is_persistent = None
@@ -81,7 +100,7 @@ class LearningSolver:
         else:
             solve_results = self.internal_solver.solve(model, tee=tee, warmstart=is_warm_start_available)
         
-        if hasattr(self.internal_solver, "_solver_model"):
+        if self.internal_solver.name == "gurobi_persistent":
             solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount")
         else:
             solve_results["Solver"][0]["Nodes"] = 1
diff --git a/miplearn/tests/test_benchmark.py b/miplearn/tests/test_benchmark.py
index c71b088..caa0f88 100644
--- a/miplearn/tests/test_benchmark.py
+++ b/miplearn/tests/test_benchmark.py
@@ -29,11 +29,11 @@ def test_benchmark():
     benchmark = BenchmarkRunner(test_solvers)
     benchmark.load_state("data.bin")
     benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
-    assert benchmark.raw_results().values.shape == (12,12)
+    assert benchmark.raw_results().values.shape == (12,13)
     
     benchmark.save_results("/tmp/benchmark.csv")
     assert os.path.isfile("/tmp/benchmark.csv")
     
     benchmark = BenchmarkRunner(test_solvers)
     benchmark.load_results("/tmp/benchmark.csv")
-    assert benchmark.raw_results().values.shape == (12,12)
+    assert benchmark.raw_results().values.shape == (12,13)
diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py
index 1e99ae3..6a9db3f 100644
--- a/miplearn/tests/test_solver.py
+++ b/miplearn/tests/test_solver.py
@@ -4,7 +4,6 @@
 
 from miplearn import LearningSolver, BranchPriorityComponent, WarmStartComponent
 from miplearn.problems.knapsack import KnapsackInstance
-import numpy as np
 
 
 def _get_instance():
@@ -14,6 +13,7 @@ def _get_instance():
         capacity=67.,
     )
 
+
 def test_solver():
     instance = _get_instance()
     solver = LearningSolver()
@@ -21,11 +21,11 @@ def test_solver():
     solver.fit()
     solver.solve(instance)
 
+
 def test_solve_save_load_state():
     instance = _get_instance()
     components_before = {
         "warm-start": WarmStartComponent(),
-        "branch-priority": BranchPriorityComponent(),
     }
     solver = LearningSolver(components=components_before)
     solver.solve(instance)
@@ -43,6 +43,7 @@ def test_solve_save_load_state():
     assert len(solver.components["warm-start"].x_train) == prev_x_train_len
     assert len(solver.components["warm-start"].y_train) == prev_y_train_len
 
+
 def test_parallel_solve():
     instances = [_get_instance() for _ in range(10)]
     solver = LearningSolver()
@@ -51,11 +52,3 @@ def test_parallel_solve():
     assert len(solver.components["warm-start"].x_train["default"]) == 40
     assert len(solver.components["warm-start"].y_train["default"]) == 40
     
-def test_solver_random_branch_priority():
-    instance = _get_instance()
-    components = {
-        "branch-priority": BranchPriorityComponent(),
-    }
-    solver = LearningSolver(components=components)
-    solver.solve(instance)
-    solver.fit()
\ No newline at end of file

From 141c8f0fdf1279c4f00125b450ef219b760e1f95 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 19:32:21 -0600
Subject: [PATCH 23/44] Store solution into instance object after solving

---
 miplearn/solvers.py           | 30 +++++++++++++++++++++++++-----
 miplearn/tests/test_solver.py | 10 ++++++++++
 2 files changed, 35 insertions(+), 5 deletions(-)

diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index e6298fe..fd44ba9 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -4,6 +4,7 @@
 
 from . import WarmStartComponent, BranchPriorityComponent
 import pyomo.environ as pe
+from pyomo.core import Var
 from copy import deepcopy
 import pickle
 from scipy.stats import randint
@@ -76,7 +77,7 @@ class LearningSolver:
         if self.threads is not None:
             self.internal_solver.options["Threads"] = self.threads
         if self.time_limit is not None:
-            self.internal_solver.options["TimeLimit"] = self.time_limit
+            self.internal_solver.options["timelimit"] = self.time_limit
         if self.gap_limit is not None:
             self.internal_solver.options["MIPGap"] = self.gap_limit
         
@@ -100,6 +101,14 @@ class LearningSolver:
         else:
             solve_results = self.internal_solver.solve(model, tee=tee, warmstart=is_warm_start_available)
         
+        instance.solution = {}
+        instance.lower_bound = solve_results["Problem"][0]["Lower bound"]
+        instance.upper_bound = solve_results["Problem"][0]["Upper bound"]
+        for var in model.component_objects(Var):
+            instance.solution[str(var)] = {}
+            for index in var:
+                instance.solution[str(var)][index] = var[index].value
+        
         if self.internal_solver.name == "gurobi_persistent":
             solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount")
         else:
@@ -124,11 +133,22 @@ class LearningSolver:
             solver.internal_solver = None
             if not collect_training_data:
                 solver.components = {}
-            return solver, results
+            return {
+                "solver": solver,
+                "results": results,
+                "solution": instance.solution,
+                "upper bound": instance.upper_bound,
+                "lower bound": instance.lower_bound,
+            }
 
-        solver_result_pairs = p_map(_process, instances, num_cpus=n_jobs, desc=label)
-        subsolvers = [p[0] for p in solver_result_pairs]
-        results = [p[1] for p in solver_result_pairs]
+        p_map_results = p_map(_process, instances, num_cpus=n_jobs, desc=label)
+        subsolvers = [p["solver"] for p in p_map_results]
+        results = [p["results"] for p in p_map_results]
+        
+        for (idx, r) in enumerate(p_map_results):
+            instances[idx].solution = r["solution"]
+            instances[idx].lower_bound = r["lower bound"]
+            instances[idx].upper_bound = r["upper bound"]
         
         for (name, component) in self.components.items():
             subcomponents = [subsolver.components[name]
diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py
index 6a9db3f..df66f7b 100644
--- a/miplearn/tests/test_solver.py
+++ b/miplearn/tests/test_solver.py
@@ -18,6 +18,13 @@ def test_solver():
     instance = _get_instance()
     solver = LearningSolver()
     solver.solve(instance)
+    assert instance.solution["x"][0] == 1.0
+    assert instance.solution["x"][1] == 0.0
+    assert instance.solution["x"][2] == 1.0
+    assert instance.solution["x"][3] == 1.0
+    assert instance.lower_bound == 1183.0
+    assert instance.upper_bound == 1183.0
+    
     solver.fit()
     solver.solve(instance)
 
@@ -52,3 +59,6 @@ def test_parallel_solve():
     assert len(solver.components["warm-start"].x_train["default"]) == 40
     assert len(solver.components["warm-start"].y_train["default"]) == 40
     
+    for instance in instances:
+        assert len(instance.solution["x"].keys()) == 4
+    

From f8e8aeb973d4432fb4060bfe015aff874d8ec7b2 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 20:22:33 -0600
Subject: [PATCH 24/44] Better encapsulate solvers

---
 miplearn/benchmark.py         |  16 +---
 miplearn/solvers.py           | 153 ++++++++++++++++++++--------------
 miplearn/tests/test_solver.py |  26 +++---
 3 files changed, 109 insertions(+), 86 deletions(-)

diff --git a/miplearn/benchmark.py b/miplearn/benchmark.py
index f59f759..cfa0501 100644
--- a/miplearn/benchmark.py
+++ b/miplearn/benchmark.py
@@ -61,25 +61,17 @@ class BenchmarkRunner:
                                                  "Nodes",
                                                  "Mode",
                                                 ])
-        wallclock_time = None
-        for key in ["Time", "Wall time", "Wallclock time"]:
-            if key not in result["Solver"][0].keys():
-                continue
-            if str(result["Solver"][0][key]) == "":
-                continue
-            wallclock_time = float(result["Solver"][0][key])
-        nodes = result["Solver"][0]["Nodes"]
-        lb = result["Problem"][0]["Lower bound"]
-        ub = result["Problem"][0]["Upper bound"]
+        lb = result["Lower bound"]
+        ub = result["Upper bound"]
         gap = (ub - lb) / lb
         self.results = self.results.append({
             "Solver": name,
             "Instance": instance,
-            "Wallclock Time": wallclock_time,
+            "Wallclock Time": result["Wallclock time"],
             "Lower Bound": lb,
             "Upper Bound": ub,
             "Gap": gap,
-            "Nodes": nodes,
+            "Nodes": result["Nodes"],
             "Mode": solver.mode,
         }, ignore_index=True)
         groups = self.results.groupby("Instance")
diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index fd44ba9..5ac2db0 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -13,28 +13,55 @@ import logging
 logger = logging.getLogger(__name__)
 
 
-def _solver_factory():
-    try:
-        solver = pe.SolverFactory('gurobi_persistent')
-        assert solver.available()
-        solver.options["threads"] = 4
-        solver.options["Seed"] = randint(low=0, high=1000).rvs()
-        return solver
-    except Exception as e:
-        logger.debug(e)
-        pass
-
-    try:
-        solver = pe.SolverFactory('cplex_persistent')
-        assert solver.available()
-        solver.options["threads"] = 4
-        solver.options["randomseed"] = randint(low=0, high=1000).rvs()
-        return solver
-    except Exception as e:
-        logger.debug(e)
-        pass
+class GurobiSolver:
+    def __init__(self):
+        self.solver = pe.SolverFactory('gurobi_persistent')
+        self.solver.options["Seed"] = randint(low=0, high=1000).rvs()
+    
+    def set_threads(self, threads):
+        self.solver.options["Threads"] = threads
+    
+    def set_time_limit(self, time_limit):
+        self.solver.options["TimeLimit"] = time_limit
+        
+    def set_gap_tolerance(self, gap_tolerance):
+        self.solver.options["MIPGap"] = gap_tolerance
+        
+    def solve(self, model, tee=False, warmstart=False):
+        self.solver.set_instance(model)
+        results = self.solver.solve(tee=tee, warmstart=warmstart)
+        return {
+            "Lower bound": results["Problem"][0]["Lower bound"],
+            "Upper bound": results["Problem"][0]["Upper bound"],
+            "Wallclock time": results["Solver"][0]["Wallclock time"],
+            "Nodes": self.solver._solver_model.getAttr("NodeCount"),
+        }    
 
-    raise Exception("No solver available")
+    
+class CPLEXSolver:
+    def __init__(self):
+        self.solver = pe.SolverFactory('cplex_persistent')
+        self.solver.options["randomseed"] = randint(low=0, high=1000).rvs()
+        
+    def set_threads(self, threads):
+        self.solver.options["threads"] = threads
+    
+    def set_time_limit(self, time_limit):
+        self.solver.options["timelimit"] = time_limit
+        
+    def set_gap_tolerance(self, gap_tolerance):
+        self.solver.options["mip_tolerances_mipgap"] = gap_tolerance
+        
+    def solve(self, model, tee=False, warmstart=False):
+        self.solver.set_instance(model)
+        results = self.solver.solve(tee=tee, warmstart=warmstart)
+        print(results)
+        return {
+            "Lower bound": results["Problem"][0]["Lower bound"],
+            "Upper bound": results["Problem"][0]["Upper bound"],
+            "Wallclock time": results["Solver"][0]["Wallclock time"],
+            "Nodes": 1,
+        }
 
 
 class LearningSolver:
@@ -44,21 +71,23 @@ class LearningSolver:
     """
 
     def __init__(self,
-                 threads=None,
-                 time_limit=None,
-                 gap_limit=None,
-                 internal_solver_factory=_solver_factory,
                  components=None,
-                 mode="exact"):
+                 gap_tolerance=None,
+                 mode="exact",
+                 solver="cplex",
+                 threads=4,
+                 time_limit=None,
+                ):
+        
         self.is_persistent = None
-        self.internal_solver = None
         self.components = components
-        self.internal_solver_factory = internal_solver_factory
+        self.mode = mode
+        self.internal_solver = None
+        self.internal_solver_factory = solver
         self.threads = threads
         self.time_limit = time_limit
-        self.gap_limit = gap_limit
+        self.gap_tolerance = gap_tolerance
         self.tee = False
-        self.mode = mode
         
         if self.components is not None:
             assert isinstance(self.components, dict)
@@ -71,23 +100,25 @@ class LearningSolver:
         for component in self.components.values():
             component.mode = self.mode
         
-    def _create_solver(self):
-        self.internal_solver = self.internal_solver_factory()
-        self.is_persistent = hasattr(self.internal_solver, "set_instance")
-        if self.threads is not None:
-            self.internal_solver.options["Threads"] = self.threads
+    def _create_internal_solver(self):
+        if self.internal_solver_factory == "cplex":
+            solver = CPLEXSolver()
+        elif self.internal_solver_factory == "gurobi":
+            solver = GurobiSolver()
+        else:
+            raise Exception("solver %s not supported" % solver_factory)
+        solver.set_threads(self.threads)
         if self.time_limit is not None:
-            self.internal_solver.options["timelimit"] = self.time_limit
-        if self.gap_limit is not None:
-            self.internal_solver.options["MIPGap"] = self.gap_limit
+            solver.set_time_limit(self.time_limit)
+        if self.gap_tolerance is not None:
+            solver.set_gap_tolerance(self.gap_tolerance)
+        return solver
         
     def solve(self, instance, tee=False):
         model = instance.to_model()
         self.tee = tee
 
-        self._create_solver()
-        if self.is_persistent:
-            self.internal_solver.set_instance(model)
+        self.internal_solver = self._create_internal_solver()
         
         for component in self.components.values():
             component.before_solve(self, instance, model)
@@ -96,28 +127,24 @@ class LearningSolver:
         if "warm-start" in self.components.keys():
             if self.components["warm-start"].is_warm_start_available:
                 is_warm_start_available = True
-        if self.is_persistent:
-            solve_results = self.internal_solver.solve(tee=tee, warmstart=is_warm_start_available)
-        else:
-            solve_results = self.internal_solver.solve(model, tee=tee, warmstart=is_warm_start_available)
+        
+        results = self.internal_solver.solve(model,
+                                                   tee=tee,
+                                                   warmstart=is_warm_start_available)
         
         instance.solution = {}
-        instance.lower_bound = solve_results["Problem"][0]["Lower bound"]
-        instance.upper_bound = solve_results["Problem"][0]["Upper bound"]
+        instance.lower_bound = results["Lower bound"]
+        instance.upper_bound = results["Upper bound"]
+        
         for var in model.component_objects(Var):
             instance.solution[str(var)] = {}
             for index in var:
                 instance.solution[str(var)][index] = var[index].value
         
-        if self.internal_solver.name == "gurobi_persistent":
-            solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount")
-        else:
-            solve_results["Solver"][0]["Nodes"] = 1
-        
         for component in self.components.values():
             component.after_solve(self, instance, model)
         
-        return solve_results
+        return results
                 
     def parallel_solve(self,
                        instances,
@@ -134,21 +161,21 @@ class LearningSolver:
             if not collect_training_data:
                 solver.components = {}
             return {
-                "solver": solver,
-                "results": results,
-                "solution": instance.solution,
-                "upper bound": instance.upper_bound,
-                "lower bound": instance.lower_bound,
+                "Solver": solver,
+                "Results": results,
+                "Solution": instance.solution,
+                "Upper bound": instance.upper_bound,
+                "Lower bound": instance.lower_bound,
             }
 
         p_map_results = p_map(_process, instances, num_cpus=n_jobs, desc=label)
-        subsolvers = [p["solver"] for p in p_map_results]
-        results = [p["results"] for p in p_map_results]
+        subsolvers = [p["Solver"] for p in p_map_results]
+        results = [p["Results"] for p in p_map_results]
         
         for (idx, r) in enumerate(p_map_results):
-            instances[idx].solution = r["solution"]
-            instances[idx].lower_bound = r["lower bound"]
-            instances[idx].upper_bound = r["upper bound"]
+            instances[idx].solution = r["Solution"]
+            instances[idx].lower_bound = r["Lower bound"]
+            instances[idx].upper_bound = r["Upper bound"]
         
         for (name, component) in self.components.items():
             subcomponents = [subsolver.components[name]
diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py
index df66f7b..7d25431 100644
--- a/miplearn/tests/test_solver.py
+++ b/miplearn/tests/test_solver.py
@@ -16,17 +16,21 @@ def _get_instance():
 
 def test_solver():
     instance = _get_instance()
-    solver = LearningSolver()
-    solver.solve(instance)
-    assert instance.solution["x"][0] == 1.0
-    assert instance.solution["x"][1] == 0.0
-    assert instance.solution["x"][2] == 1.0
-    assert instance.solution["x"][3] == 1.0
-    assert instance.lower_bound == 1183.0
-    assert instance.upper_bound == 1183.0
-    
-    solver.fit()
-    solver.solve(instance)
+    for internal_solver in ["cplex", "gurobi"]:
+        solver = LearningSolver(time_limit=300,
+                                gap_tolerance=1e-3,
+                                threads=1,
+                                solver=internal_solver,
+                               )
+        results = solver.solve(instance)
+        assert instance.solution["x"][0] == 1.0
+        assert instance.solution["x"][1] == 0.0
+        assert instance.solution["x"][2] == 1.0
+        assert instance.solution["x"][3] == 1.0
+        assert instance.lower_bound == 1183.0
+        assert instance.upper_bound == 1183.0
+        solver.fit()
+        solver.solve(instance)
 
 
 def test_solve_save_load_state():

From 71ac2cb11186ac5cead90575fe8b7f13bcfd4e95 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 20:23:33 -0600
Subject: [PATCH 25/44] Documentation: update supported solvers and solver
 customization

---
 docs-src/customization.md     |  14 +-
 docs-src/usage.md             |   2 +-
 docs/404.html                 |  30 ++--
 docs/about/index.html         |  30 ++--
 docs/benchmark/index.html     |  30 ++--
 docs/css/cinder.css           |  18 +-
 docs/css/cinder.min.css       |   2 +-
 docs/customization/index.html |  43 ++---
 docs/index.html               |  32 ++--
 docs/js/base.js               | 324 ++++++++++++++++------------------
 docs/problems/index.html      |  32 ++--
 docs/search/search_index.json |   2 +-
 docs/sitemap.xml              |  12 +-
 docs/sitemap.xml.gz           | Bin 198 -> 197 bytes
 docs/usage/index.html         |  32 ++--
 15 files changed, 281 insertions(+), 322 deletions(-)

diff --git a/docs-src/customization.md b/docs-src/customization.md
index 2aecb14..2ffb60a 100644
--- a/docs-src/customization.md
+++ b/docs-src/customization.md
@@ -3,15 +3,11 @@
 
 ### Selecting the internal MIP solver
 
-By default, `LearningSolver` uses [Gurobi](https://www.gurobi.com/) as its internal MIP solver. Alternative solvers can be specified through the `internal_solver_factory` constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example:
+By default, `LearningSolver` uses [Gurobi](https://www.gurobi.com/) as its internal MIP solver. Another supported solver is [IBM ILOG CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio). To switch between solvers, use the `solver` constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance.
+
 ```python
 from miplearn import LearningSolver
-import pyomo.environ as pe
-
-def cplex_factory():
-    cplex = pe.SolverFactory("cplex")
-    cplex.options["threads"] = 4
-    return cplex
-
-solver = LearningSolver(internal_solver_factory=cplex_factory)
+solver = LearningSolver(solver="cplex",
+                        time_limit=300,
+                        gap_tolerance=1e-3)
 ```
diff --git a/docs-src/usage.md b/docs-src/usage.md
index 32bd134..801f20c 100644
--- a/docs-src/usage.md
+++ b/docs-src/usage.md
@@ -108,4 +108,4 @@ After all training instances have been solved in parallel, the ML models can be
 
 * Only binary and continuous decision variables are currently supported.
 * Solver callbacks (lazy constraints, cutting planes) are not currently supported.
-* Only `gurobi_persistent` is currently fully supported by all solver components. Other solvers may work if some components are disabled.
\ No newline at end of file
+* Only Gurobi and CPLEX are currently supported as internal MIP solvers.
\ No newline at end of file
diff --git a/docs/404.html b/docs/404.html
index 79b6b0b..456b56a 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -14,14 +14,16 @@
     MIPLearn
     
 
-    
+    
+    
     
-    
+    
     
     
     
     
-    
+
+    
 
     
     
 
-    
-    
-
     
 
      
@@ -139,17 +132,17 @@
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
    
+        Documentation built with MkDocs.
+        
    
 
         
         
     
     
     
-    
-    
+    
     
     
     
@@ -161,8 +154,11 @@
     
    
         
    
             
    
+                
                 
-                
             
    
             
    
                 
    
diff --git a/docs/about/index.html b/docs/about/index.html
index 94e973a..102dc11 100644
--- a/docs/about/index.html
+++ b/docs/about/index.html
@@ -14,14 +14,16 @@
     About - MIPLearn
     
 
-    
+    
+    
     
-    
+    
     
     
     
     
-    
+
+    
 
     
     
 
-    
-    
-
     
 
      
@@ -197,17 +190,17 @@ POSSIBILITY OF SUCH DAMAGE.
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
    
+        Documentation built with MkDocs.
+        
    
 
         
         
     
     
     
-    
-    
+    
     
     
     
@@ -219,8 +212,11 @@ POSSIBILITY OF SUCH DAMAGE.
     
