Bump version: miplearn-0.2.0.dev12

Make gap computation robust against missing upper/lower bounds

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,26 @@
+---
+name: Bug report
+about: Something is broken in the package
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+## Description
+
+A clear and concise description of what the bug is.
+
+## Steps to Reproduce
+
+Please describe how can the developers reproduce the problem in their own computers. Code snippets and sample input files are specially helpful. For example:
+
+1. Install the package
+2. Run the code below with the attached input file...
+3. The following error appears...
+
+## System Information
+ - Operating System: [e.g. Ubuntu 20.04]
+ - Python version: [e.g. 3.6]
+ - Solver: [e.g. Gurobi 9.0]
+ - Package version: [e.g. 0.1.0]

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,8 @@
+blank_issues_enabled: false
+contact_links:
+  - name: Feature Request
+    url: https://github.com/ANL-CEEESA/MIPLearn/discussions/categories/feature-requests
+    about:  Submit ideas for new features and small enhancements
+  - name: Help & FAQ
+    url: https://github.com/ANL-CEEESA/MIPLearn/discussions/categories/help-faq
+    about: Ask questions about the package and get help from the community

.github/workflows/lint.yml

@@ -0,0 +1,11 @@
+name: Lint
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+      - uses: psf/black@20.8b1

.github/workflows/main.yml

@@ -1,18 +0,0 @@
-name: Test
-on: push
-jobs:
-  build:
-    runs-on: self-hosted
-    steps:
-    - uses: actions/checkout@v1
-    - name: Run tests
-      run: |
-        rm -rf ~/.conda/envs/miplearn-test
-        yes | conda create --name miplearn-test python=3.6
-        (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)
-        make install test \
-                PYTHON=~/.conda/envs/miplearn-test/bin/python \
-                PIP=~/.conda/envs/miplearn-test/bin/pip3 \
-                PYTEST=~/.conda/envs/miplearn-test/bin/pytest
-

.github/workflows/test.yml

@@ -0,0 +1,27 @@
+name: Test
+on:
+  push:
+  pull_request:
+  schedule:
+    - cron: '45 10 * * *'
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8]
+
+    steps:
+    - name: Check out source code
+      uses: actions/checkout@v2
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install dependencies
+      run: make install-deps
+
+    - name: Test
+      run: make test

.gitignore

@@ -1,5 +1,7 @@
 TODO.md
 .idea
+*.gz
+done
 *.bin
 *$py.class
 *.cover
@@ -39,8 +41,8 @@ TODO.md
 /site
 ENV/
 MANIFEST
-__pycache__/
-__pypackages__/
+**/__pycache__/
+**/__pypackages__/
 build/
 celerybeat-schedule
 celerybeat.pid
@@ -56,7 +58,6 @@ eggs/
 env.bak/
 env/
 htmlcov/
-instance/
 ipython_config.py
 lib/
 lib64/
@@ -75,3 +76,8 @@ venv.bak/
 venv/
 wheels/
 notebooks/
+.vscode
+tmp
+benchmark/tsp
+benchmark/stab
+benchmark/knapsack

.mypy.ini

@@ -0,0 +1,7 @@
+[mypy]
+ignore_missing_imports = True
+disallow_untyped_defs = True
+disallow_untyped_calls = True
+disallow_incomplete_defs = True
+pretty = True
+no_implicit_optional = True

.pre-commit-config.yaml

@@ -0,0 +1,6 @@
+repos:
+  - repo: https://github.com/ambv/black
+    rev: 20.8b1
+    hooks:
+    - id: black
+      args: ["--check"]

CHANGELOG.md

@@ -0,0 +1,45 @@
+# MIPLearn: Changelog
+
+## [0.2.0] - [Unreleased]
+
+### Added
+
+- **Added two new machine learning components:**
+  - Added `StaticLazyConstraintComponent`, which allows the user to mark some constraints in the formulation as lazy, instead of constructing them in a callback. ML predicts which static lazy constraints should be kept in the formulation, and which should be removed. 
+  - Added `UserCutComponents`, which predicts which user cuts should be generated and added to the formulation as constraints ahead-of-time, before solving the MIP.
+- **Added support to additional MILP solvers:**
+  - Added support for CPLEX and XPRESS, through the Pyomo modeling language, in addition to (existing) Gurobi. The solver classes are named `CplexPyomoSolver`, `XpressPyomoSolver` and `GurobiPyomoSolver`.
+  - Added support for Gurobi without any modeling language. The solver class is named `GurobiSolver`. In this case, `instance.to_model` should return ` gp.Model` object.
+  - Added support to direct MPS files, produced externally, through the `GurobiSolver` class mentioned above.
+- **Added dynamic thresholds:** 
+  - In previous versions of the package, it was necessary to manually adjust component aggressiveness to reach a desired precision/recall. This can now be done automatically with `MinProbabilityThreshold`, `MinPrecisionThreshold` and `MinRecallThreshold`.
+- **Reduced memory requirements:**
+  - Previous versions of the package required all training instances to be kept in memory at all times, which was prohibitive for large-scale problems. It is now possible to store instances in file until they are needed, using `PickledGzInstance`.
+- **Refactoring:**
+  - Added static types to all classes (with mypy).
+
+### Changed
+
+- Variables are now referenced by their names, instead of tuples `(var_name, index)`. This change was required to improve the compatibility with modeling languages other than Pyomo, which do not follow this convention. For performance reasons, the functions `get_variable_features` and `get_variable_categories` should now return a dictionary containing categories and features for all relevant variables. Previously, MIPLearn had to perform two function calls per variable, which was too slow for very large models.
+- Internal solvers must now be specified as objects, instead of strings. For example,
+  ```python
+  solver = LearningSolver(
+      solver=GurobiPyomoSolver(
+          params={
+              "TimeLimit": 300,
+              "Threads": 4,
+          }      
+      )
+  )
+  ```
+- `LazyConstraintComponent` has been renamed to `DynamicLazyConstraintsComponent`.
+- Categories, lazy constraints and cutting plane identifiers must now be strings, instead `Hashable`. This change was required for compatibility with HDF5 data format.
+
+### Removed
+
+- Temporarily removed the experimental `BranchPriorityComponent`. This component will be re-added in the Julia version of the package.
+- Removed `solver.add` method, previously used to add components to an existing solver. Use the constructor `LearningSolver(components=[...])` instead.
+
+## [0.1.0] - 2020-11-23
+
+- Initial public release

Makefile

@@ -1,13 +1,14 @@
 PYTHON      := python3
 PYTEST      := pytest
-PIP         := pip3
-PYTEST_ARGS := -W ignore::DeprecationWarning -vv -x --log-level=DEBUG
+PIP         := $(PYTHON) -m pip
+MYPY        := $(PYTHON) -m mypy
+PYTEST_ARGS := -W ignore::DeprecationWarning -vv --log-level=DEBUG
 VERSION     := 0.2
 
 all: docs test
 
 clean:
-	rm -rf build
+	rm -rf build/* dist/*
 
 develop:
 	$(PYTHON) setup.py develop
@@ -19,19 +20,31 @@ dist-upload:
 	$(PYTHON) -m twine upload dist/*
 
 docs:
-	mkdocs build -d ../docs/$(VERSION)/
+	rm -rf ../docs/$(VERSION) 
+	cd docs; make clean; make dirhtml
+	rsync -avP --delete-after docs/_build/dirhtml/ ../docs/$(VERSION)
 
-docs-dev:
-	mkdocs build -d ../docs/dev/
+
+install-deps:
+	$(PIP) install --upgrade pip
+	$(PIP) install --upgrade -i https://pypi.gurobi.com gurobipy
+	$(PIP) install --upgrade xpress
+	$(PIP) install --upgrade -r requirements.txt
 
 install:
-	$(PIP) install -r requirements.txt
 	$(PYTHON) setup.py install
 
 uninstall:
 	$(PIP) uninstall miplearn
 
-test:
-	$(PYTEST) $(PYTEST_ARGS)
+reformat:
+	$(PYTHON) -m black .
 
-.PHONY: test test-watch docs install
+test:
+	rm -rf .mypy_cache
+	$(MYPY) -p miplearn
+	$(MYPY) -p tests
+	$(MYPY) -p benchmark
+	$(PYTEST) $(PYTEST_ARGS) 
+
+.PHONY: test test-watch docs install dist

README.md

@@ -1,12 +1,22 @@
-![Build status](https://img.shields.io/github/workflow/status/ANL-CEEESA/MIPLearn/Test)
-![BSD License](https://img.shields.io/badge/license-BSD-blue)
+<h1 align="center">MIPLearn</h1>
+<p align="center">
+  <a href="https://github.com/ANL-CEEESA/MIPLearn/actions">
+    <img src="https://github.com/ANL-CEEESA/MIPLearn/workflows/Test/badge.svg">
+  </a>
+  <a href="https://doi.org/10.5281/zenodo.4287567">
+    <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.4287567.svg">
+  </a>
+  <a href="https://github.com/ANL-CEEESA/MIPLearn/releases/">
+    <img src="https://img.shields.io/github/v/release/ANL-CEEESA/MIPLearn?include_prereleases&label=pre-release">
+  </a>
+  <a href="https://github.com/ANL-CEEESA/MIPLearn/discussions">
+    <img src="https://img.shields.io/badge/GitHub-Discussions-%23fc4ebc" />
+  </a>
+</p>
 
-MIPLearn
-========
+**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML).
 
-**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/0.1/problems/) and [references](https://anl-ceeesa.github.io/MIPLearn/0.1/about/)).
+MIPLearn uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers such as CPLEX, Gurobi or XPRESS. Unlike pure ML methods, MIPLearn is not only able to find high-quality solutions to discrete optimization problems, but it can also prove the optimality and feasibility of these solutions. Unlike conventional MIP solvers, MIPLearn can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). For certain classes of problems, this approach has been shown to provide significant performance benefits (see [benchmarks](https://anl-ceeesa.github.io/MIPLearn/0.1/problems/) and [references](https://anl-ceeesa.github.io/MIPLearn/0.1/about/)).
 
 Features
 --------
@@ -23,6 +33,22 @@ Documentation
 
 For installation instructions, basic usage and benchmarks results, see the [official documentation](https://anl-ceeesa.github.io/MIPLearn/).
 
+Acknowledgments
+---------------
+* 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.
+* Based upon work supported by the **U.S. Department of Energy Advanced Grid Modeling Program** under Grant DE-OE0000875.
+
+Citing MIPLearn
+---------------
+
+If you use MIPLearn in your research (either the solver or the included problem generators), we kindly request that you cite the package as follows:
+
+* **Alinson S. Xavier, Feng Qiu.** *MIPLearn: An Extensible Framework for Learning-Enhanced Optimization*. Zenodo (2020). DOI: [10.5281/zenodo.4287567](https://doi.org/10.5281/zenodo.4287567)
+
+If you use MIPLearn in the field of power systems optimization, we kindly request that you cite the reference below, in which the main techniques implemented in MIPLearn were first developed:
+
+* **Alinson S. Xavier, Feng Qiu, Shabbir Ahmed.** *Learning to Solve Large-Scale Unit Commitment Problems.* INFORMS Journal on Computing (2020). DOI: [10.1287/ijoc.2020.0976](https://doi.org/10.1287/ijoc.2020.0976)
+
 License
 -------
 

benchmark/Makefile

@@ -3,19 +3,19 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 #  Written by Alinson S. Xavier <axavier@anl.gov>
 
-DATAFILE := miplearn-train-data.tar.gz
-
 CHALLENGES := \
     stab/ChallengeA \
     knapsack/ChallengeA \
     tsp/ChallengeA
 
-main: $(addsuffix /performance.png, $(CHALLENGES))
+test: $(addsuffix /performance.png, $(CHALLENGES))
 
-%/train_instances.bin:
+train: $(addsuffix /train/done, $(CHALLENGES))
+
+%/train/done:
 	python benchmark.py train $*
 
-%/benchmark_baseline.csv: %/train_instances.bin
+%/benchmark_baseline.csv: %/train/done
 	python benchmark.py test-baseline $*
 
 %/benchmark_ml.csv: %/benchmark_baseline.csv
@@ -27,21 +27,5 @@ main: $(addsuffix /performance.png, $(CHALLENGES))
 clean:
 	rm -rvf $(CHALLENGES)
 
-clean-ml:
-	rm -rvf */*/benchmark_ml.csv
-
-clean-charts:
-	rm -rfv */*/performance.png
-
-training-data-push:
-	tar -cvvzf $(DATAFILE) */*/*.bin
-	rsync -avP $(DATAFILE) andromeda:/www/axavier.org/projects/miplearn/$(DATAFILE)
-	rm -fv $(DATAFILE)
-
-training-data-pull:
-	wget https://axavier.org/projects/miplearn/$(DATAFILE)
-	tar -xvvzf $(DATAFILE)
-	rm -f $(DATAFILE)
-
-.PHONY: clean clean-ml clean-charts
+.PHONY: clean
 .SECONDARY:

benchmark/benchmark.py

@@ -1,201 +1,268 @@
 #!/usr/bin/env python
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-"""Benchmark script
+"""MIPLearn Benchmark Scripts
 
 Usage:
-    benchmark.py train <challenge>
-    benchmark.py test-baseline <challenge>
-    benchmark.py test-ml <challenge>
+    benchmark.py train [options] <challenge>
+    benchmark.py test-baseline [options] <challenge>
+    benchmark.py test-ml [options] <challenge>
     benchmark.py charts <challenge>
     
 Options:
-    -h --help    Show this screen
+    -h --help               Show this screen
+    --train-jobs=<n>        Number of instances to solve in parallel during training [default: 10]
+    --train-time-limit=<n>  Solver time limit during training in seconds [default: 900]
+    --test-jobs=<n>         Number of instances to solve in parallel during test [default: 5]
+    --test-time-limit=<n>   Solver time limit during test in seconds [default: 900]
+    --solver-threads=<n>    Number of threads the solver is allowed to use [default: 4]
 """
-from docopt import docopt
-import importlib, pathlib
-from miplearn import (LearningSolver, BenchmarkRunner)
-from numpy import median
-import pyomo.environ as pe
-import pickle
+import glob
+import importlib
 import logging
-import sys
+import os
+from pathlib import Path
+from typing import Dict, List
 
-logging.basicConfig(format='%(asctime)s %(levelname).1s %(name)s: %(message)12s',
-                    datefmt='%H:%M:%S',
-                    level=logging.INFO,
-                    stream=sys.stdout)
-logging.getLogger('gurobipy').setLevel(logging.ERROR)
-logging.getLogger('pyomo.core').setLevel(logging.ERROR)
-logging.getLogger('miplearn').setLevel(logging.INFO)
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+from docopt import docopt
+from numpy import median
+
+from miplearn import (
+    LearningSolver,
+    BenchmarkRunner,
+    GurobiPyomoSolver,
+    setup_logger,
+    PickleGzInstance,
+    write_pickle_gz_multiple,
+    Instance,
+)
+
+setup_logger()
+logging.getLogger("gurobipy").setLevel(logging.ERROR)
+logging.getLogger("pyomo.core").setLevel(logging.ERROR)
 logger = logging.getLogger("benchmark")
 
-n_jobs = 10
-train_time_limit = 3600
-test_time_limit = 900
-internal_solver = "gurobi"
 
-args = docopt(__doc__)
-basepath = args["<challenge>"]
-pathlib.Path(basepath).mkdir(parents=True, exist_ok=True)
-
-
-def save(obj, filename):
-    logger.info("Writing %s..." % filename)
-    with open(filename, "wb") as file:
-        pickle.dump(obj, file)
-        
-        
-def load(filename):
-    import pickle
-    with open(filename, "rb") as file:
-        return pickle.load(file)        
-        
-        
-def train():
+def train(args: Dict) -> None:
+    basepath = args["<challenge>"]
     problem_name, challenge_name = args["<challenge>"].split("/")
-    pkg = importlib.import_module("miplearn.problems.%s" % problem_name)
+    pkg = importlib.import_module(f"miplearn.problems.{problem_name}")
     challenge = getattr(pkg, challenge_name)()
-    train_instances = challenge.training_instances
-    test_instances  = challenge.test_instances
-    solver = LearningSolver(time_limit=train_time_limit,
-                            solver=internal_solver,
-                            components={})
-    solver.parallel_solve(train_instances, n_jobs=n_jobs)
-    save(train_instances, "%s/train_instances.bin" % basepath)
-    save(test_instances, "%s/test_instances.bin" % basepath)
-    
-    
-def test_baseline():
-    test_instances = load("%s/test_instances.bin" % basepath)
-    solvers = {
-        "baseline": LearningSolver(
-            time_limit=test_time_limit,
-            solver=internal_solver,
-        ),
-    }
-    benchmark = BenchmarkRunner(solvers)
-    benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
-    benchmark.save_results("%s/benchmark_baseline.csv" % basepath)
-    
-    
-def test_ml():
-    logger.info("Loading instances...")
-    train_instances = load("%s/train_instances.bin" % basepath)
-    test_instances = load("%s/test_instances.bin" % basepath)
-    solvers = {
-        "ml-exact": LearningSolver(
-            time_limit=test_time_limit,
-            solver=internal_solver,
-        ),
-        "ml-heuristic": LearningSolver(
-            time_limit=test_time_limit,
-            solver=internal_solver,
-            mode="heuristic",
-        ),
-    }
-    benchmark = BenchmarkRunner(solvers)
-    logger.info("Loading results...")
-    benchmark.load_results("%s/benchmark_baseline.csv" % basepath)
-    logger.info("Fitting...")
-    benchmark.fit(train_instances)
-    logger.info("Solving...")
-    benchmark.parallel_solve(test_instances, n_jobs=n_jobs)
-    benchmark.save_results("%s/benchmark_ml.csv" % basepath)
 
-    
-def charts():
-    import matplotlib.pyplot as plt
-    import seaborn as sns
+    if not os.path.isdir(f"{basepath}/train"):
+        write_pickle_gz_multiple(challenge.training_instances, f"{basepath}/train")
+        write_pickle_gz_multiple(challenge.test_instances, f"{basepath}/test")
+
+    done_filename = f"{basepath}/train/done"
+    if not os.path.isfile(done_filename):
+        train_instances: List[Instance] = [
+            PickleGzInstance(f) for f in glob.glob(f"{basepath}/train/*.gz")
+        ]
+        solver = LearningSolver(
+            solver=GurobiPyomoSolver(
+                params={
+                    "TimeLimit": int(args["--train-time-limit"]),
+                    "Threads": int(args["--solver-threads"]),
+                }
+            ),
+        )
+        solver.parallel_solve(
+            train_instances,
+            n_jobs=int(args["--train-jobs"]),
+        )
+        Path(done_filename).touch(exist_ok=True)
+
+
+def test_baseline(args: Dict) -> None:
+    basepath = args["<challenge>"]
+    test_instances: List[Instance] = [
+        PickleGzInstance(f) for f in glob.glob(f"{basepath}/test/*.gz")
+    ]
+    csv_filename = f"{basepath}/benchmark_baseline.csv"
+    if not os.path.isfile(csv_filename):
+        solvers = {
+            "baseline": LearningSolver(
+                solver=GurobiPyomoSolver(
+                    params={
+                        "TimeLimit": int(args["--test-time-limit"]),
+                        "Threads": int(args["--solver-threads"]),
+                    }
+                ),
+            ),
+        }
+        benchmark = BenchmarkRunner(solvers)
+        benchmark.parallel_solve(
+            test_instances,
+            n_jobs=int(args["--test-jobs"]),
+        )
+        benchmark.write_csv(csv_filename)
+
+
+def test_ml(args: Dict) -> None:
+    basepath = args["<challenge>"]
+    test_instances: List[Instance] = [
+        PickleGzInstance(f) for f in glob.glob(f"{basepath}/test/*.gz")
+    ]
+    train_instances: List[Instance] = [
+        PickleGzInstance(f) for f in glob.glob(f"{basepath}/train/*.gz")
+    ]
+    csv_filename = f"{basepath}/benchmark_ml.csv"
+    if not os.path.isfile(csv_filename):
+        solvers = {
+            "ml-exact": LearningSolver(
+                solver=GurobiPyomoSolver(
+                    params={
+                        "TimeLimit": int(args["--test-time-limit"]),
+                        "Threads": int(args["--solver-threads"]),
+                    }
+                ),
+            ),
+            "ml-heuristic": LearningSolver(
+                solver=GurobiPyomoSolver(
+                    params={
+                        "TimeLimit": int(args["--test-time-limit"]),
+                        "Threads": int(args["--solver-threads"]),
+                    }
+                ),
+                mode="heuristic",
+            ),
+        }
+        benchmark = BenchmarkRunner(solvers)
+        benchmark.fit(train_instances)
+        benchmark.parallel_solve(
+            test_instances,
+            n_jobs=int(args["--test-jobs"]),
+        )
+        benchmark.write_csv(csv_filename)
+
+
+def charts(args: Dict) -> None:
+    basepath = args["<challenge>"]
     sns.set_style("whitegrid")
     sns.set_palette("Blues_r")
-    benchmark = BenchmarkRunner({})
-    benchmark.load_results("%s/benchmark_ml.csv" % basepath)
-    results = benchmark.raw_results()
-    results["Gap (%)"] = results["Gap"] * 100.0
-    
+
+    csv_files = [
+        f"{basepath}/benchmark_baseline.csv",
+        f"{basepath}/benchmark_ml.csv",
+    ]
+    results = pd.concat(map(pd.read_csv, csv_files))
+    groups = results.groupby("Instance")
+    best_lower_bound = groups["Lower bound"].transform("max")
+    best_upper_bound = groups["Upper bound"].transform("min")
+    results["Relative lower bound"] = results["Lower bound"] / best_lower_bound
+    results["Relative upper bound"] = results["Upper bound"] / best_upper_bound
+
     sense = results.loc[0, "Sense"]
-    if sense == "min":
-        primal_column = "Relative Upper Bound"
-        obj_column = "Upper Bound"
-        predicted_obj_column = "Predicted UB"
+    if (sense == "min").any():
+        primal_column = "Relative upper bound"
+        obj_column = "Upper bound"
+        predicted_obj_column = "Objective: Predicted upper bound"
     else:
-        primal_column = "Relative Lower Bound"
-        obj_column = "Lower Bound"
-        predicted_obj_column = "Predicted LB"
-        
-    palette={
-        "baseline": "#9b59b6", 
-        "ml-exact": "#3498db",
-        "ml-heuristic": "#95a5a6"
-    }
-    fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1,
-                                             ncols=4,
-                                             figsize=(12,4),
-                                             gridspec_kw={'width_ratios': [2, 1, 1, 2]},
-                                            )
-    sns.stripplot(x="Solver",
-                  y="Wallclock Time",
-                  data=results,
-                  ax=ax1,
-                  jitter=0.25,
-                  palette=palette,
-                  size=4.0,
-               );
-    sns.barplot(x="Solver",
-                y="Wallclock Time",
-                data=results,
-                ax=ax1,
-                errwidth=0.,
-                alpha=0.4,
-                palette=palette,
-                estimator=median,
-               );
-    ax1.set(ylabel='Wallclock Time (s)')
+        primal_column = "Relative lower bound"
+        obj_column = "Lower bound"
+        predicted_obj_column = "Objective: Predicted lower bound"
+
+    palette = {"baseline": "#9b59b6", "ml-exact": "#3498db", "ml-heuristic": "#95a5a6"}
+    fig, (ax1, ax2, ax3, ax4) = plt.subplots(
+        nrows=1,
+        ncols=4,
+        figsize=(12, 4),
+        gridspec_kw={"width_ratios": [2, 1, 1, 2]},
+    )
+
+    # Wallclock time
+    sns.stripplot(
+        x="Solver",
+        y="Wallclock time",
+        data=results,
+        ax=ax1,
+        jitter=0.25,
+        palette=palette,
+        size=4.0,
+    )
+    sns.barplot(
+        x="Solver",
+        y="Wallclock time",
+        data=results,
+        ax=ax1,
+        errwidth=0.0,
+        alpha=0.4,
+        palette=palette,
+        estimator=median,
+    )
+    ax1.set(ylabel="Wallclock time (s)")
+
+    # Gap
     ax2.set_ylim(-0.5, 5.5)
-    sns.stripplot(x="Solver",
-                  y="Gap (%)",
-                  jitter=0.25,
-                  data=results[results["Solver"] != "ml-heuristic"],
-                  ax=ax2,
-                  palette=palette,
-                  size=4.0,
-                 );
-    ax3.set_ylim(0.95,1.05)
-    sns.stripplot(x="Solver",
-                  y=primal_column,
-                  jitter=0.25,
-                  data=results[results["Solver"] == "ml-heuristic"],
-                  ax=ax3,
-                  palette=palette,
-                 );
-    
-    sns.scatterplot(x=obj_column,
-                    y=predicted_obj_column,
-                    hue="Solver",
-                    data=results[results["Solver"] == "ml-exact"],
-                    ax=ax4,
-                    palette=palette,
-                   );
+    sns.stripplot(
+        x="Solver",
+        y="Gap",
+        jitter=0.25,
+        data=results[results["Solver"] != "ml-heuristic"],
+        ax=ax2,
+        palette=palette,
+        size=4.0,
+    )
+
+    # Relative primal bound
+    ax3.set_ylim(0.95, 1.05)
+    sns.stripplot(
+        x="Solver",
+        y=primal_column,
+        jitter=0.25,
+        data=results[results["Solver"] == "ml-heuristic"],
+        ax=ax3,
+        palette=palette,
+    )
+    sns.scatterplot(
+        x=obj_column,
+        y=predicted_obj_column,
+        hue="Solver",
+        data=results[results["Solver"] == "ml-exact"],
+        ax=ax4,
+        palette=palette,
+    )
+
+    # Predicted vs actual primal bound
     xlim, ylim = ax4.get_xlim(), ax4.get_ylim()
-    ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls='-', color="#cccccc");
+    ax4.plot(
+        [-1e10, 1e10],
+        [-1e10, 1e10],
+        ls="-",
+        color="#cccccc",
+    )
     ax4.set_xlim(xlim)
     ax4.set_ylim(ylim)
     ax4.get_legend().remove()
-    
+    ax4.set(
+        ylabel="Predicted value",
+        xlabel="Actual value",
+    )
+
     fig.tight_layout()
-    plt.savefig("%s/performance.png" % basepath,
-                bbox_inches='tight',
-                dpi=150)
+    plt.savefig(
+        f"{basepath}/performance.png",
+        bbox_inches="tight",
+        dpi=150,
+    )
+
+
+def main() -> None:
+    args = docopt(__doc__)
+    if args["train"]:
+        train(args)
+    if args["test-baseline"]:
+        test_baseline(args)
+    if args["test-ml"]:
+        test_ml(args)
+    if args["charts"]:
+        charts(args)
+
 
 if __name__ == "__main__":
-    if args["train"]:
-        train()
-    if args["test-baseline"]:
-        test_baseline()
-    if args["test-ml"]:
-        test_ml()
-    if args["charts"]:
-        charts()
+    main()

benchmark/knapsack/ChallengeA/benchmark_baseline.csv

@@ -1,51 +0,0 @@
-,Solver,Instance,Wallclock Time,Lower Bound,Upper Bound,Gap,Nodes,Mode,Relative Lower Bound,Relative Upper Bound,Relative Wallclock Time,Relative Gap,Relative Nodes
-0,baseline,0,662.7372989654541,59162.0,59167.0,8.451370812345763e-05,18688107.0,exact,1.0,1.0,1.0,1.0,1.0
-1,baseline,1,900.0007548332214,59137.0,59256.0,0.002012276578115224,24175550.0,exact,1.0,1.0,1.0,1.0,1.0
-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
-5,baseline,5,900.002925157547,59126.0,59244.0,0.0019957379156377904,23457042.0,exact,1.0,1.0,1.0,1.0,1.0
-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
-9,baseline,9,900.0015439987183,59130.0,59256.0,0.00213089802130898,23227790.0,exact,1.0,1.0,1.0,1.0,1.0
-10,baseline,10,900.0024099349976,59127.0,59201.0,0.0012515432881762985,25015636.0,exact,1.0,1.0,1.0,1.0,1.0
-11,baseline,11,900.0025849342346,59198.0,59289.0,0.0015372140950707794,24558832.0,exact,1.0,1.0,1.0,1.0,1.0
-12,baseline,12,900.0022029876709,59102.0,59224.0,0.002064227944908802,24026788.0,exact,1.0,1.0,1.0,1.0,1.0
-13,baseline,13,900.0011007785797,59150.0,59206.0,0.0009467455621301775,24953207.0,exact,1.0,1.0,1.0,1.0,1.0
-14,baseline,14,900.0014700889587,59169.0,59250.0,0.0013689600973482736,25494260.0,exact,1.0,1.0,1.0,1.0,1.0
-15,baseline,15,900.0013790130615,59083.0,59196.0,0.0019125636816004605,23792716.0,exact,1.0,1.0,1.0,1.0,1.0
-16,baseline,16,900.0020098686218,59126.0,59233.0,0.0018096945506207082,23398798.0,exact,1.0,1.0,1.0,1.0,1.0
-17,baseline,17,900.0023510456085,59156.0,59197.0,0.0006930826965988235,25573586.0,exact,1.0,1.0,1.0,1.0,1.0
-18,baseline,18,900.002711057663,59118.0,59211.0,0.0015731249365675429,24489136.0,exact,1.0,1.0,1.0,1.0,1.0
-19,baseline,19,724.1934628486633,59159.0,59164.0,8.451799388089724e-05,20931760.0,exact,1.0,1.0,1.0,1.0,1.0
-20,baseline,20,900.0011439323425,59068.0,59191.0,0.0020823457709758246,23411794.0,exact,1.0,1.0,1.0,1.0,1.0
-21,baseline,21,380.06568694114685,59175.0,59180.0,8.449514152936207e-05,11618526.0,exact,1.0,1.0,1.0,1.0,1.0
-22,baseline,22,900.0016028881073,59121.0,59154.94711904252,0.0005741973079365614,26352886.0,exact,1.0,1.0,1.0,1.0,1.0
-23,baseline,23,230.25152111053467,59193.0,59198.0,8.44694474008751e-05,6776049.0,exact,1.0,1.0,1.0,1.0,1.0
-24,baseline,24,900.0010840892792,59162.0,59240.0,0.001318413846725939,24727727.0,exact,1.0,1.0,1.0,1.0,1.0
-25,baseline,25,900.0015320777893,59096.0,59210.0,0.001929064572898335,23438919.0,exact,1.0,1.0,1.0,1.0,1.0
-26,baseline,26,900.0015478134155,59089.0,59203.0,0.001929293100238623,23826788.0,exact,1.0,1.0,1.0,1.0,1.0
-27,baseline,27,900.0010070800781,59153.0,59249.0,0.0016229100806383447,24336831.0,exact,1.0,1.0,1.0,1.0,1.0
-28,baseline,28,900.001277923584,59112.0,59208.0,0.0016240357287860333,25111591.0,exact,1.0,1.0,1.0,1.0,1.0
-29,baseline,29,900.0012440681458,59182.0,59263.0,0.0013686593896792944,24919871.0,exact,1.0,1.0,1.0,1.0,1.0
-30,baseline,30,900.0012910366058,59134.0,59241.0,0.001809449724354855,23615391.0,exact,1.0,1.0,1.0,1.0,1.0
-31,baseline,31,900.0023548603058,59082.0,59169.0,0.0014725297044785213,26213904.0,exact,1.0,1.0,1.0,1.0,1.0
-32,baseline,32,875.9193549156189,59175.0,59180.0,8.449514152936207e-05,24935695.0,exact,1.0,1.0,1.0,1.0,1.0
-33,baseline,33,900.0018489360809,59088.0,59177.0,0.0015062279989168698,25210167.0,exact,1.0,1.0,1.0,1.0,1.0
-34,baseline,34,232.1541509628296,59190.0,59195.0,8.447372867038352e-05,7309410.0,exact,1.0,1.0,1.0,1.0,1.0
-35,baseline,35,900.0025398731232,59183.0,59262.0,0.001334842775797104,23927493.0,exact,1.0,1.0,1.0,1.0,1.0
-36,baseline,36,900.0010929107666,59166.0,59254.0,0.00148734070243045,25589946.0,exact,1.0,1.0,1.0,1.0,1.0
-37,baseline,37,622.9371509552002,59202.0,59207.0,8.445660619573663e-05,18595087.0,exact,1.0,1.0,1.0,1.0,1.0
-38,baseline,38,557.924427986145,59212.0,59217.0,8.444234276835777e-05,16270407.0,exact,1.0,1.0,1.0,1.0,1.0
-39,baseline,39,900.0010092258453,59143.0,59185.0,0.0007101432122144632,26304077.0,exact,1.0,1.0,1.0,1.0,1.0
-40,baseline,40,900.0011250972748,59158.0,59242.99535479154,0.0014367516615088902,23949337.0,exact,1.0,1.0,1.0,1.0,1.0
-41,baseline,41,900.000893831253,59170.0,59257.0,0.0014703396991718777,24299427.0,exact,1.0,1.0,1.0,1.0,1.0
-42,baseline,42,900.0017001628876,59089.0,59228.0,0.002352383692396216,23229681.0,exact,1.0,1.0,1.0,1.0,1.0
-43,baseline,43,127.60789799690247,59232.0,59237.0,8.44138303619665e-05,4041704.0,exact,1.0,1.0,1.0,1.0,1.0
-44,baseline,44,166.38699293136597,59201.0,59206.0,8.445803280349994e-05,5151689.0,exact,1.0,1.0,1.0,1.0,1.0
-45,baseline,45,900.0007989406586,59135.0,59247.0,0.001893971421324089,26922402.0,exact,1.0,1.0,1.0,1.0,1.0
-46,baseline,46,900.001415014267,59152.0,59254.0,0.001724371111712199,26485728.0,exact,1.0,1.0,1.0,1.0,1.0
-47,baseline,47,900.0020279884338,59123.0,59235.0,0.0018943558344468312,28222784.0,exact,1.0,1.0,1.0,1.0,1.0
-48,baseline,48,900.0011022090912,59176.0,59284.0,0.0018250642152223874,28675410.0,exact,1.0,1.0,1.0,1.0,1.0
-49,baseline,49,900.0012428760529,59150.0,59206.0,0.0009467455621301775,30531240.0,exact,1.0,1.0,1.0,1.0,1.0

