ANL-CEEESA
/
MIPLearn
mirror of https://github.com/ANL-CEEESA/MIPLearn.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Implement AdaptiveSolver; reorganize imports

Browse Source
pull/1/head
Alinson S. Xavier 6 years ago
parent 1213f1d0a5
commit d3b5b43b94
4 changed files with 261 additions and 107 deletions
Show Stats Download Patch File Download Diff File

17
benchmark/benchmark.py
Unescape View File

@ -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["<challenge>"]
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(),
},
),
}

4
miplearn/__init__.py
Unescape View File

@ -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

8
miplearn/components/tests/test_warmstart.py
Unescape View File

@ -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()

337
miplearn/components/warmstart.py
Unescape View File

@ -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]
@ -50,6 +288,7 @@ class WarmStartPredictor(ABC):
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]
Write Preview
Loading…
Cancel
Save