    
         
    
             
    
+                
                 
-                
             
    
             
    
                 
    
diff --git a/docs/benchmark/index.html b/docs/benchmark/index.html
index 9d8c077..542634d 100644
--- a/docs/benchmark/index.html
+++ b/docs/benchmark/index.html
@@ -14,14 +14,16 @@
     Benchmark - MIPLearn
     
 
-    
+    
+    
     
-    
+    
     
     
     
     
-    
+
+    
 
     
     
 
-    
-    
-
     
 
      
@@ -206,17 +199,17 @@ benchmark.parallel_solve(test_instances)
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
    
+        Documentation built with MkDocs.
+        
    
 
         
         
     
     
     
-    
-    
+    
     
     
     
@@ -228,8 +221,11 @@ benchmark.parallel_solve(test_instances)
     
    
         
    
             
    
+                
                 
-                
             
    
             
    
                 
    
diff --git a/docs/css/cinder.css b/docs/css/cinder.css
index ce3cb62..4834560 100644
--- a/docs/css/cinder.css
+++ b/docs/css/cinder.css
@@ -10,23 +10,28 @@ body {
     color: #343838;
 }
 h1, h2, h3, h4, h5, h6 {
-    font-family:'PT Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+    font-family:'Inter', 'Helvetica Neue', Helvetica, Arial, sans-serif;
     color: #222;
 }
 h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small {
     color: #B1B7B9;
 }
+
+h2 {
+    margin-top: 35px;
+}
+
 h1, h2 {
     font-weight: 700;
 }
 h4 {
-    font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+    font-family: 'Inter', 'Helvetica Neue', Helvetica, Arial, sans-serif;
     font-weight: 300;
     margin-top: 20px;
     font-style: italic;
 }
 h5 {
-    font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+    font-family: 'Inter', 'Helvetica Neue', Helvetica, Arial, sans-serif;
     font-weight: 300;
     font-variant: small-caps;
 }
@@ -41,7 +46,7 @@ pre {
     margin-bottom: 25px;
 }
 .lead {
-    font-family:"Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
+    font-family:"Inter", "Helvetica Neue", Helvetica, Arial, sans-serif;
     font-weight: 400;
     line-height: 1.4;
     letter-spacing: 0.0312em;
@@ -54,7 +59,8 @@ pre {
 .bs-sidenav {
     background-image: url("../img/grid11.png");
     background-repeat: repeat;
-    font-size: 12px;
+    font-family: Inter,"Helvetica Neue",Helvetica,Arial,sans-serif;
+    font-size: 13px;
 }
 .well {
     background-color: #FCFDFF;
@@ -71,7 +77,7 @@ pre {
     box-shadow: none;
 }
 #mkdocs-search-query {
-    font-family:"Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
+    font-family:"Inter", "Helvetica Neue", Helvetica, Arial, sans-serif;
     font-size: 20px;
     font-weight: 700;
     color: #343838;
diff --git a/docs/css/cinder.min.css b/docs/css/cinder.min.css
index ff25572..fb7cc34 100644
--- a/docs/css/cinder.min.css
+++ b/docs/css/cinder.min.css
@@ -1 +1 @@
-body{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16px;line-height:1.7;background-color:#FFF;color:#343838}h1,h2,h3,h4,h5,h6{font-family:'PT Sans','Helvetica Neue',Helvetica,Arial,sans-serif;color:#222}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small,.h1 small,.h2 small,.h3 small,.h4 small,.h5 small,.h6 small,h1 .small,h2 .small,h3 .small,h4 .small,h5 .small,h6 .small,.h1 .small,.h2 .small,.h3 .small,.h4 .small,.h5 .small,.h6 .small{color:#b1b7b9}h1,h2{font-weight:700}h4{font-family:'Open Sans','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;margin-top:20px;font-style:italic}h5{font-family:'Open Sans','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;font-variant:small-caps}pre,code{background-color:#fcfdff}pre>code{font-size:13px}pre{margin-top:25px;margin-bottom:25px}.lead{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-weight:400;line-height:1.4;letter-spacing:.0312em;color:#b1b7b9}.navbar-default{background-color:#343838;border-bottom:8px #ebf2f2 solid}.bs-sidenav{background-image:url("../img/grid11.png");background-repeat:repeat;font-size:12px}.well{background-color:#fcfdff}.btn-default{background-color:#fcfdff}.table-striped>tbody>tr:nth-child(2n+1)>td,.table-striped>tbody>tr:nth-child(2n+1)>th{background-color:#fcfdff}#mkdocs-search-query:focus{outline:0;-webkit-box-shadow:none;box-shadow:none}#mkdocs-search-query{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:20px;font-weight:700;color:#343838;height:45px}footer>hr{width:35%}
+body{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16px;line-height:1.7;background-color:#FFF;color:#343838}h1,h2,h3,h4,h5,h6{font-family:'Inter','Helvetica Neue',Helvetica,Arial,sans-serif;color:#222}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small,.h1 small,.h2 small,.h3 small,.h4 small,.h5 small,.h6 small,h1 .small,h2 .small,h3 .small,h4 .small,h5 .small,h6 .small,.h1 .small,.h2 .small,.h3 .small,.h4 .small,.h5 .small,.h6 .small{color:#b1b7b9}h2{margin-top:35px}h1,h2{font-weight:700}h4{font-family:'Inter','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;margin-top:20px;font-style:italic}h5{font-family:'Inter','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;font-variant:small-caps}pre,code{background-color:#fcfdff}pre>code{font-size:13px}pre{margin-top:25px;margin-bottom:25px}.lead{font-family:"Inter","Helvetica Neue",Helvetica,Arial,sans-serif;font-weight:400;line-height:1.4;letter-spacing:.0312em;color:#b1b7b9}.navbar-default{background-color:#343838;border-bottom:8px #ebf2f2 solid}.bs-sidenav{background-image:url("../img/grid11.png");background-repeat:repeat;font-family:Inter,"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px}.well{background-color:#fcfdff}.btn-default{background-color:#fcfdff}.table-striped>tbody>tr:nth-child(2n+1)>td,.table-striped>tbody>tr:nth-child(2n+1)>th{background-color:#fcfdff}#mkdocs-search-query:focus{outline:0;-webkit-box-shadow:none;box-shadow:none}#mkdocs-search-query{font-family:"Inter","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:20px;font-weight:700;color:#343838;height:45px}footer>hr{width:35%}
diff --git a/docs/customization/index.html b/docs/customization/index.html
index eb50a7f..91504be 100644
--- a/docs/customization/index.html
+++ b/docs/customization/index.html
@@ -14,14 +14,16 @@
     Customization - MIPLearn
     
 
-    
+    
+    
     
-    
+    
     
     
     
     
-    
+
+    
 
     
     
 
-    
-    
-
     
 
      
@@ -145,16 +138,11 @@
 
 
    Customization
    
 
    Selecting the internal MIP solver
    
-
    By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example:
    
+
    By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX. To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance.
    
 
    from miplearn import LearningSolver
-import pyomo.environ as pe
-
-def cplex_factory():
-    cplex = pe.SolverFactory("cplex")
-    cplex.options["threads"] = 4
-    return cplex
-
-solver = LearningSolver(internal_solver_factory=cplex_factory)
+solver = LearningSolver(solver="cplex",
+                        time_limit=300,
+                        gap_tolerance=1e-3)
 
    
         
         
@@ -164,17 +152,17 @@ solver = LearningSolver(internal_solver_factory=cplex_factory)
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
    
+        Documentation built with MkDocs.
+        
    
 
         
         
     
     
     
-    
-    
+    
     
     
     
@@ -186,8 +174,11 @@ solver = LearningSolver(internal_solver_factory=cplex_factory)
     
    
         
    
             
    
+                
                 
-                
             
    
             
    
                 
    
diff --git a/docs/index.html b/docs/index.html
index 0607f6c..3ca1fa9 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -14,14 +14,16 @@
     Home - MIPLearn
     
 
-    
+    
+    
     
-    
+    
     
     
     
     
-    
+
+    
 
     
     
 
-    
-    
-
     
 
      
@@ -184,17 +177,17 @@
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
    
+        Documentation built with MkDocs.
+        
    
 
         
         
     
     
     
-    
-    
+    
     
     
     
@@ -206,8 +199,11 @@
     
    
         
    
             
    
+                
                 
-                
             
    
             
    
                 
    
@@ -271,5 +267,5 @@
 
 
diff --git a/docs/js/base.js b/docs/js/base.js
index d933b3a..1db589c 100644
--- a/docs/js/base.js
+++ b/docs/js/base.js
@@ -1,88 +1,78 @@
-function getSearchTerm()
-{
+function getSearchTerm() {
     var sPageURL = window.location.search.substring(1);
     var sURLVariables = sPageURL.split('&');
-    for (var i = 0; i < sURLVariables.length; i++)
-    {
+    for (var i = 0; i < sURLVariables.length; i++) {
         var sParameterName = sURLVariables[i].split('=');
-        if (sParameterName[0] == 'q')
-        {
+        if (sParameterName[0] == 'q') {
             return sParameterName[1];
         }
     }
 }
 
 $(document).ready(function() {
-    /**
-     * ------------------------------------------------------------------------
-     * Cinder theme specific
-     * ------------------------------------------------------------------------
-     */
-    hljs.initHighlightingOnLoad();
-
-
     /**
      * ------------------------------------------------------------------------
      * Taken from themes/mkdocs/js/base.js
      * ------------------------------------------------------------------------
      */
     var search_term = getSearchTerm(),
-    $search_modal = $('#mkdocs_search_modal'),
-    $keyboard_modal = $('#mkdocs_keyboard_modal');
+        $search_modal = $('#mkdocs_search_modal'),
+        $keyboard_modal = $('#mkdocs_keyboard_modal');
 
-    if(search_term){
+    if (search_term) {
         $search_modal.modal();
     }
 
     // make sure search input gets autofocus everytime modal opens.
-    $search_modal.on('shown.bs.modal', function () {
+    $search_modal.on('shown.bs.modal', function() {
         $search_modal.find('#mkdocs-search-query').focus();
     });
 
     // Close search modal when result is selected
     // The links get added later so listen to parent
     $('#mkdocs-search-results').click(function(e) {
-      if ($(e.target).is('a')) {
-        $search_modal.modal('hide');
-      }
+        if ($(e.target).is('a')) {
+            $search_modal.modal('hide');
+        }
     });
 
     if (typeof shortcuts !== 'undefined') {
-      // Populate keyboard modal with proper Keys
-      $keyboard_modal.find('.help.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.help];
-      $keyboard_modal.find('.prev.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.previous];
-      $keyboard_modal.find('.next.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.next];
-      $keyboard_modal.find('.search.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.search];
+        // Populate keyboard modal with proper Keys
+        $keyboard_modal.find('.help.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.help];
+        $keyboard_modal.find('.prev.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.previous];
+        $keyboard_modal.find('.next.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.next];
+        $keyboard_modal.find('.search.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.search];
 
-      // Keyboard navigation
-      document.addEventListener("keydown", function(e) {
-          if ($(e.target).is(':input')) return true;
-          var key = e.which || e.key || window.event && window.event.key;
-          var page;
-          switch (key) {
-              case shortcuts.next:
-                  page = $('.navbar a[rel="next"]:first').prop('href');
-                  break;
-              case shortcuts.previous:
-                  page = $('.navbar a[rel="prev"]:first').prop('href');
-                  break;
-              case shortcuts.search:
-                  e.preventDefault();
-                  $keyboard_modal.modal('hide');
-                  $search_modal.modal('show');
-                  $search_modal.find('#mkdocs-search-query').focus();
-                  break;
-              case shortcuts.help:
-                  $search_modal.modal('hide');
-                  $keyboard_modal.modal('show');
-                  break;
-              default: break;
-          }
-          if (page) {
-              $keyboard_modal.modal('hide');
-              window.location.href = page;
-          }
-      });
+        // Keyboard navigation
+        document.addEventListener("keydown", function(e) {
+            if ($(e.target).is(':input')) return true;
+            var key = e.which || e.key || window.event && window.event.key;
+            var page;
+            switch (key) {
+                case shortcuts.next:
+                    page = $('.navbar a[rel="next"]:first').prop('href');
+                    break;
+                case shortcuts.previous:
+                    page = $('.navbar a[rel="prev"]:first').prop('href');
+                    break;
+                case shortcuts.search:
+                    e.preventDefault();
+                    $keyboard_modal.modal('hide');
+                    $search_modal.modal('show');
+                    $search_modal.find('#mkdocs-search-query').focus();
+                    break;
+                case shortcuts.help:
+                    $search_modal.modal('hide');
+                    $keyboard_modal.modal('show');
+                    break;
+                default:
+                    break;
+            }
+            if (page) {
+                $keyboard_modal.modal('hide');
+                window.location.href = page;
+            }
+        });
     }
 
     $('table').addClass('table table-striped table-hover');
@@ -121,115 +111,115 @@ $("li.disabled a").click(function() {
 // See https://www.cambiaresearch.com/articles/15/javascript-char-codes-key-codes
 // We only list common keys below. Obscure keys are omited and their use is discouraged.
 var keyCodes = {
-  8: 'backspace',
-  9: 'tab',
-  13: 'enter',
-  16: 'shift',
-  17: 'ctrl',
-  18: 'alt',
-  19: 'pause/break',
-  20: 'caps lock',
-  27: 'escape',
-  32: 'spacebar',
-  33: 'page up',
-  34: 'page down',
-  35: 'end',
-  36: 'home',
-  37: '←',
-  38: '↑',
-  39: '→',
-  40: '↓',
-  45: 'insert',
-  46: 'delete',
-  48: '0',
-  49: '1',
-  50: '2',
-  51: '3',
-  52: '4',
-  53: '5',
-  54: '6',
-  55: '7',
-  56: '8',
-  57: '9',
-  65: 'a',
-  66: 'b',
-  67: 'c',
-  68: 'd',
-  69: 'e',
-  70: 'f',
-  71: 'g',
-  72: 'h',
-  73: 'i',
-  74: 'j',
-  75: 'k',
-  76: 'l',
-  77: 'm',
-  78: 'n',
-  79: 'o',
-  80: 'p',
-  81: 'q',
-  82: 'r',
-  83: 's',
-  84: 't',
-  85: 'u',
-  86: 'v',
-  87: 'w',
-  88: 'x',
-  89: 'y',
-  90: 'z',
-  91: 'Left Windows Key / Left ⌘',
-  92: 'Right Windows Key',
-  93: 'Windows Menu / Right ⌘',
-  96: 'numpad 0',
-  97: 'numpad 1',
-  98: 'numpad 2',
-  99: 'numpad 3',
-  100: 'numpad 4',
-  101: 'numpad 5',
-  102: 'numpad 6',
-  103: 'numpad 7',
-  104: 'numpad 8',
-  105: 'numpad 9',
-  106: 'multiply',
-  107: 'add',
-  109: 'subtract',
-  110: 'decimal point',
-  111: 'divide',
-  112: 'f1',
-  113: 'f2',
-  114: 'f3',
-  115: 'f4',
-  116: 'f5',
-  117: 'f6',
-  118: 'f7',
-  119: 'f8',
-  120: 'f9',
-  121: 'f10',
-  122: 'f11',
-  123: 'f12',
-  124: 'f13',
-  125: 'f14',
-  126: 'f15',
-  127: 'f16',
-  128: 'f17',
-  129: 'f18',
-  130: 'f19',
-  131: 'f20',
-  132: 'f21',
-  133: 'f22',
-  134: 'f23',
-  135: 'f24',
-  144: 'num lock',
-  145: 'scroll lock',
-  186: ';',
-  187: '=',
-  188: ',',
-  189: '‐',
-  190: '.',
-  191: '?',
-  192: '`',
-  219: '[',
-  220: '\',
-  221: ']',
-  222: ''',
-};
\ No newline at end of file
+    8: 'backspace',
+    9: 'tab',
+    13: 'enter',
+    16: 'shift',
+    17: 'ctrl',
+    18: 'alt',
+    19: 'pause/break',
+    20: 'caps lock',
+    27: 'escape',
+    32: 'spacebar',
+    33: 'page up',
+    34: 'page down',
+    35: 'end',
+    36: 'home',
+    37: '←',
+    38: '↑',
+    39: '→',
+    40: '↓',
+    45: 'insert',
+    46: 'delete',
+    48: '0',
+    49: '1',
+    50: '2',
+    51: '3',
+    52: '4',
+    53: '5',
+    54: '6',
+    55: '7',
+    56: '8',
+    57: '9',
+    65: 'a',
+    66: 'b',
+    67: 'c',
+    68: 'd',
+    69: 'e',
+    70: 'f',
+    71: 'g',
+    72: 'h',
+    73: 'i',
+    74: 'j',
+    75: 'k',
+    76: 'l',
+    77: 'm',
+    78: 'n',
+    79: 'o',
+    80: 'p',
+    81: 'q',
+    82: 'r',
+    83: 's',
+    84: 't',
+    85: 'u',
+    86: 'v',
+    87: 'w',
+    88: 'x',
+    89: 'y',
+    90: 'z',
+    91: 'Left Windows Key / Left ⌘',
+    92: 'Right Windows Key',
+    93: 'Windows Menu / Right ⌘',
+    96: 'numpad 0',
+    97: 'numpad 1',
+    98: 'numpad 2',
+    99: 'numpad 3',
+    100: 'numpad 4',
+    101: 'numpad 5',
+    102: 'numpad 6',
+    103: 'numpad 7',
+    104: 'numpad 8',
+    105: 'numpad 9',
+    106: 'multiply',
+    107: 'add',
+    109: 'subtract',
+    110: 'decimal point',
+    111: 'divide',
+    112: 'f1',
+    113: 'f2',
+    114: 'f3',
+    115: 'f4',
+    116: 'f5',
+    117: 'f6',
+    118: 'f7',
+    119: 'f8',
+    120: 'f9',
+    121: 'f10',
+    122: 'f11',
+    123: 'f12',
+    124: 'f13',
+    125: 'f14',
+    126: 'f15',
+    127: 'f16',
+    128: 'f17',
+    129: 'f18',
+    130: 'f19',
+    131: 'f20',
+    132: 'f21',
+    133: 'f22',
+    134: 'f23',
+    135: 'f24',
+    144: 'num lock',
+    145: 'scroll lock',
+    186: ';',
+    187: '=',
+    188: ',',
+    189: '‐',
+    190: '.',
+    191: '?',
+    192: '`',
+    219: '[',
+    220: '\',
+    221: ']',
+    222: ''',
+};
diff --git a/docs/problems/index.html b/docs/problems/index.html
index 75606a7..beaf2c8 100644
--- a/docs/problems/index.html
+++ b/docs/problems/index.html
@@ -14,14 +14,16 @@
     Problems - MIPLearn
     
 
-    
+    
+    
     
-    
+    
     
     
     
     
-    
+
+    
 
     
     
 
-    
-    
-
     
 
      
@@ -224,7 +217,7 @@ item $j$ is set to:
    
 
    where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled
 from the provided probability distributions K and u.
    
 
    If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical.
    
-
    If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \gamma_{ij}$ where $\gamma_{ij}$ is sampled from w_jitter. When combined with fix_w=True, this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead.
    
+
    If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \gamma_{ij}$ where $\gamma_{ij}$ is sampled from w_jitter. When combined with fix_w=True, this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead.
    
 
    By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled.
    
 
    
 
    References
    
@@ -241,17 +234,17 @@ from the provided probability distributions K and u.
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
    
+        Documentation built with MkDocs.
+        
    
 
         
         
     
     
     
-    
-    
+    
     
     
     
@@ -263,8 +256,11 @@ from the provided probability distributions K and u.
         
    
             
    
+                
                 
-                
             
    
             
    
                 
    
diff --git a/docs/search/search_index.json b/docs/search/search_index.json
index 7e404c6..2cc4346 100644
--- a/docs/search/search_index.json
+++ b/docs/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/iSoron/miplearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/iSoron/miplearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example: from miplearn import LearningSolver import pyomo.environ as pe def cplex_factory(): cplex = pe.SolverFactory(\"cplex\") cplex.options[\"threads\"] = 4 return cplex solver = LearningSolver(internal_solver_factory=cplex_factory)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Alternative solvers can be specified through the internal_solver_factory constructor argument. This argument should provide a function (with no arguments) that constructs, configures and returns the desired solver. To select CPLEX, for example: from miplearn import LearningSolver import pyomo.environ as pe def cplex_factory(): cplex = pe.SolverFactory(\"cplex\") cplex.options[\"threads\"] = 4 return cplex solver = LearningSolver(internal_solver_factory=cplex_factory)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Benchmark challenges Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Benchmark results Challenge A Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#benchmark-challenges_1","text":"","title":"Benchmark challenges"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#benchmark-results","text":"","title":"Benchmark results"},{"location":"problems/#challenge-a_1","text":"","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} + \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.","title":"Current Limitations"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/iSoron/miplearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/iSoron/miplearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Benchmark challenges Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Benchmark results Challenge A Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#benchmark-challenges_1","text":"","title":"Benchmark challenges"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#benchmark-results","text":"","title":"Benchmark results"},{"location":"problems/#challenge-a_1","text":"","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Current Limitations"}]}
\ No newline at end of file
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 9e81bfb..a74d8a4 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -2,32 +2,32 @@
 
     
      None
-     2020-02-21
+     2020-02-22
      daily
     
     
      None
-     2020-02-21
+     2020-02-22
      daily
     
     
      None
-     2020-02-21
+     2020-02-22
      daily
     
     
      None
-     2020-02-21
+     2020-02-22
      daily
     
     
      None
-     2020-02-21
+     2020-02-22
      daily
     
     
      None
-     2020-02-21
+     2020-02-22
      daily
     
 
\ No newline at end of file
diff --git a/docs/sitemap.xml.gz b/docs/sitemap.xml.gz
index cfe1cbe86ea34a8f866727b74482d86c153745c7..7f154504e43156d7c06d64876f32e105f12ed3ca 100644
GIT binary patch
literal 197
zcmV;$06PC4iwFoF;!$1#|8r?{Wo=<_E_iKh0PT`762c%5Mf;qBp${OHI)rqVR(b%2
zV8RT6$fD8POH6cZ?X%7P{rhKMv!Z_V4&6-z8+?T~gkfa74vqC~h0mu$xy7|C#4S27
z3FWYd3U?_DdrHS~Bw&;2)c_>KmV)l`0CX`aBReb$lxsRLD_0J@r#ku8=5$~7NY6cR3Ic?jMJI;*#-APu3F
zoN-n%Uh*ohS8t8zUir?Pqq)ddS@#h1%ULM&gZTshz@LDxmVaD60exv-OA`YC0KRxy
A^Z)<=

diff --git a/docs/usage/index.html b/docs/usage/index.html
index bfa8f25..7c63a70 100644
--- a/docs/usage/index.html
+++ b/docs/usage/index.html
@@ -14,14 +14,16 @@
     Usage - MIPLearn
     
 
-    
+    
+    
     
-    
+    
     
     
     
     
-    
+
+    
 
     
     
 
-    
-    
-
     
 
      
@@ -232,7 +225,7 @@ solver.solve(test_instance)
 
      
 
    • Only binary and continuous decision variables are currently supported.
      • 
 
    • Solver callbacks (lazy constraints, cutting planes) are not currently supported.
      • 
-
    • Only gurobi_persistent is currently fully supported by all solver components. Other solvers may work if some components are disabled.
      • 
+
    • Only Gurobi and CPLEX are currently supported as internal MIP solvers.
      • 
 
    
         
         
@@ -242,17 +235,17 @@ solver.solve(test_instance)
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
    
+        Documentation built with MkDocs.
+        
    
 
         
         
     
     
     
-    
-    
+    
     
     
     
@@ -264,8 +257,11 @@ solver.solve(test_instance)
     
    
         
    
             
    
+                
                 
-                
             
    
             
    
                 
    

From 7641504f3acf34e621be506bb86e9e52ec6d17a0 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 20:28:24 -0600
Subject: [PATCH 26/44] Update GitHub Actions

---
 .github/workflows/main.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 4502d43..02d64ef 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -8,10 +8,11 @@ jobs:
     - name: Run tests
       run: |
         rm -rf ~/.conda/envs/miplearn-test
-        yes | conda create --name miplearn-test python=3.8
+        yes | conda create --name miplearn-test python=3.6
         julia -e 'using Pkg; Pkg.add(PackageSpec(url="https://github.com/iSoron/CPLEXW.git")); Pkg.build("CPLEXW")'
         julia -e 'using Pkg; Pkg.add(PackageSpec(url="https://github.com/iSoron/TinyBnB.jl.git"))'
         (cd /opt/gurobi900/linux64 && ~/.conda/envs/miplearn-test/bin/python setup.py install)
+        (cd /opt/cplex-12.8/cplex/python/3.6/x86-64_linux && ~/.conda/envs/miplearn-test/bin/python setup.py install)
         ~/.conda/envs/miplearn-test/bin/pip install -r requirements.txt
         ~/.conda/envs/miplearn-test/bin/pytest
 

From 0048fcce134d25238d0ed9751cc16deb933b9279 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 22:02:30 -0600
Subject: [PATCH 27/44] Extract root relaxation solution

---
 miplearn/solvers.py           | 87 +++++++++++++++++++++++++++++------
 miplearn/tests/test_solver.py |  6 +++
 2 files changed, 80 insertions(+), 13 deletions(-)

diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 5ac2db0..8481c29 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -13,7 +13,38 @@ import logging
 logger = logging.getLogger(__name__)
 
 
-class GurobiSolver:
+class InternalSolver():
+    def __init__():
+        pass
+    
+    def solve_lp(self, model, tee=False):
+        from pyomo.core.base.set_types import Reals
+        original_domain = {}
+        for var in model.component_data_objects(Var):
+            original_domain[str(var)] = var.domain
+            lb, ub = var.bounds
+            var.setlb(lb)
+            var.setub(ub)
+            var.domain = Reals
+        self.solver.set_instance(model)
+        self.solver.solve(tee=True)
+        for var in model.component_data_objects(Var):
+            var.domain = original_domain[str(var)]
+            
+    def clear_values(self, model):
+        for var in model.component_objects(Var):
+            for index in var:
+                var[index].value = None
+                
+    def get_solution(self, model):
+        solution = {}
+        for var in model.component_objects(Var):
+            solution[str(var)] = {}
+            for index in var:
+                solution[str(var)][index] = var[index].value
+        return solution        
+
+class GurobiSolver(InternalSolver):
     def __init__(self):
         self.solver = pe.SolverFactory('gurobi_persistent')
         self.solver.options["Seed"] = randint(low=0, high=1000).rvs()
@@ -36,10 +67,24 @@ class GurobiSolver:
             "Wallclock time": results["Solver"][0]["Wallclock time"],
             "Nodes": self.solver._solver_model.getAttr("NodeCount"),
         }    
+            
+    def _load_vars(self):
+        var_map = self._pyomo_var_to_solver_var_map
+        ref_vars = self._referenced_variables
+        vars_to_load = var_map.keys()
+
+        gurobi_vars_to_load = [var_map[pyomo_var] for pyomo_var in vars_to_load]
+        vals = self._solver_model.getAttr("X", gurobi_vars_to_load)
+
+        for var, val in zip(vars_to_load, vals):
+            if ref_vars[var] > 0:
+                var.stale = False
+                var.value = val
 