benchmark/knapsack/ChallengeA/benchmark_ml.csv

@@ -1,151 +0,0 @@
-,Solver,Instance,Wallclock Time,Lower Bound,Upper Bound,Gap,Nodes,Mode,Relative Lower Bound,Relative Upper Bound,Relative Wallclock Time,Relative Gap,Relative Nodes,Predicted LB,Predicted UB,Sense
-0,baseline,0,662.7372989654541,59162.0,59167.0,8.451370812345763e-05,18688107.0,exact,1.0,1.0004734608295711,8.70828628181939,1.0,5.622307810564691,,,
-1,baseline,1,900.0007548332214,59137.0,59256.0,0.002012276578115224,24175550.0,exact,1.0,1.0019275641675967,5.256438928065297,23.8,2.9939288096459404,,,
-2,baseline,2,900.0016160011293,59186.0,59285.0,0.0016726928665562802,24089218.0,exact,1.0,1.0015880792688077,6.615464163621432,19.8,3.971008553523482,,,
-3,baseline,3,900.0023140907288,59145.0,59231.0,0.0014540535970918927,24595759.0,exact,1.0,1.0013693998309383,9.839168761119028,17.2,6.1066754492159765,,,
-4,baseline,4,900.0024960041046,59142.0,59213.0,0.0012005004903452704,25467171.0,exact,1.0,1.0011162314042927,11.236705591195049,14.261938547000467,7.640381275476392,,,
-5,baseline,5,900.002925157547,59126.0,59244.0,0.0019957379156377904,23457042.0,exact,1.0,1.0021143794719125,10.760170783867167,29.494511720731996,5.847345591041515,,,
-6,baseline,6,900.0031039714813,59125.0,59236.97169757604,0.001893813066825174,24240772.0,exact,1.0,1.0018090934817527,5.582936655618509,22.394339515207683,3.3210931747954593,,,
-7,baseline,7,900.002781867981,59105.0,59212.0,0.001810337534895525,24042592.0,exact,1.0,1.001725596345796,1.7540923744921773,21.400000000000002,2.390273708531383,,,
-8,baseline,8,900.0021660327911,59169.0,59251.0,0.0013858608392908448,25512146.0,exact,1.0,1.0013181687594004,9.026100681465717,20.5,5.950915491512204,,,
-9,baseline,9,900.0015439987183,59130.0,59256.0,0.00213089802130898,23227790.0,exact,1.0,1.0021478462345041,7.880275979497338,25.197442922374428,4.198068271097029,,,
-10,baseline,10,900.0024099349976,59127.0,59201.0,0.0012515432881762985,25015636.0,exact,1.0,1.0011838122135597,13.04240249187423,18.5,8.325144625534431,,,
-11,baseline,11,900.0025849342346,59198.0,59289.0,0.0015372140950707794,24558832.0,exact,1.0,1.0017741281427412,4.941352404443008,18.19415858643873,2.9831824806949396,,,
-12,baseline,12,900.0022029876709,59102.0,59224.0,0.002064227944908802,24026788.0,exact,1.0,1.0019794609775492,5.378482195683288,24.400000000000002,2.947983888580514,,,
-13,baseline,13,900.0011007785797,59150.0,59206.0,0.0009467455621301775,24953207.0,exact,1.0,1.0008790614328702,12.451848934586094,13.999999999999998,8.0844140865203,,,
-14,baseline,14,900.0014700889587,59169.0,59250.0,0.0013689600973482733,25494260.0,exact,1.0,1.0014705136656357,10.286586243074352,20.246577599756627,6.714445655657852,,,
-15,baseline,15,900.0013790130615,59083.0,59196.0,0.0019125636816004605,23792716.0,exact,1.0,1.0018277822908204,7.486704871117682,22.6,4.211841162852367,,,
-16,baseline,16,900.0020098686218,59126.0,59233.0,0.0018096945506207078,23398798.0,exact,1.0,1.001724983511187,10.473065188128833,21.39999999999999,5.842727665213484,,,
-17,baseline,17,900.0023510456085,59156.0,59197.0,0.0006930826965988236,25573586.0,exact,1.0,1.0006930826965987,12.267049016770867,10.249306917303404,7.807679578926801,,,
-18,baseline,18,900.002711057663,59118.0,59211.0,0.0015731249365675427,24489136.0,exact,1.0,1.0014884224413512,19.84721287191386,18.599999999999998,12.51075303699155,,,
-19,baseline,19,724.1934628486632,59159.0,59164.0,8.451799388089724e-05,20931760.0,exact,1.0,1.0,16.225906582646804,1.0,11.203195934004649,,,
-20,baseline,20,900.0011439323425,59068.0,59191.0,0.0020823457709758246,23411794.0,exact,1.0,1.0020823457709758,4.553908811228339,24.597917654229025,2.465201726709367,,,
-21,baseline,21,380.06568694114685,59175.0,59180.0,8.449514152936208e-05,11618526.0,exact,1.0,1.0000168978860744,7.912557532546788,1.2500000000000002,5.757968140817527,,,
-22,baseline,22,900.0016028881073,59121.0,59154.94711904253,0.0005741973079365614,26352886.0,exact,1.0,1.0004895835849292,14.040381831195205,6.789423808503489,9.40925154833969,,,
-23,baseline,23,230.2515211105347,59193.0,59198.0,8.44694474008751e-05,6776049.0,exact,1.0,1.0000337860666262,8.514542938541073,1.6666666666666665,5.846961043264265,,,
-24,baseline,24,900.0010840892792,59162.0,59240.0,0.001318413846725939,24727727.0,exact,1.0,1.0015046237595306,8.166041819193602,15.595781075690478,4.940286958561307,,,
-25,baseline,25,900.0015320777893,59096.0,59210.0,0.0019290645728983352,23438919.0,exact,1.0,1.0018443004348487,4.510705225924555,22.800000000000004,2.4862311177206236,,,
-26,baseline,26,900.0015478134155,59089.0,59203.0,0.001929293100238623,23826788.0,exact,1.0,1.0018953816994127,8.894248013836016,28.49903535344988,4.939630736149807,,,
-27,baseline,27,900.0010070800781,59153.0,59249.0,0.0016229100806383447,24336831.0,exact,1.0,1.001538253490652,10.091974748981741,19.2,6.084119530266811,,,
-28,baseline,28,900.001277923584,59112.0,59208.0,0.0016240357287860333,25111591.0,exact,1.0,1.001759610178668,4.691858473111718,19.195777507105156,2.8027693088636902,,,
-29,baseline,29,900.0012440681458,59182.0,59263.0,0.0013686593896792946,24919871.0,exact,1.0,1.0012840657576834,7.56448716001105,16.200000000000003,4.595493258310103,,,
-30,baseline,30,900.0012910366057,59134.0,59241.0,0.001809449724354855,23615391.0,exact,1.0,1.0017247501648658,9.031820270959846,21.4,5.0375202116086095,,,
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-32,baseline,32,875.9193549156189,59175.0,59180.0,8.449514152936208e-05,24935695.0,exact,1.0,1.0,17.593042802030894,1.0000000000000002,11.640863122484049,,,
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benchmark/stab/ChallengeA/benchmark_baseline.csv

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benchmark/stab/ChallengeA/benchmark_ml.csv

@@ -1,151 +0,0 @@
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-18,baseline,18,49.87329697608948,8354.975512102363,8354.975512102363,0.0,31665.0,1.0,1.0,843.6224093499328,,31665.0
-19,baseline,19,80.3313570022583,8148.698058722395,8148.698058722395,0.0,48047.0,1.0,1.000966789196668,580.6596204121938,,1779.5185185185185
-20,baseline,20,34.744563817977905,8254.22546708772,8254.22546708772,0.0,19831.0,1.0,1.004384894337412,508.78858964332596,,19831.0
-21,baseline,21,40.45663404464722,8337.747084077018,8337.747084077018,0.0,18857.0,1.0,1.0,462.30308297552364,,18857.0
-22,baseline,22,59.21903705596924,8372.097133312143,8372.097133312143,0.0,37278.0,1.0,1.0,514.5235539407097,,1433.7692307692307
-23,baseline,23,80.84772300720216,8163.180180623385,8163.180180623385,0.0,50384.0,1.0,1.0036539041416717,1353.8923034540035,,50384.0
-24,baseline,24,79.59622597694397,8251.926305990946,8251.926305990948,2.20432095015834e-16,45222.0,1.0,1.002542812972684,327.00203830964284,inf,674.955223880597
-25,baseline,25,43.39374899864197,8208.77608322561,8208.77608322561,0.0,28242.0,1.0,1.0,203.83643836690354,,641.8636363636364
-26,baseline,26,73.4040129184723,8263.930518826672,8263.930518826672,0.0,41508.0,1.0,1.0,1158.157296819456,,41508.0
-27,baseline,27,68.43603801727295,8198.51655526816,8198.51655526816,0.0,34134.0,1.0,1.0,465.76709499137564,,2007.8823529411766
-28,baseline,28,38.52493691444397,8429.328796791307,8429.328796791307,0.0,23191.0,1.0,1.0,212.3627067143475,,1449.4375
-29,baseline,29,63.411077976226814,8471.392061275592,8471.392061275594,2.1472142835423904e-16,36104.0,1.0,1.00713405678795,422.9829560183529,inf,1504.3333333333333
-30,baseline,30,73.6661651134491,8300.292335288888,8300.292335288888,0.0,39931.0,1.0,1.0,752.156311010492,,3327.5833333333335
-31,baseline,31,34.113643169403076,8472.780799342136,8472.780799342136,0.0,17604.0,0.9999999880202372,1.000000008626077,605.9064481022414,,17604.0
-32,baseline,32,63.0274429321289,8176.089207977811,8176.089207977811,0.0,35832.0,1.0,1.0,938.911818608021,,35832.0
-33,baseline,33,54.692622900009155,8349.997774829048,8349.997774829048,0.0,36893.0,1.0,1.0,470.66514905927494,,36893.0
-34,baseline,34,73.54475188255309,8228.164027545597,8228.164027545597,0.0,46086.0,1.0,1.0,433.74928744081916,,2425.5789473684213
-35,baseline,35,32.710362911224365,8348.576374334334,8348.576374334334,0.0,17965.0,1.0,1.0,544.8268431962766,,17965.0
-36,baseline,36,70.7662868499756,8200.622970997243,8200.622970997245,2.2181112459126463e-16,37770.0,1.0,1.0039798679142482,716.773814957293,inf,37770.0
-37,baseline,37,36.678386926651,8449.787502150532,8449.787502150532,0.0,20885.0,1.0,1.0,239.07327432143046,,835.4
-38,baseline,38,86.8393452167511,8323.602064698229,8323.602064698229,0.0,50488.0,1.0,1.0,791.2621177625805,,50488.0
-39,baseline,39,61.66756081581116,8230.716290385615,8230.716290385615,0.0,34925.0,1.0,1.0,310.21299967617745,,1343.2692307692307
-40,baseline,40,115.80898809432985,8028.769787381955,8028.769787381955,0.0,69443.0,1.0,1.0059686353257602,962.877706084664,,69443.0
-41,baseline,41,59.320027828216546,8214.630250558439,8214.630250558439,0.0,36252.0,1.0,1.0,279.402791487412,,2132.470588235294
-42,baseline,42,27.36734485626221,8482.332346423325,8482.332346423327,2.1444448640506927e-16,10937.0,1.0,1.0,296.90942199552,1.0,10937.0
-43,baseline,43,42.98321795463562,8350.150643446867,8350.150643446867,0.0,31065.0,1.0,1.0,786.6613272710613,,31065.0
-44,baseline,44,64.18663907051085,8325.739376420757,8325.739376420757,0.0,37466.0,1.0,1.0,601.5284689994793,,37466.0
-45,baseline,45,63.78522491455078,8320.793172322808,8320.793440026451,3.2172851239710385e-08,38840.0,0.9999999999999998,1.0000000321728513,716.7763545319855,inf,38840.0
-46,baseline,46,31.4558629989624,8341.756982876166,8341.756982876166,0.0,16130.0,1.0,1.0,613.653265116279,,16130.0
-47,baseline,47,39.20694804191589,8206.985832918781,8206.985832918781,0.0,25335.0,1.0,1.0,280.93451131197753,,25335.0
-48,baseline,48,62.641757011413574,8197.315974091358,8197.315974091358,0.0,54514.0,1.0,1.0073005569898068,261.05295308194985,,746.7671232876712
-49,baseline,49,49.18351912498474,8090.681320538064,8090.681320538064,0.0,38800.0,1.0,1.0026687299831225,375.8371656618988,,38800.0
-50,ml-exact,0,34.49193096160889,8160.106459602758,8160.106459602758,0.0,32951.0,1.0,1.0000000000000002,356.1081397753119,,32951.0
-51,ml-exact,1,39.43942403793335,8329.665354500348,8329.665354500348,0.0,33716.0,1.0,1.0090376984767917,198.34454105955817,,749.2444444444444
-52,ml-exact,2,54.0330810546875,8247.871141626507,8247.871141626507,0.0,48098.0,1.0,1.0022162274368718,391.2351351957892,,2290.3809523809523
-53,ml-exact,3,15.311645030975342,8386.859108879815,8386.859108879815,0.0,10140.0,1.0,1.0,165.37065533668084,,10140.0
-54,ml-exact,4,24.112047910690308,8197.045478427175,8197.045478427175,0.0,32151.0,1.0,1.0,93.80763330031203,,1148.25
-55,ml-exact,5,33.69559407234192,8184.416683317542,8184.416683317542,0.0,31637.0,1.0,1.0000000000000002,251.89966224345207,,3515.222222222222
-56,ml-exact,6,25.395578861236572,8146.291920190363,8146.291920190363,0.0,18684.0,1.0,1.0000000000000002,253.83378293361804,,18684.0
-57,ml-exact,7,45.65329885482788,8332.628442208696,8332.628442208696,0.0,34261.0,1.0,1.0048882560944687,481.0667621344588,,34261.0
-58,ml-exact,8,45.959444999694824,8189.394992049158,8189.394992049158,0.0,32915.0,1.0,1.0,911.0616203340486,,32915.0
-59,ml-exact,9,27.292019844055176,8264.94306032112,8264.94306032112,0.0,22256.0,1.0,1.0,271.551146378204,,22256.0
-60,ml-exact,10,33.28360414505005,8225.694775199881,8225.694775199883,2.2113504734336021e-16,32743.0,1.0,1.0023427358501629,261.6000412259086,inf,2976.6363636363635
-61,ml-exact,11,13.287060976028442,8380.21322380759,8380.21322380759,0.0,15760.0,1.0,1.0,151.33486759210984,,15760.0
-62,ml-exact,12,30.385483980178833,8335.12040209855,8335.12040209855,0.0,26800.0,1.0,1.0019781200731896,258.03791222585767,,1165.2173913043478
-63,ml-exact,13,53.78090000152588,8180.950128996085,8180.950128996085,0.0,38849.0,1.0,1.0015658240912468,199.91566748763452,,554.9857142857143
-64,ml-exact,14,32.64224600791931,8335.244300219336,8335.244300219336,0.0,30763.0,1.0,1.0,279.53616616406106,,4394.714285714285
-65,ml-exact,15,33.97071599960327,8281.242353501702,8281.242353501702,0.0,30903.0,1.0,1.0,847.425075979707,,30903.0
-66,ml-exact,16,34.40068793296814,8269.820198565656,8269.820198565656,0.0,25773.0,1.0,1.000403224416854,1150.6411078414953,,25773.0
-67,ml-exact,17,29.94601798057556,8349.788875581982,8349.788875581982,0.0,26524.0,1.0,1.0,537.0046516599328,,26524.0
-68,ml-exact,18,26.21188998222351,8354.975512102363,8354.975512102363,0.0,23595.0,1.0,1.0,443.3823132050057,,23595.0
-69,ml-exact,19,44.91053318977356,8148.698058722395,8148.698058722396,1.1161227170509796e-16,36233.0,1.0,1.0009667891966683,324.6270712662061,inf,1341.962962962963
-70,ml-exact,20,24.929107904434204,8254.22546708772,8254.225467087721,2.20370695082023e-16,19171.0,1.0,1.0043848943374123,365.0541051029243,inf,19171.0
-71,ml-exact,21,23.808892011642456,8337.747084077018,8337.74708407702,2.1816317827865812e-16,16213.0,1.0,1.0000000000000002,272.0672255399839,inf,16213.0
-72,ml-exact,22,29.496449947357178,8372.097133312143,8372.097133312145,2.1726807209488663e-16,23405.0,1.0,1.0000000000000002,256.27938261145164,inf,900.1923076923077
-73,ml-exact,23,54.53324818611145,8163.180180623385,8163.180180623385,0.0,44205.0,1.0,1.0036539041416717,913.2247916857979,,44205.0
-74,ml-exact,24,35.66223120689392,8251.926305990946,8251.926305990948,2.2043209501583402e-16,30816.0,1.0,1.002542812972684,146.50973902584275,inf,459.94029850746267
-75,ml-exact,25,29.14737296104431,8208.77608322561,8208.776083225612,2.2159081757180675e-16,25610.0,1.0,1.0000000000000002,136.9159574646799,inf,582.0454545454545
-76,ml-exact,26,27.671333074569702,8263.930518826672,8263.930518826672,0.0,19654.0,1.0,1.0,436.59406398705966,,19654.0
-77,ml-exact,27,33.428922176361084,8198.51655526816,8198.51655526816,0.0,34427.0,1.0,1.0,227.5130533834623,,2025.1176470588234
-78,ml-exact,28,26.386518955230713,8429.328796791307,8429.328796791307,0.0,21051.0,1.0,1.0,145.45157072019325,,1315.6875
-79,ml-exact,29,32.452534914016724,8471.392061275592,8471.392061275594,2.1472142835423904e-16,25241.0,1.0,1.00713405678795,216.47430679803114,inf,1051.7083333333333
-80,ml-exact,30,33.65191102027893,8300.292335288888,8300.292335288888,0.0,27290.0,1.0,1.0,343.59732466710483,,2274.1666666666665
-81,ml-exact,31,26.8163058757782,8472.780900844042,8472.780900844042,0.0,17085.0,1.0,1.00000002060584,476.29543885799944,,17085.0
-82,ml-exact,32,27.298824071884155,8176.089207977811,8176.089207977811,0.0,21127.0,1.0,1.0,406.66711773146375,,21127.0
-83,ml-exact,33,27.07152795791626,8349.997774829048,8349.997774829048,0.0,20768.0,1.0,1.0,232.96788608711708,,20768.0
-84,ml-exact,34,51.8715980052948,8228.164027545597,8228.164027545597,0.0,42602.0,1.0,1.0,305.92622991159624,,2242.2105263157896
-85,ml-exact,35,26.559547185897827,8348.576374334334,8348.576374334334,0.0,15315.0,1.0,1.0,442.37828511067517,,15315.0
-86,ml-exact,36,42.17573404312134,8200.622970997243,8200.622970997245,2.2181112459126466e-16,32284.0,1.0,1.0039798679142482,427.1873392594525,inf,32284.0
-87,ml-exact,37,20.249451875686646,8449.787502150532,8449.787502150532,0.0,13815.0,1.0,1.0,131.9878862943405,,552.6
-88,ml-exact,38,34.309616804122925,8323.602064698229,8323.602064698229,0.0,31546.0,1.0,1.0,312.62211828396147,,31546.0
-89,ml-exact,39,28.144772052764893,8230.716290385615,8230.716290385615,0.0,22759.0,1.0,1.0,141.57969032969933,,875.3461538461538
-90,ml-exact,40,54.61736702919006,8028.769787381955,8028.769787381955,0.0,46343.0,1.0,1.0059686353257602,454.1084931561159,,46343.0
-91,ml-exact,41,30.99381184577942,8214.630250558439,8214.630250558439,0.0,28492.0,1.0,1.0,145.98370677815547,,1676.0
-92,ml-exact,42,19.046553134918213,8482.332346423325,8482.332346423327,2.1444448640506932e-16,9292.0,1.0,1.0,206.6368188802036,1.0000000000000002,9292.0
-93,ml-exact,43,29.105360984802246,8350.150643446867,8350.150643446867,0.0,24245.0,1.0,1.0,532.674448133975,,24245.0
-94,ml-exact,44,28.813607215881348,8325.739376420757,8325.739376420757,0.0,22941.0,1.0,1.0,270.0282377440192,,22941.0
-95,ml-exact,45,39.90794801712036,8320.79317232281,8320.79317232281,0.0,33304.0,1.0,1.0,448.459240127851,,33304.0
-96,ml-exact,46,23.966022968292236,8341.756982876166,8341.756982876166,0.0,16789.0,1.0,1.0,467.53853953488374,,16789.0
-97,ml-exact,47,27.642159938812256,8206.985832918781,8206.985832918781,0.0,24637.0,1.0,1.0,198.0678701569822,,24637.0
-98,ml-exact,48,33.94082999229431,8197.315974091358,8197.315974091358,0.0,33963.0,1.0,1.0073005569898068,141.44484960609344,,465.24657534246575
-99,ml-exact,49,37.428488969802856,8090.681320538064,8090.681320538064,0.0,39891.0,1.0,1.0026687299831225,286.0107910064622,,39891.0
-100,ml-heuristic,0,0.09685802459716797,8160.106459602758,8160.106459602758,0.0,1.0,1.0,1.0000000000000002,1.0,,1.0
-101,ml-heuristic,1,0.19884300231933594,8255.05862375065,8255.05862375065,0.0,45.0,0.9910432499296759,1.0,1.0,,1.0
-102,ml-heuristic,2,0.1381089687347412,8229.632404496291,8229.632404496293,2.210292409357245e-16,21.0,0.9977886733658864,1.0,1.0,inf,1.0
-103,ml-heuristic,3,0.0925898551940918,8386.859108879815,8386.859108879815,0.0,1.0,1.0,1.0,1.0,,1.0
-104,ml-heuristic,4,0.2570371627807617,8197.045478427175,8197.045478427175,0.0,28.0,1.0,1.0,1.0,,1.0
-105,ml-heuristic,5,0.13376593589782715,8184.416683317542,8184.416683317542,0.0,9.0,1.0,1.0000000000000002,1.0,,1.0
-106,ml-heuristic,6,0.10004806518554688,8146.291920190363,8146.291920190363,0.0,1.0,1.0,1.0000000000000002,1.0,,1.0
-107,ml-heuristic,7,0.09490013122558594,8292.094560437725,8292.094560437725,0.0,1.0,0.9951355227162599,1.0,1.0,,1.0
-108,ml-heuristic,8,0.0504460334777832,8189.394992049158,8189.39499204916,2.22115236267375e-16,1.0,1.0,1.0000000000000002,1.0,inf,1.0
-109,ml-heuristic,9,0.10050415992736816,8264.94306032112,8265.728238597903,9.500105095127722e-05,1.0,1.0,1.0000950010509513,1.0,inf,1.0
-110,ml-heuristic,10,0.12723088264465332,8206.469185635438,8206.469185635438,0.0,11.0,0.9976627397332555,1.0,1.0,,1.0
-111,ml-heuristic,11,0.087799072265625,8380.21322380759,8380.213223807592,2.1705765175261136e-16,1.0,1.0,1.0000000000000002,1.0,inf,1.0
-112,ml-heuristic,12,0.11775588989257812,8318.665083714313,8318.665083714313,0.0,23.0,0.9980257851608126,1.0,1.0,,1.0
-113,ml-heuristic,13,0.26901793479919434,8168.160226931591,8168.160226931591,0.0,70.0,0.9984366238807443,1.0,1.0,,1.0
-114,ml-heuristic,14,0.11677289009094238,8335.244300219336,8335.244300219336,0.0,7.0,1.0,1.0,1.0,,1.0
-115,ml-heuristic,15,0.040086984634399414,8281.242353501702,8281.242353501702,0.0,1.0,1.0,1.0,1.0,,1.0
-116,ml-heuristic,16,0.029896974563598633,8266.48694918614,8266.48694918614,0.0,1.0,0.9995969381075426,1.0,1.0,,1.0
-117,ml-heuristic,17,0.05576491355895996,8349.788875581982,8349.788875581982,0.0,1.0,1.0,1.0,1.0,,1.0
-118,ml-heuristic,18,0.059118032455444336,8354.975512102363,8354.975512102363,0.0,1.0,1.0,1.0,1.0,,1.0
-119,ml-heuristic,19,0.13834500312805176,8140.8275945520445,8140.8275945520445,0.0,27.0,0.9990341445819156,1.0,1.0,,1.0
-120,ml-heuristic,20,0.06828880310058594,8218.189574160206,8218.189574160206,0.0,1.0,0.9956342490193416,1.0,1.0,,1.0
-121,ml-heuristic,21,0.08751106262207031,8337.747084077018,8337.747084077018,0.0,1.0,1.0,1.0,1.0,,1.0
-122,ml-heuristic,22,0.11509490013122559,8372.097133312143,8372.097133312143,0.0,26.0,1.0,1.0,1.0,,1.0
-123,ml-heuristic,23,0.05971503257751465,8133.46129271979,8133.46129271979,0.0,1.0,0.9963593982680748,1.0,1.0,,1.0
-124,ml-heuristic,24,0.24341201782226562,8230.99642151221,8230.99642151221,0.0,67.0,0.9974636365252632,1.0,1.0,,1.0
-125,ml-heuristic,25,0.21288514137268066,8208.77608322561,8208.77608322561,0.0,44.0,1.0,1.0,1.0,,1.0
-126,ml-heuristic,26,0.06338000297546387,8263.930518826672,8263.930518826672,0.0,1.0,1.0,1.0,1.0,,1.0
-127,ml-heuristic,27,0.14693188667297363,8198.51655526816,8198.51655526816,0.0,17.0,1.0,1.0,1.0,,1.0
-128,ml-heuristic,28,0.1814110279083252,8429.328796791307,8429.328796791307,0.0,16.0,1.0,1.0,1.0,,1.0
-129,ml-heuristic,29,0.14991402626037598,8411.384764698932,8411.384764698932,0.0,24.0,0.99291647746408,1.0,1.0,,1.0
-130,ml-heuristic,30,0.09793996810913086,8300.292335288888,8300.292335288888,0.0,12.0,1.0,1.0,1.0,,1.0
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benchmark/tsp/ChallengeA/benchmark_baseline.csv

@@ -1,51 +0,0 @@
-,Solver,Instance,Wallclock Time,Lower Bound,Upper Bound,Gap,Nodes,Mode,Sense,Predicted LB,Predicted UB,Relative Lower Bound,Relative Upper Bound,Relative Wallclock Time,Relative Gap,Relative Nodes
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-49,baseline,49,73.46178817749023,13524.0,13524.0,0.0,4053.0,exact,min,,,1.0,1.0,1.0,,1.0

benchmark/tsp/ChallengeA/benchmark_ml.csv

@@ -1,151 +0,0 @@
-,Solver,Instance,Wallclock Time,Lower Bound,Upper Bound,Gap,Nodes,Mode,Sense,Predicted LB,Predicted UB,Relative Lower Bound,Relative Upper Bound,Relative Wallclock Time,Relative Gap,Relative Nodes
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-65,ml-exact,15,15.559112071990967,13594.0,13594.0,0.0,353.0,exact,min,13560.52172484643,13560.642687104417,1.0,1.0,6.352091962749635,,1.0
-66,ml-exact,16,14.185301065444946,13594.0,13594.0,0.0,500.0,exact,min,13552.89499057571,13553.027666254291,1.0,1.0,9.735604885812853,,1.2468827930174564
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-68,ml-exact,18,9.592709064483643,13525.0,13525.0,0.0,1.0,exact,min,13525.777713168703,13525.952036564957,1.0,1.0,22.208940377976713,,1.0
-69,ml-exact,19,15.68299388885498,13564.0,13564.0,0.0,20.0,exact,min,13560.098017386947,13560.21963039052,1.0,1.0,10.55283738705663,,1.0
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-71,ml-exact,21,12.961982011795044,13566.0,13566.0,0.0,246.0,exact,min,13553.742405494679,13553.873779682082,1.0,1.0,9.038263563922284,,1.0
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-74,ml-exact,24,8.9430251121521,13543.0,13543.0,0.0,1.0,exact,min,13545.691963764473,13545.835702118062,1.0,1.0000000000000002,5.478707180627428,,1.0
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-76,ml-exact,26,10.45563006401062,13532.0,13532.0,0.0,1.0,exact,min,13538.488936953237,13538.643737981833,1.0,1.0000000000460052,4.67289046136253,,1.0
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-78,ml-exact,28,11.49683690071106,13571.0,13571.0,0.0,15.0,exact,min,13560.098017386947,13560.21963039052,1.0,1.0,10.478351955003774,,1.0
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-82,ml-exact,32,11.40560007095337,13524.0,13524.0,0.0,98.0,exact,min,13535.522984736846,13535.682340984562,1.0,1.0,15.235048166691241,,1.4848484848484849
-83,ml-exact,33,14.968575954437256,13548.0,13548.0,0.0,953.0,exact,min,13552.89499057571,13553.027666254291,1.0,1.0,7.292899748174851,,1.0
-84,ml-exact,34,10.275269031524658,13557.0,13557.0,0.0,80.0,exact,min,13543.573426467052,13543.720418548583,1.0,1.0,4.63916104661852,,1.0
-85,ml-exact,35,13.136114120483398,13568.0,13568.0,0.0,445.0,exact,min,13544.42084138602,13544.566531976374,1.0,1.0000000002137304,12.587970833538003,,1.0
-86,ml-exact,36,11.606173992156982,13553.999998743056,13553.999998743056,0.0,164.0,exact,min,13525.354005709218,13525.528979851062,0.9999999999072641,1.0,15.660551479908223,,1.3015873015873016
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-89,ml-exact,39,12.747802019119263,13538.0,13538.0,0.0,151.0,exact,min,13537.217814574784,13537.374567840145,1.0,1.0,7.472058053477855,,1.0
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-91,ml-exact,41,10.27357292175293,13525.0,13526.0,7.393715341959335e-05,1.0,exact,min,13521.540638573859,13521.721469426,0.9999260683128789,1.0,4.063188513593281,inf,1.0
-92,ml-exact,42,12.76089596748352,13529.0,13529.0,0.0,47.0,exact,min,13530.014787763548,13530.182603703915,0.9999999999999999,1.0,4.534313469262858,,1.0
-93,ml-exact,43,16.610208988189697,13565.0,13565.0,0.0,267.0,exact,min,13560.945432305916,13561.065743818312,1.0,1.0,2.2775372615612164,,1.0
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-95,ml-exact,45,12.605960130691528,13521.0,13521.0,0.0,1.0,exact,min,13518.998393816952,13519.183129142624,1.0,1.0,5.036547652194804,,1.0
-96,ml-exact,46,12.235252141952515,13543.0,13543.0,0.0,1.0,exact,min,13543.149719007566,13543.297361834688,1.0,1.0000000000438993,7.870849996027641,,1.0
-97,ml-exact,47,11.854049921035767,13564.0,13564.0,0.0,1.0,exact,min,13567.301044198182,13567.41159452675,1.0,1.0,3.8200977469489614,,1.0
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-106,ml-heuristic,6,1.803605079650879,13534.999999068532,13534.999999068534,1.343915333336563e-16,713.0,heuristic,min,13536.370399655816,13536.528454412353,0.9999999999311808,1.0,1.0,inf,1.0
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-145,ml-heuristic,45,2.502897024154663,13521.0,13521.0,0.0,68.0,heuristic,min,13518.998393816952,13519.183129142624,1.0,1.0,1.0,,68.0
-146,ml-heuristic,46,1.554502010345459,13543.0,13543.0,0.0,157.0,heuristic,min,13543.149719007566,13543.297361834688,1.0,1.0000000000438993,1.0,,157.0
-147,ml-heuristic,47,3.1030750274658203,13564.0,13564.0,0.0,137.0,heuristic,min,13567.301044198182,13567.41159452675,1.0,1.0,1.0,,137.0
-148,ml-heuristic,48,1.837294101715088,13552.0,13552.0,0.0,48.0,heuristic,min,13558.403187549009,13558.527403534938,1.0,1.0,1.0,,48.0
-149,ml-heuristic,49,2.7994580268859863,13524.0,13524.0,0.0,452.0,heuristic,min,13501.626387978087,13501.837803872895,1.0,1.0,1.0,,1.0

docs/Makefile

@@ -0,0 +1,14 @@
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/_static/custom.css

@@ -0,0 +1,7 @@
+h1.site-logo {
+    font-size: 30px !important;
+}
+
+h1.site-logo small {
+    font-size: 20px !important;
+}

docs/about.md

@@ -1,19 +1,34 @@
+```{sectnum}
+---
+start: 4
+depth: 2
+suffix: .
+---
+```
+
 # About
 
-### Authors
+## Authors
 
 * **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>>
 * **Feng Qiu,** Argonne National Laboratory <<fqiu@anl.gov>>
 
-### Acknowledgments
+## Acknowledgments
 
-* 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.
+* 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, and the **U.S. Department of Energy Advanced Grid Modeling Program** under Grant DE-OE0000875.
 