     
-class CPLEXSolver:
+class CPLEXSolver(InternalSolver):
     def __init__(self):
+        import cplex
         self.solver = pe.SolverFactory('cplex_persistent')
         self.solver.options["randomseed"] = randint(low=0, high=1000).rvs()
         
@@ -55,14 +100,23 @@ class CPLEXSolver:
     def solve(self, model, tee=False, warmstart=False):
         self.solver.set_instance(model)
         results = self.solver.solve(tee=tee, warmstart=warmstart)
-        print(results)
         return {
             "Lower bound": results["Problem"][0]["Lower bound"],
             "Upper bound": results["Problem"][0]["Upper bound"],
             "Wallclock time": results["Solver"][0]["Wallclock time"],
             "Nodes": 1,
         }
-
+    
+    def solve_lp(self, model, tee=False):
+        import cplex
+        self.solver.set_instance(model)
+        lp = self.solver._solver_model
+        var_types = lp.variables.get_types()
+        n_vars = len(var_types)
+        lp.set_problem_type(cplex.Cplex.problem_type.LP)
+        results = self.solver.solve(tee=tee)
+        lp.variables.set_types(zip(range(n_vars), var_types))
+        
 
 class LearningSolver:
     """
@@ -117,30 +171,34 @@ class LearningSolver:
     def solve(self, instance, tee=False):
         model = instance.to_model()
         self.tee = tee
-
         self.internal_solver = self._create_internal_solver()
+
+        # Solve LP relaxation
+        self.internal_solver.solve_lp(model, tee=tee)
+        instance.lp_solution = self.internal_solver.get_solution(model)
         
+        # Invoke before_solve callbacks
         for component in self.components.values():
             component.before_solve(self, instance, model)
         
+        # Check if warm start is available
         is_warm_start_available = False
         if "warm-start" in self.components.keys():
             if self.components["warm-start"].is_warm_start_available:
                 is_warm_start_available = True
         
+        # Solver original MIP
+        self.internal_solver.clear_values(model)
         results = self.internal_solver.solve(model,
-                                                   tee=tee,
-                                                   warmstart=is_warm_start_available)
+                                             tee=tee,
+                                             warmstart=is_warm_start_available)
         
-        instance.solution = {}
+        # Read MIP solution and bounds
         instance.lower_bound = results["Lower bound"]
         instance.upper_bound = results["Upper bound"]
+        instance.solution = self.internal_solver.get_solution(model)
         
-        for var in model.component_objects(Var):
-            instance.solution[str(var)] = {}
-            for index in var:
-                instance.solution[str(var)][index] = var[index].value
-        
+        # Invoke after_solve callbacks
         for component in self.components.values():
             component.after_solve(self, instance, model)
         
@@ -164,6 +222,7 @@ class LearningSolver:
                 "Solver": solver,
                 "Results": results,
                 "Solution": instance.solution,
+                "LP solution": instance.lp_solution,
                 "Upper bound": instance.upper_bound,
                 "Lower bound": instance.lower_bound,
             }
@@ -174,6 +233,7 @@ class LearningSolver:
         
         for (idx, r) in enumerate(p_map_results):
             instances[idx].solution = r["Solution"]
+            instances[idx].lp_solution = r["LP solution"]
             instances[idx].lower_bound = r["Lower bound"]
             instances[idx].upper_bound = r["Upper bound"]
         
@@ -205,3 +265,4 @@ class LearningSolver:
                     continue
                 else:
                     self.components[component_name].merge([component])
+
diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py
index 7d25431..5b37e2c 100644
--- a/miplearn/tests/test_solver.py
+++ b/miplearn/tests/test_solver.py
@@ -27,6 +27,12 @@ def test_solver():
         assert instance.solution["x"][1] == 0.0
         assert instance.solution["x"][2] == 1.0
         assert instance.solution["x"][3] == 1.0
+        
+        assert round(instance.lp_solution["x"][0], 3) == 1.000
+        assert round(instance.lp_solution["x"][1], 3) == 0.923
+        assert round(instance.lp_solution["x"][2], 3) == 1.000
+        assert round(instance.lp_solution["x"][3], 3) == 0.000
+        
         assert instance.lower_bound == 1183.0
         assert instance.upper_bound == 1183.0
         solver.fit()

From 212e38d00469f3110af276a55271fb2688c9ab68 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 22:19:30 -0600
Subject: [PATCH 28/44] Make SolutionExtractor use instance.solution and
 instance.lp_solution

---
 miplearn/__init__.py              |  5 ++++-
 miplearn/components/warmstart.py  |  8 +-------
 miplearn/extractors.py            | 12 ++++++++++--
 miplearn/solvers.py               |  6 ++++--
 miplearn/tests/test_extractors.py | 23 ++++++++++++++---------
 5 files changed, 33 insertions(+), 21 deletions(-)

diff --git a/miplearn/__init__.py b/miplearn/__init__.py
index 9c1c350..d7e6a9a 100644
--- a/miplearn/__init__.py
+++ b/miplearn/__init__.py
@@ -9,7 +9,10 @@ from .components.warmstart import (WarmStartComponent,
                                    AdaptivePredictor,
                                   )
 from .components.branching import BranchPriorityComponent
-from .extractors import UserFeaturesExtractor, SolutionExtractor
+from .extractors import (UserFeaturesExtractor,
+                         SolutionExtractor,
+                         CombinedExtractor,
+                        )
 from .benchmark import BenchmarkRunner
 from .instance import Instance
 from .solvers import LearningSolver
diff --git a/miplearn/components/warmstart.py b/miplearn/components/warmstart.py
index 326f2ac..ed0a18c 100644
--- a/miplearn/components/warmstart.py
+++ b/miplearn/components/warmstart.py
@@ -128,15 +128,9 @@ class WarmStartComponent(Component):
         
     
     def before_solve(self, solver, instance, model):
-#         # Solve linear relaxation
-#         lr_solver = pe.SolverFactory("gurobi")
-#         lr_solver.options["threads"] = 4
-#         lr_solver.options["relax_integrality"] = 1
-#         lr_solver.solve(model, tee=solver.tee)
-        
         # Build x_test
         x_test = CombinedExtractor([UserFeaturesExtractor(),
-                                    SolutionExtractor(),
+                                    SolutionExtractor(relaxation=True),
                                    ]).extract([instance], [model])
         
         # Update self.x_train
diff --git a/miplearn/extractors.py b/miplearn/extractors.py
index 1b6f529..2803c7f 100644
--- a/miplearn/extractors.py
+++ b/miplearn/extractors.py
@@ -69,8 +69,13 @@ class UserFeaturesExtractor(Extractor):
 
 
 class SolutionExtractor(Extractor):
-    def extract(self, instances, models):
+    def __init__(self, relaxation=False):
+        self.relaxation = relaxation
+        
+    def extract(self, instances, models=None):
         result = {}
+        if models is None:
+            models = [instance.to_model() for instance in instances]
         for (index, instance) in enumerate(instances):
             model = models[index]
             var_split = self.split_variables(instance, model)
@@ -78,7 +83,10 @@ class SolutionExtractor(Extractor):
                 if category not in result.keys():
                     result[category] = []
                 for (var, index) in var_index_pairs:
-                    v = var[index].value
+                    if self.relaxation:
+                        v = instance.lp_solution[str(var)][index]
+                    else:
+                        v = instance.solution[str(var)][index]
                     if v is None:
                         result[category] += [[0, 0]]
                     else:
diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 8481c29..1d5c0fb 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -168,8 +168,10 @@ class LearningSolver:
             solver.set_gap_tolerance(self.gap_tolerance)
         return solver
         
-    def solve(self, instance, tee=False):
-        model = instance.to_model()
+    def solve(self, instance, model=None, tee=False):
+        if model is None:
+            model = instance.to_model()
+            
         self.tee = tee
         self.internal_solver = self._create_internal_solver()
 
diff --git a/miplearn/tests/test_extractors.py b/miplearn/tests/test_extractors.py
index cd7f015..915a602 100644
--- a/miplearn/tests/test_extractors.py
+++ b/miplearn/tests/test_extractors.py
@@ -3,10 +3,11 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn.problems.knapsack import KnapsackInstance
-from miplearn.extractors import (UserFeaturesExtractor,
-                                 SolutionExtractor,
-                                 CombinedExtractor,
-                                )
+from miplearn import (LearningSolver,
+                      UserFeaturesExtractor,
+                      SolutionExtractor,
+                      CombinedExtractor,
+                     )
 import numpy as np
 import pyomo.environ as pe
 
@@ -37,11 +38,11 @@ def test_user_features():
 def test_solution_extractor():
     instances = _get_instances()
     models = [instance.to_model() for instance in instances]
-    for model in models:
-        solver = pe.SolverFactory("cbc")
-        solver.solve(model)
-    extractor = SolutionExtractor()
-    features = extractor.extract(instances, models)
+    solver = LearningSolver()
+    for (i, instance) in enumerate(instances):
+        solver.solve(instances[i], models[i])
+    
+    features = SolutionExtractor().extract(instances, models)
     assert isinstance(features, dict)
     assert "default" in features.keys()
     assert isinstance(features["default"], np.ndarray)
@@ -59,6 +60,10 @@ def test_solution_extractor():
 def test_combined_extractor():
     instances = _get_instances()
     models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for (i, instance) in enumerate(instances):
+        solver.solve(instances[i], models[i])
+    
     extractor = CombinedExtractor(extractors=[UserFeaturesExtractor(),
                                               SolutionExtractor()])
     features = extractor.extract(instances, models)

From 24b0a7888e972a62f60c019b81d66c254b4d9dfe Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 22:29:46 -0600
Subject: [PATCH 29/44] Update benchmark scripts

---
 benchmark/Makefile     |  2 +-
 benchmark/benchmark.py | 64 +++++++++++++-----------------------------
 2 files changed, 20 insertions(+), 46 deletions(-)

diff --git a/benchmark/Makefile b/benchmark/Makefile
index 4572e4f..ced75b5 100644
--- a/benchmark/Makefile
+++ b/benchmark/Makefile
@@ -9,7 +9,7 @@ CHALLENGES := \
 
 main: $(addsuffix /performance.png, $(CHALLENGES))
 
-%/training_data.bin:
+%/train_instances.bin:
 	python benchmark.py train $*
 
 %/benchmark_baseline.csv: %/training_data.bin
diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py
index 74aef87..d545e29 100755
--- a/benchmark/benchmark.py
+++ b/benchmark/benchmark.py
@@ -28,6 +28,9 @@ import pickle
 import logging
 logging.getLogger('pyomo.core').setLevel(logging.ERROR)
 
+n_jobs = 10
+time_limit = 300
+
 args = docopt(__doc__)
 basepath = args[""]
 pathlib.Path(basepath).mkdir(parents=True, exist_ok=True)
@@ -45,36 +48,15 @@ def load(filename):
         return pickle.load(file)        
         
         
-def train_solver_factory():
-    solver = pe.SolverFactory('gurobi_persistent')
-    solver.options["threads"] = 4
-    solver.options["TimeLimit"] = 300
-    return solver
-
-
-def test_solver_factory():
-    solver = pe.SolverFactory('gurobi_persistent')
-    solver.options["threads"] = 4
-    solver.options["TimeLimit"] = 300
-    return solver
-
-
 def train():
     problem_name, challenge_name = args[""].split("/")
     pkg = importlib.import_module("miplearn.problems.%s" % problem_name)
     challenge = getattr(pkg, challenge_name)()
-    train_instances = challenge.training_instances
-    test_instances  = challenge.test_instances
-    solver = LearningSolver(
-        internal_solver_factory=train_solver_factory,
-        components={
-            "warm-start": WarmStartComponent(),
-            #"branch-priority": BranchPriorityComponent(),
-        },
-    )
-    solver.parallel_solve(train_instances, n_jobs=10)
-    solver.fit(n_jobs=10)
-    solver.save_state("%s/training_data.bin" % basepath)
+    train_instances = challenge.training_instances[:10]
+    test_instances  = challenge.test_instances[:10]
+    solver = LearningSolver(time_limit=time_limit, components={})
+    solver.parallel_solve(train_instances, n_jobs=n_jobs)
+    solver.fit(n_jobs=n_jobs)
     save(train_instances, "%s/train_instances.bin" % basepath)
     save(test_instances, "%s/test_instances.bin" % basepath)
     
@@ -82,39 +64,31 @@ def train():
 def test_baseline():
     solvers = {
         "baseline": LearningSolver(
-            internal_solver_factory=test_solver_factory,
+            time_limit=time_limit,
             components={},
         ),
     }
     test_instances = load("%s/test_instances.bin" % basepath)
     benchmark = BenchmarkRunner(solvers)
-    benchmark.parallel_solve(test_instances, n_jobs=10)
+    benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
     benchmark.save_results("%s/benchmark_baseline.csv" % basepath)
     
     
 def test_ml():
     solvers = {
         "ml-exact": LearningSolver(
-            internal_solver_factory=test_solver_factory,
-            components={
-                "warm-start": WarmStartComponent(),
-                #"branch-priority": BranchPriorityComponent(),
-            },
+            time_limit=time_limit,
         ),
         "ml-heuristic": LearningSolver(
-            internal_solver_factory=test_solver_factory,
+            time_limit=time_limit,
             mode="heuristic",
-            components={
-                "warm-start": WarmStartComponent(),
-                #"branch-priority": BranchPriorityComponent(),
-            },
         ),
     }
     test_instances = load("%s/test_instances.bin" % basepath)
     benchmark = BenchmarkRunner(solvers)
     benchmark.load_state("%s/training_data.bin" % basepath)
     benchmark.load_results("%s/benchmark_baseline.csv" % basepath)
-    benchmark.parallel_solve(test_instances, n_jobs=10)
+    benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
     benchmark.save_results("%s/benchmark_ml.csv" % basepath)    
     
     
@@ -178,9 +152,9 @@ def charts():
 if __name__ == "__main__":
     if args["train"]:
         train()
-    if args["test-baseline"]:
-        test_baseline()
-    if args["test-ml"]:
-        test_ml()
-    if args["charts"]:
-        charts()
\ No newline at end of file
+    #if args["test-baseline"]:
+    #    test_baseline()
+    #if args["test-ml"]:
+    #    test_ml()
+    #if args["charts"]:
+    #    charts()
\ No newline at end of file

From 10ad8e0a9e96381e68249f64439b0afdd9e3a452 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 22:30:26 -0600
Subject: [PATCH 30/44] Make Gurobi the default solver

---
 miplearn/solvers.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 1d5c0fb..54c0024 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -44,6 +44,7 @@ class InternalSolver():
                 solution[str(var)][index] = var[index].value
         return solution        
 
+    
 class GurobiSolver(InternalSolver):
     def __init__(self):
         self.solver = pe.SolverFactory('gurobi_persistent')
@@ -128,7 +129,7 @@ class LearningSolver:
                  components=None,
                  gap_tolerance=None,
                  mode="exact",
-                 solver="cplex",
+                 solver="gurobi",
                  threads=4,
                  time_limit=None,
                 ):

From b60afab5ef837d8a1d09fef1960f44be29929df4 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 22:40:54 -0600
Subject: [PATCH 31/44] Extract value of LP relaxation

---
 miplearn/solvers.py           | 14 ++++++++++++--
 miplearn/tests/test_solver.py |  5 +++--
 2 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 54c0024..1606d30 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -27,9 +27,12 @@ class InternalSolver():
             var.setub(ub)
             var.domain = Reals
         self.solver.set_instance(model)
-        self.solver.solve(tee=True)
+        results = self.solver.solve(tee=True)
         for var in model.component_data_objects(Var):
             var.domain = original_domain[str(var)]
+        return {
+            "Optimal value": results["Problem"][0]["Lower bound"],
+        }
             
     def clear_values(self, model):
         for var in model.component_objects(Var):
@@ -117,6 +120,9 @@ class CPLEXSolver(InternalSolver):
         lp.set_problem_type(cplex.Cplex.problem_type.LP)
         results = self.solver.solve(tee=tee)
         lp.variables.set_types(zip(range(n_vars), var_types))
+        return {
+            "Optimal value": results["Problem"][0]["Lower bound"],
+        }
         
 
 class LearningSolver:
@@ -177,8 +183,10 @@ class LearningSolver:
         self.internal_solver = self._create_internal_solver()
 
         # Solve LP relaxation
-        self.internal_solver.solve_lp(model, tee=tee)
+        results = self.internal_solver.solve_lp(model, tee=tee)
         instance.lp_solution = self.internal_solver.get_solution(model)
+        print(results)
+        instance.lp_value = results["Optimal value"]
         
         # Invoke before_solve callbacks
         for component in self.components.values():
@@ -226,6 +234,7 @@ class LearningSolver:
                 "Results": results,
                 "Solution": instance.solution,
                 "LP solution": instance.lp_solution,
+                "LP value": instance.lp_value,
                 "Upper bound": instance.upper_bound,
                 "Lower bound": instance.lower_bound,
             }
@@ -237,6 +246,7 @@ class LearningSolver:
         for (idx, r) in enumerate(p_map_results):
             instances[idx].solution = r["Solution"]
             instances[idx].lp_solution = r["LP solution"]
+            instances[idx].lp_value = r["LP value"]
             instances[idx].lower_bound = r["Lower bound"]
             instances[idx].upper_bound = r["Upper bound"]
         
diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py
index 5b37e2c..3726d4d 100644
--- a/miplearn/tests/test_solver.py
+++ b/miplearn/tests/test_solver.py
@@ -27,14 +27,15 @@ def test_solver():
         assert instance.solution["x"][1] == 0.0
         assert instance.solution["x"][2] == 1.0
         assert instance.solution["x"][3] == 1.0
+        assert instance.lower_bound == 1183.0
+        assert instance.upper_bound == 1183.0
         
         assert round(instance.lp_solution["x"][0], 3) == 1.000
         assert round(instance.lp_solution["x"][1], 3) == 0.923
         assert round(instance.lp_solution["x"][2], 3) == 1.000
         assert round(instance.lp_solution["x"][3], 3) == 0.000
+        assert round(instance.lp_value, 3) == 1287.923
         
-        assert instance.lower_bound == 1183.0
-        assert instance.upper_bound == 1183.0
         solver.fit()
         solver.solve(instance)
 

From 9e4004ae8a15d9e3922139ee1992131778032ded Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 22:45:30 -0600
Subject: [PATCH 32/44] Silence Gurobi

---
 miplearn/solvers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 1606d30..f1d7b82 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -51,6 +51,7 @@ class InternalSolver():
 class GurobiSolver(InternalSolver):
     def __init__(self):
         self.solver = pe.SolverFactory('gurobi_persistent')
+        self.solver.options["OutputFlag"] = 0
         self.solver.options["Seed"] = randint(low=0, high=1000).rvs()
     
     def set_threads(self, threads):
@@ -185,7 +186,6 @@ class LearningSolver:
         # Solve LP relaxation
         results = self.internal_solver.solve_lp(model, tee=tee)
         instance.lp_solution = self.internal_solver.get_solution(model)
-        print(results)
         instance.lp_value = results["Optimal value"]
         
         # Invoke before_solve callbacks

From 15ee7f51eeabea77059694e185373e9c8314b7e8 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sat, 22 Feb 2020 22:56:53 -0600
Subject: [PATCH 33/44] Update benchmark scripts

---
 benchmark/benchmark.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py
index d545e29..8aa3387 100755
--- a/benchmark/benchmark.py
+++ b/benchmark/benchmark.py
@@ -30,6 +30,7 @@ logging.getLogger('pyomo.core').setLevel(logging.ERROR)
 
 n_jobs = 10
 time_limit = 300
+internal_solver = "gurobi"
 
 args = docopt(__doc__)
 basepath = args[""]
@@ -52,9 +53,11 @@ def train():
     problem_name, challenge_name = args[""].split("/")
     pkg = importlib.import_module("miplearn.problems.%s" % problem_name)
     challenge = getattr(pkg, challenge_name)()
-    train_instances = challenge.training_instances[:10]
-    test_instances  = challenge.test_instances[:10]
-    solver = LearningSolver(time_limit=time_limit, components={})
+    train_instances = challenge.training_instances
+    test_instances  = challenge.test_instances
+    solver = LearningSolver(time_limit=time_limit,
+                            solver=internal_solver,
+                            components={})
     solver.parallel_solve(train_instances, n_jobs=n_jobs)
     solver.fit(n_jobs=n_jobs)
     save(train_instances, "%s/train_instances.bin" % basepath)
@@ -157,4 +160,4 @@ if __name__ == "__main__":
     #if args["test-ml"]:
     #    test_ml()
     #if args["charts"]:
-    #    charts()
\ No newline at end of file
+    #    charts()

From b428a4fc36f3e33061382aae7f5e8bac45abfe5a Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sun, 23 Feb 2020 12:21:55 -0600
Subject: [PATCH 34/44] Add option to solve root relaxation only

---
 miplearn/solvers.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index f1d7b82..2867344 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -176,7 +176,12 @@ class LearningSolver:
             solver.set_gap_tolerance(self.gap_tolerance)
         return solver
         
-    def solve(self, instance, model=None, tee=False):
+    def solve(self,
+              instance,
+              model=None,
+              tee=False,
+              relaxation_only=False,
+             ):
         if model is None:
             model = instance.to_model()
             
@@ -187,6 +192,8 @@ class LearningSolver:
         results = self.internal_solver.solve_lp(model, tee=tee)
         instance.lp_solution = self.internal_solver.get_solution(model)
         instance.lp_value = results["Optimal value"]
+        if relaxation_only:
+            return results
         
         # Invoke before_solve callbacks
         for component in self.components.values():

From 7de1db047f714374b3e5631a23b1d7ec606890d1 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sun, 23 Feb 2020 12:22:40 -0600
Subject: [PATCH 35/44] Implement InstanceFeaturesExtractor and
 ObjectiveValueExtractor

---
 miplearn/__init__.py              |  2 ++
 miplearn/extractors.py            | 25 +++++++++++++++++++++++++
 miplearn/tests/test_extractors.py | 31 ++++++++++++++++---------------
 3 files changed, 43 insertions(+), 15 deletions(-)

diff --git a/miplearn/__init__.py b/miplearn/__init__.py
index d7e6a9a..daf611e 100644
--- a/miplearn/__init__.py
+++ b/miplearn/__init__.py
@@ -12,6 +12,8 @@ from .components.branching import BranchPriorityComponent
 from .extractors import (UserFeaturesExtractor,
                          SolutionExtractor,
                          CombinedExtractor,
+                         InstanceFeaturesExtractor,
+                         ObjectiveValueExtractor,
                         )
 from .benchmark import BenchmarkRunner
 from .instance import Instance
diff --git a/miplearn/extractors.py b/miplearn/extractors.py
index 2803c7f..08cec37 100644
--- a/miplearn/extractors.py
+++ b/miplearn/extractors.py
@@ -103,3 +103,28 @@ class CombinedExtractor(Extractor):
     def extract(self, instances, models):
         return self.merge([ex.extract(instances, models)
                            for ex in self.extractors])
+
+    
+class InstanceFeaturesExtractor(Extractor):
+    def extract(self, instances, models=None):
+        return np.vstack([
+            np.hstack([
+                instance.get_instance_features(),
+                instance.lp_value,
+            ])
+            for instance in instances
+        ])
+    
+    
+class ObjectiveValueExtractor(Extractor):
+    def __init__(self, kind="lp"):
+        assert kind in ["lower bound", "upper bound", "lp"]
+        self.kind = kind
+        
+    def extract(self, instances, models=None):
+        if self.kind == "lower bound":
+            return np.array([[instance.lower_bound] for instance in instances])
+        if self.kind == "upper bound":
+            return np.array([[instance.upper_bound] for instance in instances])
+        if self.kind == "lp":
+            return np.array([[instance.lp_value] for instance in instances])
diff --git a/miplearn/tests/test_extractors.py b/miplearn/tests/test_extractors.py
index 915a602..9a172b3 100644
--- a/miplearn/tests/test_extractors.py
+++ b/miplearn/tests/test_extractors.py
@@ -7,13 +7,14 @@ from miplearn import (LearningSolver,
                       UserFeaturesExtractor,
                       SolutionExtractor,
                       CombinedExtractor,
+                      InstanceFeaturesExtractor
                      )
 import numpy as np
 import pyomo.environ as pe
 
 
 def _get_instances():
-    return [
+    instances = [
         KnapsackInstance(weights=[1., 2., 3.],
                          prices=[10., 20., 30.],
                          capacity=2.5,
@@ -23,10 +24,15 @@ def _get_instances():
                          capacity=4.5,
                         ),
     ]
+    models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for (i, instance) in enumerate(instances):
+        solver.solve(instances[i], models[i])
+    return instances, models
 
 
-def test_user_features():
-    instances = _get_instances()
+def test_user_features_extractor():
+    instances, models = _get_instances()
     extractor = UserFeaturesExtractor()
     features = extractor.extract(instances)
     assert isinstance(features, dict)
@@ -36,12 +42,7 @@ def test_user_features():
 
     
 def test_solution_extractor():
-    instances = _get_instances()
-    models = [instance.to_model() for instance in instances]
-    solver = LearningSolver()
-    for (i, instance) in enumerate(instances):
-        solver.solve(instances[i], models[i])
-    
+    instances, models = _get_instances()
     features = SolutionExtractor().extract(instances, models)
     assert isinstance(features, dict)
     assert "default" in features.keys()
@@ -58,12 +59,7 @@ def test_solution_extractor():
 
     
 def test_combined_extractor():
-    instances = _get_instances()
-    models = [instance.to_model() for instance in instances]
-    solver = LearningSolver()
-    for (i, instance) in enumerate(instances):
-        solver.solve(instances[i], models[i])
-    
+    instances, models = _get_instances()
     extractor = CombinedExtractor(extractors=[UserFeaturesExtractor(),
                                               SolutionExtractor()])
     features = extractor.extract(instances, models)
@@ -72,3 +68,8 @@ def test_combined_extractor():
     assert isinstance(features["default"], np.ndarray)
     assert features["default"].shape == (6, 6)
     
+    
+def test_instance_features_extractor():
+    instances, models = _get_instances()
+    features = InstanceFeaturesExtractor().extract(instances)
+    assert features.shape == (2,3)
\ No newline at end of file

From ccd694af9b44addf9d3b5edc3e0fd78de92cf5bf Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Sun, 23 Feb 2020 15:09:57 -0600
Subject: [PATCH 36/44] Implement ObjectiveValueComponent

---
 miplearn/__init__.py                        | 13 +++---
 miplearn/components/component.py            |  2 +-
 miplearn/components/objective.py            | 49 +++++++++++++++++++++
 miplearn/components/tests/test_objective.py | 29 ++++++++++++
 miplearn/components/tests/test_warmstart.py | 38 ++++++++--------
 miplearn/solvers.py                         | 24 +++++++---
 miplearn/tests/test_solver.py               |  5 +--
 7 files changed, 124 insertions(+), 36 deletions(-)
 create mode 100644 miplearn/components/objective.py
 create mode 100644 miplearn/components/tests/test_objective.py

diff --git a/miplearn/__init__.py b/miplearn/__init__.py
index daf611e..5308f7c 100644
--- a/miplearn/__init__.py
+++ b/miplearn/__init__.py
@@ -2,19 +2,20 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+from .extractors import (UserFeaturesExtractor,
+                         SolutionExtractor,
+                         CombinedExtractor,
+                         InstanceFeaturesExtractor,
+                         ObjectiveValueExtractor,
+                        )
 from .components.component import Component
+from .components.objective import ObjectiveValueComponent
 from .components.warmstart import (WarmStartComponent,
                                    KnnWarmStartPredictor,
                                    LogisticWarmStartPredictor,
                                    AdaptivePredictor,
                                   )
 from .components.branching import BranchPriorityComponent
-from .extractors import (UserFeaturesExtractor,
-                         SolutionExtractor,
-                         CombinedExtractor,
-                         InstanceFeaturesExtractor,
-                         ObjectiveValueExtractor,
-                        )
 from .benchmark import BenchmarkRunner
 from .instance import Instance
 from .solvers import LearningSolver
diff --git a/miplearn/components/component.py b/miplearn/components/component.py
index cb794c6..fe4ca6a 100644
--- a/miplearn/components/component.py
+++ b/miplearn/components/component.py
@@ -23,5 +23,5 @@ class Component(ABC):
         pass
     
     @abstractmethod
-    def fit(self, solver):
+    def fit(self, training_instances):
         pass
diff --git a/miplearn/components/objective.py b/miplearn/components/objective.py
new file mode 100644
index 0000000..ecf2f72
--- /dev/null
+++ b/miplearn/components/objective.py
@@ -0,0 +1,49 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from .. import Component, InstanceFeaturesExtractor, ObjectiveValueExtractor
+from sklearn.linear_model import LinearRegression
+from copy import deepcopy
+import numpy as np
+import logging
+logger = logging.getLogger(__name__)
+
+
+class ObjectiveValueComponent(Component):
+    """
+    A Component which predicts the optimal objective value of the problem.
+    """
+    def __init__(self,
+                 regressor=LinearRegression()):
+        self.ub_regressor = None
+        self.lb_regressor = None
+        self.regressor_prototype = regressor
+    
+    def before_solve(self, solver, instance, model):
+        if self.ub_regressor is not None:
+            lb, ub = self.predict([instance])[0]
+            instance.predicted_ub = ub
+            instance.predicted_lb = lb
+            logger.info("Predicted objective: [%.2f, %.2f]" % (lb, ub))
+    
+    def after_solve(self, solver, instance, model):
+        pass
+    
+    def merge(self, other):
+        pass
+    
+    def fit(self, training_instances):
+        features = InstanceFeaturesExtractor().extract(training_instances)
+        ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
+        lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
+        self.ub_regressor = deepcopy(self.regressor_prototype)
+        self.lb_regressor = deepcopy(self.regressor_prototype)
+        self.ub_regressor.fit(features, ub)
+        self.lb_regressor.fit(features, lb)
+        
+    def predict(self, instances):
+        features = InstanceFeaturesExtractor().extract(instances)
+        lb = self.lb_regressor.predict(features)
+        ub = self.ub_regressor.predict(features)
+        return np.hstack([lb, ub])
diff --git a/miplearn/components/tests/test_objective.py b/miplearn/components/tests/test_objective.py
new file mode 100644
index 0000000..380ede2
--- /dev/null
+++ b/miplearn/components/tests/test_objective.py
@@ -0,0 +1,29 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from miplearn import ObjectiveValueComponent, LearningSolver
+from miplearn.problems.knapsack import KnapsackInstance
+
+def _get_instances():
+    instances = [
+        KnapsackInstance(
+            weights=[23., 26., 20., 18.],
+            prices=[505., 352., 458., 220.],
+            capacity=67.,
+        ),
+    ]
+    models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for i in range(len(instances)):
+        solver.solve(instances[i], models[i])
+    return instances, models
+
+
+def test_usage():
+    instances, models = _get_instances()
+    comp = ObjectiveValueComponent()
+    comp.fit(instances)
+    assert instances[0].lower_bound == 1183.0
+    assert instances[0].upper_bound == 1183.0
+    assert comp.predict(instances).tolist() == [[1183.0, 1183.0]]
diff --git a/miplearn/components/tests/test_warmstart.py b/miplearn/components/tests/test_warmstart.py
index e842e25..d1bd522 100644
--- a/miplearn/components/tests/test_warmstart.py
+++ b/miplearn/components/tests/test_warmstart.py
@@ -18,24 +18,24 @@ def _get_instances():
     ] * 2
 
 
-def test_warm_start_save_load():
-    state_file = tempfile.NamedTemporaryFile(mode="r")
-    solver = LearningSolver(components={"warm-start": WarmStartComponent()})
-    solver.parallel_solve(_get_instances(), n_jobs=2)
-    solver.fit()
-    comp = solver.components["warm-start"]
-    assert comp.x_train["default"].shape == (8, 6)
-    assert comp.y_train["default"].shape == (8, 2)
-    assert ("default", 0) in comp.predictors.keys()
-    assert ("default", 1) in comp.predictors.keys()
-    solver.save_state(state_file.name)
+# def test_warm_start_save_load():
+#     state_file = tempfile.NamedTemporaryFile(mode="r")
+#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
+#     solver.parallel_solve(_get_instances(), n_jobs=2)
+#     solver.fit()
+#     comp = solver.components["warm-start"]
+#     assert comp.x_train["default"].shape == (8, 6)
+#     assert comp.y_train["default"].shape == (8, 2)
+#     assert ("default", 0) in comp.predictors.keys()
+#     assert ("default", 1) in comp.predictors.keys()
+#     solver.save_state(state_file.name)
     
-    solver.solve(_get_instances()[0])
+#     solver.solve(_get_instances()[0])
     
-    solver = LearningSolver(components={"warm-start": WarmStartComponent()})
-    solver.load_state(state_file.name)
-    comp = solver.components["warm-start"]
-    assert comp.x_train["default"].shape == (8, 6)
-    assert comp.y_train["default"].shape == (8, 2)
-    assert ("default", 0) in comp.predictors.keys()
-    assert ("default", 1) in comp.predictors.keys()
+#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
+#     solver.load_state(state_file.name)
+#     comp = solver.components["warm-start"]
+#     assert comp.x_train["default"].shape == (8, 6)
+#     assert comp.y_train["default"].shape == (8, 2)
+#     assert ("default", 0) in comp.predictors.keys()
+#     assert ("default", 1) in comp.predictors.keys()
diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 2867344..ef3d1e3 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -2,7 +2,7 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from . import WarmStartComponent, BranchPriorityComponent
+from . import WarmStartComponent, BranchPriorityComponent, ObjectiveValueComponent
 import pyomo.environ as pe
 from pyomo.core import Var
 from copy import deepcopy
@@ -51,7 +51,7 @@ class InternalSolver():
 class GurobiSolver(InternalSolver):
     def __init__(self):
         self.solver = pe.SolverFactory('gurobi_persistent')
-        self.solver.options["OutputFlag"] = 0
+        #self.solver.options["OutputFlag"] = 0
         self.solver.options["Seed"] = randint(low=0, high=1000).rvs()
     
     def set_threads(self, threads):
@@ -150,12 +150,14 @@ class LearningSolver:
         self.time_limit = time_limit
         self.gap_tolerance = gap_tolerance
         self.tee = False
+        self.training_instances = []
         
         if self.components is not None:
             assert isinstance(self.components, dict)
         else:
             self.components = {
-                "warm-start": WarmStartComponent(),
+                "obj-val": ObjectiveValueComponent(),
+                #"warm-start": WarmStartComponent(),
             }
             
         assert self.mode in ["exact", "heuristic"]
@@ -192,13 +194,14 @@ class LearningSolver:
         results = self.internal_solver.solve_lp(model, tee=tee)
         instance.lp_solution = self.internal_solver.get_solution(model)
         instance.lp_value = results["Optimal value"]
-        if relaxation_only:
-            return results
         
         # Invoke before_solve callbacks
         for component in self.components.values():
             component.before_solve(self, instance, model)
         
+        if relaxation_only:
+            return results
+        
         # Check if warm start is available
         is_warm_start_available = False
         if "warm-start" in self.components.keys():
@@ -219,6 +222,9 @@ class LearningSolver:
         # Invoke after_solve callbacks
         for component in self.components.values():
             component.after_solve(self, instance, model)
+            
+        # Store instance for future training
+        self.training_instances += [instance]
         
         return results
                 
@@ -265,9 +271,13 @@ class LearningSolver:
         
         return results
 
-    def fit(self, n_jobs=1):
+    def fit(self, training_instances=None):
+        if training_instances is None:
+            training_instances = self.training_instances
+        if len(training_instances) == 0:
+            return
         for component in self.components.values():
-            component.fit(self, n_jobs=n_jobs)
+            component.fit(training_instances)
             
     def save_state(self, filename):
         with open(filename, "wb") as file:
diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py
index 3726d4d..3707f18 100644
--- a/miplearn/tests/test_solver.py
+++ b/miplearn/tests/test_solver.py
@@ -67,9 +67,8 @@ def test_parallel_solve():
     solver = LearningSolver()
     results = solver.parallel_solve(instances, n_jobs=3)
     assert len(results) == 10
-    assert len(solver.components["warm-start"].x_train["default"]) == 40
-    assert len(solver.components["warm-start"].y_train["default"]) == 40
-    
+#     assert len(solver.components["warm-start"].x_train["default"]) == 40
+#     assert len(solver.components["warm-start"].y_train["default"]) == 40
     for instance in instances:
         assert len(instance.solution["x"].keys()) == 4
     

From a2fbb9f8d8fb0a46ba3e2ba60f9bb738dcca449b Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Mon, 24 Feb 2020 10:01:21 -0600
Subject: [PATCH 37/44] Implement PrimalSolutionComponent; remove deprecated
 predictors

---
 miplearn/__init__.py                          |  12 +-
 miplearn/benchmark.py                         |   4 +-
 miplearn/components/tests/test_warmstart.py   |  41 --
 .../components/tests/test_warmstart_knn.py    |  88 -----
 .../tests/test_warmstart_logistic.py          |  64 ---
 miplearn/components/warmstart.py              | 366 ------------------
 miplearn/extractors.py                        |   5 +-
 miplearn/problems/knapsack.py                 |   4 +-
 miplearn/solvers.py                           | 106 +++--
 miplearn/tests/test_benchmark.py              |   2 +-
 miplearn/tests/test_extractors.py             |  29 +-
 miplearn/tests/test_solver.py                 |  88 ++---
 12 files changed, 140 insertions(+), 669 deletions(-)
 delete mode 100644 miplearn/components/tests/test_warmstart.py
 delete mode 100644 miplearn/components/tests/test_warmstart_knn.py
 delete mode 100644 miplearn/components/tests/test_warmstart_logistic.py
 delete mode 100644 miplearn/components/warmstart.py

diff --git a/miplearn/__init__.py b/miplearn/__init__.py
index 5308f7c..8ee19d4 100644
--- a/miplearn/__init__.py
+++ b/miplearn/__init__.py
@@ -2,19 +2,17 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from .extractors import (UserFeaturesExtractor,
-                         SolutionExtractor,
+from .extractors import (SolutionExtractor,
                          CombinedExtractor,
                          InstanceFeaturesExtractor,
                          ObjectiveValueExtractor,
+                         VariableFeaturesExtractor,
                         )
 from .components.component import Component
 from .components.objective import ObjectiveValueComponent
-from .components.warmstart import (WarmStartComponent,
-                                   KnnWarmStartPredictor,
-                                   LogisticWarmStartPredictor,
-                                   AdaptivePredictor,
-                                  )
+from .components.primal import (PrimalSolutionComponent,
+                                AdaptivePredictor,
+                               )
 from .components.branching import BranchPriorityComponent
 from .benchmark import BenchmarkRunner
 from .instance import Instance
diff --git a/miplearn/benchmark.py b/miplearn/benchmark.py
index cfa0501..8525d53 100644
--- a/miplearn/benchmark.py
+++ b/miplearn/benchmark.py
@@ -46,9 +46,9 @@ class BenchmarkRunner:
         for (name, solver) in self.solvers.items():
             solver.load_state(filename)
 
-    def fit(self):
+    def fit(self, training_instances):
         for (name, solver) in self.solvers.items():
-            solver.fit()
+            solver.fit(training_instances)
             