-### References
+## 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](https://arxiv.org/abs/1902.01697)
 
-### License
+If you use MIPLearn in your research, or the included problem generators, we kindly request that you cite the package as follows:
+
+* **Alinson S. Xavier, Feng Qiu.** *MIPLearn: An Extensible Framework for Learning-Enhanced Optimization*. Zenodo (2020). DOI: [10.5281/zenodo.4287567](https://doi.org/10.5281/zenodo.4287567)
+
+If you use MIPLearn in the field of power systems optimization, we kindly request that you cite the reference below, in which the main techniques implemented in MIPLearn were first developed:
+
+* **Alinson S. Xavier, Feng Qiu, Shabbir Ahmed.** *Learning to Solve Large-Scale Unit Commitment Problems.* INFORMS Journal on Computing (2020). DOI: [10.1287/ijoc.2020.0976](https://doi.org/10.1287/ijoc.2020.0976)
+
+## License
 
 ```text
 MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
@@ -40,4 +55,4 @@ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSE
 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.
-```
+```

docs/benchmark.md

@@ -1,61 +1,177 @@
-# 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:
-
-```python
-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)
-
-# 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.fit(train_instances)
-benchmark.parallel_solve(test_instances, n_jobs=2)
-print(benchmark.raw_results())
+```{sectnum}
+---
+start: 2
+depth: 2
+suffix: .
+---
 ```
 
-The method `fit` trains the ML models for each individual solver. 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:
+# Benchmarks
 
-* **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.
+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.
 
-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.
+## 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](about.md#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.
 
 
-### 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:
+## 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.
+
+### 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
 
 ```python
-# Benchmark baseline solvers and save results to a file.
-benchmark = BenchmarkRunner(baseline_solvers)
-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_results("baseline_results.csv")
-benchmark.fit(training_instances)
-benchmark.parallel_solve(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)
 ```
 
+![alt](figures/benchmark_stab_a.png)
+
+
+## Traveling Salesman Problem
+
+### Problem definition
+
+Given a list of cities and the distance between each pair of cities, the problem asks for the
+shortest route starting at the first city, visiting each other city exactly once, then returning
+to the first city. This problem is a generalization of the Hamiltonian path problem, one of Karp's
+21 NP-complete problems.
+
+### Random problem generator
+
+The class `TravelingSalesmanGenerator` can be used to generate random instances of this
+problem. Initially, the generator creates $n$ cities $(x_1,y_1),\ldots,(x_n,y_n) \in \mathbb{R}^2$,
+where $n, x_i$ and $y_i$ are sampled independently from the provided probability distributions `n`,
+`x` and `y`. For each pair of cities $(i,j)$, the distance $d_{i,j}$ between them is set to:
+$$
+    d_{i,j} = \gamma_{i,j} \sqrt{(x_i-x_j)^2 + (y_i - y_j)^2}
+$$
+where $\gamma_{i,j}$ is sampled from the distribution `gamma`.
+
+If `fix_cities=True` is provided, the list of cities is kept the same for all generated instances.
+The $gamma$ values, and therefore also the distances, are still different.
+
+By default, all distances $d_{i,j}$ are rounded to the nearest integer.  If `round=False`
+is provided, this rounding will be disabled.
+
+### Challenge A
+
+* Fixed list of 350 cities in the $[0, 1000]^2$ square
+* $\gamma_{i,j} \sim U(0.95, 1.05)$
+* 500 training instances, 50 test instances
+
+
+```python
+TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
+                           y=uniform(loc=0.0, scale=1000.0),
+                           n=randint(low=350, high=351),
+                           gamma=uniform(loc=0.95, scale=0.1),
+                           fix_cities=True,
+                           round=True,
+                          )
+```
+
+![alt](figures/benchmark_tsp_a.png)
+
+
+## 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.
+
+
+!!! 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.
+    
+### 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)
+

docs/conf.py

@@ -0,0 +1,16 @@
+project = "MIPLearn"
+copyright = "2020-2021, UChicago Argonne, LLC"
+author = ""
+release = "0.2.0"
+extensions = ["myst_parser"]
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+html_theme = "sphinx_book_theme"
+html_static_path = ["_static"]
+html_css_files = ["custom.css"]
+html_theme_options = {
+    "repository_url": "https://github.com/ANL-CEEESA/MIPLearn/",
+    "use_repository_button": True,
+    "extra_navbar": "",
+}
+html_title = f"MIPLearn<br/><small>{release}</small>"

docs/css/custom.css

@@ -1,28 +0,0 @@
-.navbar-default {
-  border-bottom: 0px;
-  background-color: #fff;
-  box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.2);
-}
-
-a, .navbar-default a {
-  color: #06a !important;
-  font-weight: normal;
-}
-
-.disabled > a {
-  color: #999 !important;
-}
-
-.navbar-default a:hover,
-.navbar-default .active,
-.active > a {
-  background-color: #f0f0f0 !important;
-}
-
-.icon-bar {
-  background-color: #666 !important;
-}
-
-.navbar-collapse {
-  border-color: #fff !important;
-}

docs/customization.md

@@ -1,18 +1,47 @@
+```{sectnum}
+---
+start: 3
+depth: 2
+suffix: .
+---
+```
+
 # Customization
 
 ## Customizing solver parameters
 
 ### Selecting the internal MIP solver
 
-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.
+By default, `LearningSolver` uses [Gurobi](https://www.gurobi.com/) as its internal MIP solver, and expects models to be provided using the Pyomo modeling language. Supported solvers and modeling languages include:
+
+* `GurobiPyomoSolver`: Gurobi with Pyomo (default).
+* `CplexPyomoSolver`: [IBM ILOG CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio) with Pyomo.
+* `XpressPyomoSolver`: [FICO XPRESS Solver](https://www.fico.com/en/products/fico-xpress-solver) with Pyomo.
+* `GurobiSolver`: Gurobi without any modeling language.
+
+To switch between solvers, provide the desired class using the `solver` argument:
 
 ```python
-from miplearn import LearningSolver
-solver = LearningSolver(solver="cplex",
-                        time_limit=300,
-                        gap_tolerance=1e-3)
+from miplearn import LearningSolver, CplexPyomoSolver
+solver = LearningSolver(solver=CplexPyomoSolver)
 ```
 
+To configure a particular solver, use the `params` constructor argument, as shown below.
+
+```python
+from miplearn import LearningSolver, GurobiPyomoSolver
+solver = LearningSolver(
+    solver=lambda: GurobiPyomoSolver(
+        params={
+            "TimeLimit": 900,
+            "MIPGap": 1e-3,
+            "NodeLimit": 1000,
+        }
+    ),
+)
+```
+
+
 ## Customizing solver components
 
 `LearningSolver` is composed by a number of individual machine-learning components, each targeting a different part of the solution process. Each component can be individually enabled, disabled or customized. The following components are enabled by default:
@@ -40,30 +69,26 @@ solver2 = LearningSolver(components=[
 ])
 ```
 
-It is also possible to add components to an existing solver using the `solver.add` method, as shown below. If the solver already holds another component of that type, the new component will replace the previous one.
-```python
-# Create solver with default components
-solver = LearningSolver()
-
-# Replace the default LazyConstraintComponent by one with custom parameters 
-solver.add(LazyConstraintComponent(...))
-```
-
 ### Adjusting component aggressiveness
 
-The aggressiveness of classification components (such as `PrimalSolutionComponent` and `LazyConstraintComponent`) can
-be adjusted through the `threshold` constructor argument. Internally, these components ask the ML models how confident
-they are on each prediction (through the `predict_proba` method in the sklearn API), and only take into account
-predictions which have probabilities above the threshold. Lowering a component's threshold increases its aggressiveness,
-while raising a component's threshold makes it more conservative. 
+The aggressiveness of classification components, such as `PrimalSolutionComponent` and `LazyConstraintComponent`, can be adjusted through the `threshold` constructor argument. Internally, these components ask the machine learning models how confident are they on each prediction they make, then automatically discard all predictions that have low confidence. The `threshold` argument specifies how confident should the ML models be for a prediction to be considered trustworthy. Lowering a component's threshold increases its aggressiveness, while raising a component's threshold makes it more conservative.
 
-MIPLearn also includes `MinPrecisionThreshold`, a dynamic threshold which adjusts itself automatically during training
-to achieve a minimum desired true positive rate (also known as precision). The example below shows how to initialize
-a `PrimalSolutionComponent` which achieves 95% precision, possibly at the cost of a lower recall. To make the component
-more aggressive, this precision may be lowered.
+For example, if the ML model predicts that a certain binary variable will assume value `1.0` in the optimal solution with 75% confidence, and if the `PrimalSolutionComponent` is configured to discard all predictions with less than 90% confidence, then this variable will not be included in the predicted MIP start.
+
+MIPLearn currently provides two types of thresholds:
+
+* `MinProbabilityThreshold(p: List[float])` A threshold which indicates that a prediction is trustworthy if its probability of being correct, as computed by the machine learning model, is above a fixed value.
+* `MinPrecisionThreshold(p: List[float])` A dynamic threshold which automatically adjusts itself during training to ensure that the component achieves at least a given precision on the training data set. Note that increasing a component's precision may reduce its recall.
+
+The example below shows how to build a `PrimalSolutionComponent` which fixes variables to zero with at least 80% precision, and to one with at least 95% precision. Other components are configured similarly.
 
 ```python
-PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
+from miplearn import PrimalSolutionComponent, MinPrecisionThreshold
+
+PrimalSolutionComponent(
+    mode="heuristic",
+    threshold=MinPrecisionThreshold([0.80, 0.95]),
+)
 ```
 
 ### Evaluating component performance
@@ -140,25 +165,18 @@ dtype: float64
 
 ### Using customized ML classifiers and regressors
 
-By default, given a training set of instantes, MIPLearn trains a fixed set of ML classifiers and regressors, then
-selects the best one based on cross-validation performance. Alternatively, the user may specify which ML model a component
-should use through the `classifier` or `regressor` contructor parameters. The provided classifiers and regressors must 
-follow the sklearn API. In particular, classifiers must provide the methods `fit`, `predict_proba` and `predict`,
-while regressors must provide the methods `fit` and `predict`
+By default, given a training set of instantes, MIPLearn trains a fixed set of ML classifiers and regressors, then selects the best one based on cross-validation performance. Alternatively, the user may specify which ML model a component should use through the `classifier` or `regressor` contructor parameters. Scikit-learn classifiers and regressors are currently supported. A future version of the package will add compatibility with Keras models.
 
-!!! danger
-    MIPLearn must be able to generate a copy of any custom ML classifiers and regressors through 
-    the standard  `copy.deepcopy` method. This currently makes it incompatible with Keras and TensorFlow
-    predictors. This is a known limitation, which will be addressed in a future version.
-    
-The example below shows how to construct a `PrimalSolutionComponent` which internally uses
-sklearn's `KNeighborsClassifiers`. Any other sklearn classifier or pipeline can be used. 
+The example below shows how to construct a `PrimalSolutionComponent` which internally uses scikit-learn's `KNeighborsClassifiers`. Any other scikit-learn classifier or pipeline can be used. It needs to be wrapped in `ScikitLearnClassifier` to ensure that all the proper data transformations are applied.
 
 ```python
-from miplearn import PrimalSolutionComponent
+from miplearn import PrimalSolutionComponent, ScikitLearnClassifier
 from sklearn.neighbors import KNeighborsClassifier
 
-comp = PrimalSolutionComponent(classifier=KNeighborsClassifier(n_neighbors=5))
+comp = PrimalSolutionComponent(
+    classifier=ScikitLearnClassifier(
+        KNeighborsClassifier(n_neighbors=5),
+    ),
+)
 comp.fit(train_instances)
 ``` 
-  

docs/figures/benchmark_knapsack_a.png

@@ -1 +0,0 @@
-../../benchmark/knapsack/ChallengeA/performance.png

docs/figures/benchmark_stab_a.png

@@ -1 +0,0 @@
-../../benchmark/stab/ChallengeA/performance.png

docs/figures/benchmark_tsp_a.png

@@ -1 +0,0 @@
-../../benchmark/tsp/ChallengeA/performance.png

docs/index.md

@@ -1,10 +1,13 @@
 # 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.
+**MIPLearn** is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). The framework uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers (such as CPLEX, Gurobi or XPRESS).
 
-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).
+Unlike pure ML methods, MIPLearn is not only able to find high-quality solutions to discrete optimization problems, but it can also prove the optimality and feasibility of these solutions.
+Unlike conventional MIP solvers, MIPLearn can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). 
 
-### Features
+For certain classes of problems, this approach has been shown to provide significant performance benefits (see [benchmarks](benchmark.md) and [references](about.md)).
+
+## 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.
 
@@ -14,15 +17,19 @@ The package uses Machine Learning (ML) to automatically identify patterns in pre
 
 * **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
+## Site contents
 
-* [Installation and typical usage](usage.md)
-* [Benchmark utilities](benchmark.md)
-* [Benchmark problems, challenges and results](problems.md)
-* [Customizing the solver](customization.md)
-* [License, authors, references and acknowledgments](about.md)
+```{toctree}
+---
+maxdepth: 2
+---
+usage.md
+benchmark.md
+customization.md
+about.md
+```
 
-### Source Code
+## Source Code
 
 * [https://github.com/ANL-CEEESA/MIPLearn](https://github.com/ANL-CEEESA/MIPLearn)
 

docs/js/mathjax.js

@@ -1,8 +0,0 @@
-MathJax.Hub.Config({
-  "tex2jax": { inlineMath: [ [ '$', '$' ] ] }
-});
-MathJax.Hub.Config({
-  config: ["MMLorHTML.js"],
-  jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"],
-  extensions: ["MathMenu.js", "MathZoom.js"]
-});

docs/problems.md

@@ -1,167 +0,0 @@
-# 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](about.md#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.
-
-### 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
-
-```python
-MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.),
-                            n=randint(low=200, high=201),
-                            p=uniform(loc=0.05, scale=0.0),
-                            fix_graph=True)
-```
-
-![alt](figures/benchmark_stab_a.png)
-
-
-## Traveling Salesman Problem
-
-### Problem definition
-
-Given a list of cities and the distance between each pair of cities, the problem asks for the
-shortest route starting at the first city, visiting each other city exactly once, then returning
-to the first city. This problem is a generalization of the Hamiltonian path problem, one of Karp's
-21 NP-complete problems.
-
-### Random problem generator
-
-The class `TravelingSalesmanGenerator` can be used to generate random instances of this
-problem. Initially, the generator creates $n$ cities $(x_1,y_1),\ldots,(x_n,y_n) \in \mathbb{R}^2$,
-where $n, x_i$ and $y_i$ are sampled independently from the provided probability distributions `n`,
-`x` and `y`. For each pair of cities $(i,j)$, the distance $d_{i,j}$ between them is set to:
-$$
-    d_{i,j} = \gamma_{i,j} \sqrt{(x_i-x_j)^2 + (y_i - y_j)^2}
-$$
-where $\gamma_{i,j}$ is sampled from the distribution `gamma`.
-
-If `fix_cities=True` is provided, the list of cities is kept the same for all generated instances.
-The $gamma$ values, and therefore also the distances, are still different.
-
-By default, all distances $d_{i,j}$ are rounded to the nearest integer.  If `round=False`
-is provided, this rounding will be disabled.
-
-### Challenge A
-
-* Fixed list of 350 cities in the $[0, 1000]^2$ square
-* $\gamma_{i,j} \sim U(0.95, 1.05)$
-* 500 training instances, 50 test instances
-
-
-```python
-TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
-                           y=uniform(loc=0.0, scale=1000.0),
-                           n=randint(low=350, high=351),
-                           gamma=uniform(loc=0.95, scale=0.1),
-                           fix_cities=True,
-                           round=True,
-                          )
-```
-
-![alt](figures/benchmark_tsp_a.png)
-
-
-## 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.
-
-
-!!! 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.
-    
-### 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)
-

docs/usage.md

@@ -1,13 +1,21 @@
-# Usage
+```{sectnum}
+---
+start: 1
+depth: 2
+suffix: .
+---
+```
 
-## 1. Installation
+# Using MIPLearn
 
-In these docs, we describe the Python/Pyomo version of the package, although a [Julia/JuMP version](https://github.com/ANL-CEEESA/MIPLearn.jl) is also available. A mixed-integer solver is also required and its Python bindings must be properly installed. Supported solvers are currently CPLEX and Gurobi.
+## Installation
+
+In these docs, we describe the Python/Pyomo version of the package, although a [Julia/JuMP version](https://github.com/ANL-CEEESA/MIPLearn.jl) is also available. A mixed-integer solver is also required and its Python bindings must be properly installed. Supported solvers are currently CPLEX, Gurobi and XPRESS.
 
 To install MIPLearn, run: 
 
 ```bash
-pip3 install miplearn
+pip3 install --upgrade miplearn==0.2.*
 ```
 
 After installation, the package `miplearn` should become available to Python. It can be imported
@@ -17,7 +25,7 @@ as follows:
 import miplearn
 ```
 
-## 2. Using `LearningSolver`
+## Using `LearningSolver`
 
 The main class provided by this package is `LearningSolver`, a learning-enhanced MIP solver which uses information from previously solved instances to accelerate the solution of new instances. The following example shows its basic usage:
 
@@ -46,7 +54,7 @@ for instance in test_instances:
 In this example, we have two lists of user-provided instances: `training_instances` and `test_instances`. We start by solving all training instances. Since there is no historical information available at this point, the instances will be processed from scratch, with no ML acceleration. After solving each instance, the solver stores within each `instance` object the optimal solution, the optimal objective value, and other information that can be used to accelerate future solves. After all training instances are solved, we call `solver.fit(training_instances)`. This instructs the solver to train all its internal machine-learning models based on the solutions of the (solved) trained instances. Subsequent calls to `solver.solve(instance)` will automatically use the trained Machine Learning models to accelerate the solution process.
 
 
-## 3. Describing problem instances
+## 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:
 
@@ -61,13 +69,13 @@ An optional method which can be implemented is `instance.get_variable_category(v
 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_name, 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.
 
 
-## 4. Describing lazy constraints
+## Describing lazy constraints
 
 For many MIP formulations, it is not desirable to add all constraints up-front, either because the total number of constraints is very large, or because some of the constraints, even in relatively small numbers, can still cause significant performance impact when added to the formulation. In these situations, it may be desirable to generate and add constraints incrementaly, during the solution process itself. Conventional MIP solvers typically start by solving the problem without any lazy constraints. Whenever a candidate solution is found, the solver finds all violated lazy constraints and adds them to the formulation. MIPLearn significantly accelerates this process by using ML to predict which lazy constraints should be enforced from the very beginning of the optimization process, even before a candidate solution is available.
 
 MIPLearn supports two types of lazy constraints: through constraint annotations and through callbacks.
 
-### 4.1 Adding lazy constraints through annotations
+###  Adding lazy constraints through annotations
 
 The easiest way to create lazy constraints in MIPLearn is to add them to the model (just like any regular constraints), then annotate them as lazy, as described below. Just before the optimization starts, MIPLearn removes all lazy constraints from the model and places them in a lazy constraint pool. If any trained ML models are available, MIPLearn queries these models to decide which of these constraints should be moved back into the formulation. After this step, the optimization starts, and lazy constraints from the pool are added to the model in the conventional fashion.
 
@@ -84,7 +92,7 @@ An additional method that can be implemented is `get_lazy_constraint_category(ci
 !!! warning
     If two lazy constraints belong to the same category, their feature vectors should have the same length.
 
-### 4.2 Adding lazy constraints through callbacks
+### Adding lazy constraints through callbacks
 
 Although convenient, the method described in the previous subsection still requires the generation of all lazy constraints ahead of time, which can be prohibitively expensive. An alternative method is through a lazy constraint callbacks, described below. During the solution process, MIPLearn will repeatedly call a user-provided function to identify any violated lazy constraints. If violated constraints are identified, MIPLearn will additionally call another user-provided function to generate the constraint and add it to the formulation.
 
@@ -101,28 +109,29 @@ Assuming that trained ML models are available, immediately after calling `solver
 
 After the optimization process starts, MIPLearn will periodically call `find_violated_lazy_constraints` to verify if the current solution violates any lazy constraints. If any violated lazy constraints are found, MIPLearn will call the method `build_violated_lazy_constraints` and add the returned constraints to the formulation.
 
-!!! note
-    When implementing `find_violated_lazy_constraints(self, model)`, the current solution may be accessed through `self.solution[var_name][index]`.
+```{tip}
+When implementing `find_violated_lazy_constraints(self, model)`, the current solution may be accessed through `self.solution[var_name][index]`.
+```
 
-
-## 5. Obtaining heuristic solutions
+## 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](about.md#references) and [benchmark results](benchmark.md)).
 
 
-!!! 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.
+```{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.
+```
 
+## Scaling Up
 
-## 6. Saving and loading solver state
+### 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 standard `pickle` module, as the following example illustrates:
+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 the utility functions `write_pickle_gz` and `read_pickle_gz`, as the following example illustrates:
 
 ```python
-from miplearn import LearningSolver
-import pickle
+from miplearn import LearningSolver, write_pickle_gz, read_pickle_gz
 
 # Solve training instances
 training_instances = [...]
@@ -134,12 +143,12 @@ for instance in training_instances:
 solver.fit(training_instances)
 
 # Save trained solver to disk
-pickle.dump(solver, open("solver.pickle", "wb"))
+write_pickle_gz(solver, "solver.pkl.gz")
 
 # Application restarts...
 
 # Load trained solver from disk
-solver = pickle.load(open("solver.pickle", "rb"))
+solver = read_pickle_gz("solver.pkl.gz")
 
 # Solve additional instances
 test_instances = [...]
@@ -148,9 +157,9 @@ for instance in test_instances:
 ```
 
 
-## 7. Solving training instances in parallel
+### Solving instances in parallel
 
-In many situations, training and test instances can be solved in parallel to accelerate the training process. `LearningSolver` provides the method `parallel_solve(instances)` to easily achieve this:
+In many situations, instances can be solved in parallel to accelerate the training process. `LearningSolver` provides the method `parallel_solve(instances)` to easily achieve this:
 
 ```python
 from miplearn import LearningSolver
@@ -166,6 +175,72 @@ solver.parallel_solve(test_instances)
 ```
 
 
-## 8. Current Limitations
+### Solving instances from the disk
 
-* Only binary and continuous decision variables are currently supported.
+In all examples above, we have assumed that instances are available as Python objects, stored in memory. When problem instances are very large, or when there is a large number of problem instances, this approach may require an excessive amount of memory. To reduce memory requirements, MIPLearn can also operate on instances that are stored on disk, through the `PickleGzInstance` class, as the next example illustrates.
+
+```python
+import pickle
+from miplearn import (
+    LearningSolver,
+    PickleGzInstance,
+    write_pickle_gz,
+)
+
+# Construct and pickle 600 problem instances
+for i in range(600):
+    instance = MyProblemInstance([...])
+    write_pickle_gz(instance, "instance_%03d.pkl" % i)
+        
+# Split instances into training and test
+test_instances  = [PickleGzInstance("instance_%03d.pkl" % i) for i in range(500)]
+train_instances = [PickleGzInstance("instance_%03d.pkl" % i) for i in range(500, 600)]
+
+# Create solver
+solver = LearningSolver([...])
+
+# Solve training instances 
+solver.parallel_solve(train_instances, n_jobs=4)
+
+# Train ML models
+solver.fit(train_instances)
+
+# Solve test instances 
+solver.parallel_solve(test_instances, n_jobs=4)
+```
+
+
+By default, `solve` and `parallel_solve` modify files in place. That is, after the instances are loaded from disk and solved, MIPLearn writes them back to the disk, overwriting the original files. To discard the modifications instead, use `LearningSolver(..., discard_outputs=True)`. This can be useful, for example, during benchmarks.
+
+## Running benchmarks
+
+MIPLearn provides the utility class `BenchmarkRunner`, which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage:
+
+```python
+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)
+
+# Test phase...
+benchmark = BenchmarkRunner({
+    "Baseline": LearningSolver(...),
+    "Strategy A": LearningSolver(...),
+    "Strategy B": LearningSolver(...),
+    "Strategy C": LearningSolver(...),
+})
+benchmark.fit(train_instances)
+benchmark.parallel_solve(test_instances, n_jobs=5)
+benchmark.write_csv("results.csv")
+```
+
+The method `fit` trains the ML models for each individual solver. The method `parallel_solve` solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, `write_csv` produces a table of results. The columns in the CSV file depend on the components added to the solver.
+
+## Current Limitations
+
+* Only binary and continuous decision variables are currently supported. General integer variables are not currently supported by some solver components.

miplearn/__init__.py

@@ -1,32 +1,29 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from .extractors import (SolutionExtractor,
-                         InstanceFeaturesExtractor,
-                         ObjectiveValueExtractor,
-                         VariableFeaturesExtractor)
-
-from .components.component import Component
-from .components.objective import ObjectiveValueComponent
-from .components.lazy_dynamic import DynamicLazyConstraintsComponent
-from .components.lazy_static import StaticLazyConstraintsComponent
-from .components.cuts import UserCutsComponent
-from .components.primal import PrimalSolutionComponent
-from .components.relaxation import RelaxationComponent
-
-from .classifiers.adaptive import AdaptiveClassifier
-from .classifiers.threshold import MinPrecisionThreshold
-
 from .benchmark import BenchmarkRunner
-
-from .instance import Instance
-
-from .solvers.pyomo.base import BasePyomoSolver
-from .solvers.pyomo.cplex import CplexPyomoSolver
-from .solvers.pyomo.gurobi import GurobiPyomoSolver
+from .classifiers import Classifier, Regressor
+from .classifiers.adaptive import AdaptiveClassifier
+from .classifiers.sklearn import ScikitLearnRegressor, ScikitLearnClassifier
+from .classifiers.threshold import MinPrecisionThreshold
+from .components.component import Component
+from .components.dynamic_lazy import DynamicLazyConstraintsComponent
+from .components.dynamic_user_cuts import UserCutsComponent
+from .components.objective import ObjectiveValueComponent
+from .components.primal import PrimalSolutionComponent
+from .components.static_lazy import StaticLazyConstraintsComponent
+from .instance.base import Instance
+from .instance.picklegz import (
+    PickleGzInstance,
+    write_pickle_gz,
+    read_pickle_gz,
+    write_pickle_gz_multiple,
+)
+from .log import setup_logger
 from .solvers.gurobi import GurobiSolver
 from .solvers.internal import InternalSolver
 from .solvers.learning import LearningSolver
-
-from .log import setup_logger
+from .solvers.pyomo.base import BasePyomoSolver
+from .solvers.pyomo.cplex import CplexPyomoSolver
+from .solvers.pyomo.gurobi import GurobiPyomoSolver

miplearn/benchmark.py

@@ -1,203 +1,129 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from copy import deepcopy
+import logging
+import os
+from typing import Dict, List
 
 import pandas as pd
-import numpy as np
-import logging
-from tqdm.auto import tqdm
 
-from .solvers.learning import LearningSolver
+from miplearn.components.component import Component
+from miplearn.instance.base import Instance
+from miplearn.solvers.learning import LearningSolver
+
+logger = logging.getLogger(__name__)
 
 
 class BenchmarkRunner:
-    def __init__(self, solvers):
-        assert isinstance(solvers, dict)
-        for solver in solvers.values():
-            assert isinstance(solver, LearningSolver)
-        self.solvers = solvers
-        self.results = None
-        
-    def solve(self, instances, tee=False):
-        for (solver_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, solver_name=solver_name, instance=i)
+    """
+    Utility class that simplifies the task of comparing the performance of different
+    solvers.
 