     def _push_result(self, result, solver, name, instance):
         if self.results is None:
diff --git a/miplearn/components/tests/test_warmstart.py b/miplearn/components/tests/test_warmstart.py
deleted file mode 100644
index d1bd522..0000000
--- a/miplearn/components/tests/test_warmstart.py
+++ /dev/null
@@ -1,41 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-
-from miplearn import WarmStartComponent, LearningSolver
-from miplearn.problems.knapsack import KnapsackInstance
-import numpy as np
-import tempfile
-
-
-def _get_instances():
-    return [
-        KnapsackInstance(
-            weights=[23., 26., 20., 18.],
-            prices=[505., 352., 458., 220.],
-            capacity=67.,
-        ),
-    ] * 2
-
-
-# def test_warm_start_save_load():
-#     state_file = tempfile.NamedTemporaryFile(mode="r")
-#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
-#     solver.parallel_solve(_get_instances(), n_jobs=2)
-#     solver.fit()
-#     comp = solver.components["warm-start"]
-#     assert comp.x_train["default"].shape == (8, 6)
-#     assert comp.y_train["default"].shape == (8, 2)
-#     assert ("default", 0) in comp.predictors.keys()
-#     assert ("default", 1) in comp.predictors.keys()
-#     solver.save_state(state_file.name)
-    
-#     solver.solve(_get_instances()[0])
-    
-#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
-#     solver.load_state(state_file.name)
-#     comp = solver.components["warm-start"]
-#     assert comp.x_train["default"].shape == (8, 6)
-#     assert comp.y_train["default"].shape == (8, 2)
-#     assert ("default", 0) in comp.predictors.keys()
-#     assert ("default", 1) in comp.predictors.keys()
diff --git a/miplearn/components/tests/test_warmstart_knn.py b/miplearn/components/tests/test_warmstart_knn.py
deleted file mode 100644
index 4031c6d..0000000
--- a/miplearn/components/tests/test_warmstart_knn.py
+++ /dev/null
@@ -1,88 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-
-from miplearn import KnnWarmStartPredictor
-from sklearn.metrics import accuracy_score, precision_score
-import numpy as np
-
-
-def test_knn_with_consensus():
-    x_train = np.array([
-        [0.0, 0.0],
-        [0.1, 0.0],
-        [0.0, 0.1],
-        [1.0, 1.0],
-    ])
-    y_train = np.array([
-        [0., 1.],
-        [0., 1.],
-        [0., 1.],
-        [1., 0.],
-    ])
-    ws = KnnWarmStartPredictor(k=3, thr_clip=[0.75, 0.75])
-    ws.fit(x_train, y_train)
-    
-    x_test = np.array([[0.0, 0.0]])
-    y_test = np.array([[0, 1]])
-    assert (ws.predict(x_test) == y_test).all()
-    
-def test_knn_without_consensus():
-    x_train = np.array([
-        [0.0, 0.0],
-        [0.1, 0.1],
-        [0.9, 0.9],
-        [1.0, 1.0],
-    ])
-    y_train = np.array([
-        [0., 1.],
-        [0., 1.],
-        [1., 0.],
-        [1., 0.],
-    ])
-    ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
-    ws.fit(x_train, y_train)
-    
-    x_test = np.array([[0.5, 0.5]])
-    y_test = np.array([[0, 0]])
-    assert (ws.predict(x_test) == y_test).all()    
-    
-def test_knn_always_true():
-    x_train = np.array([
-        [0.0, 0.0],
-        [0.1, 0.1],
-        [0.9, 0.9],
-        [1.0, 1.0],
-    ])
-    y_train = np.array([
-        [1., 0.],
-        [1., 0.],
-        [1., 0.],
-        [1., 0.],
-    ])
-    ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
-    ws.fit(x_train, y_train)
-    
-    x_test = np.array([[0.5, 0.5]])
-    y_test = np.array([[1, 0]])
-    assert (ws.predict(x_test) == y_test).all()       
-    
-def test_knn_always_false():
-    x_train = np.array([
-        [0.0, 0.0],
-        [0.1, 0.1],
-        [0.9, 0.9],
-        [1.0, 1.0],
-    ])
-    y_train = np.array([
-        [0., 1.],
-        [0., 1.],
-        [0., 1.],
-        [0., 1.],
-    ])
-    ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
-    ws.fit(x_train, y_train)
-    
-    x_test = np.array([[0.5, 0.5]])
-    y_test = np.array([[0, 1]])
-    assert (ws.predict(x_test) == y_test).all()           
\ No newline at end of file
diff --git a/miplearn/components/tests/test_warmstart_logistic.py b/miplearn/components/tests/test_warmstart_logistic.py
deleted file mode 100644
index 23bada0..0000000
--- a/miplearn/components/tests/test_warmstart_logistic.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-
-from miplearn import LogisticWarmStartPredictor
-from sklearn.metrics import accuracy_score, precision_score
-import numpy as np
-
-
-def _generate_dataset(ground_truth, n_samples=10_000):
-    x_train = np.random.rand(n_samples,5)
-    x_test = np.random.rand(n_samples,5)
-    y_train = ground_truth(x_train)
-    y_test = ground_truth(x_test)
-    return x_train, y_train, x_test, y_test
-
-
-def _is_sum_greater_than_two(x):
-    y = (np.sum(x, axis=1) > 2.0).astype(int)
-    return np.vstack([y, 1 - y]).transpose()
-
-
-def _always_zero(x):
-    y = np.zeros((1, x.shape[0]))
-    return np.vstack([y, 1 - y]).transpose()
-
-
-def _random_values(x):
-    y = np.random.randint(2, size=x.shape[0])
-    return np.vstack([y, 1 - y]).transpose()
-    
-    
-def test_logistic_ws_with_balanced_labels():
-    x_train, y_train, x_test, y_test = _generate_dataset(_is_sum_greater_than_two)
-    ws = LogisticWarmStartPredictor()
-    ws.fit(x_train, y_train)
-    y_pred = ws.predict(x_test)
-    assert accuracy_score(y_test[:,0], y_pred[:,0]) > 0.99
-    assert accuracy_score(y_test[:,1], y_pred[:,1]) > 0.99
-    
-    
-def test_logistic_ws_with_unbalanced_labels():
-    x_train, y_train, x_test, y_test = _generate_dataset(_always_zero)
-    ws = LogisticWarmStartPredictor()
-    ws.fit(x_train, y_train)
-    y_pred = ws.predict(x_test)
-    assert accuracy_score(y_test[:,0], y_pred[:,0]) == 1.0
-    assert accuracy_score(y_test[:,1], y_pred[:,1]) == 1.0
-
-    
-def test_logistic_ws_with_unpredictable_labels():
-    x_train, y_train, x_test, y_test = _generate_dataset(_random_values)
-    ws = LogisticWarmStartPredictor()
-    ws.fit(x_train, y_train)
-    y_pred = ws.predict(x_test)
-    assert np.sum(y_pred) == 0
-
-    
-def test_logistic_ws_with_small_sample_size():
-    x_train, y_train, x_test, y_test = _generate_dataset(_random_values, n_samples=3)
-    ws = LogisticWarmStartPredictor()
-    ws.fit(x_train, y_train)
-    y_pred = ws.predict(x_test)
-    assert np.sum(y_pred) == 0
diff --git a/miplearn/components/warmstart.py b/miplearn/components/warmstart.py
deleted file mode 100644
index ed0a18c..0000000
--- a/miplearn/components/warmstart.py
+++ /dev/null
@@ -1,366 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-
-from .component import Component
-from ..extractors import *
-
-from abc import ABC, abstractmethod
-from copy import deepcopy
-import numpy as np
-from sklearn.pipeline import make_pipeline
-from sklearn.linear_model import LogisticRegression
-from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import cross_val_score
-from sklearn.metrics import roc_curve
-from sklearn.neighbors import KNeighborsClassifier
-from tqdm.auto import tqdm
-import pyomo.environ as pe
-import logging
-logger = logging.getLogger(__name__)
-
-
-class AdaptivePredictor:
-    def __init__(self,
-                 predictor=None,
-                 min_samples_predict=1,
-                 min_samples_cv=100,
-                 thr_fix=0.999,
-                 thr_alpha=0.50,
-                 thr_balance=1.0,
-                ):
-        self.min_samples_predict = min_samples_predict
-        self.min_samples_cv = min_samples_cv
-        self.thr_fix = thr_fix
-        self.thr_alpha = thr_alpha
-        self.thr_balance = thr_balance
-        self.predictor_factory = predictor
-        
-    def fit(self, x_train, y_train):
-        n_samples = x_train.shape[0]
-        
-        # If number of samples is too small, don't predict anything.
-        if n_samples < self.min_samples_predict:
-            logger.debug("    Too few samples (%d); always predicting false" % n_samples)
-            self.predictor = 0
-            return
-        
-        # If vast majority of observations are false, always return false.
-        y_train_avg = np.average(y_train)
-        if y_train_avg <= 1.0 - self.thr_fix:
-            logger.debug("    Most samples are negative (%.3f); always returning false" % y_train_avg)
-            self.predictor = 0
-            return
-        
-        # If vast majority of observations are true, always return true.
-        if y_train_avg >= self.thr_fix:
-            logger.debug("    Most samples are positive (%.3f); always returning true" % y_train_avg)
-            self.predictor = 1
-            return
-        
-        # If classes are too unbalanced, don't predict anything.
-        if y_train_avg < (1 - self.thr_balance) or y_train_avg > self.thr_balance:
-            logger.debug("    Classes are too unbalanced (%.3f); always returning false" % y_train_avg)
-            self.predictor = 0
-            return
-
-        # Select ML model if none is provided
-        if self.predictor_factory is None:
-            if n_samples < 30:
-                self.predictor_factory = KNeighborsClassifier(n_neighbors=n_samples)
-            else:
-                self.predictor_factory = make_pipeline(StandardScaler(), LogisticRegression())
-        
-        # Create predictor
-        if callable(self.predictor_factory):
-            pred = self.predictor_factory()
-        else:
-            pred = deepcopy(self.predictor_factory)
-        
-        # Skip cross-validation if number of samples is too small
-        if n_samples < self.min_samples_cv:
-            logger.debug("    Too few samples (%d); skipping cross validation" % n_samples)
-            self.predictor = pred
-            self.predictor.fit(x_train, y_train)
-            return
-        
-        # Calculate cross-validation score
-        cv_score = np.mean(cross_val_score(pred, x_train, y_train, cv=5))
-        dummy_score = max(y_train_avg, 1 - y_train_avg)
-        cv_thr = 1. * self.thr_alpha + dummy_score * (1 - self.thr_alpha)
-
-        # If cross-validation score is too low, don't predict anything.
-        if cv_score < cv_thr:
-            logger.debug("    Score is too low (%.3f < %.3f); always returning false" % (cv_score, cv_thr))
-            self.predictor = 0
-        else:
-            logger.debug("    Score is acceptable (%.3f > %.3f); training classifier" % (cv_score, cv_thr))
-            self.predictor = pred
-            self.predictor.fit(x_train, y_train)
-        
-    def predict_proba(self, x_test):
-        if isinstance(self.predictor, int):
-            y_pred = np.zeros((x_test.shape[0], 2))
-            y_pred[:, self.predictor] = 1.0
-            return y_pred
-        else:
-            return self.predictor.predict_proba(x_test)
-
-        
-class WarmStartComponent(Component):
-    def __init__(self,
-                 predictor=AdaptivePredictor(),
-                 mode="exact",
-                 max_fpr=[0.01, 0.01],
-                 min_threshold=[0.75, 0.75],
-                 dynamic_thresholds=False,
-                ):
-        self.mode = mode
-        self.x_train = {}
-        self.y_train = {}
-        self.predictors = {}
-        self.is_warm_start_available = False
-        self.max_fpr = max_fpr
-        self.min_threshold = min_threshold
-        self.thresholds = {}
-        self.predictor_factory = predictor
-        self.dynamic_thresholds = dynamic_thresholds
-        
-    
-    def before_solve(self, solver, instance, model):
-        # Build x_test
-        x_test = CombinedExtractor([UserFeaturesExtractor(),
-                                    SolutionExtractor(relaxation=True),
-                                   ]).extract([instance], [model])
-        
-        # Update self.x_train
-        self.x_train = Extractor.merge([self.x_train, x_test],
-                                       vertical=True)
-        
-        # Predict solutions
-        count_total, count_fixed = 0, 0
-        var_split = Extractor.split_variables(instance, model)
-        for category in var_split.keys():
-            var_index_pairs = var_split[category]
-            
-            # Clear current values
-            for i in range(len(var_index_pairs)):
-                var, index = var_index_pairs[i]
-                var[index].value = None
-            
-            # Make predictions
-            for label in [0,1]:
-                if (category, label) not in self.predictors.keys():
-                    continue
-                ws = self.predictors[category, label].predict_proba(x_test[category])
-                assert ws.shape == (len(var_index_pairs), 2)
-                for i in range(len(var_index_pairs)):
-                    count_total += 1
-                    var, index = var_index_pairs[i]
-                    logger.debug("%s[%s] ws=%.6f threshold=%.6f" % (var, index, ws[i, 1], self.thresholds[category, label]))
-                    if ws[i, 1] > self.thresholds[category, label]:
-                        logger.debug("Setting %s[%s] to %d" % (var, index, label))
-                        count_fixed += 1
-                        if self.mode == "heuristic":
-                            var[index].fix(label)
-                            if solver.is_persistent:
-                                solver.internal_solver.update_var(var[index])
-                        else:
-                            var[index].value = label
-                            self.is_warm_start_available = True
-                            
-            # Clear current values
-            for i in range(len(var_index_pairs)):
-                var, index = var_index_pairs[i]
-                if var[index].value is None:
-                    logger.debug("Variable %s[%s] not set" % (var, index))
-                else:
-                    logger.debug("Varible %s[%s] set to %.2f" % (var, index, var[index].value))
-
-                
-        logger.info("Setting values for %d variables (out of %d)" % (count_fixed, count_total // 2))
-
-
-    def after_solve(self, solver, instance, model):
-        y_test = SolutionExtractor().extract([instance], [model])
-        self.y_train = Extractor.merge([self.y_train, y_test], vertical=True)        
-                
-    def fit(self, solver, n_jobs=1):
-        for category in tqdm(self.x_train.keys(), desc="Fit (warm start)"):
-            x_train = self.x_train[category]
-            y_train = self.y_train[category]
-            for label in [0, 1]:
-                logger.debug("Fitting predictors[%s, %s]:" % (category, label))
-                
-                if callable(self.predictor_factory):
-                    pred = self.predictor_factory(category, label)
-                else:
-                    pred = deepcopy(self.predictor_factory)
-                self.predictors[category, label] = pred
-                y = y_train[:, label].astype(int)
-                pred.fit(x_train, y)
-
-                # If y is either always one or always zero, set fixed threshold
-                y_avg = np.average(y)
-                if (not self.dynamic_thresholds) or y_avg <= 0.001 or y_avg >= 0.999:
-                    self.thresholds[category, label] = self.min_threshold[label]
-                    logger.debug("    Setting threshold to %.4f" % self.min_threshold[label])
-                    continue
-                
-                # Calculate threshold dynamically using ROC curve
-                y_scores = pred.predict_proba(x_train)[:, 1]
-                fpr, tpr, thresholds = roc_curve(y, y_scores)
-                k = 0
-                while True:
-                    if (k + 1) > len(fpr):
-                        break
-                    if fpr[k + 1] > self.max_fpr[label]:
-                        break
-                    if thresholds[k + 1] < self.min_threshold[label]:
-                        break
-                    k = k + 1
-                logger.debug("    Setting threshold to %.4f (fpr=%.4f, tpr=%.4f)" % (thresholds[k], fpr[k], tpr[k]))
-                self.thresholds[category, label] = thresholds[k]
-                
-
-    def merge(self, other_components):
-        # Merge x_train and y_train
-        keys = set(self.x_train.keys())
-        for comp in other_components:
-            keys = keys.union(set(comp.x_train.keys()))
-        for key in keys:
-            x_train_submatrices = [comp.x_train[key]
-                                   for comp in other_components
-                                   if key in comp.x_train.keys()]
-            y_train_submatrices = [comp.y_train[key]
-                                   for comp in other_components
-                                   if key in comp.y_train.keys()]
-            if key in self.x_train.keys():
-                x_train_submatrices += [self.x_train[key]]
-                y_train_submatrices += [self.y_train[key]]
-            self.x_train[key] = np.vstack(x_train_submatrices)
-            self.y_train[key] = np.vstack(y_train_submatrices)
-
-        # Merge trained predictors
-        for comp in other_components:
-            for key in comp.predictors.keys():
-                if key not in self.predictors.keys():
-                    self.predictors[key] = comp.predictors[key]
-                    self.thresholds[key] = comp.thresholds[key]
-
-                    
-# Deprecated               
-class WarmStartPredictor(ABC):
-    def __init__(self, thr_clip=[0.50, 0.50]):
-        self.models = [None, None]
-        self.thr_clip = thr_clip
-        
-    def fit(self, x_train, y_train):
-        assert isinstance(x_train, np.ndarray)
-        assert isinstance(y_train, np.ndarray)
-        y_train = y_train.astype(int)
-        assert y_train.shape[0] == x_train.shape[0]
-        assert y_train.shape[1] == 2
-        for i in [0,1]:
-            self.models[i] = self._fit(x_train, y_train[:, i],  i)
-
-    def predict(self, x_test):
-        assert isinstance(x_test, np.ndarray)
-        y_pred = np.zeros((x_test.shape[0], 2))
-        for i in [0,1]:
-            if isinstance(self.models[i], int):
-                y_pred[:, i] = self.models[i]
-            else:
-                y = self.models[i].predict_proba(x_test)[:,1]
-                y[y < self.thr_clip[i]] = 0.
-                y[y > 0.] = 1.
-                y_pred[:, i] = y
-        return y_pred.astype(int)
-
-    @abstractmethod
-    def _fit(self, x_train, y_train, label):
-        pass
-
-    
-# Deprecated               
-class LogisticWarmStartPredictor(WarmStartPredictor):
-    def __init__(self,
-                 min_samples=100,
-                 thr_fix=[0.99, 0.99],
-                 thr_balance=[0.80, 0.80],
-                 thr_alpha=[0.50, 0.50],
-                ):
-        super().__init__()
-        self.min_samples = min_samples
-        self.thr_fix = thr_fix
-        self.thr_balance = thr_balance
-        self.thr_alpha = thr_alpha
-
-    def _fit(self, x_train, y_train, label):
-        y_train_avg = np.average(y_train)
-
-        # If number of samples is too small, don't predict anything.
-        if x_train.shape[0] < self.min_samples:
-            return 0
-        
-        # If vast majority of observations are true, always return true.
-        if y_train_avg > self.thr_fix[label]:
-            return 1
-        
-        # If dataset is not balanced enough, don't predict anything.
-        if y_train_avg < (1 - self.thr_balance[label]) or y_train_avg > self.thr_balance[label]:
-            return 0
-            
-        reg = make_pipeline(StandardScaler(), LogisticRegression())
-        reg_score = np.mean(cross_val_score(reg, x_train, y_train, cv=5))
-        dummy_score = max(y_train_avg, 1 - y_train_avg)
-        reg_thr = 1. * self.thr_alpha[label] + dummy_score * (1 - self.thr_alpha[label])
-
-        # If cross-validation score is too low, don't predict anything.
-        if reg_score < reg_thr:
-            return 0
-        
-        reg.fit(x_train, y_train.astype(int))
-        return reg
-    
-    
-# Deprecated
-class KnnWarmStartPredictor(WarmStartPredictor):
-    def __init__(self,
-                 k=50,
-                 min_samples=1,
-                 thr_clip=[0.80, 0.80],
-                 thr_fix=[1.0, 1.0],
-                ):
-        super().__init__(thr_clip=thr_clip)
-        self.k = k
-        self.thr_fix = thr_fix
-        self.min_samples = min_samples
-
-    def _fit(self, x_train, y_train, label):
-        y_train_avg = np.average(y_train)
-
-        # If number of training samples is too small, don't predict anything.
-        if x_train.shape[0] < self.min_samples:
-            logger.debug("Too few samples; return 0")
-            return 0
-        
-        # If vast majority of observations are true, always return true.
-        if y_train_avg >= self.thr_fix[label]:
-            logger.debug("Consensus reached; return 1")
-            return 1
-        
-        # If vast majority of observations are false, always return false.
-        if y_train_avg <= (1 - self.thr_fix[label]):
-            logger.debug("Consensus reached; return 0")
-            return 0
-        
-        logger.debug("Training classifier...")
-        k = min(self.k, x_train.shape[0])
-        knn = KNeighborsClassifier(n_neighbors=k)
-        knn.fit(x_train, y_train)
-        return knn
-    
-    
-    
-                
\ No newline at end of file
diff --git a/miplearn/extractors.py b/miplearn/extractors.py
index 08cec37..f966250 100644
--- a/miplearn/extractors.py
+++ b/miplearn/extractors.py
@@ -42,8 +42,8 @@ class Extractor(ABC):
                 results[category] = np.hstack(results[category])
         return results
 
-
-class UserFeaturesExtractor(Extractor):
+    
+class VariableFeaturesExtractor(Extractor):
     def extract(self,
                 instances,
                 models=None,
@@ -62,6 +62,7 @@ class UserFeaturesExtractor(Extractor):
                     result[category] += [np.hstack([
                         instance_features,
                         instance.get_variable_features(var, index),
+                        instance.lp_solution[str(var)][index],
                     ])]
         for category in result.keys():
             result[category] = np.vstack(result[category])
diff --git a/miplearn/problems/knapsack.py b/miplearn/problems/knapsack.py
index 5fb9415..78576c7 100644
--- a/miplearn/problems/knapsack.py
+++ b/miplearn/problems/knapsack.py
@@ -91,8 +91,8 @@ class MultiKnapsackInstance(Instance):
             self.weights[:, index],
         ])
 
-    def get_variable_category(self, var, index):
-        return index
+#     def get_variable_category(self, var, index):
+#         return index
 
 
 class MultiKnapsackGenerator:
diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index ef3d1e3..7ff976c 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -2,7 +2,7 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from . import WarmStartComponent, BranchPriorityComponent, ObjectiveValueComponent
+from . import ObjectiveValueComponent, PrimalSolutionComponent
 import pyomo.environ as pe
 from pyomo.core import Var
 from copy import deepcopy
@@ -13,45 +13,87 @@ import logging
 logger = logging.getLogger(__name__)
 
 
-class InternalSolver():
-    def __init__():
+class InternalSolver:
+    def __init__(self):
+        self.is_warm_start_available = False
+        self.model = None
         pass
     
-    def solve_lp(self, model, tee=False):
+    def solve_lp(self, tee=False):
+        # Relax domain
         from pyomo.core.base.set_types import Reals
         original_domain = {}
-        for var in model.component_data_objects(Var):
+        for var in self.model.component_data_objects(Var):
             original_domain[str(var)] = var.domain
             lb, ub = var.bounds
             var.setlb(lb)
             var.setub(ub)
             var.domain = Reals
-        self.solver.set_instance(model)
-        results = self.solver.solve(tee=True)
-        for var in model.component_data_objects(Var):
+        
+        # Solve LP relaxation
+        self.solver.set_instance(self.model)
+        results = self.solver.solve(tee=tee)
+        
+        # Restore domains
+        for var in self.model.component_data_objects(Var):
             var.domain = original_domain[str(var)]
+            
+        # Reload original model
+        self.solver.set_instance(self.model)
+        
         return {
             "Optimal value": results["Problem"][0]["Lower bound"],
         }
             
-    def clear_values(self, model):
-        for var in model.component_objects(Var):
+    def clear_values(self):
+        for var in self.model.component_objects(Var):
             for index in var:
                 var[index].value = None
                 
-    def get_solution(self, model):
+    def get_solution(self):
         solution = {}
-        for var in model.component_objects(Var):
+        for var in self.model.component_objects(Var):
             solution[str(var)] = {}
             for index in var:
                 solution[str(var)][index] = var[index].value
-        return solution        
+        return solution   
+    
+    def set_warm_start(self, ws):
+        self.is_warm_start_available = True
+        self.clear_values()
+        count_total, count_fixed = 0, 0
+        for var in ws.keys():
+            for index in var:
+                count_total += 1
+                var[index].value = ws[var][index]
+                if ws[var][index] is not None:
+                    count_fixed += 1
+        logger.info("Setting start values for %d variables (out of %d)" %
+                    (count_fixed, count_total))
+
+                
+    def set_model(self, model):
+        self.model = model
+        self.solver.set_instance(model)
+        
+    def fix(self, ws):
+        count_total, count_fixed = 0, 0
+        for var in ws.keys():
+            for index in var:
+                count_total += 1
+                if ws[var][index] is None:
+                    continue
+                count_fixed += 1
+                var[index].fix(ws[var][index])
+                self.solver.update_var(var[index])        
+        logger.info("Fixing values for %d variables (out of %d)" %
+                    (count_fixed, count_total))
 
     
 class GurobiSolver(InternalSolver):
     def __init__(self):
+        super().__init__()
         self.solver = pe.SolverFactory('gurobi_persistent')
-        #self.solver.options["OutputFlag"] = 0
         self.solver.options["Seed"] = randint(low=0, high=1000).rvs()
     
     def set_threads(self, threads):
@@ -63,9 +105,8 @@ class GurobiSolver(InternalSolver):
     def set_gap_tolerance(self, gap_tolerance):
         self.solver.options["MIPGap"] = gap_tolerance
         
-    def solve(self, model, tee=False, warmstart=False):
-        self.solver.set_instance(model)
-        results = self.solver.solve(tee=tee, warmstart=warmstart)
+    def solve(self, tee=False):
+        results = self.solver.solve(tee=tee, warmstart=self.is_warm_start_available)
         return {
             "Lower bound": results["Problem"][0]["Lower bound"],
             "Upper bound": results["Problem"][0]["Upper bound"],
@@ -89,6 +130,7 @@ class GurobiSolver(InternalSolver):
     
 class CPLEXSolver(InternalSolver):
     def __init__(self):
+        super().__init__()
         import cplex
         self.solver = pe.SolverFactory('cplex_persistent')
         self.solver.options["randomseed"] = randint(low=0, high=1000).rvs()
@@ -102,9 +144,8 @@ class CPLEXSolver(InternalSolver):
     def set_gap_tolerance(self, gap_tolerance):
         self.solver.options["mip_tolerances_mipgap"] = gap_tolerance
         
-    def solve(self, model, tee=False, warmstart=False):
-        self.solver.set_instance(model)
-        results = self.solver.solve(tee=tee, warmstart=warmstart)
+    def solve(self, tee=False):
+        results = self.solver.solve(tee=tee, warmstart=self.is_warm_start_available)
         return {
             "Lower bound": results["Problem"][0]["Lower bound"],
             "Upper bound": results["Problem"][0]["Upper bound"],
@@ -112,9 +153,8 @@ class CPLEXSolver(InternalSolver):
             "Nodes": 1,
         }
     
-    def solve_lp(self, model, tee=False):
+    def solve_lp(self, tee=False):
         import cplex
-        self.solver.set_instance(model)
         lp = self.solver._solver_model
         var_types = lp.variables.get_types()
         n_vars = len(var_types)
@@ -156,8 +196,8 @@ class LearningSolver:
             assert isinstance(self.components, dict)
         else:
             self.components = {
-                "obj-val": ObjectiveValueComponent(),
-                #"warm-start": WarmStartComponent(),
+                "ObjectiveValue": ObjectiveValueComponent(),
+                "PrimalSolution": PrimalSolutionComponent(),
             }
             
         assert self.mode in ["exact", "heuristic"]
@@ -189,10 +229,11 @@ class LearningSolver:
             
         self.tee = tee
         self.internal_solver = self._create_internal_solver()
+        self.internal_solver.set_model(model)
 
         # Solve LP relaxation
-        results = self.internal_solver.solve_lp(model, tee=tee)
-        instance.lp_solution = self.internal_solver.get_solution(model)
+        results = self.internal_solver.solve_lp(tee=tee)
+        instance.lp_solution = self.internal_solver.get_solution()
         instance.lp_value = results["Optimal value"]
         
         # Invoke before_solve callbacks
@@ -202,22 +243,13 @@ class LearningSolver:
         if relaxation_only:
             return results
         
-        # Check if warm start is available
-        is_warm_start_available = False
-        if "warm-start" in self.components.keys():
-            if self.components["warm-start"].is_warm_start_available:
-                is_warm_start_available = True
-        
         # Solver original MIP
-        self.internal_solver.clear_values(model)
-        results = self.internal_solver.solve(model,
-                                             tee=tee,
-                                             warmstart=is_warm_start_available)
+        results = self.internal_solver.solve(tee=tee)
         
         # Read MIP solution and bounds
         instance.lower_bound = results["Lower bound"]
         instance.upper_bound = results["Upper bound"]
-        instance.solution = self.internal_solver.get_solution(model)
+        instance.solution = self.internal_solver.get_solution()
         
         # Invoke after_solve callbacks
         for component in self.components.values():
diff --git a/miplearn/tests/test_benchmark.py b/miplearn/tests/test_benchmark.py
index caa0f88..094eee4 100644
--- a/miplearn/tests/test_benchmark.py
+++ b/miplearn/tests/test_benchmark.py
@@ -2,7 +2,7 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from miplearn import LearningSolver, BenchmarkRunner, KnnWarmStartPredictor
+from miplearn import LearningSolver, BenchmarkRunner
 from miplearn.problems.stab import MaxWeightStableSetGenerator
 from scipy.stats import randint
 import numpy as np
diff --git a/miplearn/tests/test_extractors.py b/miplearn/tests/test_extractors.py
index 9a172b3..eb57df7 100644
--- a/miplearn/tests/test_extractors.py
+++ b/miplearn/tests/test_extractors.py
@@ -4,10 +4,10 @@
 
 from miplearn.problems.knapsack import KnapsackInstance
 from miplearn import (LearningSolver,
-                      UserFeaturesExtractor,
                       SolutionExtractor,
                       CombinedExtractor,
-                      InstanceFeaturesExtractor
+                      InstanceFeaturesExtractor,
+                      VariableFeaturesExtractor,
                      )
 import numpy as np
 import pyomo.environ as pe
@@ -31,16 +31,6 @@ def _get_instances():
     return instances, models
 
 
-def test_user_features_extractor():
-    instances, models = _get_instances()
-    extractor = UserFeaturesExtractor()
-    features = extractor.extract(instances)
-    assert isinstance(features, dict)
-    assert "default" in features.keys()
-    assert isinstance(features["default"], np.ndarray)
-    assert features["default"].shape == (6, 4)
-
-    
 def test_solution_extractor():
     instances, models = _get_instances()
     features = SolutionExtractor().extract(instances, models)
@@ -60,16 +50,25 @@ def test_solution_extractor():
     
 def test_combined_extractor():
     instances, models = _get_instances()
-    extractor = CombinedExtractor(extractors=[UserFeaturesExtractor(),
+    extractor = CombinedExtractor(extractors=[VariableFeaturesExtractor(),
                                               SolutionExtractor()])
     features = extractor.extract(instances, models)
     assert isinstance(features, dict)
     assert "default" in features.keys()
     assert isinstance(features["default"], np.ndarray)
-    assert features["default"].shape == (6, 6)
+    assert features["default"].shape == (6, 7)
     
     
 def test_instance_features_extractor():
     instances, models = _get_instances()
     features = InstanceFeaturesExtractor().extract(instances)
-    assert features.shape == (2,3)
\ No newline at end of file
+    assert features.shape == (2,3)
+    
+    
+def test_variable_features_extractor():
+    instances, models = _get_instances()
+    features = VariableFeaturesExtractor().extract(instances)
+    assert isinstance(features, dict)
+    assert "default" in features
+    assert features["default"].shape == (6,5)
+    
\ No newline at end of file
diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py
index 3707f18..c8b662e 100644
--- a/miplearn/tests/test_solver.py
+++ b/miplearn/tests/test_solver.py
@@ -2,7 +2,7 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from miplearn import LearningSolver, BranchPriorityComponent, WarmStartComponent
+from miplearn import LearningSolver, BranchPriorityComponent
 from miplearn.problems.knapsack import KnapsackInstance
 
 
@@ -16,50 +16,52 @@ def _get_instance():
 
 def test_solver():
     instance = _get_instance()
-    for internal_solver in ["cplex", "gurobi"]:
-        solver = LearningSolver(time_limit=300,
-                                gap_tolerance=1e-3,
-                                threads=1,
-                                solver=internal_solver,
-                               )
-        results = solver.solve(instance)
-        assert instance.solution["x"][0] == 1.0
-        assert instance.solution["x"][1] == 0.0
-        assert instance.solution["x"][2] == 1.0
-        assert instance.solution["x"][3] == 1.0
-        assert instance.lower_bound == 1183.0
-        assert instance.upper_bound == 1183.0
-        
-        assert round(instance.lp_solution["x"][0], 3) == 1.000
-        assert round(instance.lp_solution["x"][1], 3) == 0.923
-        assert round(instance.lp_solution["x"][2], 3) == 1.000
-        assert round(instance.lp_solution["x"][3], 3) == 0.000
-        assert round(instance.lp_value, 3) == 1287.923
-        
-        solver.fit()
-        solver.solve(instance)
+    for mode in ["exact", "heuristic"]:
+        for internal_solver in ["cplex", "gurobi"]:
+            solver = LearningSolver(time_limit=300,
+                                    gap_tolerance=1e-3,
+                                    threads=1,
+                                    solver=internal_solver,
+                                    mode=mode,
+                                   )
+            results = solver.solve(instance)
+            assert instance.solution["x"][0] == 1.0
+            assert instance.solution["x"][1] == 0.0
+            assert instance.solution["x"][2] == 1.0
+            assert instance.solution["x"][3] == 1.0
+            assert instance.lower_bound == 1183.0
+            assert instance.upper_bound == 1183.0
 
+            assert round(instance.lp_solution["x"][0], 3) == 1.000
+            assert round(instance.lp_solution["x"][1], 3) == 0.923
+            assert round(instance.lp_solution["x"][2], 3) == 1.000
+            assert round(instance.lp_solution["x"][3], 3) == 0.000
+            assert round(instance.lp_value, 3) == 1287.923
 
-def test_solve_save_load_state():
-    instance = _get_instance()
-    components_before = {
-        "warm-start": WarmStartComponent(),
-    }
-    solver = LearningSolver(components=components_before)
-    solver.solve(instance)
-    solver.fit()
-    solver.save_state("/tmp/knapsack_train.bin")
-    prev_x_train_len = len(solver.components["warm-start"].x_train)
-    prev_y_train_len = len(solver.components["warm-start"].y_train)
+            solver.fit()
+            solver.solve(instance)
+
+
+# def test_solve_save_load_state():
+#     instance = _get_instance()
+#     components_before = {
+#         "warm-start": WarmStartComponent(),
+#     }
+#     solver = LearningSolver(components=components_before)
+#     solver.solve(instance)
+#     solver.fit()
+#     solver.save_state("/tmp/knapsack_train.bin")
+#     prev_x_train_len = len(solver.components["warm-start"].x_train)
+#     prev_y_train_len = len(solver.components["warm-start"].y_train)
     
-    components_after = {
-        "warm-start": WarmStartComponent(),
-    }
-    solver = LearningSolver(components=components_after)
-    solver.load_state("/tmp/knapsack_train.bin")
-    assert len(solver.components.keys()) == 1
-    assert len(solver.components["warm-start"].x_train) == prev_x_train_len
-    assert len(solver.components["warm-start"].y_train) == prev_y_train_len
+#     components_after = {
+#         "warm-start": WarmStartComponent(),
+#     }
+#     solver = LearningSolver(components=components_after)
+#     solver.load_state("/tmp/knapsack_train.bin")
+#     assert len(solver.components.keys()) == 1
+#     assert len(solver.components["warm-start"].x_train) == prev_x_train_len
+#     assert len(solver.components["warm-start"].y_train) == prev_y_train_len
 
 
 def test_parallel_solve():
@@ -67,8 +69,6 @@ def test_parallel_solve():
     solver = LearningSolver()
     results = solver.parallel_solve(instances, n_jobs=3)
     assert len(results) == 10
-#     assert len(solver.components["warm-start"].x_train["default"]) == 40
-#     assert len(solver.components["warm-start"].y_train["default"]) == 40
     for instance in instances:
         assert len(instance.solution["x"].keys()) == 4
     

From bd1d2117c52b80bf54c1820ef1423c2dee5aa562 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Mon, 24 Feb 2020 10:01:56 -0600
Subject: [PATCH 38/44] Update benchmark scripts

---
 benchmark/Makefile     |  2 +-
 benchmark/benchmark.py | 31 ++++++++++++++-----------------
 2 files changed, 15 insertions(+), 18 deletions(-)

diff --git a/benchmark/Makefile b/benchmark/Makefile
index ced75b5..eb4e224 100644
--- a/benchmark/Makefile
+++ b/benchmark/Makefile
@@ -12,7 +12,7 @@ main: $(addsuffix /performance.png, $(CHALLENGES))
 %/train_instances.bin:
 	python benchmark.py train $*
 
-%/benchmark_baseline.csv: %/training_data.bin
+%/benchmark_baseline.csv: %/train_instances.bin
 	python benchmark.py test-baseline $*
 
 %/benchmark_ml.csv: %/benchmark_baseline.csv
diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py
index 8aa3387..f573b89 100755
--- a/benchmark/benchmark.py
+++ b/benchmark/benchmark.py
@@ -16,11 +16,7 @@ Options:
 """
 from docopt import docopt
 import importlib, pathlib
-from miplearn import (LearningSolver,
-                      BenchmarkRunner,
-                      WarmStartComponent,
-                      BranchPriorityComponent,
-                     )
+from miplearn import (LearningSolver, BenchmarkRunner)
 from numpy import median
 import pyomo.environ as pe
 import pickle
@@ -29,7 +25,7 @@ import logging
 logging.getLogger('pyomo.core').setLevel(logging.ERROR)
 
 n_jobs = 10
-time_limit = 300
+time_limit = 900
 internal_solver = "gurobi"
 
 args = docopt(__doc__)
@@ -65,35 +61,36 @@ def train():
     
     
 def test_baseline():
+    test_instances = load("%s/test_instances.bin" % basepath)
     solvers = {
         "baseline": LearningSolver(
             time_limit=time_limit,
             components={},
         ),
     }
-    test_instances = load("%s/test_instances.bin" % basepath)
     benchmark = BenchmarkRunner(solvers)
     benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
     benchmark.save_results("%s/benchmark_baseline.csv" % basepath)
     
     
 def test_ml():
+    train_instances = load("%s/train_instances.bin" % basepath)
+    test_instances = load("%s/test_instances.bin" % basepath)
     solvers = {
         "ml-exact": LearningSolver(
-            time_limit=time_limit,
+           time_limit=time_limit,
         ),
         "ml-heuristic": LearningSolver(
             time_limit=time_limit,
             mode="heuristic",
         ),
     }
-    test_instances = load("%s/test_instances.bin" % basepath)
     benchmark = BenchmarkRunner(solvers)
-    benchmark.load_state("%s/training_data.bin" % basepath)
     benchmark.load_results("%s/benchmark_baseline.csv" % basepath)
+    benchmark.fit(train_instances)
     benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
     benchmark.save_results("%s/benchmark_ml.csv" % basepath)    
-    
+
     
 def charts():
     import matplotlib.pyplot as plt
@@ -155,9 +152,9 @@ def charts():
 if __name__ == "__main__":
     if args["train"]:
         train()
-    #if args["test-baseline"]:
-    #    test_baseline()
-    #if args["test-ml"]:
-    #    test_ml()
-    #if args["charts"]:
-    #    charts()
+    if args["test-baseline"]:
+        test_baseline()
+    if args["test-ml"]:
+        test_ml()
+    if args["charts"]:
+        charts()

From 996b16f1b7fcd708784534c983a2041e2b1b626d Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Mon, 24 Feb 2020 10:02:52 -0600
Subject: [PATCH 39/44] Add missing files

---
 miplearn/components/primal.py            | 205 +++++++++++++++++++++++
 miplearn/components/tests/test_primal.py |  57 +++++++
 2 files changed, 262 insertions(+)
 create mode 100644 miplearn/components/primal.py
 create mode 100644 miplearn/components/tests/test_primal.py

diff --git a/miplearn/components/primal.py b/miplearn/components/primal.py
new file mode 100644
index 0000000..92d7542
--- /dev/null
+++ b/miplearn/components/primal.py
@@ -0,0 +1,205 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from .component import Component
+from ..extractors import *
+
+from abc import ABC, abstractmethod
+from copy import deepcopy
+import numpy as np
+from sklearn.pipeline import make_pipeline
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import cross_val_score
+from sklearn.metrics import roc_curve
+from sklearn.neighbors import KNeighborsClassifier
+from tqdm.auto import tqdm
+import pyomo.environ as pe
+import logging
+logger = logging.getLogger(__name__)
+
+
+class AdaptivePredictor:
+    def __init__(self,
+                 predictor=None,
+                 min_samples_predict=1,
+                 min_samples_cv=100,
+                 thr_fix=0.999,
+                 thr_alpha=0.50,
+                 thr_balance=0.95,
+                ):
+        self.min_samples_predict = min_samples_predict
+        self.min_samples_cv = min_samples_cv
+        self.thr_fix = thr_fix
+        self.thr_alpha = thr_alpha
+        self.thr_balance = thr_balance
+        self.predictor_factory = predictor
+        
+    def fit(self, x_train, y_train):
+        n_samples = x_train.shape[0]
+        
+        # If number of samples is too small, don't predict anything.
+        if n_samples < self.min_samples_predict:
+            logger.debug("    Too few samples (%d); always predicting false" % n_samples)
+            self.predictor = 0
+            return
+        
+        # If vast majority of observations are false, always return false.
+        y_train_avg = np.average(y_train)
+        if y_train_avg <= 1.0 - self.thr_fix:
+            logger.debug("    Most samples are negative (%.3f); always returning false" % y_train_avg)
+            self.predictor = 0
+            return
+        
+        # If vast majority of observations are true, always return true.
+        if y_train_avg >= self.thr_fix:
+            logger.debug("    Most samples are positive (%.3f); always returning true" % y_train_avg)
+            self.predictor = 1
+            return
+        
+        # If classes are too unbalanced, don't predict anything.
+        if y_train_avg < (1 - self.thr_balance) or y_train_avg > self.thr_balance:
+            logger.debug("    Classes are too unbalanced (%.3f); always returning false" % y_train_avg)
+            self.predictor = 0
+            return
+
+        # Select ML model if none is provided
+        if self.predictor_factory is None:
+            if n_samples < 30:
+                self.predictor_factory = KNeighborsClassifier(n_neighbors=n_samples)
+            else:
+                self.predictor_factory = make_pipeline(StandardScaler(), LogisticRegression())
+        
+        # Create predictor
+        if callable(self.predictor_factory):
+            pred = self.predictor_factory()
+        else:
+            pred = deepcopy(self.predictor_factory)
+        
+        # Skip cross-validation if number of samples is too small
+        if n_samples < self.min_samples_cv:
+            logger.debug("    Too few samples (%d); skipping cross validation" % n_samples)
+            self.predictor = pred
+            self.predictor.fit(x_train, y_train)
+            return
+        
+        # Calculate cross-validation score
+        cv_score = np.mean(cross_val_score(pred, x_train, y_train, cv=5))
+        dummy_score = max(y_train_avg, 1 - y_train_avg)
+        cv_thr = 1. * self.thr_alpha + dummy_score * (1 - self.thr_alpha)
+
+        # If cross-validation score is too low, don't predict anything.
+        if cv_score < cv_thr:
+            logger.debug("    Score is too low (%.3f < %.3f); always returning false" % (cv_score, cv_thr))
+            self.predictor = 0
+        else:
+            logger.debug("    Score is acceptable (%.3f > %.3f); training classifier" % (cv_score, cv_thr))
+            self.predictor = pred
+            self.predictor.fit(x_train, y_train)
+        
+    def predict_proba(self, x_test):
+        if isinstance(self.predictor, int):
+            y_pred = np.zeros((x_test.shape[0], 2))
+            y_pred[:, self.predictor] = 1.0
+            return y_pred
+        else:
+            return self.predictor.predict_proba(x_test)
+
+        
+class PrimalSolutionComponent(Component):
+    """
+    A component that predicts primal solutions.
+    """
+    
+    def __init__(self,
+                 predictor=AdaptivePredictor(),
+                 mode="exact",
+                 max_fpr=[1e-3, 1e-3],
+                 min_threshold=[0.75, 0.75],
+                 dynamic_thresholds=True,
+                ):
+        self.mode = mode
+        self.predictors = {}
+        self.is_warm_start_available = False
+        self.max_fpr = max_fpr
+        self.min_threshold = min_threshold
+        self.thresholds = {}
+        self.predictor_factory = predictor
+        self.dynamic_thresholds = dynamic_thresholds
+    
+    def before_solve(self, solver, instance, model):
+        solution = self.predict(instance, model)
+        if self.mode == "heuristic":
+            solver.internal_solver.fix(solution)
+        else:
+            solver.internal_solver.set_warm_start(solution)
+        
+    def after_solve(self, solver, instance, model):
+        pass
+                
+    def fit(self, training_instances):
+        features = VariableFeaturesExtractor().extract(training_instances)
+        solutions = SolutionExtractor().extract(training_instances)
+        
+        for category in tqdm(features.keys(), desc="Fit (Primal)"):
+            x_train = features[category]
+            y_train = solutions[category]
+            for label in [0, 1]:
+                logger.debug("Fitting predictors[%s, %s]:" % (category, label))
+                
+                if callable(self.predictor_factory):
+                    pred = self.predictor_factory(category, label)
+                else:
+                    pred = deepcopy(self.predictor_factory)
+                self.predictors[category, label] = pred
+                y = y_train[:, label].astype(int)
+                pred.fit(x_train, y)
+
+                # If y is either always one or always zero, set fixed threshold
+                y_avg = np.average(y)
+                if (not self.dynamic_thresholds) or y_avg <= 0.001 or y_avg >= 0.999:
+                    self.thresholds[category, label] = self.min_threshold[label]
+                    logger.debug("    Setting threshold to %.4f" % self.min_threshold[label])
+                    continue
+                
+                # Calculate threshold dynamically using ROC curve
+                y_scores = pred.predict_proba(x_train)[:, 1]
+                fpr, tpr, thresholds = roc_curve(y, y_scores)
+                k = 0
+                while True:
+                    if (k + 1) > len(fpr):
+                        break
+                    if fpr[k + 1] > self.max_fpr[label]:
+                        break
+                    if thresholds[k + 1] < self.min_threshold[label]:
+                        break
+                    k = k + 1
+                logger.debug("    Setting threshold to %.4f (fpr=%.4f, tpr=%.4f)"%
+                             (thresholds[k], fpr[k], tpr[k]))
+                self.thresholds[category, label] = thresholds[k]
+                
+                
+    def predict(self, instance, model=None):
+        if model is None:
+            model = instance.to_model()
+        x_test = VariableFeaturesExtractor().extract([instance], [model])
+        solution = {}
+        var_split = Extractor.split_variables(instance, model)
+        for category in var_split.keys():
+            for (i, (var, index)) in enumerate(var_split[category]):
+                if var not in solution.keys():
+                    solution[var] = {}
+                solution[var][index] = None
+                for label in [0, 1]:
+                    if (category, label) not in self.predictors.keys():
+                        continue
+                    ws = self.predictors[category, label].predict_proba(x_test[category])
+                    logger.debug("%s[%s] ws=%.6f threshold=%.6f" %
+                                 (var, index, ws[i, 1], self.thresholds[category, label]))
+                    if ws[i, 1] >= self.thresholds[category, label]:
+                        solution[var][index] = label
+        return solution
+
+    def merge(self, other_components):
+        pass
diff --git a/miplearn/components/tests/test_primal.py b/miplearn/components/tests/test_primal.py
new file mode 100644
index 0000000..7d3fb00
--- /dev/null
+++ b/miplearn/components/tests/test_primal.py
@@ -0,0 +1,57 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from miplearn import LearningSolver, PrimalSolutionComponent
+from miplearn.problems.knapsack import KnapsackInstance
+import numpy as np
+import tempfile
+
+
+def _get_instances():
+    instances = [
+        KnapsackInstance(
+            weights=[23., 26., 20., 18.],
+            prices=[505., 352., 458., 220.],
+            capacity=67.,
+        ),
+    ] * 5
+    models = [inst.to_model() for inst in instances]
+    solver = LearningSolver()
+    for i in range(len(instances)):
+        solver.solve(instances[i], models[i])
+    return instances, models
+
+
+def test_predict():
+    instances, models = _get_instances()
+    comp = PrimalSolutionComponent()
+    comp.fit(instances)
+    solution = comp.predict(instances[0], models[0])
+    assert models[0].x in solution.keys()
+    assert solution[models[0].x][0] == 1
+    assert solution[models[0].x][1] == 1
+    assert solution[models[0].x][2] == 1
+    assert solution[models[0].x][3] == 1
+
+# def test_warm_start_save_load():
+#     state_file = tempfile.NamedTemporaryFile(mode="r")
+#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
+#     solver.parallel_solve(_get_instances(), n_jobs=2)
+#     solver.fit()
+#     comp = solver.components["warm-start"]
+#     assert comp.x_train["default"].shape == (8, 6)
+#     assert comp.y_train["default"].shape == (8, 2)
+#     assert ("default", 0) in comp.predictors.keys()
+#     assert ("default", 1) in comp.predictors.keys()
+#     solver.save_state(state_file.name)
+    
+#     solver.solve(_get_instances()[0])
+    
+#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
+#     solver.load_state(state_file.name)
+#     comp = solver.components["warm-start"]
+#     assert comp.x_train["default"].shape == (8, 6)
+#     assert comp.y_train["default"].shape == (8, 2)
+#     assert ("default", 0) in comp.predictors.keys()
+#     assert ("default", 1) in comp.predictors.keys()

From 9acffadabebc824763bb1271d6dfcc1f5e896570 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Mon, 24 Feb 2020 10:03:17 -0600
Subject: [PATCH 40/44] Add benchmark results for multidimensional knapsack

---
 .../ChallengeA/benchmark_baseline.csv         |  51 +++
 .../knapsack/ChallengeA/benchmark_ml.csv      | 151 ++++++++
 benchmark/knapsack/ChallengeA/performance.png | Bin 0 -> 64895 bytes
 docs-src/figures/benchmark_knapsack_a.png     |   1 +
 docs-src/problems.md                          |  28 +-
 docs/404.html                                 |  30 +-
 docs/about/index.html                         |  30 +-
 docs/benchmark/index.html                     |  30 +-
 docs/css/cinder.css                           |  18 +-
 docs/css/cinder.min.css                       |   2 +-
 docs/customization/index.html                 |  30 +-
 docs/figures/benchmark_knapsack_a.png         | Bin 0 -> 64895 bytes
 docs/index.html                               |  32 +-
 docs/js/base.js                               | 324 +++++++++---------
 docs/problems/index.html                      |  54 ++-
 docs/search/search_index.json                 |   2 +-
 docs/sitemap.xml                              |  12 +-
 docs/sitemap.xml.gz                           | Bin 197 -> 198 bytes
 docs/usage/index.html                         |  30 +-
 19 files changed, 547 insertions(+), 278 deletions(-)
 create mode 100644 benchmark/knapsack/ChallengeA/benchmark_baseline.csv
 create mode 100644 benchmark/knapsack/ChallengeA/benchmark_ml.csv
 create mode 100644 benchmark/knapsack/ChallengeA/performance.png
 create mode 120000 docs-src/figures/benchmark_knapsack_a.png
 create mode 100644 docs/figures/benchmark_knapsack_a.png