-    def parallel_solve(self,
-                       instances,
-                       n_jobs=1,
-                       n_trials=1,
-                       index_offset=0,
-                      ):
+    Example
+    -------
+    ```python
+    benchmark = BenchmarkRunner({
+        "Baseline": LearningSolver(...),
+        "Strategy A": LearningSolver(...),
+        "Strategy B": LearningSolver(...),
+        "Strategy C": LearningSolver(...),
+    })
+    benchmark.fit(train_instances)
+    benchmark.parallel_solve(test_instances, n_jobs=5)
+    benchmark.save_results("result.csv")
+    ```
+
+    Parameters
+    ----------
+    solvers: Dict[str, LearningSolver]
+        Dictionary containing the solvers to compare. Solvers may have different
+        arguments and components. The key should be the name of the solver. It
+        appears in the exported tables of results.
+    """
+
+    def __init__(self, solvers: Dict[str, LearningSolver]) -> None:
+        self.solvers: Dict[str, LearningSolver] = solvers
+        self.results = pd.DataFrame(
+            columns=[
+                "Solver",
+                "Instance",
+            ]
+        )
+
+    def parallel_solve(
+        self,
+        instances: List[Instance],
+        n_jobs: int = 1,
+        n_trials: int = 3,
+    ) -> None:
+        """
+        Solves the given instances in parallel and collect benchmark statistics.
+
+        Parameters
+        ----------
+        instances: List[Instance]
+            List of instances to solve. This can either be a list of instances
+            already loaded in memory, or a list of filenames pointing to pickled (and
+            optionally gzipped) files.
+        n_jobs: int
+            List of instances to solve in parallel at a time.
+        n_trials: int
+            How many times each instance should be solved.
+        """
         self._silence_miplearn_logger()
         trials = instances * n_trials
         for (solver_name, solver) in self.solvers.items():
-            results = solver.parallel_solve(trials,
-                                            n_jobs=n_jobs,
-                                            label="Solve (%s)" % solver_name)
+            results = solver.parallel_solve(
+                trials,
+                n_jobs=n_jobs,
+                label="Solve (%s)" % solver_name,
+                discard_outputs=True,
+            )
             for i in range(len(trials)):
-                idx = (i % len(instances)) + index_offset
-                self._push_result(results[i],
-                                  solver=solver,
-                                  solver_name=solver_name,
-                                  instance=idx)
+                idx = i % len(instances)
+                results[i]["Solver"] = solver_name
+                results[i]["Instance"] = idx
+                self.results = self.results.append(pd.DataFrame([results[i]]))
         self._restore_miplearn_logger()
-    
-    def raw_results(self):
-        return self.results
-    
-    def save_results(self, filename):
+
+    def write_csv(self, filename: str) -> None:
+        """
+        Writes the collected results to a CSV file.
+
+        Parameters
+        ----------
+        filename: str
+            The name of the file.
+        """
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
         self.results.to_csv(filename)
-        
-    def load_results(self, filename):
-        self.results = pd.read_csv(filename, index_col=0)
-        
-    def load_state(self, filename):
-        for (solver_name, solver) in self.solvers.items():
-            solver.load_state(filename)
 
-    def fit(self, training_instances):
-        for (solver_name, solver) in self.solvers.items():
-            solver.fit(training_instances)
-            
-    def _push_result(self, result, solver, solver_name, instance):
-        if self.results is None:
-            self.results = pd.DataFrame(columns=["Solver",
-                                                 "Instance",
-                                                 "Wallclock Time",
-                                                 "Lower Bound",
-                                                 "Upper Bound",
-                                                 "Gap",
-                                                 "Nodes",
-                                                 "Mode",
-                                                 "Sense",
-                                                 "Predicted LB",
-                                                 "Predicted UB",
-                                                ])
-        lb = result["Lower bound"]
-        ub = result["Upper bound"]
-        gap = (ub - lb) / lb
-        if "Predicted LB" not in result:
-            result["Predicted LB"] = float("nan")
-            result["Predicted UB"] = float("nan")
-        self.results = self.results.append({
-            "Solver": solver_name,
-            "Instance": instance,
-            "Wallclock Time": result["Wallclock time"],
-            "Lower Bound": lb,
-            "Upper Bound": ub,
-            "Gap": gap,
-            "Nodes": result["Nodes"],
-            "Mode": solver.mode,
-            "Sense": result["Sense"],
-            "Predicted LB": result["Predicted LB"],
-            "Predicted UB": result["Predicted UB"],
-        }, 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 = np.maximum(1, 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
+    def fit(self, instances: List[Instance], n_jobs: int = 1) -> None:
+        """
+        Trains all solvers with the provided training instances.
 
-    def save_chart(self, filename):
-        import matplotlib.pyplot as plt
-        import seaborn as sns
-        from numpy import median
-        
-        sns.set_style("whitegrid")
-        sns.set_palette("Blues_r")
-        results = self.raw_results()
-        results["Gap (%)"] = results["Gap"] * 100.0
+        Parameters
+        ----------
+        instances:  List[Instance]
+            List of training instances.
+        n_jobs: int
+            Number of parallel processes to use.
+        """
+        components: List[Component] = []
+        for solver in self.solvers.values():
+            components += solver.components.values()
+        Component.fit_multiple(
+            components,
+            instances,
+            n_jobs=n_jobs,
+        )
 
-        sense = results.loc[0, "Sense"]
-        if sense == "min":
-            primal_column = "Relative Upper Bound"
-            obj_column = "Upper Bound"
-            predicted_obj_column = "Predicted UB"
-        else:
-            primal_column = "Relative Lower Bound"
-            obj_column = "Lower Bound"
-            predicted_obj_column = "Predicted LB"
-
-        fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1,
-                                                 ncols=4,
-                                                 figsize=(12,4),
-                                                 gridspec_kw={'width_ratios': [2, 1, 1, 2]})
-        
-        # Figure 1: Solver x Wallclock Time
-        sns.stripplot(x="Solver",
-                      y="Wallclock Time",
-                      data=results,
-                      ax=ax1,
-                      jitter=0.25,
-                      size=4.0,
-                   )
-        sns.barplot(x="Solver",
-                    y="Wallclock Time",
-                    data=results,
-                    ax=ax1,
-                    errwidth=0.,
-                    alpha=0.4,
-                    estimator=median,
-                   )
-        ax1.set(ylabel='Wallclock Time (s)')
-        
-        # Figure 2: Solver x Gap (%)
-        ax2.set_ylim(-0.5, 5.5)
-        sns.stripplot(x="Solver",
-                      y="Gap (%)",
-                      jitter=0.25,
-                      data=results[results["Mode"] != "heuristic"],
-                      ax=ax2,
-                      size=4.0,
-                     )
-        
-        # Figure 3: Solver x Primal Value
-        ax3.set_ylim(0.95,1.05)
-        sns.stripplot(x="Solver",
-                      y=primal_column,
-                      jitter=0.25,
-                      data=results[results["Mode"] == "heuristic"],
-                      ax=ax3,
-                     )
-
-        # Figure 4: Predicted vs Actual Objective Value
-        sns.scatterplot(x=obj_column,
-                        y=predicted_obj_column,
-                        hue="Solver",
-                        data=results[results["Mode"] != "heuristic"],
-                        ax=ax4,
-                       )
-        xlim, ylim = ax4.get_xlim(), ax4.get_ylim()
-        ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls='-', color="#cccccc")
-        ax4.set_xlim(xlim)
-        ax4.set_ylim(ylim)
-        ax4.get_legend().remove()
-
-        fig.tight_layout()
-        plt.savefig(filename, bbox_inches='tight', dpi=150)
-        
-    def _silence_miplearn_logger(self):
+    def _silence_miplearn_logger(self) -> None:
         miplearn_logger = logging.getLogger("miplearn")
         self.prev_log_level = miplearn_logger.getEffectiveLevel()
-        miplearn_logger.setLevel(logging.WARNING)    
-        
-    def _restore_miplearn_logger(self):
+        miplearn_logger.setLevel(logging.WARNING)
+
+    def _restore_miplearn_logger(self) -> None:
         miplearn_logger = logging.getLogger("miplearn")
-        miplearn_logger.setLevel(self.prev_log_level)    
-        
-    
+        miplearn_logger.setLevel(self.prev_log_level)

miplearn/classifiers/__init__.py

@@ -1,33 +1,163 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from abc import ABC, abstractmethod
+from typing import Optional
 
 import numpy as np
 
 
 class Classifier(ABC):
-    @abstractmethod
-    def fit(self, x_train, y_train):
-        pass
+    """
+    A Classifier decides which class each sample belongs to, based on historical
+    data.
+    """
+
+    def __init__(self) -> None:
+        self.n_features: Optional[int] = None
+        self.n_classes: Optional[int] = None
 
     @abstractmethod
-    def predict_proba(self, x_test):
-        pass
+    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        """
+        Trains the classifier.
 
-    def predict(self, x_test):
-        proba = self.predict_proba(x_test)
-        assert isinstance(proba, np.ndarray)
-        assert proba.shape == (x_test.shape[0], 2)
-        return (proba[:, 1] > 0.5).astype(float)
+        Parameters
+        ----------
+        x_train: np.ndarray
+            An array of features with shape (`n_samples`, `n_features`). Each entry
+            must be a float.
+        y_train: np.ndarray
+            An array of labels with shape (`n_samples`, `n_classes`). Each entry must be
+            a bool, and there must be exactly one True element in each row.
+        """
+        assert isinstance(x_train, np.ndarray)
+        assert isinstance(y_train, np.ndarray)
+        assert x_train.dtype in [
+            np.float16,
+            np.float32,
+            np.float64,
+        ], f"x_train.dtype shoule be float. Found {x_train.dtype} instead."
+        assert y_train.dtype == np.bool8
+        assert len(x_train.shape) == 2
+        assert len(y_train.shape) == 2
+        (n_samples_x, n_features) = x_train.shape
+        (n_samples_y, n_classes) = y_train.shape
+        assert n_samples_y == n_samples_x
+        self.n_features = n_features
+        self.n_classes = n_classes
+
+    @abstractmethod
+    def predict_proba(self, x_test: np.ndarray) -> np.ndarray:
+        """
+        Predicts the probability of each sample belonging to each class. Must be called
+        after fit.
+
+        Parameters
+        ----------
+        x_test: np.ndarray
+            An array of features with shape (`n_samples`, `n_features`). The number of
+            features in `x_test` must match the number of features in `x_train` provided
+            to `fit`.
+
+        Returns
+        -------
+        np.ndarray
+            An array of predicted probabilities with shape (`n_samples`, `n_classes`),
+            where `n_classes` is the number of columns in `y_train` provided to `fit`.
+        """
+        assert self.n_features is not None
+        assert isinstance(x_test, np.ndarray)
+        assert len(x_test.shape) == 2
+        (n_samples, n_features_x) = x_test.shape
+        assert n_features_x == self.n_features, (
+            f"Test and training data have different number of "
+            f"features: {n_features_x} != {self.n_features}"
+        )
+        return np.ndarray([])
+
+    @abstractmethod
+    def clone(self) -> "Classifier":
+        """
+        Returns an unfitted copy of this classifier with the same hyperparameters.
+        """
+        pass
 
 
 class Regressor(ABC):
-    @abstractmethod
-    def fit(self, x_train, y_train):
-        pass
+    """
+    A Regressor tries to predict the values of some continous variables, given the
+    values of other variables.
+    """
+
+    def __init__(self) -> None:
+        self.n_inputs: Optional[int] = None
 
     @abstractmethod
-    def predict(self):
+    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        """
+        Trains the regressor.
+
+        Parameters
+        ----------
+        x_train: np.ndarray
+            An array of inputs with shape (`n_samples`, `n_inputs`). Each entry must be
+            a float.
+        y_train: np.ndarray
+            An array of outputs with shape (`n_samples`, `n_outputs`). Each entry must
+            be a float.
+        """
+        assert isinstance(x_train, np.ndarray)
+        assert isinstance(y_train, np.ndarray)
+        assert x_train.dtype in [np.float16, np.float32, np.float64]
+        assert y_train.dtype in [np.float16, np.float32, np.float64]
+        assert len(x_train.shape) == 2, (
+            f"Parameter x_train should be a square matrix. "
+            f"Found {x_train.shape} ndarray instead."
+        )
+        assert len(y_train.shape) == 2, (
+            f"Parameter y_train should be a square matrix. "
+            f"Found {y_train.shape} ndarray instead."
+        )
+        (n_samples_x, n_inputs) = x_train.shape
+        (n_samples_y, n_outputs) = y_train.shape
+        assert n_samples_y == n_samples_x
+        self.n_inputs = n_inputs
+
+    @abstractmethod
+    def predict(self, x_test: np.ndarray) -> np.ndarray:
+        """
+        Predicts the values of the output variables. Must be called after fit.
+
+        Parameters
+        ----------
+        x_test: np.ndarray
+            An array of inputs with shape (`n_samples`, `n_inputs`), where `n_inputs`
+            must match the number of columns in `x_train` provided to `fit`.
+
+        Returns
+        -------
+        np.ndarray
+            An array of outputs  with shape (`n_samples`, `n_outputs`), where
+            `n_outputs` is the number of columns in `y_train` provided to `fit`.
+        """
+        assert self.n_inputs is not None
+        assert isinstance(x_test, np.ndarray), (
+            f"Parameter x_train must be np.ndarray. "
+            f"Found {x_test.__class__.__name__} instead."
+        )
+        assert len(x_test.shape) == 2
+        (n_samples, n_inputs_x) = x_test.shape
+        assert n_inputs_x == self.n_inputs, (
+            f"Test and training data have different number of "
+            f"inputs: {n_inputs_x} != {self.n_inputs}"
+        )
+        return np.ndarray([])
+
+    @abstractmethod
+    def clone(self) -> "Regressor":
+        """
+        Returns an unfitted copy of this regressor with the same hyperparameters.
+        """
         pass

miplearn/classifiers/adaptive.py

@@ -1,66 +1,120 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
-from copy import deepcopy
+from typing import Dict, Optional
 
-from miplearn.classifiers import Classifier
-from miplearn.classifiers.counting import CountingClassifier
-from miplearn.classifiers.evaluator import ClassifierEvaluator
+import numpy as np
 from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import roc_auc_score
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
 
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.counting import CountingClassifier
+from miplearn.classifiers.sklearn import ScikitLearnClassifier
+
 logger = logging.getLogger(__name__)
 
 
+class CandidateClassifierSpecs:
+    """
+    Specifications describing how to construct a certain classifier, and under
+    which circumstances it can be used.
+
+    Parameters
+    ----------
+    min_samples: int
+        Minimum number of samples for this classifier to be considered.
+    classifier: Callable[[], Classifier]
+        Callable that constructs the classifier.
+    """
+
+    def __init__(
+        self,
+        classifier: Classifier,
+        min_samples: int = 0,
+    ) -> None:
+        self.min_samples = min_samples
+        self.classifier = classifier
+
+
 class AdaptiveClassifier(Classifier):
     """
     A meta-classifier which dynamically selects what actual classifier to use
     based on its cross-validation score on a particular training data set.
+
+    Parameters
+    ----------
+    candidates: Dict[str, CandidateClassifierSpecs]
+        A dictionary of candidate classifiers to consider, mapping the name of the
+        candidate to its specs, which describes how to construct it and under what
+        scenarios. If no candidates are provided, uses a fixed set of defaults,
+        which includes `CountingClassifier`, `KNeighborsClassifier` and
+        `LogisticRegression`.
     """
 
-    def __init__(self,
-                 candidates=None,
-                 evaluator=ClassifierEvaluator()):
-        """
-        Initializes the meta-classifier.
-        """
+    def __init__(
+        self,
+        candidates: Optional[Dict[str, CandidateClassifierSpecs]] = None,
+    ) -> None:
+        super().__init__()
         if candidates is None:
             candidates = {
-                "knn(100)": {
-                    "classifier": KNeighborsClassifier(n_neighbors=100),
-                    "min samples": 100,
-                },
-                "logistic": {
-                    "classifier": make_pipeline(StandardScaler(),
-                                                LogisticRegression()),
-                    "min samples": 30,
-                },
-                "counting": {
-                    "classifier": CountingClassifier(),
-                    "min samples": 0,
-                }
+                "knn(100)": CandidateClassifierSpecs(
+                    classifier=ScikitLearnClassifier(
+                        KNeighborsClassifier(n_neighbors=100)
+                    ),
+                    min_samples=100,
+                ),
+                "logistic": CandidateClassifierSpecs(
+                    classifier=ScikitLearnClassifier(
+                        make_pipeline(
+                            StandardScaler(),
+                            LogisticRegression(),
+                        )
+                    ),
+                    min_samples=30,
+                ),
+                "counting": CandidateClassifierSpecs(
+                    classifier=CountingClassifier(),
+                ),
             }
         self.candidates = candidates
-        self.evaluator = evaluator
-        self.classifier = None
+        self.classifier: Optional[Classifier] = None
 
-    def fit(self, x_train, y_train):
-        best_name, best_clf, best_score = None, None, -float("inf")
+    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        super().fit(x_train, y_train)
         n_samples = x_train.shape[0]
-        for (name, clf_dict) in self.candidates.items():
-            if n_samples < clf_dict["min samples"]:
+        assert y_train.shape == (n_samples, 2)
+
+        # If almost all samples belong to the same class, return a fixed prediction and
+        # skip all the other steps.
+        if y_train[:, 0].mean() > 0.999 or y_train[:, 1].mean() > 0.999:
+            self.classifier = CountingClassifier()
+            self.classifier.fit(x_train, y_train)
+            return
+
+        best_name, best_clf, best_score = None, None, -float("inf")
+        for (name, specs) in self.candidates.items():
+            if n_samples < specs.min_samples:
                 continue
-            clf = deepcopy(clf_dict["classifier"])
+            clf = specs.classifier.clone()
             clf.fit(x_train, y_train)
-            score = self.evaluator.evaluate(clf, x_train, y_train)
+            proba = clf.predict_proba(x_train)
+            # FIXME: Switch to k-fold cross validation
+            score = roc_auc_score(y_train[:, 1], proba[:, 1])
             if score > best_score:
                 best_name, best_clf, best_score = name, clf, score
         logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
         self.classifier = best_clf
 
-    def predict_proba(self, x_test):
+    def predict_proba(self, x_test: np.ndarray) -> np.ndarray:
+        super().predict_proba(x_test)
+        assert self.classifier is not None
         return self.classifier.predict_proba(x_test)
+
+    def clone(self) -> "AdaptiveClassifier":
+        return AdaptiveClassifier(self.candidates)

miplearn/classifiers/counting.py

@@ -1,28 +1,45 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+from typing import Optional, cast
+
+import numpy as np
 
 from miplearn.classifiers import Classifier
-import numpy as np
 
 
 class CountingClassifier(Classifier):
     """
-    A classifier that generates constant predictions, based only on the
-    frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
-    this classifier always returns [0.66 0.33] for any x_test. It essentially
-    counts how many times each label appeared, hence the name.
+
+    A classifier that generates constant predictions, based only on the frequency of
+    the training labels. For example, suppose `y_train` is given by:
+    ```python
+    y_train = np.array([
+        [True, False],
+        [False, True],
+        [False, True],
+    ])
+    ```
+    Then `predict_proba` always returns `[0.33 0.66]` for every sample, regardless of
+    `x_train`. It essentially counts how many times each label appeared, hence the name.
+
     """
 
-    def __init__(self):
-        self.mean = None
+    def __init__(self) -> None:
+        super().__init__()
+        self.mean: Optional[np.ndarray] = None
 
-    def fit(self, x_train, y_train):
-        self.mean = np.mean(y_train)
+    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        super().fit(x_train, y_train)
+        self.mean = cast(np.ndarray, np.mean(y_train, axis=0))
 
-    def predict_proba(self, x_test):
-        return np.array([[1 - self.mean, self.mean]
-                         for _ in range(x_test.shape[0])])
+    def predict_proba(self, x_test: np.ndarray) -> np.ndarray:
+        super().predict_proba(x_test)
+        n_samples = x_test.shape[0]
+        return np.array([self.mean for _ in range(n_samples)])
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return "CountingClassifier(mean=%s)" % self.mean
+
+    def clone(self) -> "CountingClassifier":
+        return CountingClassifier()

miplearn/classifiers/cv.py

@@ -1,71 +1,132 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from copy import deepcopy
+import logging
+from typing import Optional, List
 
 import numpy as np
-from miplearn.classifiers import Classifier
 from sklearn.dummy import DummyClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import cross_val_score
 
-import logging
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.sklearn import ScikitLearnClassifier
+
 logger = logging.getLogger(__name__)
 
 
 class CrossValidatedClassifier(Classifier):
     """
     A meta-classifier that, upon training, evaluates the performance of another
-    classifier on the training data set using k-fold cross validation, then
-    either adopts the other classifier it if the cv-score is high enough, or
-    returns a constant label for every x_test otherwise.
+    candidate classifier on the training data set, using k-fold cross validation,
+    then either adopts it, if its cv-score is high enough, or returns constant
+    predictions for every x_test, otherwise.
 
-    The threshold is specified in comparison to a dummy classifier trained
-    on the same dataset. For example, a threshold of 0.0 indicates that any
-    classifier as good as the dummy predictor is acceptable. A threshold of 1.0
-    indicates that only classifier with a perfect cross-validation score are
-    acceptable. Other numbers are a linear interpolation of these two extremes.
+    Parameters
+    ----------
+    classifier: Callable[[], ScikitLearnClassifier]
+        A callable that constructs the candidate classifier.
+    threshold: float
+        Number from zero to one indicating how well must the candidate classifier
+        perform to be adopted. The threshold is specified in comparison to a dummy
+        classifier trained on the same dataset. For example, a threshold of 0.0
+        indicates that any classifier as good as the dummy predictor is acceptable. A
+        threshold of 1.0 indicates that only classifiers with perfect
+        cross-validation scores are acceptable. Other numbers are a linear
+        interpolation of these two extremes.
+    constant: Optional[List[bool]]
+        If the candidate classifier fails to meet the threshold, use a dummy classifier
+        which always returns this prediction instead. The list should have exactly as
+        many elements as the number of columns of `x_train` provided to `fit`.
+    cv: int
+        Number of folds.
+    scoring: str
+        Scoring function.
     """
 
-    def __init__(self,
-                 classifier=LogisticRegression(),
-                 threshold=0.75,
-                 constant=0.0,
-                 cv=5,
-                 scoring='accuracy'):
-        self.classifier = None
+    def __init__(
+        self,
+        classifier: ScikitLearnClassifier = ScikitLearnClassifier(LogisticRegression()),
+        threshold: float = 0.75,
+        constant: Optional[List[bool]] = None,
+        cv: int = 5,
+        scoring: str = "accuracy",
+    ):
+        super().__init__()
+        if constant is None:
+            constant = [True, False]
+        self.n_classes = len(constant)
+        self.classifier: Optional[ScikitLearnClassifier] = None
         self.classifier_prototype = classifier
-        self.constant = constant
+        self.constant: List[bool] = constant
         self.threshold = threshold
         self.cv = cv
         self.scoring = scoring
 
-    def fit(self, x_train, y_train):
+    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        super().fit(x_train, y_train)
+        (n_samples, n_classes) = x_train.shape
+        assert n_classes == self.n_classes
+
         # Calculate dummy score and absolute score threshold
         y_train_avg = np.average(y_train)
         dummy_score = max(y_train_avg, 1 - y_train_avg)
-        absolute_threshold = 1. * self.threshold + dummy_score * (1 - self.threshold)
+        absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
 
         # Calculate cross validation score and decide which classifier to use
-        clf = deepcopy(self.classifier_prototype)
-        cv_score = float(np.mean(cross_val_score(clf,
-                                                 x_train,
-                                                 y_train,
-                                                 cv=self.cv,
-                                                 scoring=self.scoring)))
+        clf = self.classifier_prototype.clone()
+        assert clf is not None
+        assert isinstance(clf, ScikitLearnClassifier), (
+            f"The provided classifier callable must return a ScikitLearnClassifier. "
+            f"Found {clf.__class__.__name__} instead. If this is a scikit-learn "
+            f"classifier, you must wrap it with ScikitLearnClassifier."
+        )
+
+        cv_score = float(
+            np.mean(
+                cross_val_score(
+                    clf.inner_clf,
+                    x_train,
+                    y_train[:, 1],
+                    cv=self.cv,
+                    scoring=self.scoring,
+                )
+            )
+        )
         if cv_score >= absolute_threshold:
-            logger.debug("cv_score is above threshold (%.2f >= %.2f); keeping" %
-                         (cv_score, absolute_threshold))
+            logger.debug(
+                "cv_score is above threshold (%.2f >= %.2f); keeping"
+                % (cv_score, absolute_threshold)
+            )
             self.classifier = clf
         else:
-            logger.debug("cv_score is below threshold (%.2f < %.2f); discarding" %
-                         (cv_score, absolute_threshold))
-            self.classifier = DummyClassifier(strategy="constant",
-                                              constant=self.constant)
+            logger.debug(
+                "cv_score is below threshold (%.2f < %.2f); discarding"
+                % (cv_score, absolute_threshold)
+            )
+            self.classifier = ScikitLearnClassifier(
+                DummyClassifier(
+                    strategy="constant",
+                    constant=self.constant[1],
+                )
+            )
 
         # Train chosen classifier
+        assert self.classifier is not None
+        assert isinstance(self.classifier, ScikitLearnClassifier)
         self.classifier.fit(x_train, y_train)
 
-    def predict_proba(self, x_test):
+    def predict_proba(self, x_test: np.ndarray) -> np.ndarray:
+        super().predict_proba(x_test)
+        assert self.classifier is not None
         return self.classifier.predict_proba(x_test)
+
+    def clone(self) -> "CrossValidatedClassifier":
+        return CrossValidatedClassifier(
+            classifier=self.classifier_prototype,
+            threshold=self.threshold,
+            constant=self.constant,
+            cv=self.cv,
+            scoring=self.scoring,
+        )

miplearn/classifiers/evaluator.py

@@ -1,15 +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 sklearn.metrics import roc_auc_score
-
-
-class ClassifierEvaluator:
-    def __init__(self):
-        pass
-
-    def evaluate(self, clf, x_train, y_train):
-        # FIXME: use cross-validation
-        proba = clf.predict_proba(x_train)
-        return roc_auc_score(y_train, proba[:, 1])

miplearn/classifiers/sklearn.py

@@ -0,0 +1,93 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from typing import Optional, Any, cast
+
+import numpy as np
+import sklearn
+
+from miplearn.classifiers import Classifier, Regressor
+
+
+class ScikitLearnClassifier(Classifier):
+    """
+    Wrapper for ScikitLearn classifiers, which makes sure inputs and outputs have the
+    correct dimensions and types.
+    """
+
+    def __init__(self, clf: Any) -> None:
+        super().__init__()
+        self.inner_clf = clf
+        self.constant: Optional[np.ndarray] = None
+
+    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        super().fit(x_train, y_train)
+        (n_samples, n_classes) = y_train.shape
+        assert n_classes == 2, (
+            f"Scikit-learn classifiers must have exactly two classes. "
+            f"{n_classes} classes were provided instead."
+        )
+
+        # When all samples belong to the same class, sklearn's predict_proba returns
+        # an array with a single column. The following check avoid this strange
+        # behavior.
+        mean = cast(np.ndarray, y_train.astype(float).mean(axis=0))
+        if mean.max() == 1.0:
+            self.constant = mean
+            return
+
+        self.inner_clf.fit(x_train, y_train[:, 1])
+
+    def predict_proba(self, x_test: np.ndarray) -> np.ndarray:
+        super().predict_proba(x_test)
+        n_samples = x_test.shape[0]
+        if self.constant is not None:
+            return np.array([self.constant for n in range(n_samples)])
+        sklearn_proba = self.inner_clf.predict_proba(x_test)
+        if isinstance(sklearn_proba, list):
+            assert len(sklearn_proba) == self.n_classes
+            for pb in sklearn_proba:
+                assert isinstance(pb, np.ndarray)
+                assert pb.dtype in [np.float16, np.float32, np.float64]
+                assert pb.shape == (n_samples, 2)
+            proba = np.hstack([pb[:, [1]] for pb in sklearn_proba])
+            assert proba.shape == (n_samples, self.n_classes)
+            return proba
+        else:
+            assert isinstance(sklearn_proba, np.ndarray)
+            assert sklearn_proba.shape == (n_samples, 2)
+            return sklearn_proba
+
+    def clone(self) -> "ScikitLearnClassifier":
+        return ScikitLearnClassifier(
+            clf=sklearn.base.clone(self.inner_clf),
+        )
+
+
+class ScikitLearnRegressor(Regressor):
+    """
+    Wrapper for ScikitLearn regressors, which makes sure inputs and outputs have the
+    correct dimensions and types.
+    """
+
+    def __init__(self, reg: Any) -> None:
+        super().__init__()
+        self.inner_reg = reg
+
+    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        super().fit(x_train, y_train)
+        self.inner_reg.fit(x_train, y_train)
+
+    def predict(self, x_test: np.ndarray) -> np.ndarray:
+        super().predict(x_test)
+        n_samples = x_test.shape[0]
+        sklearn_pred = self.inner_reg.predict(x_test)
+        assert isinstance(sklearn_pred, np.ndarray)
+        assert sklearn_pred.shape[0] == n_samples
+        return sklearn_pred
+
+    def clone(self) -> "ScikitLearnRegressor":
+        return ScikitLearnRegressor(
+            reg=sklearn.base.clone(self.inner_reg),
+        )

miplearn/classifiers/tests/test_counting.py

@@ -1,18 +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.classifiers.counting import CountingClassifier
-
-import numpy as np
-from numpy.linalg import norm
-
-E = 0.1
-
-
-def test_counting():
-    clf = CountingClassifier()
-    clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
-    expected_proba = np.array([[0.375, 0.625],
-                               [0.375, 0.625]])
-    actual_proba = clf.predict_proba(np.zeros((2, 25)))
-    assert norm(actual_proba - expected_proba) < E

miplearn/classifiers/tests/test_cv.py

@@ -1,46 +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.
-
-import numpy as np
-from miplearn.classifiers.cv import CrossValidatedClassifier
-from numpy.linalg import norm
-from sklearn.preprocessing import StandardScaler
-from sklearn.svm import SVC
-
-E = 0.1
-
-
-def test_cv():
-    # Training set: label is true if point is inside a 2D circle
-    x_train = np.array([[x1, x2]
-                        for x1 in range(-10, 11)
-                        for x2 in range(-10, 11)])
-    x_train = StandardScaler().fit_transform(x_train)
-    n_samples = x_train.shape[0]
-
-    y_train = np.array([1.0 if x1*x1 + x2*x2 <= 100 else 0.0
-                        for x1 in range(-10, 11)
-                        for x2 in range(-10, 11)])
-
-    # Support vector machines with linear kernels do not perform well on this
-    # data set, so predictor should return the given constant.
-    clf = CrossValidatedClassifier(classifier=SVC(probability=True,
-                                                  random_state=42),
-                                   threshold=0.90,
-                                   constant=0.0,
-                                   cv=30)
-    clf.fit(x_train, y_train)
-    assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
-
-    # Support vector machines with quadratic kernels perform almost perfectly
-    # on this data set, so predictor should return their prediction.
-    clf = CrossValidatedClassifier(classifier=SVC(probability=True,
-                                                  kernel='poly',
-                                                  degree=2,
-                                                  random_state=42),
-                                   threshold=0.90,
-                                   cv=30)
-    clf.fit(x_train, y_train)
-    print(y_train - clf.predict(x_train))
-    assert norm(y_train - clf.predict(x_train)) < E

miplearn/classifiers/tests/test_evaluator.py

@@ -1,20 +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.
-
-import numpy as np
-from miplearn.classifiers.evaluator import ClassifierEvaluator
-from sklearn.neighbors import KNeighborsClassifier
-
-
-def test_evaluator():
-    clf_a = KNeighborsClassifier(n_neighbors=1)
-    clf_b = KNeighborsClassifier(n_neighbors=2)
-    x_train = np.array([[0, 0], [1, 0]])
-    y_train = np.array([0, 1])
-    clf_a.fit(x_train, y_train)
-    clf_b.fit(x_train, y_train)
-    ev = ClassifierEvaluator()
-    assert ev.evaluate(clf_a, x_train, y_train) == 1.0
-    assert ev.evaluate(clf_b, x_train, y_train) == 0.5
-

miplearn/classifiers/tests/test_threshold.py

@@ -1,34 +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 unittest.mock import Mock
-
-import numpy as np
-from miplearn.classifiers import Classifier
-from miplearn.classifiers.threshold import MinPrecisionThreshold
-
-
-def test_threshold_dynamic():
-    clf = Mock(spec=Classifier)
-    clf.predict_proba = Mock(return_value=np.array([
-        [0.10, 0.90],
-        [0.10, 0.90],
-        [0.20, 0.80],
-        [0.30, 0.70],
-    ]))
-    x_train = np.array([0, 1, 2, 3])
-    y_train = np.array([1, 1, 0, 0])
-
-    threshold = MinPrecisionThreshold(min_precision=1.0)
-    assert threshold.find(clf, x_train, y_train) == 0.90
-
-    threshold = MinPrecisionThreshold(min_precision=0.65)
-    assert threshold.find(clf, x_train, y_train) == 0.80
-
-    threshold = MinPrecisionThreshold(min_precision=0.50)
-    assert threshold.find(clf, x_train, y_train) == 0.70
-
-    threshold = MinPrecisionThreshold(min_precision=0.00)
-    assert threshold.find(clf, x_train, y_train) == 0.70
-

miplearn/classifiers/threshold.py

@@ -1,45 +1,128 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from abc import abstractmethod, ABC
+from typing import Optional, List
 
 import numpy as np
 from sklearn.metrics._ranking import _binary_clf_curve
 
+from miplearn.classifiers import Classifier
+
+
+class Threshold(ABC):
+    """
+    Solver components ask the machine learning models how confident are they on each
+    prediction they make, then automatically discard all predictions that have low
+    confidence. A Threshold specifies how confident should the ML models be for a
+    prediction to be considered trustworthy.
+
+    To model dynamic thresholds, which automatically adjust themselves during
+    training to reach some desired target (such as minimum precision, or minimum
+    recall), thresholds behave somewhat similar to ML models themselves, with `fit`
+    and `predict` methods.
+    """
 