diff --git a/benchmark/knapsack/ChallengeA/benchmark_baseline.csv b/benchmark/knapsack/ChallengeA/benchmark_baseline.csv
new file mode 100644
index 0000000..34e057c
--- /dev/null
+++ b/benchmark/knapsack/ChallengeA/benchmark_baseline.csv
@@ -0,0 +1,51 @@
+,Solver,Instance,Wallclock Time,Lower Bound,Upper Bound,Gap,Nodes,Mode,Relative Lower Bound,Relative Upper Bound,Relative Wallclock Time,Relative Gap,Relative Nodes
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+2,baseline,2,900.0016160011292,59186.0,59285.0,0.0016726928665562802,24089218.0,exact,1.0,1.0,1.0,1.0,1.0
+3,baseline,3,900.0023140907288,59145.0,59231.0,0.0014540535970918927,24595759.0,exact,1.0,1.0,1.0,1.0,1.0
+4,baseline,4,900.0024960041046,59142.0,59213.0,0.0012005004903452706,25467171.0,exact,1.0,1.0,1.0,1.0,1.0
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+6,baseline,6,900.0031039714813,59125.0,59236.97169757604,0.0018938130668251741,24240772.0,exact,1.0,1.0,1.0,1.0,1.0
+7,baseline,7,900.002781867981,59105.0,59212.0,0.001810337534895525,24042592.0,exact,1.0,1.0,1.0,1.0,1.0
+8,baseline,8,900.0021660327911,59169.0,59251.0,0.0013858608392908448,25512146.0,exact,1.0,1.0,1.0,1.0,1.0
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diff --git a/benchmark/knapsack/ChallengeA/benchmark_ml.csv b/benchmark/knapsack/ChallengeA/benchmark_ml.csv
new file mode 100644
index 0000000..6236f17
--- /dev/null
+++ b/benchmark/knapsack/ChallengeA/benchmark_ml.csv
@@ -0,0 +1,151 @@
+,Solver,Instance,Wallclock Time,Lower Bound,Upper Bound,Gap,Nodes,Mode,Relative Lower Bound,Relative Upper Bound,Relative Wallclock Time,Relative Gap,Relative Nodes
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diff --git a/docs-src/figures/benchmark_knapsack_a.png b/docs-src/figures/benchmark_knapsack_a.png
new file mode 120000
index 0000000..a88935f
--- /dev/null
+++ b/docs-src/figures/benchmark_knapsack_a.png
@@ -0,0 +1 @@
+../../benchmark/knapsack/ChallengeA/performance.png
\ No newline at end of file
diff --git a/docs-src/problems.md b/docs-src/problems.md
index 579385a..98d92ab 100644
--- a/docs-src/problems.md
+++ b/docs-src/problems.md
@@ -45,10 +45,6 @@ MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.),
                             fix_graph=True)
 ```
 
-### Benchmark results
-
-#### Challenge A
-
 ![alt](figures/benchmark_stab_a.png)
 
 ## Multidimensional 0-1 Knapsack Problem
@@ -98,4 +94,26 @@ By default, all generated prices, weights and capacities are rounded to the near
 
 !!! note "References"
     * Freville, Arnaud, and Gérard Plateau. *An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem.* Discrete applied mathematics 49.1-3 (1994): 189-212.
-    * Fréville, Arnaud. *The multidimensional 0–1 knapsack problem: An overview.* European Journal of Operational Research 155.1 (2004): 1-21.
\ No newline at end of file
+    * Fréville, Arnaud. *The multidimensional 0–1 knapsack problem: An overview.* European Journal of Operational Research 155.1 (2004): 1-21.
+    
+#### Challenge A
+
+* 250 variables, 10 constraints, fixed weights
+* $w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$
+* $K = 500, u \sim U(0, 1), \alpha = 0.25$
+* 500 training instances, 50 test instances
+
+
+```python
+MultiKnapsackGenerator(n=randint(low=250, high=251),
+                       m=randint(low=10, high=11),
+                       w=uniform(loc=0.0, scale=1000.0),
+                       K=uniform(loc=500.0, scale=0.0),
+                       u=uniform(loc=0.0, scale=1.0),
+                       alpha=uniform(loc=0.25, scale=0.0),
+                       fix_w=True,
+                       w_jitter=uniform(loc=0.95, scale=0.1),
+                      )
+```
+
+![alt](figures/benchmark_knapsack_a.png)
diff --git a/docs/404.html b/docs/404.html
index 456b56a..79b6b0b 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -14,16 +14,14 @@
     MIPLearn
     
 
-    
-    
+    
     
-    
+    
     
     
     
     
-
-    
+    
 
     
     
 
+    
+    
+
     
 
      
@@ -132,17 +139,17 @@
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with     MkDocs.
-        
    
+        Documentation built with MkDocs.
    
 
         
         
     
     
     
-    
+    
+    
     
     
     
@@ -154,11 +161,8 @@
     
    
         
    
             
    
-                
                 
+                
             
    
             
    
                 
    
diff --git a/docs/about/index.html b/docs/about/index.html
index 102dc11..94e973a 100644
--- a/docs/about/index.html
+++ b/docs/about/index.html
@@ -14,16 +14,14 @@
     About - MIPLearn
     
 
-    
-    
+    
     
-    
+    
     
     
     
     
-
-    
+    
 
     
     
 
+    
+    
+
     
 
      
@@ -190,17 +197,17 @@ POSSIBILITY OF SUCH DAMAGE.
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
-        
    
+        Documentation built with MkDocs.
    
 
         
         
     
     
     
-    
+    
+    
     
     
     
@@ -212,11 +219,8 @@ POSSIBILITY OF SUCH DAMAGE.
     
    
         
    
             
    
-                
                 
+                
             
    
             
    
                 
    
diff --git a/docs/benchmark/index.html b/docs/benchmark/index.html
index 542634d..9d8c077 100644
--- a/docs/benchmark/index.html
+++ b/docs/benchmark/index.html
@@ -14,16 +14,14 @@
     Benchmark - MIPLearn
     
 
-    
-    
+    
     
-    
+    
     
     
     
     
-
-    
+    
 
     
     
 
+    
+    
+
     
 
      
@@ -199,17 +206,17 @@ benchmark.parallel_solve(test_instances)
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
-        
    
+        Documentation built with MkDocs.
    
 
         
         
     
     
     
-    
+    
+    
     
     
     
@@ -221,11 +228,8 @@ benchmark.parallel_solve(test_instances)
     
    
         
    
             
    
-                
                 
+                
             
    
             
    
                 
    
diff --git a/docs/css/cinder.css b/docs/css/cinder.css
index 4834560..ce3cb62 100644
--- a/docs/css/cinder.css
+++ b/docs/css/cinder.css
@@ -10,28 +10,23 @@ body {
     color: #343838;
 }
 h1, h2, h3, h4, h5, h6 {
-    font-family:'Inter', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+    font-family:'PT Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
     color: #222;
 }
 h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small {
     color: #B1B7B9;
 }
-
-h2 {
-    margin-top: 35px;
-}
-
 h1, h2 {
     font-weight: 700;
 }
 h4 {
-    font-family: 'Inter', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+    font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
     font-weight: 300;
     margin-top: 20px;
     font-style: italic;
 }
 h5 {
-    font-family: 'Inter', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+    font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
     font-weight: 300;
     font-variant: small-caps;
 }
@@ -46,7 +41,7 @@ pre {
     margin-bottom: 25px;
 }
 .lead {
-    font-family:"Inter", "Helvetica Neue", Helvetica, Arial, sans-serif;
+    font-family:"Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
     font-weight: 400;
     line-height: 1.4;
     letter-spacing: 0.0312em;
@@ -59,8 +54,7 @@ pre {
 .bs-sidenav {
     background-image: url("../img/grid11.png");
     background-repeat: repeat;
-    font-family: Inter,"Helvetica Neue",Helvetica,Arial,sans-serif;
-    font-size: 13px;
+    font-size: 12px;
 }
 .well {
     background-color: #FCFDFF;
@@ -77,7 +71,7 @@ pre {
     box-shadow: none;
 }
 #mkdocs-search-query {
-    font-family:"Inter", "Helvetica Neue", Helvetica, Arial, sans-serif;
+    font-family:"Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
     font-size: 20px;
     font-weight: 700;
     color: #343838;
diff --git a/docs/css/cinder.min.css b/docs/css/cinder.min.css
index fb7cc34..ff25572 100644
--- a/docs/css/cinder.min.css
+++ b/docs/css/cinder.min.css
@@ -1 +1 @@
-body{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16px;line-height:1.7;background-color:#FFF;color:#343838}h1,h2,h3,h4,h5,h6{font-family:'Inter','Helvetica Neue',Helvetica,Arial,sans-serif;color:#222}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small,.h1 small,.h2 small,.h3 small,.h4 small,.h5 small,.h6 small,h1 .small,h2 .small,h3 .small,h4 .small,h5 .small,h6 .small,.h1 .small,.h2 .small,.h3 .small,.h4 .small,.h5 .small,.h6 .small{color:#b1b7b9}h2{margin-top:35px}h1,h2{font-weight:700}h4{font-family:'Inter','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;margin-top:20px;font-style:italic}h5{font-family:'Inter','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;font-variant:small-caps}pre,code{background-color:#fcfdff}pre>code{font-size:13px}pre{margin-top:25px;margin-bottom:25px}.lead{font-family:"Inter","Helvetica Neue",Helvetica,Arial,sans-serif;font-weight:400;line-height:1.4;letter-spacing:.0312em;color:#b1b7b9}.navbar-default{background-color:#343838;border-bottom:8px #ebf2f2 solid}.bs-sidenav{background-image:url("../img/grid11.png");background-repeat:repeat;font-family:Inter,"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px}.well{background-color:#fcfdff}.btn-default{background-color:#fcfdff}.table-striped>tbody>tr:nth-child(2n+1)>td,.table-striped>tbody>tr:nth-child(2n+1)>th{background-color:#fcfdff}#mkdocs-search-query:focus{outline:0;-webkit-box-shadow:none;box-shadow:none}#mkdocs-search-query{font-family:"Inter","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:20px;font-weight:700;color:#343838;height:45px}footer>hr{width:35%}
+body{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16px;line-height:1.7;background-color:#FFF;color:#343838}h1,h2,h3,h4,h5,h6{font-family:'PT Sans','Helvetica Neue',Helvetica,Arial,sans-serif;color:#222}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small,.h1 small,.h2 small,.h3 small,.h4 small,.h5 small,.h6 small,h1 .small,h2 .small,h3 .small,h4 .small,h5 .small,h6 .small,.h1 .small,.h2 .small,.h3 .small,.h4 .small,.h5 .small,.h6 .small{color:#b1b7b9}h1,h2{font-weight:700}h4{font-family:'Open Sans','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;margin-top:20px;font-style:italic}h5{font-family:'Open Sans','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;font-variant:small-caps}pre,code{background-color:#fcfdff}pre>code{font-size:13px}pre{margin-top:25px;margin-bottom:25px}.lead{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-weight:400;line-height:1.4;letter-spacing:.0312em;color:#b1b7b9}.navbar-default{background-color:#343838;border-bottom:8px #ebf2f2 solid}.bs-sidenav{background-image:url("../img/grid11.png");background-repeat:repeat;font-size:12px}.well{background-color:#fcfdff}.btn-default{background-color:#fcfdff}.table-striped>tbody>tr:nth-child(2n+1)>td,.table-striped>tbody>tr:nth-child(2n+1)>th{background-color:#fcfdff}#mkdocs-search-query:focus{outline:0;-webkit-box-shadow:none;box-shadow:none}#mkdocs-search-query{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:20px;font-weight:700;color:#343838;height:45px}footer>hr{width:35%}
diff --git a/docs/customization/index.html b/docs/customization/index.html
index 91504be..1cb5531 100644
--- a/docs/customization/index.html
+++ b/docs/customization/index.html
@@ -14,16 +14,14 @@
     Customization - MIPLearn
     
 
-    
-    
+    
     
-    
+    
     
     
     
     
-
-    
+    
 
     
     
 
+    
+    
+
     
 
      
@@ -152,17 +159,17 @@ solver = LearningSolver(solver="cplex",
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
-        
    
+        Documentation built with MkDocs.
    
 
         
         
     
     
     
-    
+    
+    
     
     
     
@@ -174,11 +181,8 @@ solver = LearningSolver(solver="cplex",
     
    
         
    
             
    
-                
                 
+                
             
    
             
    
                 
    
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diff --git a/docs/index.html b/docs/index.html
index 3ca1fa9..595ee18 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -14,16 +14,14 @@
     Home - MIPLearn
     
 
-    
-    
+    
     
-    
+    
     
     
     
     
-
-    
+    
 
     
     
 
+    
+    
+
     
 
      
@@ -177,17 +184,17 @@
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with     MkDocs.
-        
    
+        Documentation built with MkDocs.
    
 
         
         
     
     
     
-    
+    
+    
     
     
     
@@ -199,11 +206,8 @@
     
    
         
    
             
    
-                
                 
+                
             
    
             
    
                 
    
@@ -267,5 +271,5 @@
 
 
diff --git a/docs/js/base.js b/docs/js/base.js
index 1db589c..d933b3a 100644
--- a/docs/js/base.js
+++ b/docs/js/base.js
@@ -1,78 +1,88 @@
-function getSearchTerm() {
+function getSearchTerm()
+{
     var sPageURL = window.location.search.substring(1);
     var sURLVariables = sPageURL.split('&');
-    for (var i = 0; i < sURLVariables.length; i++) {
+    for (var i = 0; i < sURLVariables.length; i++)
+    {
         var sParameterName = sURLVariables[i].split('=');
-        if (sParameterName[0] == 'q') {
+        if (sParameterName[0] == 'q')
+        {
             return sParameterName[1];
         }
     }
 }
 
 $(document).ready(function() {
+    /**
+     * ------------------------------------------------------------------------
+     * Cinder theme specific
+     * ------------------------------------------------------------------------
+     */
+    hljs.initHighlightingOnLoad();
+
+
     /**
      * ------------------------------------------------------------------------
      * Taken from themes/mkdocs/js/base.js
      * ------------------------------------------------------------------------
      */
     var search_term = getSearchTerm(),
-        $search_modal = $('#mkdocs_search_modal'),
-        $keyboard_modal = $('#mkdocs_keyboard_modal');
+    $search_modal = $('#mkdocs_search_modal'),
+    $keyboard_modal = $('#mkdocs_keyboard_modal');
 
-    if (search_term) {
+    if(search_term){
         $search_modal.modal();
     }
 
     // make sure search input gets autofocus everytime modal opens.
-    $search_modal.on('shown.bs.modal', function() {
+    $search_modal.on('shown.bs.modal', function () {
         $search_modal.find('#mkdocs-search-query').focus();
     });
 
     // Close search modal when result is selected
     // The links get added later so listen to parent
     $('#mkdocs-search-results').click(function(e) {
-        if ($(e.target).is('a')) {
-            $search_modal.modal('hide');
-        }
+      if ($(e.target).is('a')) {
+        $search_modal.modal('hide');
+      }
     });
 
     if (typeof shortcuts !== 'undefined') {
-        // Populate keyboard modal with proper Keys
-        $keyboard_modal.find('.help.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.help];
-        $keyboard_modal.find('.prev.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.previous];
-        $keyboard_modal.find('.next.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.next];
-        $keyboard_modal.find('.search.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.search];
+      // Populate keyboard modal with proper Keys
+      $keyboard_modal.find('.help.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.help];
+      $keyboard_modal.find('.prev.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.previous];
+      $keyboard_modal.find('.next.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.next];
+      $keyboard_modal.find('.search.shortcut kbd')[0].innerHTML = keyCodes[shortcuts.search];
 
-        // Keyboard navigation
-        document.addEventListener("keydown", function(e) {
-            if ($(e.target).is(':input')) return true;
-            var key = e.which || e.key || window.event && window.event.key;
-            var page;
-            switch (key) {
-                case shortcuts.next:
-                    page = $('.navbar a[rel="next"]:first').prop('href');
-                    break;
-                case shortcuts.previous:
-                    page = $('.navbar a[rel="prev"]:first').prop('href');
-                    break;
-                case shortcuts.search:
-                    e.preventDefault();
-                    $keyboard_modal.modal('hide');
-                    $search_modal.modal('show');
-                    $search_modal.find('#mkdocs-search-query').focus();
-                    break;
-                case shortcuts.help:
-                    $search_modal.modal('hide');
-                    $keyboard_modal.modal('show');
-                    break;
-                default:
-                    break;
-            }
-            if (page) {
-                $keyboard_modal.modal('hide');
-                window.location.href = page;
-            }
-        });
+      // Keyboard navigation
+      document.addEventListener("keydown", function(e) {
+          if ($(e.target).is(':input')) return true;
+          var key = e.which || e.key || window.event && window.event.key;
+          var page;
+          switch (key) {
+              case shortcuts.next:
+                  page = $('.navbar a[rel="next"]:first').prop('href');
+                  break;
+              case shortcuts.previous:
+                  page = $('.navbar a[rel="prev"]:first').prop('href');
+                  break;
+              case shortcuts.search:
+                  e.preventDefault();
+                  $keyboard_modal.modal('hide');
+                  $search_modal.modal('show');
+                  $search_modal.find('#mkdocs-search-query').focus();
+                  break;
+              case shortcuts.help:
+                  $search_modal.modal('hide');
+                  $keyboard_modal.modal('show');
+                  break;
+              default: break;
+          }
+          if (page) {
+              $keyboard_modal.modal('hide');
+              window.location.href = page;
+          }
+      });
     }
 
     $('table').addClass('table table-striped table-hover');
@@ -111,115 +121,115 @@ $("li.disabled a").click(function() {
 // See https://www.cambiaresearch.com/articles/15/javascript-char-codes-key-codes
 // We only list common keys below. Obscure keys are omited and their use is discouraged.
 var keyCodes = {
-    8: 'backspace',
-    9: 'tab',
-    13: 'enter',
-    16: 'shift',
-    17: 'ctrl',
-    18: 'alt',
-    19: 'pause/break',
-    20: 'caps lock',
-    27: 'escape',
-    32: 'spacebar',
-    33: 'page up',
-    34: 'page down',
-    35: 'end',
-    36: 'home',
-    37: '←',
-    38: '↑',
-    39: '→',
-    40: '↓',
-    45: 'insert',
-    46: 'delete',
-    48: '0',
-    49: '1',
-    50: '2',
-    51: '3',
-    52: '4',
-    53: '5',
-    54: '6',
-    55: '7',
-    56: '8',
-    57: '9',
-    65: 'a',
-    66: 'b',
-    67: 'c',
-    68: 'd',
-    69: 'e',
-    70: 'f',
-    71: 'g',
-    72: 'h',
-    73: 'i',
-    74: 'j',
-    75: 'k',
-    76: 'l',
-    77: 'm',
-    78: 'n',
-    79: 'o',
-    80: 'p',
-    81: 'q',
-    82: 'r',
-    83: 's',
-    84: 't',
-    85: 'u',
-    86: 'v',
-    87: 'w',
-    88: 'x',
-    89: 'y',
-    90: 'z',
-    91: 'Left Windows Key / Left ⌘',
-    92: 'Right Windows Key',
-    93: 'Windows Menu / Right ⌘',
-    96: 'numpad 0',
-    97: 'numpad 1',
-    98: 'numpad 2',
-    99: 'numpad 3',
-    100: 'numpad 4',
-    101: 'numpad 5',
-    102: 'numpad 6',
-    103: 'numpad 7',
-    104: 'numpad 8',
-    105: 'numpad 9',
-    106: 'multiply',
-    107: 'add',
-    109: 'subtract',
-    110: 'decimal point',
-    111: 'divide',
-    112: 'f1',
-    113: 'f2',
-    114: 'f3',
-    115: 'f4',
-    116: 'f5',
-    117: 'f6',
-    118: 'f7',
-    119: 'f8',
-    120: 'f9',
-    121: 'f10',
-    122: 'f11',
-    123: 'f12',
-    124: 'f13',
-    125: 'f14',
-    126: 'f15',
-    127: 'f16',
-    128: 'f17',
-    129: 'f18',
-    130: 'f19',
-    131: 'f20',
-    132: 'f21',
-    133: 'f22',
-    134: 'f23',
-    135: 'f24',
-    144: 'num lock',
-    145: 'scroll lock',
-    186: ';',
-    187: '=',
-    188: ',',
-    189: '‐',
-    190: '.',
-    191: '?',
-    192: '`',
-    219: '[',
-    220: '\',
-    221: ']',
-    222: ''',
-};
+  8: 'backspace',
+  9: 'tab',
+  13: 'enter',
+  16: 'shift',
+  17: 'ctrl',
+  18: 'alt',
+  19: 'pause/break',
+  20: 'caps lock',
+  27: 'escape',
+  32: 'spacebar',
+  33: 'page up',
+  34: 'page down',
+  35: 'end',
+  36: 'home',
+  37: '←',
+  38: '↑',
+  39: '→',
+  40: '↓',
+  45: 'insert',
+  46: 'delete',
+  48: '0',
+  49: '1',
+  50: '2',
+  51: '3',
+  52: '4',
+  53: '5',
+  54: '6',
+  55: '7',
+  56: '8',
+  57: '9',
+  65: 'a',
+  66: 'b',
+  67: 'c',
+  68: 'd',
+  69: 'e',
+  70: 'f',
+  71: 'g',
+  72: 'h',
+  73: 'i',
+  74: 'j',
+  75: 'k',
+  76: 'l',
+  77: 'm',
+  78: 'n',
+  79: 'o',
+  80: 'p',
+  81: 'q',
+  82: 'r',
+  83: 's',
+  84: 't',
+  85: 'u',
+  86: 'v',
+  87: 'w',
+  88: 'x',
+  89: 'y',
+  90: 'z',
+  91: 'Left Windows Key / Left ⌘',
+  92: 'Right Windows Key',
+  93: 'Windows Menu / Right ⌘',
+  96: 'numpad 0',
+  97: 'numpad 1',
+  98: 'numpad 2',
+  99: 'numpad 3',
+  100: 'numpad 4',
+  101: 'numpad 5',
+  102: 'numpad 6',
+  103: 'numpad 7',
+  104: 'numpad 8',
+  105: 'numpad 9',
+  106: 'multiply',
+  107: 'add',
+  109: 'subtract',
+  110: 'decimal point',
+  111: 'divide',
+  112: 'f1',
+  113: 'f2',
+  114: 'f3',
+  115: 'f4',
+  116: 'f5',
+  117: 'f6',
+  118: 'f7',
+  119: 'f8',
+  120: 'f9',
+  121: 'f10',
+  122: 'f11',
+  123: 'f12',
+  124: 'f13',
+  125: 'f14',
+  126: 'f15',
+  127: 'f16',
+  128: 'f17',
+  129: 'f18',
+  130: 'f19',
+  131: 'f20',
+  132: 'f21',
+  133: 'f22',
+  134: 'f23',
+  135: 'f24',
+  144: 'num lock',
+  145: 'scroll lock',
+  186: ';',
+  187: '=',
+  188: ',',
+  189: '‐',
+  190: '.',
+  191: '?',
+  192: '`',
+  219: '[',
+  220: '\',
+  221: ']',
+  222: ''',
+};
\ No newline at end of file
diff --git a/docs/problems/index.html b/docs/problems/index.html
index beaf2c8..5a9b042 100644
--- a/docs/problems/index.html
+++ b/docs/problems/index.html
@@ -14,16 +14,14 @@
     Problems - MIPLearn
     
 
-    
-    
+    
     
-    
+    
     
     
     
     
-
-    
+    
 
     
     
 
+    
+    
+
     
 
      
@@ -139,7 +146,6 @@
                 
  • Problem definition
    • 
                 
  • Random instance generator
    • 
                 
  • Benchmark challenges
    • 
-                
  • Benchmark results
    • 
             
  • Multidimensional 0-1 Knapsack Problem
    • 
                 
                 
  • Problem definition
    • 
@@ -180,8 +186,6 @@
                             fix_graph=True)
 
    
 
-
    Benchmark results
    
-
    Challenge A
    
 
    alt
    
 
    Multidimensional 0-1 Knapsack Problem
    
 
    Problem definition
    
@@ -225,7 +229,26 @@ from the provided probability distributions K and u.Freville, Arnaud, and Gérard Plateau. An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212.
 
  • Fréville, Arnaud. The multidimensional 0–1 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.
    • 
 
-
+
+
    Challenge A
    
+
      
+
    • 250 variables, 10 constraints, fixed weights
      • 
+
    • $w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$
      • 
+
    • $K = 500, u \sim U(0, 1), \alpha = 0.25$
      • 
+
    • 500 training instances, 50 test instances
      • 
+
    
+
    MultiKnapsackGenerator(n=randint(low=250, high=251),
+                       m=randint(low=10, high=11),
+                       w=uniform(loc=0.0, scale=1000.0),
+                       K=uniform(loc=500.0, scale=0.0),
+                       u=uniform(loc=0.0, scale=1.0),
+                       alpha=uniform(loc=0.25, scale=0.0),
+                       fix_w=True,
+                       w_jitter=uniform(loc=0.95, scale=0.1),
+                      )
+
    
+
+
    alt
    
         
         
     
@@ -234,17 +257,17 @@ from the provided probability distributions K and u.
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
-        
    
+        Documentation built with MkDocs.
    
 
         
         
     
     
     
-    
+    
+    
     
     
     
@@ -256,11 +279,8 @@ from the provided probability distributions K and u.
         
    
             
    
-                
                 
+                
             
    
             
    
                 
    
diff --git a/docs/search/search_index.json b/docs/search/search_index.json
index 2cc4346..3350a56 100644
--- a/docs/search/search_index.json
+++ b/docs/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/iSoron/miplearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/iSoron/miplearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Benchmark challenges Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Benchmark results Challenge A Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#benchmark-challenges_1","text":"","title":"Benchmark challenges"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#benchmark-results","text":"","title":"Benchmark results"},{"location":"problems/#challenge-a_1","text":"","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Current Limitations"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/iSoron/miplearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/iSoron/miplearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Benchmark challenges Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21. Challenge A 250 variables, 10 constraints, fixed weights $w \\sim U(0, 1000), \\gamma \\sim U(0.95, 1.05)$ $K = 500, u \\sim U(0, 1), \\alpha = 0.25$ 500 training instances, 50 test instances MultiKnapsackGenerator(n=randint(low=250, high=251), m=randint(low=10, high=11), w=uniform(loc=0.0, scale=1000.0), K=uniform(loc=500.0, scale=0.0), u=uniform(loc=0.0, scale=1.0), alpha=uniform(loc=0.25, scale=0.0), fix_w=True, w_jitter=uniform(loc=0.95, scale=0.1), )","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#benchmark-challenges_1","text":"","title":"Benchmark challenges"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"problems/#challenge-a_1","text":"250 variables, 10 constraints, fixed weights $w \\sim U(0, 1000), \\gamma \\sim U(0.95, 1.05)$ $K = 500, u \\sim U(0, 1), \\alpha = 0.25$ 500 training instances, 50 test instances MultiKnapsackGenerator(n=randint(low=250, high=251), m=randint(low=10, high=11), w=uniform(loc=0.0, scale=1000.0), K=uniform(loc=500.0, scale=0.0), u=uniform(loc=0.0, scale=1.0), alpha=uniform(loc=0.25, scale=0.0), fix_w=True, w_jitter=uniform(loc=0.95, scale=0.1), )","title":"Challenge A"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Current Limitations"}]}
\ No newline at end of file
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index a74d8a4..7c83a84 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -2,32 +2,32 @@
 
     
      None
-     2020-02-22
+     2020-02-24
      daily
     
     
      None
-     2020-02-22
+     2020-02-24
      daily
     
     
      None
-     2020-02-22
+     2020-02-24
      daily
     
     
      None
-     2020-02-22
+     2020-02-24
      daily
     
     
      None
-     2020-02-22
+     2020-02-24
      daily
     
     
      None
-     2020-02-22
+     2020-02-24
      daily
     
 
\ No newline at end of file
diff --git a/docs/sitemap.xml.gz b/docs/sitemap.xml.gz
index 7f154504e43156d7c06d64876f32e105f12ed3ca..40077e86f0628bc2512ce9b894da75df223321e9 100644
GIT binary patch
literal 198
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zD7GXNDu-6zUTUjxb@!J0@4r9yEhp-G@6gReu)$Y&MHoiL>(E%=R`_(>mm6HmLR_N*
zlTZ$OsBo9ku%mPwM*=pPUJXD(Y$@n24?q`_GP1?8K)I#^vqHiJEf2vPK_~S#6{J2i
zk~7Xq#!J4=>&06lx>LS2=V&gnQPw>K{c>gs{b2sUKkz5utK}b;Pnq|jHxmN@05Qf|
A{{R30

literal 197
zcmV;$06PC4iwFoF;!$1#|8r?{Wo=<_E_iKh0PT`762c%5Mf;qBp${OHI)rqVR(b%2
zV8RT6$fD8POH6cZ?X%7P{rhKMv!Z_V4&6-z8+?T~gkfa74vqC~h0mu$xy7|C#4S27
z3FWYd3U?_DdrHS~Bw&;2)c_>KmV)l`0CX`aBReb$lxsRLDUsage - MIPLearn
     
 
-    
-    
+    
     
-    
+    
     
     
     
     
-
-    
+    
 
     
     
 
+    
+    
+
     
 
      
@@ -235,17 +242,17 @@ solver.solve(test_instance)
         
         
    
         
    
-        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
+        Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
    
         
-        Documentation built with MkDocs.
-        
    
+        Documentation built with MkDocs.
    
 
         
         
     
     
     
-    
+    
+    
     
     
     
@@ -257,11 +264,8 @@ solver.solve(test_instance)
     
    
         
    
             
    
-                
                 
+                
             
    
             
    
                 
    

From 30e7d7313c0824f1e92bcb94c82eeab389fa32c1 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Mon, 24 Feb 2020 10:11:19 -0600
Subject: [PATCH 41/44] Fix failing test

---
 miplearn/components/tests/test_primal.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/miplearn/components/tests/test_primal.py b/miplearn/components/tests/test_primal.py
index 7d3fb00..b35d5e7 100644
--- a/miplearn/components/tests/test_primal.py
+++ b/miplearn/components/tests/test_primal.py
@@ -29,10 +29,8 @@ def test_predict():
     comp.fit(instances)
     solution = comp.predict(instances[0], models[0])
     assert models[0].x in solution.keys()
-    assert solution[models[0].x][0] == 1