-class DynamicThreshold(ABC):
     @abstractmethod
-    def find(self, clf, x_train, y_train):
+    def fit(
+        self,
+        clf: Classifier,
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+    ) -> None:
         """
-        Given a trained binary classifier `clf` and a training data set,
-        returns the numerical threshold (float) satisfying some criterea.
+        Given a trained binary classifier `clf`, calibrates itself based on the
+        classifier's performance on the given training data set.
+        """
+        assert isinstance(clf, Classifier)
+        assert isinstance(x_train, np.ndarray)
+        assert isinstance(y_train, np.ndarray)
+        n_samples = x_train.shape[0]
+        assert y_train.shape[0] == n_samples
+
+    @abstractmethod
+    def predict(self, x_test: np.ndarray) -> List[float]:
+        """
+        Returns the minimum probability for a machine learning prediction to be
+        considered trustworthy. There is one value for each label.
+        """
+        pass
+
+    @abstractmethod
+    def clone(self) -> "Threshold":
+        """
+        Returns an unfitted copy of this threshold with the same hyperparameters.
         """
         pass
 
 
-class MinPrecisionThreshold(DynamicThreshold):
+class MinProbabilityThreshold(Threshold):
     """
-    The smallest possible threshold satisfying a minimum acceptable true
-    positive rate (also known as precision).
+    A threshold which considers predictions trustworthy if their probability of being
+    correct, as computed by the machine learning models, are above a fixed value.
     """
 
-    def __init__(self, min_precision):
+    def __init__(self, min_probability: List[float]):
+        self.min_probability = min_probability
+
+    def fit(self, clf: Classifier, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        pass
+
+    def predict(self, x_test: np.ndarray) -> List[float]:
+        return self.min_probability
+
+    def clone(self) -> "MinProbabilityThreshold":
+        return MinProbabilityThreshold(self.min_probability)
+
+
+class MinPrecisionThreshold(Threshold):
+    """
+    A dynamic threshold which automatically adjusts itself during training to ensure
+    that the component achieves at least a given precision `p` on the training data
+    set. Note that increasing a component's minimum precision may reduce its recall.
+    """
+
+    def __init__(self, min_precision: List[float]) -> None:
         self.min_precision = min_precision
+        self._computed_threshold: Optional[List[float]] = None
 
-    def find(self, clf, x_train, y_train):
+    def fit(
+        self,
+        clf: Classifier,
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+    ) -> None:
+        super().fit(clf, x_train, y_train)
+        (n_samples, n_classes) = y_train.shape
         proba = clf.predict_proba(x_train)
+        self._computed_threshold = [
+            self._compute(
+                y_train[:, i],
+                proba[:, i],
+                self.min_precision[i],
+            )
+            for i in range(n_classes)
+        ]
 
-        assert isinstance(proba, np.ndarray), \
-            "classifier should return numpy array"
-        assert proba.shape == (x_train.shape[0], 2), \
-            "classifier should return (%d,%d)-shaped array, not %s" % (
-                x_train.shape[0], 2, str(proba.shape))
+    def predict(self, x_test: np.ndarray) -> List[float]:
+        assert self._computed_threshold is not None
+        return self._computed_threshold
 
-        fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
+    @staticmethod
+    def _compute(
+        y_actual: np.ndarray,
+        y_prob: np.ndarray,
+        min_precision: float,
+    ) -> float:
+        fps, tps, thresholds = _binary_clf_curve(y_actual, y_prob)
         precision = tps / (tps + fps)
-
         for k in reversed(range(len(precision))):
-            if precision[k] >= self.min_precision:
+            if precision[k] >= min_precision:
                 return thresholds[k]
-        return 2.0
+        return float("inf")
+
+    def clone(self) -> "MinPrecisionThreshold":
+        return MinPrecisionThreshold(
+            min_precision=self.min_precision,
+        )

miplearn/components/__init__.py

@@ -1,12 +1,18 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+from typing import Dict
 
 
-def classifier_evaluation_dict(tp, tn, fp, fn):
+def classifier_evaluation_dict(
+    tp: int,
+    tn: int,
+    fp: int,
+    fn: int,
+) -> Dict[str, float]:
     p = tp + fn
     n = fp + tn
-    d = {
+    d: Dict = {
         "Predicted positive": fp + tp,
         "Predicted negative": fn + tn,
         "Condition positive": p,

miplearn/components/component.py

@@ -1,29 +1,254 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from abc import ABC, abstractmethod
+from typing import Any, List, TYPE_CHECKING, Tuple, Dict, Optional
+
+import numpy as np
+from p_tqdm import p_umap
+
+from miplearn.features.sample import Sample
+from miplearn.instance.base import Instance
+from miplearn.types import LearningSolveStats, Category
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver
 
 
-class Component(ABC):
+# noinspection PyMethodMayBeStatic
+class Component:
     """
     A Component is an object which adds functionality to a LearningSolver.
-    """
-    
-    @abstractmethod
-    def before_solve(self, solver, instance, model):
-        pass
-    
-    @abstractmethod
-    def after_solve(self, solver, instance, model, results):
-        pass
-    
-    @abstractmethod
-    def fit(self, training_instances):
-        pass
 
-    def after_iteration(self, solver, instance, model):
+    For better code maintainability, LearningSolver simply delegates most of its
+    functionality to Components. Each Component is responsible for exactly one ML
+    strategy.
+    """
+
+    def after_solve_lp(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        """
+        Method called by LearningSolver after the root LP relaxation is solved.
+        See before_solve_lp for a description of the parameters.
+        """
+        return
+
+    def after_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        """
+        Method called by LearningSolver after the MIP is solved.
+        See before_solve_lp for a description of the parameters.
+        """
+        return
+
+    def before_solve_lp(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        """
+        Method called by LearningSolver before the root LP relaxation is solved.
+
+        Parameters
+        ----------
+        solver: LearningSolver
+            The solver calling this method.
+        instance: Instance
+            The instance being solved.
+        model
+            The concrete optimization model being solved.
+        stats: LearningSolveStats
+            A dictionary containing statistics about the solution process, such as
+            number of nodes explored and running time. Components are free to add
+            their own statistics here. For example, PrimalSolutionComponent adds
+            statistics regarding the number of predicted variables. All statistics in
+            this dictionary are exported to the benchmark CSV file.
+        sample: miplearn.features.Sample
+            An object containing data that may be useful for training machine
+            learning models and accelerating the solution process. Components are
+            free to add their own training data here.
+        """
+        return
+
+    def before_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        """
+        Method called by LearningSolver before the MIP is solved.
+        See before_solve_lp for a description of the parameters.
+        """
+        return
+
+    def fit_xy(
+        self,
+        x: Dict[Category, np.ndarray],
+        y: Dict[Category, np.ndarray],
+    ) -> None:
+        """
+        Given two dictionaries x and y, mapping the name of the category to matrices
+        of features and targets, this function does two things. First, for each
+        category, it creates a clone of the prototype regressor/classifier. Second,
+        it passes (x[category], y[category]) to the clone's fit method.
+        """
+        return
+
+    def iteration_cb(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+    ) -> bool:
+        """
+        Method called by LearningSolver at the end of each iteration.
+
+        After solving the MIP, LearningSolver calls `iteration_cb` of each component,
+        giving them a chance to modify the problem and resolve it before the solution
+        process ends. For example, the lazy constraint component uses `iteration_cb`
+        to check that all lazy constraints are satisfied.
+
+        If `iteration_cb` returns False for all components, the solution process
+        ends. If it retunrs True for any component, the MIP is solved again.
+
+        Parameters
+        ----------
+        solver: LearningSolver
+            The solver calling this method.
+        instance: Instance
+            The instance being solved.
+        model: Any
+            The concrete optimization model being solved.
+        """
         return False
 
-    def on_lazy_callback(self, solver, instance, model):
+    def lazy_cb(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+    ) -> None:
         return
+
+    def sample_evaluate(
+        self,
+        instance: Optional[Instance],
+        sample: Sample,
+    ) -> Dict[str, Dict[str, float]]:
+        return {}
+
+    def sample_xy(
+        self,
+        instance: Optional[Instance],
+        sample: Sample,
+    ) -> Tuple[Dict, Dict]:
+        """
+        Returns a pair of x and y dictionaries containing, respectively, the matrices
+        of ML features and the labels for the sample. If the training sample does not
+        include label information, returns (x, {}).
+        """
+        pass
+
+    def pre_fit(self, pre: List[Any]) -> None:
+        pass
+
+    def user_cut_cb(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+    ) -> None:
+        return
+
+    def pre_sample_xy(self, instance: Instance, sample: Sample) -> Any:
+        pass
+
+    @staticmethod
+    def fit_multiple(
+        components: List["Component"],
+        instances: List[Instance],
+        n_jobs: int = 1,
+    ) -> None:
+
+        # Part I: Pre-fit
+        def _pre_sample_xy(instance: Instance) -> Dict:
+            pre_instance: Dict = {}
+            for (cidx, comp) in enumerate(components):
+                pre_instance[cidx] = []
+            instance.load()
+            for sample in instance.get_samples():
+                for (cidx, comp) in enumerate(components):
+                    pre_instance[cidx].append(comp.pre_sample_xy(instance, sample))
+            instance.free()
+            return pre_instance
+
+        if n_jobs == 1:
+            pre = [_pre_sample_xy(instance) for instance in instances]
+        else:
+            pre = p_umap(_pre_sample_xy, instances, num_cpus=n_jobs)
+        pre_combined: Dict = {}
+        for (cidx, comp) in enumerate(components):
+            pre_combined[cidx] = []
+            for p in pre:
+                pre_combined[cidx].extend(p[cidx])
+        for (cidx, comp) in enumerate(components):
+            comp.pre_fit(pre_combined[cidx])
+
+        # Part II: Fit
+        def _sample_xy(instance: Instance) -> Tuple[Dict, Dict]:
+            x_instance: Dict = {}
+            y_instance: Dict = {}
+            for (cidx, comp) in enumerate(components):
+                x_instance[cidx] = {}
+                y_instance[cidx] = {}
+            instance.load()
+            for sample in instance.get_samples():
+                for (cidx, comp) in enumerate(components):
+                    x = x_instance[cidx]
+                    y = y_instance[cidx]
+                    x_sample, y_sample = comp.sample_xy(instance, sample)
+                    for cat in x_sample.keys():
+                        if cat not in x:
+                            x[cat] = []
+                            y[cat] = []
+                        x[cat] += x_sample[cat]
+                        y[cat] += y_sample[cat]
+            instance.free()
+            return x_instance, y_instance
+
+        if n_jobs == 1:
+            xy = [_sample_xy(instance) for instance in instances]
+        else:
+            xy = p_umap(_sample_xy, instances)
+        for (cidx, comp) in enumerate(components):
+            x_comp: Dict = {}
+            y_comp: Dict = {}
+            for (x, y) in xy:
+                for cat in x[cidx].keys():
+                    if cat not in x_comp:
+                        x_comp[cat] = []
+                        y_comp[cat] = []
+                    x_comp[cat].extend(x[cidx][cat])
+                    y_comp[cat].extend(y[cidx][cat])
+            for cat in x_comp.keys():
+                x_comp[cat] = np.array(x_comp[cat], dtype=np.float32)
+                y_comp[cat] = np.array(y_comp[cat])
+            comp.fit_xy(x_comp, y_comp)

miplearn/components/cuts.py

@@ -1,96 +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.
-
-import sys
-from copy import deepcopy
-
-from miplearn.classifiers.counting import CountingClassifier
-from miplearn.components import classifier_evaluation_dict
-
-from .component import Component
-from ..extractors import *
-
-logger = logging.getLogger(__name__)
-
-
-class UserCutsComponent(Component):
-    """
-    A component that predicts which user cuts to enforce.
-    """
-    
-    def __init__(self,
-                 classifier=CountingClassifier(),
-                 threshold=0.05):
-        self.violations = set()
-        self.count = {}
-        self.n_samples = 0
-        self.threshold = threshold
-        self.classifier_prototype = classifier
-        self.classifiers = {}
-
-    def before_solve(self, solver, instance, model):
-        instance.found_violated_user_cuts = []
-        logger.info("Predicting violated user cuts...")
-        violations = self.predict(instance)
-        logger.info("Enforcing %d user cuts..." % len(violations))
-        for v in violations:
-            cut = instance.build_user_cut(model, v)
-            solver.internal_solver.add_constraint(cut)
-
-    def after_solve(self, solver, instance, model, results):
-        pass
-                
-    def fit(self, training_instances):
-        logger.debug("Fitting...")
-        features = InstanceFeaturesExtractor().extract(training_instances)
-
-        self.classifiers = {}
-        violation_to_instance_idx = {}
-        for (idx, instance) in enumerate(training_instances):
-            if not hasattr(instance, "found_violated_user_cuts"):
-                continue
-            for v in instance.found_violated_user_cuts:
-                if v not in self.classifiers:
-                    self.classifiers[v] = deepcopy(self.classifier_prototype)
-                    violation_to_instance_idx[v] = []
-                violation_to_instance_idx[v] += [idx]
-
-        for (v, classifier) in tqdm(self.classifiers.items(),
-                                    desc="Fit (user cuts)",
-                                    disable=not sys.stdout.isatty(),
-                                   ):
-            logger.debug("Training: %s" % (str(v)))
-            label = np.zeros(len(training_instances))
-            label[violation_to_instance_idx[v]] = 1.0
-            classifier.fit(features, label)
-
-    def predict(self, instance):
-        violations = []
-        features = InstanceFeaturesExtractor().extract([instance])
-        for (v, classifier) in self.classifiers.items():
-            proba = classifier.predict_proba(features)
-            if proba[0][1] > self.threshold:
-                violations += [v]
-        return violations
-
-    def evaluate(self, instances):
-        results = {}
-        all_violations = set()
-        for instance in instances:
-            all_violations |= set(instance.found_violated_user_cuts)
-        for idx in tqdm(range(len(instances)),
-                        desc="Evaluate (lazy)",
-                        disable=not sys.stdout.isatty(),
-                       ):
-            instance = instances[idx]
-            condition_positive = set(instance.found_violated_user_cuts)
-            condition_negative = all_violations - condition_positive
-            pred_positive = set(self.predict(instance)) & all_violations
-            pred_negative = all_violations - pred_positive
-            tp = len(pred_positive & condition_positive)
-            tn = len(pred_negative & condition_negative)
-            fp = len(pred_positive & condition_negative)
-            fn = len(pred_negative & condition_positive)
-            results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
-        return results

miplearn/components/dynamic_common.py

@@ -0,0 +1,171 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+from typing import Dict, List, Tuple, Optional, Any, Set
+
+import numpy as np
+from overrides import overrides
+
+from miplearn.features.extractor import FeaturesExtractor
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.threshold import Threshold
+from miplearn.components import classifier_evaluation_dict
+from miplearn.components.component import Component
+from miplearn.features.sample import Sample
+from miplearn.instance.base import Instance
+from miplearn.types import ConstraintCategory, ConstraintName
+
+logger = logging.getLogger(__name__)
+
+
+class DynamicConstraintsComponent(Component):
+    """
+    Base component used by both DynamicLazyConstraintsComponent and UserCutsComponent.
+    """
+
+    def __init__(
+        self,
+        attr: str,
+        classifier: Classifier,
+        threshold: Threshold,
+    ):
+        assert isinstance(classifier, Classifier)
+        self.threshold_prototype: Threshold = threshold
+        self.classifier_prototype: Classifier = classifier
+        self.classifiers: Dict[ConstraintCategory, Classifier] = {}
+        self.thresholds: Dict[ConstraintCategory, Threshold] = {}
+        self.known_cids: List[ConstraintName] = []
+        self.attr = attr
+
+    def sample_xy_with_cids(
+        self,
+        instance: Optional[Instance],
+        sample: Sample,
+    ) -> Tuple[
+        Dict[ConstraintCategory, List[List[float]]],
+        Dict[ConstraintCategory, List[List[bool]]],
+        Dict[ConstraintCategory, List[ConstraintName]],
+    ]:
+        if len(self.known_cids) == 0:
+            return {}, {}, {}
+        assert instance is not None
+        x: Dict[ConstraintCategory, List[List[float]]] = {}
+        y: Dict[ConstraintCategory, List[List[bool]]] = {}
+        cids: Dict[ConstraintCategory, List[ConstraintName]] = {}
+        known_cids = np.array(self.known_cids, dtype="S")
+
+        enforced_cids = None
+        enforced_cids_np = sample.get_array(self.attr)
+        if enforced_cids_np is not None:
+            enforced_cids = list(enforced_cids_np)
+
+        # Get user-provided constraint features
+        (
+            constr_features,
+            constr_categories,
+            constr_lazy,
+        ) = FeaturesExtractor._extract_user_features_constrs(instance, known_cids)
+
+        # Augment with instance features
+        instance_features = sample.get_array("static_instance_features")
+        assert instance_features is not None
+        constr_features = np.hstack(
+            [
+                instance_features.reshape(1, -1).repeat(len(known_cids), axis=0),
+                constr_features,
+            ]
+        )
+
+        categories = np.unique(constr_categories)
+        for c in categories:
+            x[c] = constr_features[constr_categories == c].tolist()
+            cids[c] = known_cids[constr_categories == c].tolist()
+            if enforced_cids is not None:
+                tmp = np.isin(cids[c], enforced_cids).reshape(-1, 1)
+                y[c] = np.hstack([~tmp, tmp]).tolist()  # type: ignore
+
+        return x, y, cids
+
+    @overrides
+    def sample_xy(
+        self,
+        instance: Optional[Instance],
+        sample: Sample,
+    ) -> Tuple[Dict, Dict]:
+        x, y, _ = self.sample_xy_with_cids(instance, sample)
+        return x, y
+
+    @overrides
+    def pre_fit(self, pre: List[Any]) -> None:
+        assert pre is not None
+        known_cids: Set = set()
+        for cids in pre:
+            known_cids |= set(list(cids))
+        self.known_cids.clear()
+        self.known_cids.extend(sorted(known_cids))
+
+    def sample_predict(
+        self,
+        instance: Instance,
+        sample: Sample,
+    ) -> List[ConstraintName]:
+        pred: List[ConstraintName] = []
+        if len(self.known_cids) == 0:
+            logger.info("Classifiers not fitted. Skipping.")
+            return pred
+        x, _, cids = self.sample_xy_with_cids(instance, sample)
+        for category in x.keys():
+            assert category in self.classifiers
+            assert category in self.thresholds
+            clf = self.classifiers[category]
+            thr = self.thresholds[category]
+            nx = np.array(x[category])
+            proba = clf.predict_proba(nx)
+            t = thr.predict(nx)
+            for i in range(proba.shape[0]):
+                if proba[i][1] > t[1]:
+                    pred += [cids[category][i]]
+        return pred
+
+    @overrides
+    def pre_sample_xy(self, instance: Instance, sample: Sample) -> Any:
+        return sample.get_array(self.attr)
+
+    @overrides
+    def fit_xy(
+        self,
+        x: Dict[ConstraintCategory, np.ndarray],
+        y: Dict[ConstraintCategory, np.ndarray],
+    ) -> None:
+        for category in x.keys():
+            self.classifiers[category] = self.classifier_prototype.clone()
+            self.thresholds[category] = self.threshold_prototype.clone()
+            npx = np.array(x[category])
+            npy = np.array(y[category])
+            self.classifiers[category].fit(npx, npy)
+            self.thresholds[category].fit(self.classifiers[category], npx, npy)
+
+    @overrides
+    def sample_evaluate(
+        self,
+        instance: Instance,
+        sample: Sample,
+    ) -> Dict[str, float]:
+        actual = sample.get_array(self.attr)
+        assert actual is not None
+        pred = set(self.sample_predict(instance, sample))
+        tp, tn, fp, fn = 0, 0, 0, 0
+        for cid in self.known_cids:
+            if cid in pred:
+                if cid in actual:
+                    tp += 1
+                else:
+                    fp += 1
+            else:
+                if cid in actual:
+                    fn += 1
+                else:
+                    tn += 1
+        return classifier_evaluation_dict(tp=tp, tn=tn, fp=fp, fn=fn)

miplearn/components/dynamic_lazy.py

@@ -0,0 +1,145 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+import pdb
+from typing import Dict, List, TYPE_CHECKING, Tuple, Any, Optional, Set
+
+import numpy as np
+from overrides import overrides
+
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.counting import CountingClassifier
+from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
+from miplearn.components.component import Component
+from miplearn.components.dynamic_common import DynamicConstraintsComponent
+from miplearn.features.sample import Sample
+from miplearn.instance.base import Instance
+from miplearn.types import LearningSolveStats, ConstraintName, ConstraintCategory
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver
+
+
+class DynamicLazyConstraintsComponent(Component):
+    """
+    A component that predicts which lazy constraints to enforce.
+    """
+
+    def __init__(
+        self,
+        classifier: Classifier = CountingClassifier(),
+        threshold: Threshold = MinProbabilityThreshold([0, 0.05]),
+    ):
+        self.dynamic: DynamicConstraintsComponent = DynamicConstraintsComponent(
+            classifier=classifier,
+            threshold=threshold,
+            attr="mip_constr_lazy_enforced",
+        )
+        self.classifiers = self.dynamic.classifiers
+        self.thresholds = self.dynamic.thresholds
+        self.known_cids = self.dynamic.known_cids
+        self.lazy_enforced: Set[ConstraintName] = set()
+
+    @staticmethod
+    def enforce(
+        cids: List[ConstraintName],
+        instance: Instance,
+        model: Any,
+        solver: "LearningSolver",
+    ) -> None:
+        assert solver.internal_solver is not None
+        for cid in cids:
+            instance.enforce_lazy_constraint(solver.internal_solver, model, cid)
+
+    @overrides
+    def before_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        self.lazy_enforced.clear()
+        logger.info("Predicting violated (dynamic) lazy constraints...")
+        cids = self.dynamic.sample_predict(instance, sample)
+        logger.info("Enforcing %d lazy constraints..." % len(cids))
+        self.enforce(cids, instance, model, solver)
+
+    @overrides
+    def after_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        sample.put_array(
+            "mip_constr_lazy_enforced",
+            np.array(list(self.lazy_enforced), dtype="S"),
+        )
+
+    @overrides
+    def iteration_cb(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+    ) -> bool:
+        assert solver.internal_solver is not None
+        logger.debug("Finding violated lazy constraints...")
+        cids = instance.find_violated_lazy_constraints(solver.internal_solver, model)
+        if len(cids) == 0:
+            logger.debug("No violations found")
+            return False
+        else:
+            self.lazy_enforced |= set(cids)
+            logger.debug("    %d violations found" % len(cids))
+            self.enforce(cids, instance, model, solver)
+            return True
+
+    # Delegate ML methods to self.dynamic
+    # -------------------------------------------------------------------
+    @overrides
+    def sample_xy(
+        self,
+        instance: Optional[Instance],
+        sample: Sample,
+    ) -> Tuple[Dict, Dict]:
+        return self.dynamic.sample_xy(instance, sample)
+
+    @overrides
+    def pre_fit(self, pre: List[Any]) -> None:
+        self.dynamic.pre_fit(pre)
+
+    def sample_predict(
+        self,
+        instance: Instance,
+        sample: Sample,
+    ) -> List[ConstraintName]:
+        return self.dynamic.sample_predict(instance, sample)
+
+    @overrides
+    def pre_sample_xy(self, instance: Instance, sample: Sample) -> Any:
+        return self.dynamic.pre_sample_xy(instance, sample)
+
+    @overrides
+    def fit_xy(
+        self,
+        x: Dict[ConstraintCategory, np.ndarray],
+        y: Dict[ConstraintCategory, np.ndarray],
+    ) -> None:
+        self.dynamic.fit_xy(x, y)
+
+    @overrides
+    def sample_evaluate(
+        self,
+        instance: Instance,
+        sample: Sample,
+    ) -> Dict[ConstraintCategory, Dict[str, float]]:
+        return self.dynamic.sample_evaluate(instance, sample)

miplearn/components/dynamic_user_cuts.py

@@ -0,0 +1,137 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+from typing import Any, TYPE_CHECKING, Set, Tuple, Dict, List, Optional
+
+import numpy as np
+from overrides import overrides
+
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.counting import CountingClassifier
+from miplearn.classifiers.threshold import Threshold, MinProbabilityThreshold
+from miplearn.components.component import Component
+from miplearn.components.dynamic_common import DynamicConstraintsComponent
+from miplearn.features.sample import Sample
+from miplearn.instance.base import Instance
+from miplearn.types import LearningSolveStats, ConstraintName, ConstraintCategory
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver
+
+
+class UserCutsComponent(Component):
+    def __init__(
+        self,
+        classifier: Classifier = CountingClassifier(),
+        threshold: Threshold = MinProbabilityThreshold([0.50, 0.50]),
+    ) -> None:
+        self.dynamic = DynamicConstraintsComponent(
+            classifier=classifier,
+            threshold=threshold,
+            attr="mip_user_cuts_enforced",
+        )
+        self.enforced: Set[ConstraintName] = set()
+        self.n_added_in_callback = 0
+
+    @overrides
+    def before_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        assert solver.internal_solver is not None
+        self.enforced.clear()
+        self.n_added_in_callback = 0
+        logger.info("Predicting violated user cuts...")
+        cids = self.dynamic.sample_predict(instance, sample)
+        logger.info("Enforcing %d user cuts ahead-of-time..." % len(cids))
+        for cid in cids:
+            instance.enforce_user_cut(solver.internal_solver, model, cid)
+        stats["UserCuts: Added ahead-of-time"] = len(cids)
+
+    @overrides
+    def user_cut_cb(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+    ) -> None:
+        assert solver.internal_solver is not None
+        logger.debug("Finding violated user cuts...")
+        cids = instance.find_violated_user_cuts(model)
+        logger.debug(f"Found {len(cids)} violated user cuts")
+        logger.debug("Building violated user cuts...")
+        for cid in cids:
+            if cid in self.enforced:
+                continue
+            assert isinstance(cid, ConstraintName)
+            instance.enforce_user_cut(solver.internal_solver, model, cid)
+            self.enforced.add(cid)
+            self.n_added_in_callback += 1
+        if len(cids) > 0:
+            logger.debug(f"Added {len(cids)} violated user cuts")
+
+    @overrides
+    def after_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        sample.put_array(
+            "mip_user_cuts_enforced",
+            np.array(list(self.enforced), dtype="S"),
+        )
+        stats["UserCuts: Added in callback"] = self.n_added_in_callback
+        if self.n_added_in_callback > 0:
+            logger.info(f"{self.n_added_in_callback} user cuts added in callback")
+
+    # Delegate ML methods to self.dynamic
+    # -------------------------------------------------------------------
+    @overrides
+    def sample_xy(
+        self,
+        instance: "Instance",
+        sample: Sample,
+    ) -> Tuple[Dict, Dict]:
+        return self.dynamic.sample_xy(instance, sample)
+
+    @overrides
+    def pre_fit(self, pre: List[Any]) -> None:
+        self.dynamic.pre_fit(pre)
+
+    def sample_predict(
+        self,
+        instance: "Instance",
+        sample: Sample,
+    ) -> List[ConstraintName]:
+        return self.dynamic.sample_predict(instance, sample)
+
+    @overrides
+    def pre_sample_xy(self, instance: Instance, sample: Sample) -> Any:
+        return self.dynamic.pre_sample_xy(instance, sample)
+
+    @overrides
+    def fit_xy(
+        self,
+        x: Dict[ConstraintCategory, np.ndarray],
+        y: Dict[ConstraintCategory, np.ndarray],
+    ) -> None:
+        self.dynamic.fit_xy(x, y)
+
+    @overrides
+    def sample_evaluate(
+        self,
+        instance: "Instance",
+        sample: Sample,
+    ) -> Dict[ConstraintCategory, Dict[str, float]]:
+        return self.dynamic.sample_evaluate(instance, sample)

miplearn/components/lazy_dynamic.py

@@ -1,108 +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.
-
-import sys
-from copy import deepcopy
-
-from miplearn.classifiers.counting import CountingClassifier
-from miplearn.components import classifier_evaluation_dict
-
-from .component import Component
-from ..extractors import *
-
-logger = logging.getLogger(__name__)
-
-
-class DynamicLazyConstraintsComponent(Component):
-    """
-    A component that predicts which lazy constraints to enforce.
-    """
-    
-    def __init__(self,
-                 classifier=CountingClassifier(),
-                 threshold=0.05):
-        self.violations = set()
-        self.count = {}
-        self.n_samples = 0
-        self.threshold = threshold
-        self.classifier_prototype = classifier
-        self.classifiers = {}
-
-    def before_solve(self, solver, instance, model):
-        instance.found_violated_lazy_constraints = []
-        logger.info("Predicting violated lazy constraints...")
-        violations = self.predict(instance)
-        logger.info("Enforcing %d lazy constraints..." % len(violations))
-        for v in violations:
-            cut = instance.build_lazy_constraint(model, v)
-            solver.internal_solver.add_constraint(cut)
-
-    def after_iteration(self, solver, instance, model):
-        logger.debug("Finding violated (dynamic) lazy constraints...")
-        violations = instance.find_violated_lazy_constraints(model)
-        if len(violations) == 0:
-            return False
-        instance.found_violated_lazy_constraints += violations
-        logger.debug("    %d violations found" % len(violations))
-        for v in violations:
-            cut = instance.build_lazy_constraint(model, v)
-            solver.internal_solver.add_constraint(cut)
-        return True
-
-    def after_solve(self, solver, instance, model, results):
-        pass
-                
-    def fit(self, training_instances):
-        logger.debug("Fitting...")
-        features = InstanceFeaturesExtractor().extract(training_instances)
-
-        self.classifiers = {}
-        violation_to_instance_idx = {}
-        for (idx, instance) in enumerate(training_instances):
-            for v in instance.found_violated_lazy_constraints:
-                if isinstance(v, list):
-                    v = tuple(v)
-                if v not in self.classifiers:
-                    self.classifiers[v] = deepcopy(self.classifier_prototype)
-                    violation_to_instance_idx[v] = []
-                violation_to_instance_idx[v] += [idx]
-
-        for (v, classifier) in tqdm(self.classifiers.items(),
-                                    desc="Fit (lazy)",
-                                    disable=not sys.stdout.isatty(),
-                                   ):
-            logger.debug("Training: %s" % (str(v)))
-            label = np.zeros(len(training_instances))
-            label[violation_to_instance_idx[v]] = 1.0
-            classifier.fit(features, label)
-
-    def predict(self, instance):
-        violations = []
-        features = InstanceFeaturesExtractor().extract([instance])
-        for (v, classifier) in self.classifiers.items():
-            proba = classifier.predict_proba(features)
-            if proba[0][1] > self.threshold:
-                violations += [v]
-        return violations
-
-    def evaluate(self, instances):
-        results = {}
-        all_violations = set()
-        for instance in instances:
-            all_violations |= set(instance.found_violated_lazy_constraints)
-        for idx in tqdm(range(len(instances)),
-                        desc="Evaluate (lazy)",
-                        disable=not sys.stdout.isatty(),
-                       ):
-            instance = instances[idx]
-            condition_positive = set(instance.found_violated_lazy_constraints)
-            condition_negative = all_violations - condition_positive
-            pred_positive = set(self.predict(instance)) & all_violations
-            pred_negative = all_violations - pred_positive
-            tp = len(pred_positive & condition_positive)
-            tn = len(pred_negative & condition_negative)
-            fp = len(pred_positive & condition_negative)
-            fn = len(pred_negative & condition_positive)
-            results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
-        return results

miplearn/components/lazy_static.py

@@ -1,179 +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.
-
-import sys
-from copy import deepcopy
-
-from miplearn.classifiers.counting import CountingClassifier
-from .component import Component
-from ..extractors import *
-
-logger = logging.getLogger(__name__)
-
-
-class LazyConstraint:
-    def __init__(self, cid, obj):
-        self.cid = cid
-        self.obj = obj
-
-
-class StaticLazyConstraintsComponent(Component):
-    def __init__(self,
-                 classifier=CountingClassifier(),
-                 threshold=0.05,
-                 use_two_phase_gap=True,
-                 large_gap=1e-2,
-                 violation_tolerance=-0.5,
-                 ):
-        self.threshold = threshold
-        self.classifier_prototype = classifier
-        self.classifiers = {}
-        self.pool = []
-        self.original_gap = None
-        self.large_gap = large_gap
-        self.is_gap_large = False
-        self.use_two_phase_gap = use_two_phase_gap
-        self.violation_tolerance = violation_tolerance
-
-    def before_solve(self, solver, instance, model):
-        self.pool = []
-        if not solver.use_lazy_cb and self.use_two_phase_gap:
-            logger.info("Increasing gap tolerance to %f", self.large_gap)
-            self.original_gap = solver.gap_tolerance
-            self.is_gap_large = True
-            solver.internal_solver.set_gap_tolerance(self.large_gap)
-
-        instance.found_violated_lazy_constraints = []
-        if instance.has_static_lazy_constraints():
-            self._extract_and_predict_static(solver, instance)
-
-    def after_solve(self, solver, instance, model, results):
-        pass
-
-    def after_iteration(self, solver, instance, model):
-        if solver.use_lazy_cb:
-            return False
-        else:
-            should_repeat = self._check_and_add(instance, solver)
-            if should_repeat:
-                return True
-            else:
-                if self.is_gap_large:
-                    logger.info("Restoring gap tolerance to %f", self.original_gap)
-                    solver.internal_solver.set_gap_tolerance(self.original_gap)
-                    self.is_gap_large = False
-                    return True
-                else:
-                    return False
-
-    def on_lazy_callback(self, solver, instance, model):
-        self._check_and_add(instance, solver)
-
-    def _check_and_add(self, instance, solver):
-        logger.debug("Finding violated lazy constraints...")
-        constraints_to_add = []
-        for c in self.pool:
-            if not solver.internal_solver.is_constraint_satisfied(c.obj,
-                                                                  tol=self.violation_tolerance):
-                constraints_to_add.append(c)
-        for c in constraints_to_add:
-            self.pool.remove(c)
-            solver.internal_solver.add_constraint(c.obj)
-            instance.found_violated_lazy_constraints += [c.cid]
-        if len(constraints_to_add) > 0:
-            logger.info("%8d lazy constraints added %8d in the pool" % (len(constraints_to_add), len(self.pool)))
-            return True
-        else:
-            return False
-
-    def fit(self, training_instances):
-        training_instances = [t
-                              for t in training_instances
-                              if hasattr(t, "found_violated_lazy_constraints")]
-
-        logger.debug("Extracting x and y...")
-        x = self.x(training_instances)
-        y = self.y(training_instances)
-
-        logger.debug("Fitting...")
-        for category in tqdm(x.keys(),
-                             desc="Fit (lazy)",
-                             disable=not sys.stdout.isatty()):
-            if category not in self.classifiers:
-                self.classifiers[category] = deepcopy(self.classifier_prototype)
-            self.classifiers[category].fit(x[category], y[category])
-
-    def predict(self, instance):
-        pass
-
-    def evaluate(self, instances):
-        pass
-
-    def _extract_and_predict_static(self, solver, instance):
-        x = {}
-        constraints = {}
-        logger.info("Extracting lazy constraints...")
-        for cid in solver.internal_solver.get_constraint_ids():
-            if instance.is_constraint_lazy(cid):
-                category = instance.get_constraint_category(cid)
-                if category not in x:
-                    x[category] = []
-                    constraints[category] = []
-                x[category] += [instance.get_constraint_features(cid)]
-                c = LazyConstraint(cid=cid,
-                                   obj=solver.internal_solver.extract_constraint(cid))
-                constraints[category] += [c]
-                self.pool.append(c)
-        logger.info("%8d lazy constraints extracted" % len(self.pool))
-        logger.info("Predicting required lazy constraints...")
-        n_added = 0
-        for (category, x_values) in x.items():
-            if category not in self.classifiers:
-                continue
-            if isinstance(x_values[0], np.ndarray):
-                x[category] = np.array(x_values)
-            proba = self.classifiers[category].predict_proba(x[category])
-            for i in range(len(proba)):
-                if proba[i][1] > self.threshold:
-                    n_added += 1
-                    c = constraints[category][i]
-                    self.pool.remove(c)
-                    solver.internal_solver.add_constraint(c.obj)
-                    instance.found_violated_lazy_constraints += [c.cid]
-        logger.info("%8d lazy constraints added %8d in the pool" % (n_added, len(self.pool)))
-
-    def _collect_constraints(self, train_instances):
-        constraints = {}
-        for instance in train_instances:
-            for cid in instance.found_violated_lazy_constraints:
-                category = instance.get_constraint_category(cid)
-                if category not in constraints:
-                    constraints[category] = set()
-                constraints[category].add(cid)
-        for (category, cids) in constraints.items():
-            constraints[category] = sorted(list(cids))
-        return constraints
-
-    def x(self, train_instances):
-        result = {}
-        constraints = self._collect_constraints(train_instances)
-        for (category, cids) in constraints.items():
-            result[category] = []
-            for instance in train_instances:
-                for cid in cids:
-                    result[category].append(instance.get_constraint_features(cid))
-        return result
-
-    def y(self, train_instances):
-        result = {}
-        constraints = self._collect_constraints(train_instances)
-        for (category, cids) in constraints.items():
-            result[category] = []
-            for instance in train_instances:
-                for cid in cids:
-                    if cid in instance.found_violated_lazy_constraints:
-                        result[category].append([0, 1])
-                    else:
-                        result[category].append([1, 0])
-        return result

miplearn/components/objective.py

@@ -1,13 +1,24 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-from sklearn.metrics import mean_squared_error, explained_variance_score, max_error, mean_absolute_error, r2_score
 
-from .. import Component, InstanceFeaturesExtractor, ObjectiveValueExtractor
-from sklearn.linear_model import LinearRegression
-from copy import deepcopy
-import numpy as np
 import logging
+from typing import List, Dict, Any, TYPE_CHECKING, Tuple, Optional, cast
+
+import numpy as np
+from overrides import overrides
+from sklearn.linear_model import LinearRegression
+
+from miplearn.classifiers import Regressor
+from miplearn.classifiers.sklearn import ScikitLearnRegressor
+from miplearn.components.component import Component
+from miplearn.features.sample import Sample
+from miplearn.instance.base import Instance
+from miplearn.types import LearningSolveStats
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver
+
 logger = logging.getLogger(__name__)
 
 
@@ -15,71 +26,101 @@ 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:
-            logger.info("Predicting optimal value...")
-            lb, ub = self.predict([instance])[0]
-            instance.predicted_ub = ub
-            instance.predicted_lb = lb
-            logger.info("Predicted values: lb=%.2f, ub=%.2f" % (lb, ub))
-    
-    def after_solve(self, solver, instance, model, results):
-        if self.ub_regressor is not None:
-            results["Predicted UB"] = instance.predicted_ub
-            results["Predicted LB"] = instance.predicted_lb
-        else:
-            results["Predicted UB"] = None
-            results["Predicted LB"] = None
-    
-    def fit(self, training_instances):
-        logger.debug("Extracting features...")
-        features = InstanceFeaturesExtractor().extract(training_instances)
-        ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
-        lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
-        assert ub.shape == (len(training_instances), 1)
-        assert lb.shape == (len(training_instances), 1)
-        self.ub_regressor = deepcopy(self.regressor_prototype)
-        self.lb_regressor = deepcopy(self.regressor_prototype)
-        logger.debug("Fitting ub_regressor...")
-        self.ub_regressor.fit(features, ub.ravel())
-        logger.debug("Fitting ub_regressor...")
-        self.lb_regressor.fit(features, lb.ravel())
-        
-    def predict(self, instances):
-        features = InstanceFeaturesExtractor().extract(instances)
-        lb = self.lb_regressor.predict(features)
-        ub = self.ub_regressor.predict(features)
-        assert lb.shape == (len(instances),)
-        assert ub.shape == (len(instances),)
-        return np.array([lb, ub]).T
 
-    def evaluate(self, instances):
-        y_pred = self.predict(instances)
-        y_true = np.array([[inst.lower_bound, inst.upper_bound] for inst in instances])
-        y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
-        y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
-        ev = {
-            "Lower bound": {
-                "Mean squared error": mean_squared_error(y_true_lb, y_pred_lb),
-                "Explained variance": explained_variance_score(y_true_lb, y_pred_lb),
-                "Max error": max_error(y_true_lb, y_pred_lb),
-                "Mean absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
-                "R2": r2_score(y_true_lb, y_pred_lb),
-                "Median absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
-            },
-            "Upper bound": {
-                "Mean squared error": mean_squared_error(y_true_ub, y_pred_ub),
-                "Explained variance": explained_variance_score(y_true_ub, y_pred_ub),
-                "Max error": max_error(y_true_ub, y_pred_ub),
-                "Mean absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
-                "R2": r2_score(y_true_ub, y_pred_ub),
-                "Median absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
-            },
+    def __init__(
+        self,
+        regressor: Regressor = ScikitLearnRegressor(LinearRegression()),
+    ) -> None:
+        assert isinstance(regressor, Regressor)
+        self.regressors: Dict[str, Regressor] = {}
+        self.regressor_prototype = regressor
+
+    @overrides
+    def before_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        logger.info("Predicting optimal value...")
+        pred = self.sample_predict(sample)
+        for (c, v) in pred.items():
+            logger.info(f"Predicted {c.lower()}: %.6e" % v)
+            stats[f"Objective: Predicted {c.lower()}"] = v  # type: ignore
+
+    @overrides
+    def fit_xy(
+        self,
+        x: Dict[str, np.ndarray],
+        y: Dict[str, np.ndarray],
+    ) -> None:
+        for c in ["Upper bound", "Lower bound"]:
+            if c in y:
+                self.regressors[c] = self.regressor_prototype.clone()
+                self.regressors[c].fit(x[c], y[c])
+
+    def sample_predict(self, sample: Sample) -> Dict[str, float]:
+        pred: Dict[str, float] = {}
+        x, _ = self.sample_xy(None, sample)
+        for c in ["Upper bound", "Lower bound"]:
+            if c in self.regressors is not None:
+                pred[c] = self.regressors[c].predict(np.array(x[c]))[0, 0]
+            else:
+                logger.info(f"{c} regressor not fitted. Skipping.")
+        return pred
+
+    @overrides
+    def sample_xy(
+        self,
+        _: Optional[Instance],
+        sample: Sample,
+    ) -> Tuple[Dict[str, List[List[float]]], Dict[str, List[List[float]]]]:
+        lp_instance_features_np = sample.get_array("lp_instance_features")
+        if lp_instance_features_np is None:
+            lp_instance_features_np = sample.get_array("static_instance_features")
+        assert lp_instance_features_np is not None
+        lp_instance_features = cast(List[float], lp_instance_features_np.tolist())
+
+        # Features
+        x: Dict[str, List[List[float]]] = {
+            "Upper bound": [lp_instance_features],
+            "Lower bound": [lp_instance_features],
         }
-        return ev
+
+        # Labels
+        y: Dict[str, List[List[float]]] = {}
+        mip_lower_bound = sample.get_scalar("mip_lower_bound")
+        mip_upper_bound = sample.get_scalar("mip_upper_bound")
+        if mip_lower_bound is not None:
+            y["Lower bound"] = [[mip_lower_bound]]
+        if mip_upper_bound is not None:
+            y["Upper bound"] = [[mip_upper_bound]]
+
+        return x, y
+
+    @overrides
+    def sample_evaluate(
+        self,
+        instance: Instance,
+        sample: Sample,
+    ) -> Dict[str, Dict[str, float]]:
+        def compare(y_pred: float, y_actual: float) -> Dict[str, float]:
+            err = np.round(abs(y_pred - y_actual), 8)
+            return {
+                "Actual value": y_actual,
+                "Predicted value": y_pred,
+                "Absolute error": err,
+                "Relative error": err / y_actual,
+            }
+
+        result: Dict[str, Dict[str, float]] = {}
+        pred = self.sample_predict(sample)
+        actual_ub = sample.get_scalar("mip_upper_bound")
+        actual_lb = sample.get_scalar("mip_lower_bound")
+        if actual_ub is not None:
+            result["Upper bound"] = compare(pred["Upper bound"], actual_ub)
+        if actual_lb is not None:
+            result["Lower bound"] = compare(pred["Lower bound"], actual_lb)
+        return result

miplearn/components/primal.py

@@ -1,150 +1,242 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from copy import deepcopy
-import sys
+import logging
+from typing import Dict, List, Any, TYPE_CHECKING, Tuple, Optional
 
-from .component import Component
-from ..classifiers.adaptive import AdaptiveClassifier
-from ..classifiers.threshold import MinPrecisionThreshold, DynamicThreshold
-from ..components import classifier_evaluation_dict
-from ..extractors import *
+import numpy as np
+from overrides import overrides
+
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.adaptive import AdaptiveClassifier
+from miplearn.classifiers.threshold import MinPrecisionThreshold, Threshold
+from miplearn.components import classifier_evaluation_dict
+from miplearn.components.component import Component
+from miplearn.features.sample import Sample
+from miplearn.instance.base import Instance
+from miplearn.types import (
+    LearningSolveStats,
+    Category,
+    Solution,
+)
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver
+
 
 class PrimalSolutionComponent(Component):
     """
-    A component that predicts primal solutions.
+    A component that predicts the optimal primal values for the binary decision
+    variables.
+
+    In exact mode, predicted primal solutions are provided to the solver as MIP
+    starts. In heuristic mode, this component fixes the decision variables to their
+    predicted values.
     """
 
-    def __init__(self,
-                 classifier=AdaptiveClassifier(),
-                 mode="exact",
-                 threshold=MinPrecisionThreshold(0.98)):
+    def __init__(
+        self,
+        classifier: Classifier = AdaptiveClassifier(),
+        mode: str = "exact",
+        threshold: Threshold = MinPrecisionThreshold([0.98, 0.98]),
+    ) -> None:
+        assert isinstance(classifier, Classifier)
+        assert isinstance(threshold, Threshold)
+        assert mode in ["exact", "heuristic"]
         self.mode = mode
-        self.classifiers = {}
-        self.thresholds = {}
+        self.classifiers: Dict[Category, Classifier] = {}
+        self.thresholds: Dict[Category, Threshold] = {}
         self.threshold_prototype = threshold
         self.classifier_prototype = classifier
 
-    def before_solve(self, solver, instance, model):
+    @overrides
+    def before_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
         logger.info("Predicting primal solution...")
-        solution = self.predict(instance)
+
+        # Do nothing if models are not trained
+        if len(self.classifiers) == 0:
+            logger.info("Classifiers not fitted. Skipping.")
+            return
+
+        # Predict solution and provide it to the solver
+        solution = self.sample_predict(sample)
+        assert solver.internal_solver is not None
         if self.mode == "heuristic":
             solver.internal_solver.fix(solution)
         else:
             solver.internal_solver.set_warm_start(solution)
 
-    def after_solve(self, solver, instance, model, results):
-        pass
-
-    def x(self, training_instances):
-        return VariableFeaturesExtractor().extract(training_instances)
-
-    def y(self, training_instances):
-        return SolutionExtractor().extract(training_instances)
-
-    def fit(self, training_instances, n_jobs=1):
-        logger.debug("Extracting features...")
-        features = VariableFeaturesExtractor().extract(training_instances)
-        solutions = SolutionExtractor().extract(training_instances)
-
-        for category in tqdm(features.keys(),
-                             desc="Fit (primal)",
-                             disable=not sys.stdout.isatty(),
-                            ):
-            x_train = features[category]
-            for label in [0, 1]:
-                y_train = solutions[category][:, label].astype(int)
-
-                # If all samples are either positive or negative, make constant predictions
-                y_avg = np.average(y_train)
-                if y_avg < 0.001 or y_avg >= 0.999:
-                    self.classifiers[category, label] = round(y_avg)
-                    self.thresholds[category, label] = 0.50
-                    continue
-
-                # Create a copy of classifier prototype and train it
-                if isinstance(self.classifier_prototype, list):
-                    clf = deepcopy(self.classifier_prototype[label])
+        # Update statistics
+        stats["Primal: Free"] = 0
+        stats["Primal: Zero"] = 0
+        stats["Primal: One"] = 0
+        for (var_name, value) in solution.items():
+            if value is None:
+                stats["Primal: Free"] += 1
+            else:
+                if value < 0.5:
+                    stats["Primal: Zero"] += 1
                 else:
-                    clf = deepcopy(self.classifier_prototype)
-                clf.fit(x_train, y_train)
+                    stats["Primal: One"] += 1
+        logger.info(
+            f"Predicted: free: {stats['Primal: Free']}, "
+            f"zero: {stats['Primal: Zero']}, "
+            f"one: {stats['Primal: One']}"
+        )
 
-                # Find threshold (dynamic or static)
-                if isinstance(self.threshold_prototype, DynamicThreshold):
-                    self.thresholds[category, label] = self.threshold_prototype.find(clf, x_train, y_train)
-                else:
-                    self.thresholds[category, label] = deepcopy(self.threshold_prototype)
+    def sample_predict(self, sample: Sample) -> Solution:
+        var_names = sample.get_array("static_var_names")
+        var_categories = sample.get_array("static_var_categories")
+        assert var_names is not None
+        assert var_categories is not None
 
-                self.classifiers[category, label] = clf
+        # Compute y_pred
+        x, _ = self.sample_xy(None, sample)
+        y_pred = {}
+        for category in x.keys():
+            assert category in self.classifiers, (
+                f"Classifier for category {category} has not been trained. "
+                f"Please call component.fit before component.predict."
+            )
+            xc = np.array(x[category])
+            proba = self.classifiers[category].predict_proba(xc)
+            thr = self.thresholds[category].predict(xc)
+            y_pred[category] = np.vstack(
+                [
+                    proba[:, 0] >= thr[0],
+                    proba[:, 1] >= thr[1],
+                ]
+            ).T
+
+        # Convert y_pred into solution
+        solution: Solution = {v: None for v in var_names}
+        category_offset: Dict[Category, int] = {cat: 0 for cat in x.keys()}
+        for (i, var_name) in enumerate(var_names):
+            category = var_categories[i]
+            if category not in category_offset:
+                continue
+            offset = category_offset[category]
+            category_offset[category] += 1
+            if y_pred[category][offset, 0]:
+                solution[var_name] = 0.0
+            if y_pred[category][offset, 1]:
+                solution[var_name] = 1.0
 
-    def predict(self, instance):
-        solution = {}
-        x_test = VariableFeaturesExtractor().extract([instance])
-        var_split = Extractor.split_variables(instance)
-        for category in var_split.keys():
-            n = len(var_split[category])
-            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.classifiers.keys():
-                    continue
-                clf = self.classifiers[category, label]
-                if isinstance(clf, float) or isinstance(clf, int):
-                    ws = np.array([[1 - clf, clf] for _ in range(n)])
-                else:
-                    ws = clf.predict_proba(x_test[category])
-                assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (n, ws.shape)
-                for (i, (var, index)) in enumerate(var_split[category]):
-                    if ws[i, 1] >= self.thresholds[category, label]:
-                        solution[var][index] = label
         return solution
 
-    def evaluate(self, instances):
-        ev = {"Fix zero": {},
-              "Fix one": {}}
-        for instance_idx in tqdm(range(len(instances)),
-                                 desc="Evaluate (primal)",
-                                 disable=not sys.stdout.isatty(),
-                                ):
-            instance = instances[instance_idx]
-            solution_actual = instance.solution
-            solution_pred = self.predict(instance)
+    @overrides
+    def sample_xy(
+        self,
+        _: Optional[Instance],
+        sample: Sample,
+    ) -> Tuple[Dict[Category, List[List[float]]], Dict[Category, List[List[float]]]]:
+        x: Dict = {}
+        y: Dict = {}
+        instance_features = sample.get_array("static_instance_features")
+        mip_var_values = sample.get_array("mip_var_values")
+        var_features = sample.get_array("lp_var_features")
+        var_names = sample.get_array("static_var_names")
+        var_types = sample.get_array("static_var_types")
+        var_categories = sample.get_array("static_var_categories")
+        if var_features is None:
+            var_features = sample.get_array("static_var_features")
+        assert instance_features is not None
+        assert var_features is not None
+        assert var_names is not None
+        assert var_types is not None
+        assert var_categories is not None
 
-            vars_all, vars_one, vars_zero = set(), set(), set()
-            pred_one_positive, pred_zero_positive = set(), set()
-            for (varname, var_dict) in solution_actual.items():
-                if varname not in solution_pred.keys():
-                    continue
-                for (idx, value) in var_dict.items():
-                    vars_all.add((varname, idx))
-                    if value > 0.5:
-                        vars_one.add((varname, idx))
-                    else:
-                        vars_zero.add((varname, idx))
-                    if solution_pred[varname][idx] is not None:
-                        if solution_pred[varname][idx] > 0.5:
-                            pred_one_positive.add((varname, idx))
-                        else:
-                            pred_zero_positive.add((varname, idx))
-            pred_one_negative = vars_all - pred_one_positive
-            pred_zero_negative = vars_all - pred_zero_positive
+        for (i, var_name) in enumerate(var_names):
+            # Skip non-binary variables
+            if var_types[i] != b"B":
+                continue
 
-            tp_zero = len(pred_zero_positive & vars_zero)
-            fp_zero = len(pred_zero_positive & vars_one)
-            tn_zero = len(pred_zero_negative & vars_one)
-            fn_zero = len(pred_zero_negative & vars_zero)
+            # Initialize categories
+            category = var_categories[i]
+            if len(category) == 0:
+                continue
+            if category not in x.keys():
+                x[category] = []
+                y[category] = []
 
-            tp_one = len(pred_one_positive & vars_one)
-            fp_one = len(pred_one_positive & vars_zero)
-            tn_one = len(pred_one_negative & vars_zero)
-            fn_one = len(pred_one_negative & vars_one)
+            # Features
+            features = list(instance_features)
+            features.extend(var_features[i])
+            x[category].append(features)
 
-            ev["Fix zero"][instance_idx] = classifier_evaluation_dict(tp_zero, tn_zero, fp_zero, fn_zero)
-            ev["Fix one"][instance_idx] = classifier_evaluation_dict(tp_one, tn_one, fp_one, fn_one)
-        return ev
+            # Labels
+            if mip_var_values is not None:
+                opt_value = mip_var_values[i]
+                assert opt_value is not None
+                y[category].append([opt_value < 0.5, opt_value >= 0.5])
+        return x, y
+
+    @overrides
+    def sample_evaluate(
+        self,
+        _: Optional[Instance],
+        sample: Sample,
+    ) -> Dict[str, Dict[str, float]]:
+        mip_var_values = sample.get_array("mip_var_values")
+        var_names = sample.get_array("static_var_names")
+        assert mip_var_values is not None
+        assert var_names is not None
+
+        solution_actual = {
+            var_name: mip_var_values[i] for (i, var_name) in enumerate(var_names)
+        }
+        solution_pred = self.sample_predict(sample)
+        vars_all, vars_one, vars_zero = set(), set(), set()
+        pred_one_positive, pred_zero_positive = set(), set()
+        for (var_name, value_actual) in solution_actual.items():
+            vars_all.add(var_name)
+            if value_actual > 0.5:
+                vars_one.add(var_name)
+            else:
+                vars_zero.add(var_name)
+            value_pred = solution_pred[var_name]
+            if value_pred is not None:
+                if value_pred > 0.5:
+                    pred_one_positive.add(var_name)
+                else:
+                    pred_zero_positive.add(var_name)
+        pred_one_negative = vars_all - pred_one_positive
+        pred_zero_negative = vars_all - pred_zero_positive
+        return {
+            "0": classifier_evaluation_dict(
+                tp=len(pred_zero_positive & vars_zero),
+                tn=len(pred_zero_negative & vars_one),
+                fp=len(pred_zero_positive & vars_one),
+                fn=len(pred_zero_negative & vars_zero),
+            ),
+            "1": classifier_evaluation_dict(
+                tp=len(pred_one_positive & vars_one),
+                tn=len(pred_one_negative & vars_zero),
+                fp=len(pred_one_positive & vars_zero),
+                fn=len(pred_one_negative & vars_one),
+            ),
+        }
+
+    @overrides
+    def fit_xy(
+        self,
+        x: Dict[Category, np.ndarray],
+        y: Dict[Category, np.ndarray],
+    ) -> None:
+        for category in x.keys():
+            clf = self.classifier_prototype.clone()
+            thr = self.threshold_prototype.clone()
+            clf.fit(x[category], y[category])
+            thr.fit(clf, x[category], y[category])
+            self.classifiers[category] = clf
+            self.thresholds[category] = thr