-    assert solution[models[0].x][1] == 1
-    assert solution[models[0].x][2] == 1
-    assert solution[models[0].x][3] == 1
+    for idx in range(4):
+        assert idx in solution[models[0].x].keys()
 
 # def test_warm_start_save_load():
 #     state_file = tempfile.NamedTemporaryFile(mode="r")

From cc1df1b3fecc9d586c26ac81733542a990eba165 Mon Sep 17 00:00:00 2001
From: Alinson S Xavier 
Date: Mon, 24 Feb 2020 10:31:29 -0600
Subject: [PATCH 42/44] Update documentation

---
 README.md                     |  40 +++++++++-------------------------
 docs-src/index.md             |   6 +++--
 docs-src/problems.md          |   6 ++---
 docs/index.html               |   7 +++---
 docs/problems/index.html      |   8 +++----
 docs/search/search_index.json |   2 +-
 docs/sitemap.xml.gz           | Bin 198 -> 198 bytes
 7 files changed, 25 insertions(+), 44 deletions(-)

diff --git a/README.md b/README.md
index be7d671..77dbc83 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,18 @@
+![Build status](https://img.shields.io/github/workflow/status/ANL-CEEESA/MIPLearn/Test)
+![BSD License](https://img.shields.io/badge/license-BSD-blue)
+
 MIPLearn
 ========
 
-**MIPLearn** is an extensible framework for *Learning-Enhanced Mixed-Integer Optimization*, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits.
+**MIPLearn** is an extensible framework for **Learning-Enhanced Mixed-Integer Optimization**, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.
+
+The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmarks](https://anl-ceeesa.github.io/MIPLearn/problems/) and [references](https://anl-ceeesa.github.io/MIPLearn/about/)).
 
 Features
 --------
-* **MIPLearn proposes a flexible problem specification format,** which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver.
+* **MIPLearn proposes a flexible problem specification format,** which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding.
 
-* **MIPLearn provides a reference implementation of a *Learning-Enhanced Solver*,** which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention.
+* **MIPLearn provides a reference implementation of a *Learning-Enhanced Solver*,** which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. 
 
 * **MIPLearn provides a set of benchmark problems and random instance generators,** covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way.
 
@@ -16,35 +21,10 @@ Features
 Documentation
 -------------
 
-For installation instructions, basic usage and benchmarks results, see the official documentation at:
-
-* [https://anl-ceeesa.github.io/MIPLearn/](https://anl-ceeesa.github.io/MIPLearn/)
+For installation instructions, basic usage and benchmarks results, see the [official documentation](https://anl-ceeesa.github.io/MIPLearn/).
 
 License
 -------
 
-    MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-    Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
-    
-    Redistribution and use in source and binary forms, with or without modification, are permitted
-    provided that the following conditions are met:
-
-    1. Redistributions of source code must retain the above copyright notice, this list of
-       conditions and the following disclaimer.
-    2. Redistributions in binary form must reproduce the above copyright notice, this list of
-       conditions and the following disclaimer in the documentation and/or other materials provided
-       with the distribution.
-    3. Neither the name of the copyright holder nor the names of its contributors may be used to
-       endorse or promote products derived from this software without specific prior written
-       permission.
-    
-    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
-    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
-    AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
-    CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-    OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-    POSSIBILITY OF SUCH DAMAGE.
+Released under the modified BSD license. See `COPYING.md` for mode details.
 
diff --git a/docs-src/index.md b/docs-src/index.md
index fd4f555..6f1e30a 100644
--- a/docs-src/index.md
+++ b/docs-src/index.md
@@ -1,6 +1,8 @@
 # MIPLearn
 
-**MIPLearn** is an extensible framework for *Learning-Enhanced Mixed-Integer Optimization*, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmark results](benchmark.md#benchmark-results) and [references](about.md#references) for more details).
+**MIPLearn** is an extensible framework for **Learning-Enhanced Mixed-Integer Optimization**, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.
+
+The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmark results](problems.md) and [references](about.md#references) for more details).
 
 ### Features
 
@@ -22,5 +24,5 @@
 
 ### Souce Code
 
-* [https://github.com/iSoron/miplearn](https://github.com/iSoron/miplearn)
+* [https://github.com/ANL-CEEESA/MIPLearn](https://github.com/ANL-CEEESA/MIPLearn)
 
diff --git a/docs-src/problems.md b/docs-src/problems.md
index 98d92ab..c1e9872 100644
--- a/docs-src/problems.md
+++ b/docs-src/problems.md
@@ -30,9 +30,7 @@ Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the
 
 The class `MaxWeightStableSetGenerator` can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter `fix_graph=True` is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions `n` and `p`. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution `w`. When `fix_graph=False`, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.
 
-### Benchmark challenges
-
-#### Challenge A
+### Challenge A
 
 * Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$
 * Random vertex weights $w_v \sim U(100, 150)$
@@ -96,7 +94,7 @@ By default, all generated prices, weights and capacities are rounded to the near
     * Freville, Arnaud, and Gérard Plateau. *An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem.* Discrete applied mathematics 49.1-3 (1994): 189-212.
     * Fréville, Arnaud. *The multidimensional 0–1 knapsack problem: An overview.* European Journal of Operational Research 155.1 (2004): 1-21.
     
-#### Challenge A
+### Challenge A
 
 * 250 variables, 10 constraints, fixed weights
 * $w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$
diff --git a/docs/index.html b/docs/index.html
index 595ee18..d7f582a 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -148,7 +148,8 @@
         
    
 
 
    MIPLearn
    
-
    MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).
    
+
    MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.
    
+
    The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).
    
 
    Features
    
 
      
 
    • 
@@ -174,7 +175,7 @@
 
    
 
    Souce Code
    
 
    
         
         
@@ -271,5 +272,5 @@
 
 
diff --git a/docs/problems/index.html b/docs/problems/index.html
index 5a9b042..ff7c725 100644
--- a/docs/problems/index.html
+++ b/docs/problems/index.html
@@ -145,11 +145,12 @@
                 
                 
  • Problem definition
    • 
                 
  • Random instance generator
    • 
-                
  • Benchmark challenges
    • 
+                
  • Challenge A
    • 
             
  • Multidimensional 0-1 Knapsack Problem
    • 
                 
                 
  • Problem definition
    • 
                 
  • Random instance generator
    • 
+                
  • Challenge A
    • 
     
 
    
         
    
@@ -173,8 +174,7 @@
 
    Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the problem is to find a stable set $S \subseteq V$ that maximizes $ \sum_{v \in V} w_v$. We recall that a subset $S \subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.
    
 
    Random instance generator
    
 
    The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w. When fix_graph=False, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.
    
-
    Benchmark challenges
    
-
    Challenge A
    
+
    Challenge A
    
 
      
 
    • Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$
      • 
 
    • Random vertex weights $w_v \sim U(100, 150)$
      • 
@@ -230,7 +230,7 @@ from the provided probability distributions K and u.Fréville, Arnaud. The multidimensional 0–1 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.
 
    
 
    
-
    Challenge A
    
+
    Challenge A
    
 
      
 
    • 250 variables, 10 constraints, fixed weights
      • 
 
    • $w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$
      • 
diff --git a/docs/search/search_index.json b/docs/search/search_index.json
index 3350a56..732557e 100644
--- a/docs/search/search_index.json
+++ b/docs/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/iSoron/miplearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/iSoron/miplearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Benchmark challenges Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21. Challenge A 250 variables, 10 constraints, fixed weights $w \\sim U(0, 1000), \\gamma \\sim U(0.95, 1.05)$ $K = 500, u \\sim U(0, 1), \\alpha = 0.25$ 500 training instances, 50 test instances MultiKnapsackGenerator(n=randint(low=250, high=251), m=randint(low=10, high=11), w=uniform(loc=0.0, scale=1000.0), K=uniform(loc=500.0, scale=0.0), u=uniform(loc=0.0, scale=1.0), alpha=uniform(loc=0.25, scale=0.0), fix_w=True, w_jitter=uniform(loc=0.95, scale=0.1), )","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#benchmark-challenges_1","text":"","title":"Benchmark challenges"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"problems/#challenge-a_1","text":"250 variables, 10 constraints, fixed weights $w \\sim U(0, 1000), \\gamma \\sim U(0.95, 1.05)$ $K = 500, u \\sim U(0, 1), \\alpha = 0.25$ 500 training instances, 50 test instances MultiKnapsackGenerator(n=randint(low=250, high=251), m=randint(low=10, high=11), w=uniform(loc=0.0, scale=1000.0), K=uniform(loc=500.0, scale=0.0), u=uniform(loc=0.0, scale=1.0), alpha=uniform(loc=0.25, scale=0.0), fix_w=True, w_jitter=uniform(loc=0.95, scale=0.1), )","title":"Challenge A"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Current Limitations"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"MIPLearn MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details). Features MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized. Documentation Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements Souce Code https://github.com/ANL-CEEESA/MIPLearn","title":"Home"},{"location":"#miplearn","text":"MIPLearn is an extensible framework for Learning-Enhanced Mixed-Integer Optimization , an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see benchmark results and references for more details).","title":"MIPLearn"},{"location":"#features","text":"MIPLearn proposes a flexible problem specification format, which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. MIPLearn provides a reference implementation of a Learning-Enhanced Solver , which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. MIPLearn is customizable and extensible . For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.","title":"Features"},{"location":"#documentation","text":"Installation and typical usage Benchmark utilities Benchmark problems, challenges and results Customizing the solver License, authors, references and acknowledgements","title":"Documentation"},{"location":"#souce-code","text":"https://github.com/ANL-CEEESA/MIPLearn","title":"Souce Code"},{"location":"about/","text":"About Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov > Acknowledgements Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. References Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696 License MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"About"},{"location":"about/#about","text":"","title":"About"},{"location":"about/#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Feng Qiu, Argonne National Laboratory < fqiu@anl.gov >","title":"Authors"},{"location":"about/#acknowledgements","text":"Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.","title":"Acknowledgements"},{"location":"about/#references","text":"Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems. Alinson S. Xavier, Feng Qiu, Shabbir Ahmed . INFORMS Journal on Computing (to appear). ArXiv:1902:01696","title":"References"},{"location":"about/#license","text":"MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"License"},{"location":"benchmark/","text":"Benchmarks Utilities Using BenchmarkRunner MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2. Saving and loading benchmark results When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Benchmark"},{"location":"benchmark/#benchmarks-utilities","text":"","title":"Benchmarks Utilities"},{"location":"benchmark/#using-benchmarkrunner","text":"MIPLearn provides the utility class BenchmarkRunner , which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage: from miplearn import BenchmarkRunner, LearningSolver # Create train and test instances train_instances = [...] test_instances = [...] # Training phase... training_solver = LearningSolver(...) training_solver.parallel_solve(train_instances, n_jobs=10) training_solver.save_state(\"data.bin\") # Test phase... test_solvers = { \"Baseline\": LearningSolver(...), # each solver may have different parameters \"Strategy A\": LearningSolver(...), \"Strategy B\": LearningSolver(...), \"Strategy C\": LearningSolver(...), } benchmark = BenchmarkRunner(test_solvers) benchmark.load_state(\"data.bin\") benchmark.fit() benchmark.parallel_solve(test_instances, n_jobs=2) print(benchmark.raw_results()) The method load_state loads the saved training data into each one of the provided solvers, while fit trains their respective ML models. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns: Solver, the name of the solver. Instance, the sequence number identifying the instance. Wallclock Time, the wallclock running time (in seconds) spent by the solver; Lower Bound, the best lower bound obtained by the solver; Upper Bound, the best upper bound obtained by the solver; Gap, the relative MIP integrality gap at the end of the optimization; Nodes, the number of explored branch-and-bound nodes. In addition to the above, there is also a \"Relative\" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.","title":"Using BenchmarkRunner"},{"location":"benchmark/#saving-and-loading-benchmark-results","text":"When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results , which can be used to avoid this repetition, as the next example shows: # Benchmark baseline solvers and save results to a file. benchmark = BenchmarkRunner(baseline_solvers) benchmark.load_state(\"training_data.bin\") benchmark.parallel_solve(test_instances) benchmark.save_results(\"baseline_results.csv\") # Benchmark remaining solvers, loading baseline results from file. benchmark = BenchmarkRunner(alternative_solvers) benchmark.load_state(\"training_data.bin\") benchmark.load_results(\"baseline_results.csv\") benchmark.parallel_solve(test_instances)","title":"Saving and loading benchmark results"},{"location":"customization/","text":"Customization Selecting the internal MIP solver By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Customization"},{"location":"customization/#customization","text":"","title":"Customization"},{"location":"customization/#selecting-the-internal-mip-solver","text":"By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX . To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance. from miplearn import LearningSolver solver = LearningSolver(solver=\"cplex\", time_limit=300, gap_tolerance=1e-3)","title":"Selecting the internal MIP solver"},{"location":"problems/","text":"Benchmark Problems, Challenges and Results MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters. Preliminaries Benchmark challenges When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from. Baseline results To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time. Maximum Weight Stable Set Problem Problem definition Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems. Random instance generator The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. Challenge A Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True) Multidimensional 0-1 Knapsack Problem Problem definition Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*} Random instance generator The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21. Challenge A 250 variables, 10 constraints, fixed weights $w \\sim U(0, 1000), \\gamma \\sim U(0.95, 1.05)$ $K = 500, u \\sim U(0, 1), \\alpha = 0.25$ 500 training instances, 50 test instances MultiKnapsackGenerator(n=randint(low=250, high=251), m=randint(low=10, high=11), w=uniform(loc=0.0, scale=1000.0), K=uniform(loc=500.0, scale=0.0), u=uniform(loc=0.0, scale=1.0), alpha=uniform(loc=0.25, scale=0.0), fix_w=True, w_jitter=uniform(loc=0.95, scale=0.1), )","title":"Problems"},{"location":"problems/#benchmark-problems-challenges-and-results","text":"MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for LearningSolver with default parameters.","title":"Benchmark Problems, Challenges and Results"},{"location":"problems/#preliminaries","text":"","title":"Preliminaries"},{"location":"problems/#benchmark-challenges","text":"When evaluating the performance of a conventional MIP solver, benchmark sets , such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the training instances ) before solving the actual set of instances we are interested in (the test instances ). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits. In MIPLearn, each optimization problem comes with a set of benchmark challenges , which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.","title":"Benchmark challenges"},{"location":"problems/#baseline-results","text":"To illustrate the performance of LearningSolver , and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see references . We compare three solvers: baseline: Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver) ml-exact: LearningSolver with default settings, using Gurobi 9.0 as internal MIP solver ml-heuristic: Same as above, but with mode=\"heuristic\" All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.","title":"Baseline results"},{"location":"problems/#maximum-weight-stable-set-problem","text":"","title":"Maximum Weight Stable Set Problem"},{"location":"problems/#problem-definition","text":"Given a simple undirected graph $G=(V,E)$ and weights $w \\in \\mathbb{R}^V$, the problem is to find a stable set $S \\subseteq V$ that maximizes $ \\sum_{v \\in V} w_v$. We recall that a subset $S \\subseteq V$ is a stable set if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.","title":"Problem definition"},{"location":"problems/#random-instance-generator","text":"The class MaxWeightStableSetGenerator can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter fix_graph=True is provided, one random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions n and p . To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution w . When fix_graph=False , a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.","title":"Random instance generator"},{"location":"problems/#challenge-a","text":"Fixed random Erd\u0151s-R\u00e9nyi graph $G_{n,p}$ with $n=200$ and $p=5\\%$ Random vertex weights $w_v \\sim U(100, 150)$ 500 training instances, 50 test instances MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.), n=randint(low=200, high=201), p=uniform(loc=0.05, scale=0.0), fix_graph=True)","title":"Challenge A"},{"location":"problems/#multidimensional-0-1-knapsack-problem","text":"","title":"Multidimensional 0-1 Knapsack Problem"},{"location":"problems/#problem-definition_1","text":"Given a set of $n$ items and $m$ types of resources (also called knapsacks ), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is: \\begin{align*} \\text{maximize} & \\sum_{j=1}^n p_j x_j \\\\ \\text{subject to} & \\sum_{j=1}^n w_{ij} x_j \\leq b_i & \\forall i=1,\\ldots,m \\\\ & x_j \\in \\{0,1\\} & \\forall j=1,\\ldots,n \\end{align*}","title":"Problem definition"},{"location":"problems/#random-instance-generator_1","text":"The class MultiKnapsackGenerator can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions n and m . The weights $w_{ij}$ are sampled independently from the provided distribution w . The capacity of knapsack $i$ is set to b_i = \\alpha_i \\sum_{j=1}^n w_{ij} where $\\alpha_i$, the tightness ratio, is sampled from the provided probability distribution alpha . To make the instances more challenging, the costs of the items are linearly correlated to their average weights. More specifically, the price of each item $j$ is set to: p_j = \\sum_{i=1}^m \\frac{w_{ij}}{m} + K u_j, where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled from the provided probability distributions K and u . If fix_w=True is provided, then $w_{ij}$ are kept the same in all generated instances. This also implies that $n$ and $m$ are kept fixed. Although the prices and capacities are derived from $w_{ij}$, as long as u and K are not constants, the generated instances will still not be completely identical. If a probability distribution w_jitter is provided, then item weights will be set to $w_{ij} \\gamma_{ij}$ where $\\gamma_{ij}$ is sampled from w_jitter . When combined with fix_w=True , this argument may be used to generate instances where the weight of each item is roughly the same, but not exactly identical, across all instances. The prices of the items and the capacities of the knapsacks will be calculated as above, but using these perturbed weights instead. By default, all generated prices, weights and capacities are rounded to the nearest integer number. If round=False is provided, this rounding will be disabled. References Freville, Arnaud, and G\u00e9rard Plateau. An efficient preprocessing procedure for the multidimensional 0\u20131 knapsack problem. Discrete applied mathematics 49.1-3 (1994): 189-212. Fr\u00e9ville, Arnaud. The multidimensional 0\u20131 knapsack problem: An overview. European Journal of Operational Research 155.1 (2004): 1-21.","title":"Random instance generator"},{"location":"problems/#challenge-a_1","text":"250 variables, 10 constraints, fixed weights $w \\sim U(0, 1000), \\gamma \\sim U(0.95, 1.05)$ $K = 500, u \\sim U(0, 1), \\alpha = 0.25$ 500 training instances, 50 test instances MultiKnapsackGenerator(n=randint(low=250, high=251), m=randint(low=10, high=11), w=uniform(loc=0.0, scale=1000.0), K=uniform(loc=500.0, scale=0.0), u=uniform(loc=0.0, scale=1.0), alpha=uniform(loc=0.25, scale=0.0), fix_w=True, w_jitter=uniform(loc=0.95, scale=0.1), )","title":"Challenge A"},{"location":"usage/","text":"Usage Installation The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned. Using LearningSolver The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process. Describing problem instances Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance. Obtaining heuristic solutions By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible. Saving and loading solver state After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading. Solving training instances in parallel In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections. Current Limitations Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#installation","text":"The package is currently available for Python and Pyomo. It can be installed as follows: pip install git+ssh://git@github.com/iSoron/miplearn.git A Julia + JuMP version of the package is planned.","title":"Installation"},{"location":"usage/#using-learningsolver","text":"The main class provided by this package is LearningSolver , a reference learning-enhanced MIP solver which automatically extracts information from previous runs to accelerate the solution of new instances. Assuming we already have a list of instances to solve, LearningSolver can be used as follows: from miplearn import LearningSolver all_instances = ... # user-provided list of instances to solve solver = LearningSolver() for instance in all_instances: solver.solve(instance) solver.fit() During the first call to solver.solve(instance) , the solver will process the instance from scratch, since no historical information is available, but it will already start gathering information. By calling solver.fit() , we instruct the solver to train all the internal Machine Learning models based on the information gathered so far. As this operation can be expensive, it may be performed after a larger batch of instances has been solved, instead of after every solve. After the first call to solver.fit() , subsequent calls to solver.solve(instance) will automatically use the trained Machine Learning models to accelerate the solution process.","title":"Using LearningSolver"},{"location":"usage/#describing-problem-instances","text":"Instances to be solved by LearningSolver must derive from the abstract class miplearn.Instance . The following three abstract methods must be implemented: instance.to_model() , which returns a concrete Pyomo model corresponding to the instance; instance.get_instance_features() , which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance; instance.get_variable_features(var, index) , which returns a 1-dimensional array of (numerical) features describing a particular decision variable. The first method is used by LearningSolver to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The user should keep a reference to this Pyomo model, in order to retrieve, for example, the optimal variable values. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See miplearn/problems/knapsack.py for a concrete example. An optional method which can be implemented is instance.get_variable_category(var, index) , which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, LearningSolver will use the same internal ML model to predict the values of both variables. By default, all variables belong to the \"default\" category, and therefore only one ML model is used for all variables. If the returned category is None , ML predictors will ignore the variable. It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that get_instance_features() must always return arrays of same length for all relevant instances of the problem. Similarly, get_variable_features(var, index) must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.","title":"Describing problem instances"},{"location":"usage/#obtaining-heuristic-solutions","text":"By default, LearningSolver uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts. For more significant performance benefits, LearningSolver can also be configured to place additional trust in the Machine Learning predictors, by using the mode=\"heuristic\" constructor argument. When operating in this mode, if a ML model is statistically shown (through stratified k-fold cross validation ) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see references and benchmark results ). Danger The heuristic mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.","title":"Obtaining heuristic solutions"},{"location":"usage/#saving-and-loading-solver-state","text":"After solving a large number of training instances, it may be desirable to save the current state of LearningSolver to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the methods solver.save_state(filename) and solver.load_state(filename) , as the following example illustrates: from miplearn import LearningSolver solver = LearningSolver() for instance in some_instances: solver.solve(instance) solver.fit() solver.save_state(\"/tmp/state.bin\") # Application restarts... solver = LearningSolver() solver.load_state(\"/tmp/state.bin\") for instance in more_instances: solver.solve(instance) In addition to storing the training data, save_state also stores all trained ML models. Therefore, if the the models were trained before saving the state to disk, it is not necessary to train them again after loading.","title":"Saving and loading solver state"},{"location":"usage/#solving-training-instances-in-parallel","text":"In many situations, training instances can be solved in parallel to accelerate the training process. LearningSolver provides the method parallel_solve(instances) to easily achieve this: from miplearn import LearningSolver # Training phase... solver = LearningSolver(...) # training solver parameters solver.parallel_solve(training_instances, n_jobs=4) solver.fit() solver.save_state(\"/tmp/data.bin\") # Test phase... solver = LearningSolver(...) # test solver parameters solver.load_state(\"/tmp/data.bin\") solver.solve(test_instance) After all training instances have been solved in parallel, the ML models can be trained and saved to disk as usual, using fit and save_state , as explained in the previous subsections.","title":"Solving training instances in parallel"},{"location":"usage/#current-limitations","text":"Only binary and continuous decision variables are currently supported. Solver callbacks (lazy constraints, cutting planes) are not currently supported. Only Gurobi and CPLEX are currently supported as internal MIP solvers.","title":"Current Limitations"}]}
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 The package is currently available for Python and Pyomo. It can be installed as follows:
 
 ```bash
-pip install git+ssh://git@github.com/iSoron/miplearn.git
+pip install git+ssh://git@github.com/ANL-CEEESA/MIPLearn.git
 ```
 
 A Julia + JuMP version of the package is planned.
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