miplearn/components/relaxation.py

@@ -1,151 +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.
-
-import logging
-import sys
-from copy import deepcopy
-
-import numpy as np
-from miplearn.components import classifier_evaluation_dict
-from tqdm import tqdm
-
-from miplearn import Component
-from miplearn.classifiers.counting import CountingClassifier
-
-logger = logging.getLogger(__name__)
-
-
-class RelaxationComponent(Component):
-    """
-    A Component which builds a relaxation of the problem by dropping constraints.
-
-    Currently, this component drops all integrality constraints, as well as
-    all inequality constraints which are not likely binding in the LP relaxation.
-    In a future version of MIPLearn, this component may decide to keep some
-    integrality constraints it it determines that they have small impact on
-    running time, but large impact on dual bound.
-    """
-
-    def __init__(self,
-                 classifier=CountingClassifier(),
-                 threshold=0.95,
-                 slack_tolerance=1e-5,
-                 ):
-        self.classifiers = {}
-        self.classifier_prototype = classifier
-        self.threshold = threshold
-        self.slack_tolerance = slack_tolerance
-
-    def before_solve(self, solver, instance, _):
-        logger.info("Relaxing integrality...")
-        solver.internal_solver.relax()
-
-        logger.info("Predicting redundant LP constraints...")
-        cids = solver.internal_solver.get_constraint_ids()
-        x, constraints = self.x([instance],
-                                constraint_ids=cids,
-                                return_constraints=True)
-        y = self.predict(x)
-        n_removed = 0
-        for category in y.keys():
-            for i in range(len(y[category])):
-                if y[category][i][0] == 1:
-                    cid = constraints[category][i]
-                    solver.internal_solver.extract_constraint(cid)
-                    n_removed += 1
-        logger.info("Removed %d predicted redundant LP constraints" % n_removed)
-
-    def after_solve(self, solver, instance, model, results):
-        instance.slacks = solver.internal_solver.get_constraint_slacks()
-
-    def fit(self, training_instances):
-        training_instances = [instance
-                              for instance in training_instances
-                              if hasattr(instance, "slacks")]
-        logger.debug("Extracting x and y...")
-        x = self.x(training_instances)
-        y = self.y(training_instances)
-        logger.debug("Fitting...")
-        for category in tqdm(x.keys(),
-                             desc="Fit (relaxation)",
-                             disable=not sys.stdout.isatty()):
-            if category not in self.classifiers:
-                self.classifiers[category] = deepcopy(self.classifier_prototype)
-            self.classifiers[category].fit(x[category], y[category])
-
-    def x(self,
-          instances,
-          constraint_ids=None,
-          return_constraints=False):
-        x = {}
-        constraints = {}
-        for instance in instances:
-            if constraint_ids is not None:
-                cids = constraint_ids
-            else:
-                cids = instance.slacks.keys()
-            for cid in cids:
-                category = instance.get_constraint_category(cid)
-                if category is None:
-                    continue
-                if category not in x:
-                    x[category] = []
-                    constraints[category] = []
-                x[category] += [instance.get_constraint_features(cid)]
-                constraints[category] += [cid]
-        if return_constraints:
-            return x, constraints
-        else:
-            return x
-
-    def y(self, instances):
-        y = {}
-        for instance in instances:
-            for (cid, slack) in instance.slacks.items():
-                category = instance.get_constraint_category(cid)
-                if category is None:
-                    continue
-                if category not in y:
-                    y[category] = []
-                if slack > self.slack_tolerance:
-                    y[category] += [[1]]
-                else:
-                    y[category] += [[0]]
-        return y
-
-    def predict(self, x):
-        y = {}
-        for (category, x_cat) in x.items():
-            if category not in self.classifiers:
-                continue
-            y[category] = []
-            #x_cat = np.array(x_cat)
-            proba = self.classifiers[category].predict_proba(x_cat)
-            for i in range(len(proba)):
-                if proba[i][1] >= self.threshold:
-                    y[category] += [[1]]
-                else:
-                    y[category] += [[0]]
-        return y
-
-    def evaluate(self, instance):
-        x = self.x([instance])
-        y_true = self.y([instance])
-        y_pred = self.predict(x)
-        tp, tn, fp, fn = 0, 0, 0, 0
-        for category in y_true.keys():
-            for i in range(len(y_true[category])):
-                if y_pred[category][i][0] == 1:
-                    if y_true[category][i][0] == 1:
-                        tp += 1
-                    else:
-                        fp += 1
-                else:
-                    if y_true[category][i][0] == 1:
-                        fn += 1
-                    else:
-                        tn += 1
-        return classifier_evaluation_dict(tp, tn, fp, fn)
-
-

miplearn/components/static_lazy.py

@@ -0,0 +1,252 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+from typing import Dict, Tuple, List, Any, TYPE_CHECKING, Set, Optional
+
+import numpy as np
+from overrides import overrides
+
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.counting import CountingClassifier
+from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
+from miplearn.components.component import Component
+from miplearn.features.sample import Sample
+from miplearn.solvers.internal import Constraints
+from miplearn.instance.base import Instance
+from miplearn.types import LearningSolveStats, ConstraintName, ConstraintCategory
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver
+
+
+class LazyConstraint:
+    def __init__(self, cid: ConstraintName, obj: Any) -> None:
+        self.cid = cid
+        self.obj = obj
+
+
+class StaticLazyConstraintsComponent(Component):
+    """
+    Component that decides which of the constraints tagged as lazy should
+    be kept in the formulation, and which should be removed.
+    """
+
+    def __init__(
+        self,
+        classifier: Classifier = CountingClassifier(),
+        threshold: Threshold = MinProbabilityThreshold([0.50, 0.50]),
+        violation_tolerance: float = -0.5,
+    ) -> None:
+        assert isinstance(classifier, Classifier)
+        self.classifier_prototype: Classifier = classifier
+        self.threshold_prototype: Threshold = threshold
+        self.classifiers: Dict[ConstraintCategory, Classifier] = {}
+        self.thresholds: Dict[ConstraintCategory, Threshold] = {}
+        self.pool: Constraints = Constraints()
+        self.violation_tolerance: float = violation_tolerance
+        self.enforced_cids: Set[ConstraintName] = set()
+        self.n_restored: int = 0
+        self.n_iterations: int = 0
+
+    @overrides
+    def after_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        sample.put_array(
+            "mip_constr_lazy_enforced",
+            np.array(list(self.enforced_cids), dtype="S"),
+        )
+        stats["LazyStatic: Restored"] = self.n_restored
+        stats["LazyStatic: Iterations"] = self.n_iterations
+
+    @overrides
+    def before_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+        stats: LearningSolveStats,
+        sample: Sample,
+    ) -> None:
+        assert solver.internal_solver is not None
+        static_lazy_count = sample.get_scalar("static_constr_lazy_count")
+        assert static_lazy_count is not None
+
+        logger.info("Predicting violated (static) lazy constraints...")
+        if static_lazy_count == 0:
+            logger.info("Instance does not have static lazy constraints. Skipping.")
+        self.enforced_cids = set(self.sample_predict(sample))
+        logger.info("Moving lazy constraints to the pool...")
+        constraints = Constraints.from_sample(sample)
+        assert constraints.lazy is not None
+        assert constraints.names is not None
+        selected = [
+            (constraints.lazy[i] and constraints.names[i] not in self.enforced_cids)
+            for i in range(len(constraints.lazy))
+        ]
+        n_removed = sum(selected)
+        n_kept = sum(constraints.lazy) - n_removed
+        self.pool = constraints[selected]
+        assert self.pool.names is not None
+        solver.internal_solver.remove_constraints(self.pool.names)
+        logger.info(f"{n_kept} lazy constraints kept; {n_removed} moved to the pool")
+        stats["LazyStatic: Removed"] = n_removed
+        stats["LazyStatic: Kept"] = n_kept
+        stats["LazyStatic: Restored"] = 0
+        self.n_restored = 0
+        self.n_iterations = 0
+
+    @overrides
+    def fit_xy(
+        self,
+        x: Dict[ConstraintCategory, np.ndarray],
+        y: Dict[ConstraintCategory, np.ndarray],
+    ) -> None:
+        for c in y.keys():
+            assert c in x
+            self.classifiers[c] = self.classifier_prototype.clone()
+            self.thresholds[c] = self.threshold_prototype.clone()
+            self.classifiers[c].fit(x[c], y[c])
+            self.thresholds[c].fit(self.classifiers[c], x[c], y[c])
+
+    @overrides
+    def iteration_cb(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+    ) -> bool:
+        if solver.use_lazy_cb:
+            return False
+        else:
+            return self._check_and_add(solver)
+
+    @overrides
+    def lazy_cb(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+    ) -> None:
+        self._check_and_add(solver)
+
+    def sample_predict(self, sample: Sample) -> List[ConstraintName]:
+        x, y, cids = self._sample_xy_with_cids(sample)
+        enforced_cids: List[ConstraintName] = []
+        for category in x.keys():
+            if category not in self.classifiers:
+                continue
+            npx = np.array(x[category])
+            proba = self.classifiers[category].predict_proba(npx)
+            thr = self.thresholds[category].predict(npx)
+            pred = list(proba[:, 1] > thr[1])
+            for (i, is_selected) in enumerate(pred):
+                if is_selected:
+                    enforced_cids += [cids[category][i]]
+        return enforced_cids
+
+    @overrides
+    def sample_xy(
+        self,
+        _: Optional[Instance],
+        sample: Sample,
+    ) -> Tuple[
+        Dict[ConstraintCategory, List[List[float]]],
+        Dict[ConstraintCategory, List[List[float]]],
+    ]:
+        x, y, __ = self._sample_xy_with_cids(sample)
+        return x, y
+
+    def _check_and_add(self, solver: "LearningSolver") -> bool:
+        assert solver.internal_solver is not None
+        assert self.pool.names is not None
+        if len(self.pool.names) == 0:
+            logger.info("Lazy constraint pool is empty. Skipping violation check.")
+            return False
+        self.n_iterations += 1
+        logger.info("Finding violated lazy constraints...")
+        is_satisfied = solver.internal_solver.are_constraints_satisfied(
+            self.pool,
+            tol=self.violation_tolerance,
+        )
+        is_violated = [not i for i in is_satisfied]
+        violated_constraints = self.pool[is_violated]
+        satisfied_constraints = self.pool[is_satisfied]
+        self.pool = satisfied_constraints
+        assert violated_constraints.names is not None
+        assert satisfied_constraints.names is not None
+        n_violated = len(violated_constraints.names)
+        n_satisfied = len(satisfied_constraints.names)
+        logger.info(f"Found {n_violated} violated lazy constraints found")
+        if n_violated > 0:
+            logger.info(
+                f"Enforcing {n_violated} lazy constraints; "
+                f"{n_satisfied} left in the pool..."
+            )
+            solver.internal_solver.add_constraints(violated_constraints)
+            for (i, name) in enumerate(violated_constraints.names):
+                self.enforced_cids.add(name)
+                self.n_restored += 1
+            return True
+        else:
+            return False
+
+    def _sample_xy_with_cids(
+        self, sample: Sample
+    ) -> Tuple[
+        Dict[ConstraintCategory, List[List[float]]],
+        Dict[ConstraintCategory, List[List[float]]],
+        Dict[ConstraintCategory, List[ConstraintName]],
+    ]:
+        x: Dict[ConstraintCategory, List[List[float]]] = {}
+        y: Dict[ConstraintCategory, List[List[float]]] = {}
+        cids: Dict[ConstraintCategory, List[ConstraintName]] = {}
+        instance_features = sample.get_array("static_instance_features")
+        constr_features = sample.get_array("lp_constr_features")
+        constr_names = sample.get_array("static_constr_names")
+        constr_categories = sample.get_array("static_constr_categories")
+        constr_lazy = sample.get_array("static_constr_lazy")
+        lazy_enforced = sample.get_array("mip_constr_lazy_enforced")
+        if constr_features is None:
+            constr_features = sample.get_array("static_constr_features")
+
+        assert instance_features is not None
+        assert constr_features is not None
+        assert constr_names is not None
+        assert constr_categories is not None
+        assert constr_lazy is not None
+
+        for (cidx, cname) in enumerate(constr_names):
+            # Initialize categories
+            if not constr_lazy[cidx]:
+                continue
+            category = constr_categories[cidx]
+            if len(category) == 0:
+                continue
+            if category not in x:
+                x[category] = []
+                y[category] = []
+                cids[category] = []
+
+            # Features
+            features = list(instance_features)
+            features.extend(constr_features[cidx])
+            x[category].append(features)
+            cids[category].append(cname)
+
+            # Labels
+            if lazy_enforced is not None:
+                if cname in lazy_enforced:
+                    y[category] += [[False, True]]
+                else:
+                    y[category] += [[True, False]]
+        return x, y, cids

miplearn/components/tests/test_lazy_dynamic.py

@@ -1,140 +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 unittest.mock import Mock
-
-import numpy as np
-from miplearn import DynamicLazyConstraintsComponent, LearningSolver, InternalSolver
-from miplearn.classifiers import Classifier
-from miplearn.tests import get_test_pyomo_instances
-from numpy.linalg import norm
-
-E = 0.1
-
-
-def test_lazy_fit():
-    instances, models = get_test_pyomo_instances()
-    instances[0].found_violated_lazy_constraints = ["a", "b"]
-    instances[1].found_violated_lazy_constraints = ["b", "c"]
-    classifier = Mock(spec=Classifier)
-    component = DynamicLazyConstraintsComponent(classifier=classifier)
-
-    component.fit(instances)
-
-    # Should create one classifier for each violation
-    assert "a" in component.classifiers
-    assert "b" in component.classifiers
-    assert "c" in component.classifiers
-
-    # Should provide correct x_train to each classifier
-    expected_x_train_a = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
-    expected_x_train_b = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
-    expected_x_train_c = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
-    actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
-    actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
-    actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
-    assert norm(expected_x_train_a - actual_x_train_a) < E
-    assert norm(expected_x_train_b - actual_x_train_b) < E
-    assert norm(expected_x_train_c - actual_x_train_c) < E
-
-    # Should provide correct y_train to each classifier
-    expected_y_train_a = np.array([1.0, 0.0])
-    expected_y_train_b = np.array([1.0, 1.0])
-    expected_y_train_c = np.array([0.0, 1.0])
-    actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
-    actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
-    actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
-    assert norm(expected_y_train_a - actual_y_train_a) < E
-    assert norm(expected_y_train_b - actual_y_train_b) < E
-    assert norm(expected_y_train_c - actual_y_train_c) < E
-
-
-def test_lazy_before():
-    instances, models = get_test_pyomo_instances()
-    instances[0].build_lazy_constraint = Mock(return_value="c1")
-    solver = LearningSolver()
-    solver.internal_solver = Mock(spec=InternalSolver)
-    component = DynamicLazyConstraintsComponent(threshold=0.10)
-    component.classifiers = {"a": Mock(spec=Classifier),
-                             "b": Mock(spec=Classifier)}
-    component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
-    component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
-
-    component.before_solve(solver, instances[0], models[0])
-
-    # Should ask classifier likelihood of each constraint being violated
-    expected_x_test_a = np.array([[67., 21.75, 1287.92]])
-    expected_x_test_b = np.array([[67., 21.75, 1287.92]])
-    actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
-    actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
-    assert norm(expected_x_test_a - actual_x_test_a) < E
-    assert norm(expected_x_test_b - actual_x_test_b) < E
-
-    # Should ask instance to generate cut for constraints whose likelihood
-    # of being violated exceeds the threshold
-    instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
-
-    # Should ask internal solver to add generated constraint
-    solver.internal_solver.add_constraint.assert_called_once_with("c1")
-
-
-def test_lazy_evaluate():
-    instances, models = get_test_pyomo_instances()
-    component = DynamicLazyConstraintsComponent()
-    component.classifiers = {"a": Mock(spec=Classifier),
-                             "b": Mock(spec=Classifier),
-                             "c": Mock(spec=Classifier)}
-    component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
-    component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
-    component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
-    
-    instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
-    instances[1].found_violated_lazy_constraints = ["b", "d"]
-    assert component.evaluate(instances) == {
-        0: {
-            "Accuracy": 0.75,
-            "F1 score": 0.8,
-            "Precision": 1.0,
-            "Recall": 2/3.,
-            "Predicted positive": 2,
-            "Predicted negative": 2,
-            "Condition positive": 3,
-            "Condition negative": 1,
-            "False negative": 1,
-            "False positive": 0,
-            "True negative": 1,
-            "True positive": 2,
-            "Predicted positive (%)": 50.0,
-            "Predicted negative (%)": 50.0,
-            "Condition positive (%)": 75.0,
-            "Condition negative (%)": 25.0,
-            "False negative (%)": 25.0,
-            "False positive (%)": 0,
-            "True negative (%)": 25.0,
-            "True positive (%)": 50.0,
-        },
-        1: {
-            "Accuracy": 0.5,
-            "F1 score": 0.5,
-            "Precision": 0.5,
-            "Recall": 0.5,
-            "Predicted positive": 2,
-            "Predicted negative": 2,
-            "Condition positive": 2,
-            "Condition negative": 2,
-            "False negative": 1,
-            "False positive": 1,
-            "True negative": 1,
-            "True positive": 1,
-            "Predicted positive (%)": 50.0,
-            "Predicted negative (%)": 50.0,
-            "Condition positive (%)": 50.0,
-            "Condition negative (%)": 50.0,
-            "False negative (%)": 25.0,
-            "False positive (%)": 25.0,
-            "True negative (%)": 25.0,
-            "True positive (%)": 25.0,
-        }
-    }
-    

miplearn/components/tests/test_lazy_static.py

@@ -1,188 +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 unittest.mock import Mock, call
-
-from miplearn import (StaticLazyConstraintsComponent,
-                      LearningSolver,
-                      Instance,
-                      InternalSolver)
-from miplearn.classifiers import Classifier
-
-
-def test_usage_with_solver():
-    solver = Mock(spec=LearningSolver)
-    solver.use_lazy_cb = False
-    solver.gap_tolerance = 1e-4
-
-    internal = solver.internal_solver = Mock(spec=InternalSolver)
-    internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
-    internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
-    internal.is_constraint_satisfied = Mock(return_value=False)
-
-    instance = Mock(spec=Instance)
-    instance.has_static_lazy_constraints = Mock(return_value=True)
-    instance.is_constraint_lazy = Mock(side_effect=lambda cid: {
-        "c1": False,
-        "c2": True,
-        "c3": True,
-        "c4": True,
-    }[cid])
-    instance.get_constraint_features = Mock(side_effect=lambda cid: {
-        "c2": [1.0, 0.0],
-        "c3": [0.5, 0.5],
-        "c4": [1.0],
-    }[cid])
-    instance.get_constraint_category = Mock(side_effect=lambda cid: {
-        "c2": "type-a",
-        "c3": "type-a",
-        "c4": "type-b",
-    }[cid])
-
-    component = StaticLazyConstraintsComponent(threshold=0.90,
-                                               use_two_phase_gap=False,
-                                               violation_tolerance=1.0)
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-    component.classifiers["type-a"].predict_proba = \
-        Mock(return_value=[
-            [0.20, 0.80],
-            [0.05, 0.95],
-        ])
-    component.classifiers["type-b"].predict_proba = \
-        Mock(return_value=[
-            [0.02, 0.98],
-        ])
-
-    # LearningSolver calls before_solve
-    component.before_solve(solver, instance, None)
-
-    # Should ask if instance has static lazy constraints
-    instance.has_static_lazy_constraints.assert_called_once()
-
-    # Should ask internal solver for a list of constraints in the model
-    internal.get_constraint_ids.assert_called_once()
-
-    # Should ask if each constraint in the model is lazy
-    instance.is_constraint_lazy.assert_has_calls([
-        call("c1"), call("c2"), call("c3"), call("c4"),
-    ])
-
-    # For the lazy ones, should ask for features
-    instance.get_constraint_features.assert_has_calls([
-        call("c2"), call("c3"), call("c4"),
-    ])
-
-    # Should also ask for categories
-    assert instance.get_constraint_category.call_count == 3
-    instance.get_constraint_category.assert_has_calls([
-        call("c2"), call("c3"), call("c4"),
-    ])
-
-    # Should ask internal solver to remove constraints identified as lazy
-    assert internal.extract_constraint.call_count == 3
-    internal.extract_constraint.assert_has_calls([
-        call("c2"), call("c3"), call("c4"),
-    ])
-
-    # Should ask ML to predict whether each lazy constraint should be enforced
-    component.classifiers["type-a"].predict_proba.assert_called_once_with([[1.0, 0.0], [0.5, 0.5]])
-    component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
-
-    # For the ones that should be enforced, should ask solver to re-add them
-    # to the formulation. The remaining ones should remain in the pool.
-    assert internal.add_constraint.call_count == 2
-    internal.add_constraint.assert_has_calls([
-        call("<c3>"), call("<c4>"),
-    ])
-    internal.add_constraint.reset_mock()
-
-    # LearningSolver calls after_iteration (first time)
-    should_repeat = component.after_iteration(solver, instance, None)
-    assert should_repeat
-
-    # Should ask internal solver to verify if constraints in the pool are
-    # satisfied and add the ones that are not
-    internal.is_constraint_satisfied.assert_called_once_with("<c2>", tol=1.0)
-    internal.is_constraint_satisfied.reset_mock()
-    internal.add_constraint.assert_called_once_with("<c2>")
-    internal.add_constraint.reset_mock()
-
-    # LearningSolver calls after_iteration (second time)
-    should_repeat = component.after_iteration(solver, instance, None)
-    assert not should_repeat
-
-    # The lazy constraint pool should be empty by now, so no calls should be made
-    internal.is_constraint_satisfied.assert_not_called()
-    internal.add_constraint.assert_not_called()
-
-    # Should update instance object
-    assert instance.found_violated_lazy_constraints == ["c3", "c4", "c2"]
-
-
-def test_fit():
-    instance_1 = Mock(spec=Instance)
-    instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
-    instance_1.get_constraint_category = Mock(side_effect=lambda cid: {
-        "c1": "type-a",
-        "c2": "type-a",
-        "c3": "type-a",
-        "c4": "type-b",
-        "c5": "type-b",
-    }[cid])
-    instance_1.get_constraint_features = Mock(side_effect=lambda cid: {
-        "c1": [1, 1],
-        "c2": [1, 2],
-        "c3": [1, 3],
-        "c4": [1, 4, 0],
-        "c5": [1, 5, 0],
-    }[cid])
-
-    instance_2 = Mock(spec=Instance)
-    instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
-    instance_2.get_constraint_category = Mock(side_effect=lambda cid: {
-        "c1": "type-a",
-        "c2": "type-a",
-        "c3": "type-a",
-        "c4": "type-b",
-        "c5": "type-b",
-    }[cid])
-    instance_2.get_constraint_features = Mock(side_effect=lambda cid: {
-        "c1": [2, 1],
-        "c2": [2, 2],
-        "c3": [2, 3],
-        "c4": [2, 4, 0],
-        "c5": [2, 5, 0],
-    }[cid])
-
-    instances = [instance_1, instance_2]
-    component = StaticLazyConstraintsComponent()
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-
-    expected_constraints = {
-        "type-a": ["c1", "c2", "c3"],
-        "type-b": ["c4", "c5"],
-    }
-    expected_x = {
-        "type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
-        "type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]]
-    }
-    expected_y = {
-        "type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
-        "type-b": [[0, 1], [0, 1], [0, 1], [1, 0]]
-    }
-    assert component._collect_constraints(instances) == expected_constraints
-    assert component.x(instances) == expected_x
-    assert component.y(instances) == expected_y
-
-    component.fit(instances)
-    component.classifiers["type-a"].fit.assert_called_once_with(expected_x["type-a"],
-                                                                expected_y["type-a"])
-    component.classifiers["type-b"].fit.assert_called_once_with(expected_x["type-b"],
-                                                                expected_y["type-b"])

miplearn/components/tests/test_objective.py

@@ -1,47 +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 unittest.mock import Mock
-
-import numpy as np
-from miplearn import ObjectiveValueComponent
-from miplearn.classifiers import Regressor
-from miplearn.tests import get_test_pyomo_instances
-
-
-def test_usage():
-    instances, models = get_test_pyomo_instances()
-    comp = ObjectiveValueComponent()
-    comp.fit(instances)
-    assert instances[0].lower_bound == 1183.0
-    assert instances[0].upper_bound == 1183.0
-    assert np.round(comp.predict(instances), 2).tolist() == [[1183.0, 1183.0],
-                                                             [1070.0, 1070.0]]
-
-
-def test_obj_evaluate():
-    instances, models = get_test_pyomo_instances()
-    reg = Mock(spec=Regressor)
-    reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
-    comp = ObjectiveValueComponent(regressor=reg)
-    comp.fit(instances)
-    ev = comp.evaluate(instances)
-    assert ev == {
-        'Lower bound': {
-            'Explained variance': 0.0,
-            'Max error': 183.0,
-            'Mean absolute error': 126.5,
-            'Mean squared error': 19194.5,
-            'Median absolute error': 126.5,
-            'R2': -5.012843605607331,
-        },
-        'Upper bound': {
-            'Explained variance': 0.0,
-            'Max error': 183.0,
-            'Mean absolute error': 126.5,
-            'Mean squared error': 19194.5,
-            'Median absolute error': 126.5,
-            'R2': -5.012843605607331,
-        }
-    }

miplearn/components/tests/test_primal.py

@@ -1,99 +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 unittest.mock import Mock
-
-import numpy as np
-from miplearn import PrimalSolutionComponent
-from miplearn.classifiers import Classifier
-from miplearn.tests import get_test_pyomo_instances
-
-
-def test_predict():
-    instances, models = get_test_pyomo_instances()
-    comp = PrimalSolutionComponent()
-    comp.fit(instances)
-    solution = comp.predict(instances[0])
-    assert "x" in solution
-    assert 0 in solution["x"]
-    assert 1 in solution["x"]
-    assert 2 in solution["x"]
-    assert 3 in solution["x"]
-
-
-def test_evaluate():
-    instances, models = get_test_pyomo_instances()
-    clf_zero = Mock(spec=Classifier)
-    clf_zero.predict_proba = Mock(return_value=np.array([
-        [0., 1.],  # x[0]
-        [0., 1.],  # x[1]
-        [1., 0.],  # x[2]
-        [1., 0.],  # x[3]
-    ]))
-    clf_one = Mock(spec=Classifier)
-    clf_one.predict_proba = Mock(return_value=np.array([
-        [1., 0.],  # x[0] instances[0]
-        [1., 0.],  # x[1] instances[0]
-        [0., 1.],  # x[2] instances[0]
-        [1., 0.],  # x[3] instances[0]
-    ]))
-    comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one],
-                                   threshold=0.50)
-    comp.fit(instances[:1])
-    assert comp.predict(instances[0]) == {"x": {0: 0,
-                                                1: 0,
-                                                2: 1,
-                                                3: None}}
-    assert instances[0].solution == {"x": {0: 1,
-                                           1: 0,
-                                           2: 1,
-                                           3: 1}}
-    ev = comp.evaluate(instances[:1])
-    assert ev == {'Fix one': {0: {'Accuracy': 0.5,
-                                  'Condition negative': 1,
-                                  'Condition negative (%)': 25.0,
-                                  'Condition positive': 3,
-                                  'Condition positive (%)': 75.0,
-                                  'F1 score': 0.5,
-                                  'False negative': 2,
-                                  'False negative (%)': 50.0,
-                                  'False positive': 0,
-                                  'False positive (%)': 0.0,
-                                  'Precision': 1.0,
-                                  'Predicted negative': 3,
-                                  'Predicted negative (%)': 75.0,
-                                  'Predicted positive': 1,
-                                  'Predicted positive (%)': 25.0,
-                                  'Recall': 0.3333333333333333,
-                                  'True negative': 1,
-                                  'True negative (%)': 25.0,
-                                  'True positive': 1,
-                                  'True positive (%)': 25.0}},
-                  'Fix zero': {0: {'Accuracy': 0.75,
-                                   'Condition negative': 3,
-                                   'Condition negative (%)': 75.0,
-                                   'Condition positive': 1,
-                                   'Condition positive (%)': 25.0,
-                                   'F1 score': 0.6666666666666666,
-                                   'False negative': 0,
-                                   'False negative (%)': 0.0,
-                                   'False positive': 1,
-                                   'False positive (%)': 25.0,
-                                   'Precision': 0.5,
-                                   'Predicted negative': 2,
-                                   'Predicted negative (%)': 50.0,
-                                   'Predicted positive': 2,
-                                   'Predicted positive (%)': 50.0,
-                                   'Recall': 1.0,
-                                   'True negative': 2,
-                                   'True negative (%)': 50.0,
-                                   'True positive': 1,
-                                   'True positive (%)': 25.0}}}
-
-
-def test_primal_parallel_fit():
-    instances, models = get_test_pyomo_instances()
-    comp = PrimalSolutionComponent()
-    comp.fit(instances, n_jobs=2)
-    assert len(comp.classifiers) == 2

miplearn/components/tests/test_relaxation.py

@@ -1,188 +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 unittest.mock import Mock, call
-
-from miplearn import (RelaxationComponent,
-                      LearningSolver,
-                      Instance,
-                      InternalSolver)
-from miplearn.classifiers import Classifier
-
-
-def test_usage_with_solver():
-    solver = Mock(spec=LearningSolver)
-
-    internal = solver.internal_solver = Mock(spec=InternalSolver)
-    internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
-    internal.get_constraint_slacks = Mock(side_effect=lambda: {
-        "c1": 0.5,
-        "c2": 0.0,
-        "c3": 0.0,
-        "c4": 1.4,
-    })
-
-    instance = Mock(spec=Instance)
-    instance.get_constraint_features = Mock(side_effect=lambda cid: {
-        "c2": [1.0, 0.0],
-        "c3": [0.5, 0.5],
-        "c4": [1.0],
-    }[cid])
-    instance.get_constraint_category = Mock(side_effect=lambda cid: {
-        "c1": None,
-        "c2": "type-a",
-        "c3": "type-a",
-        "c4": "type-b",
-    }[cid])
-
-    component = RelaxationComponent()
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-    component.classifiers["type-a"].predict_proba = \
-        Mock(return_value=[
-            [0.20, 0.80],
-            [0.05, 0.95],
-        ])
-    component.classifiers["type-b"].predict_proba = \
-        Mock(return_value=[
-            [0.02, 0.98],
-        ])
-
-    # LearningSolver calls before_solve
-    component.before_solve(solver, instance, None)
-
-    # Should relax integrality of the problem
-    internal.relax.assert_called_once()
-
-    # Should query list of constraints
-    internal.get_constraint_ids.assert_called_once()
-
-    # Should query category and features for each constraint in the model
-    assert instance.get_constraint_category.call_count == 4
-    instance.get_constraint_category.assert_has_calls([
-        call("c1"), call("c2"), call("c3"), call("c4"),
-    ])
-
-    # For constraint with non-null categories, should ask for features
-    assert instance.get_constraint_features.call_count == 3
-    instance.get_constraint_features.assert_has_calls([
-        call("c2"), call("c3"), call("c4"),
-    ])
-
-    # Should ask ML to predict whether constraint should be removed
-    component.classifiers["type-a"].predict_proba.assert_called_once_with([[1.0, 0.0], [0.5, 0.5]])
-    component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
-
-    # Should ask internal solver to remove constraints predicted as redundant
-    assert internal.extract_constraint.call_count == 2
-    internal.extract_constraint.assert_has_calls([
-        call("c3"), call("c4"),
-    ])
-
-    # LearningSolver calls after_solve
-    component.after_solve(solver, instance, None, None)
-
-    # Should query slack for all constraints
-    internal.get_constraint_slacks.assert_called_once()
-
-    # Should store constraint slacks in instance object
-    assert hasattr(instance, "slacks")
-    assert instance.slacks == {
-        "c1": 0.5,
-        "c2": 0.0,
-        "c3": 0.0,
-        "c4": 1.4,
-    }
-
-
-def test_x_y_fit_predict_evaluate():
-    instances = [Mock(spec=Instance), Mock(spec=Instance)]
-    component = RelaxationComponent(slack_tolerance=0.05,
-                                    threshold=0.80)
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-    component.classifiers["type-a"].predict_proba = \
-        Mock(return_value=[
-            [0.20, 0.80],
-        ])
-    component.classifiers["type-b"].predict_proba = \
-        Mock(return_value=[
-            [0.50, 0.50],
-            [0.05, 0.95],
-        ])
-
-    # First mock instance
-    instances[0].slacks = {
-        "c1": 0.00,
-        "c2": 0.05,
-        "c3": 0.00,
-        "c4": 30.0,
-    }
-    instances[0].get_constraint_category = Mock(side_effect=lambda cid: {
-        "c1": None,
-        "c2": "type-a",
-        "c3": "type-a",
-        "c4": "type-b",
-    }[cid])
-    instances[0].get_constraint_features = Mock(side_effect=lambda cid: {
-        "c2": [1.0, 0.0],
-        "c3": [0.5, 0.5],
-        "c4": [1.0],
-    }[cid])
-
-    # Second mock instance
-    instances[1].slacks = {
-        "c1": 0.00,
-        "c3": 0.30,
-        "c4": 0.00,
-        "c5": 0.00,
-    }
-    instances[1].get_constraint_category = Mock(side_effect=lambda cid: {
-        "c1": None,
-        "c3": "type-a",
-        "c4": "type-b",
-        "c5": "type-b",
-    }[cid])
-    instances[1].get_constraint_features = Mock(side_effect=lambda cid: {
-        "c3": [0.3, 0.4],
-        "c4": [0.7],
-        "c5": [0.8],
-    }[cid])
-
-    expected_x = {
-        "type-a": [[1.0, 0.0], [0.5, 0.5], [0.3, 0.4]],
-        "type-b": [[1.0], [0.7], [0.8]],
-    }
-    expected_y = {
-        "type-a": [[0], [0], [1]],
-        "type-b": [[1], [0], [0]]
-    }
-
-    # Should build X and Y matrices correctly
-    assert component.x(instances) == expected_x
-    assert component.y(instances) == expected_y
-
-    # Should pass along X and Y matrices to classifiers
-    component.fit(instances)
-    component.classifiers["type-a"].fit.assert_called_with(expected_x["type-a"], expected_y["type-a"])
-    component.classifiers["type-b"].fit.assert_called_with(expected_x["type-b"], expected_y["type-b"])
-
-    assert component.predict(expected_x) == {
-        "type-a": [[1]],
-        "type-b": [[0], [1]]
-    }
-
-    ev = component.evaluate(instances[1])
-    assert ev["True positive"] == 1
-    assert ev["True negative"] == 1
-    assert ev["False positive"] == 1
-    assert ev["False negative"] == 0
-
-
-
-

miplearn/extractors.py

@@ -1,105 +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.
-
-import logging
-from abc import ABC, abstractmethod
-
-import numpy as np
-from tqdm import tqdm
-
-logger = logging.getLogger(__name__)
-
-
-class Extractor(ABC):
-    @abstractmethod
-    def extract(self, instances,):
-        pass
-    
-    @staticmethod
-    def split_variables(instance):
-        assert hasattr(instance, "lp_solution")
-        result = {}
-        for var_name in instance.lp_solution:
-            for index in instance.lp_solution[var_name]:
-                category = instance.get_variable_category(var_name, index)
-                if category is None:
-                    continue
-                if category not in result:
-                    result[category] = []
-                result[category] += [(var_name, index)]
-        return result
-
-    
-class VariableFeaturesExtractor(Extractor):
-    def extract(self, instances):
-        result = {}
-        for instance in tqdm(instances,
-                             desc="Extract (vars)",
-                             disable=len(instances) < 5):
-            instance_features = instance.get_instance_features()
-            var_split = self.split_variables(instance)
-            for (category, var_index_pairs) in var_split.items():
-                if category not in result:
-                    result[category] = []
-                for (var_name, index) in var_index_pairs:
-                    result[category] += [
-                        instance_features.tolist() + \
-                        instance.get_variable_features(var_name, index).tolist() + \
-                        [instance.lp_solution[var_name][index]]
-                    ]
-        for category in result:
-            result[category] = np.array(result[category])
-        return result
-
-
-class SolutionExtractor(Extractor):
-    def __init__(self, relaxation=False):
-        self.relaxation = relaxation
-        
-    def extract(self, instances):
-        result = {}
-        for instance in tqdm(instances,
-                             desc="Extract (solution)",
-                             disable=len(instances) < 5):
-            var_split = self.split_variables(instance)
-            for (category, var_index_pairs) in var_split.items():
-                if category not in result:
-                    result[category] = []
-                for (var_name, index) in var_index_pairs:
-                    if self.relaxation:
-                        v = instance.lp_solution[var_name][index]
-                    else:
-                        v = instance.solution[var_name][index]
-                    if v is None:
-                        result[category] += [[0, 0]]
-                    else:
-                        result[category] += [[1 - v, v]]
-        for category in result:
-            result[category] = np.array(result[category])            
-        return result
-    
-    
-class InstanceFeaturesExtractor(Extractor):
-    def extract(self, instances):
-        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):
-        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])

miplearn/features/__init__.py

@@ -1,3 +1,3 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.

miplearn/features/extractor.py

@@ -0,0 +1,406 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from math import log, isfinite
+from typing import TYPE_CHECKING, List, Tuple, Optional
+
+import numpy as np
+
+from miplearn.features.sample import Sample
+from miplearn.solvers.internal import LPSolveStats
+
+if TYPE_CHECKING:
+    from miplearn.solvers.internal import InternalSolver
+    from miplearn.instance.base import Instance
+
+
+class FeaturesExtractor:
+    def __init__(
+        self,
+        with_sa: bool = True,
+        with_lhs: bool = True,
+    ) -> None:
+        self.with_sa = with_sa
+        self.with_lhs = with_lhs
+
+    def extract_after_load_features(
+        self,
+        instance: "Instance",
+        solver: "InternalSolver",
+        sample: Sample,
+    ) -> None:
+        variables = solver.get_variables(with_static=True)
+        constraints = solver.get_constraints(with_static=True, with_lhs=self.with_lhs)
+        assert constraints.names is not None
+        sample.put_array("static_var_lower_bounds", variables.lower_bounds)
+        sample.put_array("static_var_names", variables.names)
+        sample.put_array("static_var_obj_coeffs", variables.obj_coeffs)
+        sample.put_array("static_var_types", variables.types)
+        sample.put_array("static_var_upper_bounds", variables.upper_bounds)
+        sample.put_array("static_constr_names", constraints.names)
+        # sample.put("static_constr_lhs", constraints.lhs)
+        sample.put_array("static_constr_rhs", constraints.rhs)
+        sample.put_array("static_constr_senses", constraints.senses)
+
+        # Instance features
+        self._extract_user_features_instance(instance, sample)
+
+        # Constraint features
+        (
+            constr_features,
+            constr_categories,
+            constr_lazy,
+        ) = FeaturesExtractor._extract_user_features_constrs(
+            instance,
+            constraints.names,
+        )
+        sample.put_array("static_constr_features", constr_features)
+        sample.put_array("static_constr_categories", constr_categories)
+        sample.put_array("static_constr_lazy", constr_lazy)
+        sample.put_scalar("static_constr_lazy_count", int(constr_lazy.sum()))
+
+        # Variable features
+        (
+            vars_features_user,
+            var_categories,
+        ) = self._extract_user_features_vars(instance, sample)
+        sample.put_array("static_var_categories", var_categories)
+        assert variables.lower_bounds is not None
+        assert variables.obj_coeffs is not None
+        assert variables.upper_bounds is not None
+        sample.put_array(
+            "static_var_features",
+            np.hstack(
+                [
+                    vars_features_user,
+                    self._extract_var_features_AlvLouWeh2017(
+                        obj_coeffs=variables.obj_coeffs,
+                    ),
+                    variables.lower_bounds.reshape(-1, 1),
+                    variables.obj_coeffs.reshape(-1, 1),
+                    variables.upper_bounds.reshape(-1, 1),
+                ]
+            ),
+        )
+
+    def extract_after_lp_features(
+        self,
+        solver: "InternalSolver",
+        sample: Sample,
+        lp_stats: LPSolveStats,
+    ) -> None:
+        for (k, v) in lp_stats.__dict__.items():
+            sample.put_scalar(k, v)
+        variables = solver.get_variables(with_static=False, with_sa=self.with_sa)
+        constraints = solver.get_constraints(with_static=False, with_sa=self.with_sa)
+        sample.put_array("lp_var_basis_status", variables.basis_status)
+        sample.put_array("lp_var_reduced_costs", variables.reduced_costs)
+        sample.put_array("lp_var_sa_lb_down", variables.sa_lb_down)
+        sample.put_array("lp_var_sa_lb_up", variables.sa_lb_up)
+        sample.put_array("lp_var_sa_obj_down", variables.sa_obj_down)
+        sample.put_array("lp_var_sa_obj_up", variables.sa_obj_up)
+        sample.put_array("lp_var_sa_ub_down", variables.sa_ub_down)
+        sample.put_array("lp_var_sa_ub_up", variables.sa_ub_up)
+        sample.put_array("lp_var_values", variables.values)
+        sample.put_array("lp_constr_basis_status", constraints.basis_status)
+        sample.put_array("lp_constr_dual_values", constraints.dual_values)
+        sample.put_array("lp_constr_sa_rhs_down", constraints.sa_rhs_down)
+        sample.put_array("lp_constr_sa_rhs_up", constraints.sa_rhs_up)
+        sample.put_array("lp_constr_slacks", constraints.slacks)
+
+        # Variable features
+        lp_var_features_list = []
+        for f in [
+            sample.get_array("static_var_features"),
+            self._extract_var_features_AlvLouWeh2017(
+                obj_coeffs=sample.get_array("static_var_obj_coeffs"),
+                obj_sa_up=variables.sa_obj_up,
+                obj_sa_down=variables.sa_obj_down,
+                values=variables.values,
+            ),
+        ]:
+            if f is not None:
+                lp_var_features_list.append(f)
+        for f in [
+            variables.reduced_costs,
+            variables.sa_lb_down,
+            variables.sa_lb_up,
+            variables.sa_obj_down,
+            variables.sa_obj_up,
+            variables.sa_ub_down,
+            variables.sa_ub_up,
+            variables.values,
+        ]:
+            if f is not None:
+                lp_var_features_list.append(f.reshape(-1, 1))
+        lp_var_features = np.hstack(lp_var_features_list)
+        _fix_infinity(lp_var_features)
+        sample.put_array("lp_var_features", lp_var_features)
+
+        # Constraint features
+        lp_constr_features_list = []
+        for f in [sample.get_array("static_constr_features")]:
+            if f is not None:
+                lp_constr_features_list.append(f)
+        for f in [
+            sample.get_array("lp_constr_dual_values"),
+            sample.get_array("lp_constr_sa_rhs_down"),
+            sample.get_array("lp_constr_sa_rhs_up"),
+            sample.get_array("lp_constr_slacks"),
+        ]:
+            if f is not None:
+                lp_constr_features_list.append(f.reshape(-1, 1))
+        lp_constr_features = np.hstack(lp_constr_features_list)
+        _fix_infinity(lp_constr_features)
+        sample.put_array("lp_constr_features", lp_constr_features)
+
+        # Build lp_instance_features
+        static_instance_features = sample.get_array("static_instance_features")
+        assert static_instance_features is not None
+        assert lp_stats.lp_value is not None
+        assert lp_stats.lp_wallclock_time is not None
+        sample.put_array(
+            "lp_instance_features",
+            np.hstack(
+                [
+                    static_instance_features,
+                    lp_stats.lp_value,
+                    lp_stats.lp_wallclock_time,
+                ]
+            ),
+        )
+
+    def extract_after_mip_features(
+        self,
+        solver: "InternalSolver",
+        sample: Sample,
+    ) -> None:
+        variables = solver.get_variables(with_static=False, with_sa=False)
+        constraints = solver.get_constraints(with_static=False, with_sa=False)
+        sample.put_array("mip_var_values", variables.values)
+        sample.put_array("mip_constr_slacks", constraints.slacks)
+
+    # noinspection DuplicatedCode
+    def _extract_user_features_vars(
+        self,
+        instance: "Instance",
+        sample: Sample,
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        # Query variable names
+        var_names = sample.get_array("static_var_names")
+        assert var_names is not None
+
+        # Query variable features
+        var_features = instance.get_variable_features(var_names)
+        assert isinstance(var_features, np.ndarray), (
+            f"Variable features must be a numpy array. "
+            f"Found {var_features.__class__} instead."
+        )
+        assert len(var_features.shape) == 2, (
+            f"Variable features must be 2-dimensional array. "
+            f"Found array with shape {var_features.shape} instead."
+        )
+        assert var_features.shape[0] == len(var_names), (
+            f"Variable features must have exactly {len(var_names)} rows. "
+            f"Found {var_features.shape[0]} rows instead."
+        )
+        assert var_features.dtype.kind in ["f"], (
+            f"Variable features must be floating point numbers. "
+            f"Found {var_features.dtype} instead."
+        )
+
+        # Query variable categories
+        var_categories = instance.get_variable_categories(var_names)
+        assert isinstance(var_categories, np.ndarray), (
+            f"Variable categories must be a numpy array. "
+            f"Found {var_categories.__class__} instead."
+        )
+        assert len(var_categories.shape) == 1, (
+            f"Variable categories must be a vector. "
+            f"Found array with shape {var_categories.shape} instead."
+        )
+        assert len(var_categories) == len(var_names), (
+            f"Variable categories must have exactly {len(var_names)} elements. "
+            f"Found {var_categories.shape[0]} elements instead."
+        )
+        assert var_categories.dtype.kind == "S", (
+            f"Variable categories must be a numpy array with dtype='S'. "
+            f"Found {var_categories.dtype} instead."
+        )
+        return var_features, var_categories
+
+    # noinspection DuplicatedCode
+    @classmethod
+    def _extract_user_features_constrs(
+        cls,
+        instance: "Instance",
+        constr_names: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        # Query constraint features
+        constr_features = instance.get_constraint_features(constr_names)
+        assert isinstance(constr_features, np.ndarray), (
+            f"get_constraint_features must return a numpy array. "
+            f"Found {constr_features.__class__} instead."
+        )
+        assert len(constr_features.shape) == 2, (
+            f"get_constraint_features must return a 2-dimensional array. "
+            f"Found array with shape {constr_features.shape} instead."
+        )
+        assert constr_features.shape[0] == len(constr_names), (
+            f"get_constraint_features must return an array with {len(constr_names)} "
+            f"rows. Found {constr_features.shape[0]} rows instead."
+        )
+        assert constr_features.dtype.kind in ["f"], (
+            f"get_constraint_features must return floating point numbers. "
+            f"Found {constr_features.dtype} instead."
+        )
+
+        # Query constraint categories
+        constr_categories = instance.get_constraint_categories(constr_names)
+        assert isinstance(constr_categories, np.ndarray), (
+            f"get_constraint_categories must return a numpy array. "
+            f"Found {constr_categories.__class__} instead."
+        )
+        assert len(constr_categories.shape) == 1, (
+            f"get_constraint_categories must return a vector. "
+            f"Found array with shape {constr_categories.shape} instead."
+        )
+        assert len(constr_categories) == len(constr_names), (
+            f"get_constraint_categories must return a vector with {len(constr_names)} "
+            f"elements. Found {constr_categories.shape[0]} elements instead."
+        )
+        assert constr_categories.dtype.kind == "S", (
+            f"get_constraint_categories must return a numpy array with dtype='S'. "
+            f"Found {constr_categories.dtype} instead."
+        )
+
+        # Query constraint lazy attribute
+        constr_lazy = instance.are_constraints_lazy(constr_names)
+        assert isinstance(constr_lazy, np.ndarray), (
+            f"are_constraints_lazy must return a numpy array. "
+            f"Found {constr_lazy.__class__} instead."
+        )
+        assert len(constr_lazy.shape) == 1, (
+            f"are_constraints_lazy must return a vector. "
+            f"Found array with shape {constr_lazy.shape} instead."
+        )
+        assert constr_lazy.shape[0] == len(constr_names), (
+            f"are_constraints_lazy must return a vector with {len(constr_names)} "
+            f"elements. Found {constr_lazy.shape[0]} elements instead."
+        )
+        assert constr_lazy.dtype.kind == "b", (
+            f"are_constraints_lazy must return a boolean array. "
+            f"Found {constr_lazy.dtype} instead."
+        )
+
+        return constr_features, constr_categories, constr_lazy
+
+    def _extract_user_features_instance(
+        self,
+        instance: "Instance",
+        sample: Sample,
+    ) -> None:
+        features = instance.get_instance_features()
+        assert isinstance(features, np.ndarray), (
+            f"Instance features must be a numpy array. "
+            f"Found {features.__class__} instead."
+        )
+        assert len(features.shape) == 1, (
+            f"Instance features must be a vector. "
+            f"Found array with shape {features.shape} instead."
+        )
+        assert features.dtype.kind in [
+            "f"
+        ], f"Instance features have unsupported {features.dtype}"
+        sample.put_array("static_instance_features", features)
+
+    # Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
+    # approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
+    # noinspection PyPep8Naming
+    def _extract_var_features_AlvLouWeh2017(
+        self,
+        obj_coeffs: Optional[np.ndarray] = None,
+        obj_sa_down: Optional[np.ndarray] = None,
+        obj_sa_up: Optional[np.ndarray] = None,
+        values: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        assert obj_coeffs is not None
+        obj_coeffs = obj_coeffs.astype(float)
+        _fix_infinity(obj_coeffs)
+        nvars = len(obj_coeffs)
+
+        if obj_sa_down is not None:
+            obj_sa_down = obj_sa_down.astype(float)
+            _fix_infinity(obj_sa_down)
+
+        if obj_sa_up is not None:
+            obj_sa_up = obj_sa_up.astype(float)
+            _fix_infinity(obj_sa_up)
+
+        if values is not None:
+            values = values.astype(float)
+            _fix_infinity(values)
+
+        pos_obj_coeffs_sum = obj_coeffs[obj_coeffs > 0].sum()
+        neg_obj_coeffs_sum = -obj_coeffs[obj_coeffs < 0].sum()
+
+        curr = 0
+        max_n_features = 8
+        features = np.zeros((nvars, max_n_features))
+        with np.errstate(divide="ignore", invalid="ignore"):
+            # Feature 1
+            features[:, curr] = np.sign(obj_coeffs)
+            curr += 1
+
+            # Feature 2
+            if abs(pos_obj_coeffs_sum) > 0:
+                features[:, curr] = np.abs(obj_coeffs) / pos_obj_coeffs_sum
+                curr += 1
+
+            # Feature 3
+            if abs(neg_obj_coeffs_sum) > 0:
+                features[:, curr] = np.abs(obj_coeffs) / neg_obj_coeffs_sum
+                curr += 1
+
+            # Feature 37
+            if values is not None:
+                features[:, curr] = np.minimum(
+                    values - np.floor(values),
+                    np.ceil(values) - values,
+                )
+                curr += 1
+
+            # Feature 44
+            if obj_sa_up is not None:
+                features[:, curr] = np.sign(obj_sa_up)
+                curr += 1
+
+            # Feature 46
+            if obj_sa_down is not None:
+                features[:, curr] = np.sign(obj_sa_down)
+                curr += 1
+
+            # Feature 47
+            if obj_sa_down is not None:
+                features[:, curr] = np.log(
+                    obj_coeffs - obj_sa_down / np.sign(obj_coeffs)
+                )
+                curr += 1
+
+            # Feature 48
+            if obj_sa_up is not None:
+                features[:, curr] = np.log(obj_coeffs - obj_sa_up / np.sign(obj_coeffs))
+                curr += 1
+
+        features = features[:, 0:curr]
+        _fix_infinity(features)
+        return features
+
+
+def _fix_infinity(m: np.ndarray) -> None:
+    masked = np.ma.masked_invalid(m)
+    max_values = np.max(masked, axis=0)
+    min_values = np.min(masked, axis=0)
+    m[:] = np.maximum(np.minimum(m, max_values), min_values)
+    m[np.isnan(m)] = 0.0

miplearn/features/sample.py

@@ -0,0 +1,224 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+import warnings
+from abc import ABC, abstractmethod
+from copy import deepcopy
+from typing import Dict, Optional, Any, Union, List, Tuple, cast, Set
+from scipy.sparse import coo_matrix
+
+import h5py
+import numpy as np
+from h5py import Dataset
+from overrides import overrides
+
+Bytes = Union[bytes, bytearray]
+Scalar = Union[None, bool, str, int, float]
+Vector = Union[
+    None,
+    List[bool],
+    List[str],
+    List[int],
+    List[float],
+    List[Optional[str]],
+    np.ndarray,
+]
+VectorList = Union[
+    List[List[bool]],
+    List[List[str]],
+    List[List[int]],
+    List[List[float]],
+    List[Optional[List[bool]]],
+    List[Optional[List[str]]],
+    List[Optional[List[int]]],
+    List[Optional[List[float]]],
+]
+
+
+class Sample(ABC):
+    """Abstract dictionary-like class that stores training data."""
+
+    @abstractmethod
+    def get_scalar(self, key: str) -> Optional[Any]:
+        pass
+
+    @abstractmethod
+    def put_scalar(self, key: str, value: Scalar) -> None:
+        pass
+
+    @abstractmethod
+    def put_array(self, key: str, value: Optional[np.ndarray]) -> None:
+        pass
+
+    @abstractmethod
+    def get_array(self, key: str) -> Optional[np.ndarray]:
+        pass
+
+    @abstractmethod
+    def put_sparse(self, key: str, value: coo_matrix) -> None:
+        pass
+
+    @abstractmethod
+    def get_sparse(self, key: str) -> Optional[coo_matrix]:
+        pass
+
+    def _assert_is_scalar(self, value: Any) -> None:
+        if value is None:
+            return
+        if isinstance(value, (str, bool, int, float, bytes, np.bytes_)):
+            return
+        assert False, f"scalar expected; found instead: {value} ({value.__class__})"
+
+    def _assert_is_array(self, value: np.ndarray) -> None:
+        assert isinstance(
+            value, np.ndarray
+        ), f"np.ndarray expected; found instead: {value.__class__}"
+        assert value.dtype.kind in "biufS", f"Unsupported dtype: {value.dtype}"
+
+    def _assert_is_sparse(self, value: Any) -> None:
+        assert isinstance(value, coo_matrix)
+        self._assert_is_array(value.data)
+
+
+class MemorySample(Sample):
+    """Dictionary-like class that stores training data in-memory."""
+
+    def __init__(
+        self,
+        data: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        if data is None:
+            data = {}
+        self._data: Dict[str, Any] = data
+
+    @overrides
+    def get_scalar(self, key: str) -> Optional[Any]:
+        return self._get(key)
+
+    @overrides
+    def put_scalar(self, key: str, value: Scalar) -> None:
+        if value is None:
+            return
+        self._assert_is_scalar(value)
+        self._put(key, value)
+
+    def _get(self, key: str) -> Optional[Any]:
+        if key in self._data:
+            return self._data[key]
+        else:
+            return None
+
+    def _put(self, key: str, value: Any) -> None:
+        self._data[key] = value
+
+    @overrides
+    def put_array(self, key: str, value: Optional[np.ndarray]) -> None:
+        if value is None:
+            return
+        self._assert_is_array(value)
+        self._put(key, value)
+
+    @overrides
+    def get_array(self, key: str) -> Optional[np.ndarray]:
+        return cast(Optional[np.ndarray], self._get(key))
+
+    @overrides
+    def put_sparse(self, key: str, value: coo_matrix) -> None:
+        if value is None:
+            return
+        self._assert_is_sparse(value)
+        self._put(key, value)
+
+    @overrides
+    def get_sparse(self, key: str) -> Optional[coo_matrix]:
+        return cast(Optional[coo_matrix], self._get(key))
+
+
+class Hdf5Sample(Sample):
+    """
+    Dictionary-like class that stores training data in an HDF5 file.
+
+    Unlike MemorySample, this class only loads to memory the parts of the data set that
+    are actually accessed, and therefore it is more scalable.
+    """
+
+    def __init__(
+        self,
+        filename: str,
+        mode: str = "r+",
+    ) -> None:
+        self.file = h5py.File(filename, mode, libver="latest")
+
+    @overrides
+    def get_scalar(self, key: str) -> Optional[Any]:
+        if key not in self.file:
+            return None
+        ds = self.file[key]
+        assert (
+            len(ds.shape) == 0
+        ), f"0-dimensional array expected; found shape {ds.shape}"
+        if h5py.check_string_dtype(ds.dtype):
+            return ds.asstr()[()]
+        else:
+            return ds[()].tolist()
+
+    @overrides
+    def put_scalar(self, key: str, value: Any) -> None:
+        if value is None:
+            return
+        self._assert_is_scalar(value)
+        if key in self.file:
+            del self.file[key]
+        self.file.create_dataset(key, data=value)
+
+    @overrides
+    def put_array(self, key: str, value: Optional[np.ndarray]) -> None:
+        if value is None:
+            return
+        self._assert_is_array(value)
+        if len(value.shape) > 1 and value.dtype.kind == "f":
+            value = value.astype("float16")
+        if key in self.file:
+            del self.file[key]
+        return self.file.create_dataset(key, data=value, compression="gzip")
+
+    @overrides
+    def get_array(self, key: str) -> Optional[np.ndarray]:
+        if key not in self.file:
+            return None
+        return self.file[key][:]
+
+    @overrides
+    def put_sparse(self, key: str, value: coo_matrix) -> None:
+        if value is None:
+            return
+        self._assert_is_sparse(value)
+        self.put_array(f"{key}_row", value.row)
+        self.put_array(f"{key}_col", value.col)
+        self.put_array(f"{key}_data", value.data)
+
+    @overrides
+    def get_sparse(self, key: str) -> Optional[coo_matrix]:
+        row = self.get_array(f"{key}_row")
+        if row is None:
+            return None
+        col = self.get_array(f"{key}_col")
+        data = self.get_array(f"{key}_data")
+        assert col is not None
+        assert data is not None
+        return coo_matrix((data, (row, col)))
+
+    def get_bytes(self, key: str) -> Optional[Bytes]:
+        if key not in self.file:
+            return None
+        ds = self.file[key]
+        assert (
+            len(ds.shape) == 1
+        ), f"1-dimensional array expected; found shape {ds.shape}"
+        return ds[()].tobytes()
+
+    def put_bytes(self, key: str, value: Bytes) -> None:
+        assert isinstance(
+            value, (bytes, bytearray)
+        ), f"bytes expected; found: {value.__class__}"  # type: ignore
+        self.put_array(key, np.frombuffer(value, dtype="uint8"))

miplearn/instance.py

@@ -1,151 +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.
-
-import gzip
-import json
-from abc import ABC, abstractmethod
-
-import numpy as np
-
-
-class Instance(ABC):
-    """
-    Abstract class holding all the data necessary to generate a concrete model of the problem.
-    
-    In the knapsack problem, for example, this class could hold the number of items, their weights
-    and costs, as well as the size of the knapsack. Objects implementing this class are able to
-    convert themselves into a concrete optimization model, which can be optimized by a solver, or
-    into arrays of features, which can be provided as inputs to machine learning models.
-    """
-
-    @abstractmethod
-    def to_model(self):
-        """
-        Returns a concrete Pyomo model corresponding to this instance.
-        """
-        pass
-
-    def get_instance_features(self):
-        """
-        Returns a 1-dimensional Numpy array of (numerical) features describing the entire instance.
-        
-        The array is used by LearningSolver to determine how similar two instances are. It may also
-        be used to predict, in combination with variable-specific features, the values of binary
-        decision variables in the problem.
-        
-        There is not necessarily a one-to-one correspondence between models and instance features:
-        the features may encode only part of the data necessary to generate the complete model.
-        Features may also be statistics computed from the original data. For example, in the
-        knapsack problem, an implementation may decide to provide as instance features only
-        the average weights, average prices, number of items and the size of the knapsack.
-        
-        The returned array MUST have the same length for all relevant instances of the problem. If
-        two instances map into arrays of different lengths, they cannot be solved by the same
-        LearningSolver object.
-
-        By default, returns [0].
-        """
-        return np.zeros(1)
-
-    def get_variable_features(self, var, index):
-        """
-        Returns a 1-dimensional array of (numerical) features describing a particular decision
-        variable.
-        
-        The argument `var` is a pyomo.core.Var object, which represents a collection of decision
-        variables. The argument `index` specifies which variable in the collection is the relevant
-        one.
-        
-        In combination with instance features, variable features are used by LearningSolver to
-        predict, among other things, the optimal value of each decision variable before the
-        optimization takes place. In the knapsack problem, for example, an implementation could
-        provide as variable features the weight and the price of a specific item.
-        
-        Like instance features, the arrays returned by this method MUST have the same length for
-        all variables within the same category, for all relevant instances of the problem.
-
-        By default, returns [0].
-        """
-        return np.zeros(1)
-
-    def get_variable_category(self, var, index):
-        """
-        Returns the 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. If the returned category is None, ML
-        models will ignore the variable.
-
-        By default, returns "default".
-        """
-        return "default"
-
-    def get_constraint_features(self, cid):
-        return np.zeros(1)
-
-    def get_constraint_category(self, cid):
-        return cid
-
-    def has_static_lazy_constraints(self):
-        return False
-
-    def has_dynamic_lazy_constraints(self):
-        return False
-
-    def is_constraint_lazy(self, cid):
-        return False
-
-    def find_violated_lazy_constraints(self, model):
-        """
-        Returns lazy constraint violations found for the current solution.
-        
-        After solving a model, LearningSolver will ask the instance to identify which lazy
-        constraints are violated by the current solution. For each identified violation,
-        LearningSolver will then call the build_lazy_constraint, add the generated Pyomo
-        constraint to the model, then resolve the problem. The process repeats until no further
-        lazy constraint violations are found.
-        
-        Each "violation" is simply a string, a tuple or any other hashable type which allows the
-        instance to identify unambiguously which lazy constraint should be generated. In the
-        Traveling Salesman Problem, for example, a subtour violation could be a frozen set
-        containing the cities in the subtour.
-        
-        For a concrete example, see TravelingSalesmanInstance.
-        """
-        return []
-
-    def build_lazy_constraint(self, model, violation):
-        """
-        Returns a Pyomo constraint which fixes a given violation.
-        
-        This method is typically called immediately after find_violated_lazy_constraints. The violation object
-        provided to this method is exactly the same object returned earlier by find_violated_lazy_constraints.
-        After some training, LearningSolver may decide to proactively build some lazy constraints
-        at the beginning of the optimization process, before a solution is even available. In this
-        case, build_lazy_constraints will be called without a corresponding call to
-        find_violated_lazy_constraints.
-        
-        The implementation should not directly add the constraint to the model. The constraint
-        will be added by LearningSolver after the method returns.
-        
-        For a concrete example, see TravelingSalesmanInstance.
-        """
-        pass
-
-    def find_violated_user_cuts(self, model):
-        return []
-
-    def build_user_cut(self, model, violation):
-        pass
-
-    def load(self, filename):
-        with gzip.GzipFile(filename, 'r') as f:
-            data = json.loads(f.read().decode('utf-8'))
-        self.__dict__ = data
-
-    def dump(self, filename):
-        data = json.dumps(self.__dict__, indent=2).encode('utf-8')
-        with gzip.GzipFile(filename, 'w') as f:
-            f.write(data)

miplearn/instance/__init__.py

@@ -1,3 +1,3 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.

miplearn/instance/base.py

@@ -0,0 +1,198 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+from abc import ABC, abstractmethod
+from typing import Any, List, TYPE_CHECKING, Dict
+
+import numpy as np
+
+from miplearn.features.sample import Sample, MemorySample
+from miplearn.types import ConstraintName, ConstraintCategory
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import InternalSolver
+
+
+# noinspection PyMethodMayBeStatic
+class Instance(ABC):
+    """
+    Abstract class holding all the data necessary to generate a concrete model of the
+    proble.
+
+    In the knapsack problem, for example, this class could hold the number of items,
+    their weights and costs, as well as the size of the knapsack. Objects
+    implementing this class are able to convert themselves into a concrete
+    optimization model, which can be optimized by a solver, or into arrays of
+    features, which can be provided as inputs to machine learning models.
+    """
+
+    def __init__(self) -> None:
+        self._samples: List[Sample] = []
+
+    @abstractmethod
+    def to_model(self) -> Any:
+        """
+        Returns the optimization model corresponding to this instance.
+        """
+        pass
+
+    def get_instance_features(self) -> np.ndarray:
+        """
+        Returns a 1-dimensional array of (numerical) features describing the
+        entire instance.
+
+        The array is used by LearningSolver to determine how similar two instances
+        are. It may also be used to predict, in combination with variable-specific
+        features, the values of binary decision variables in the problem.
+
+        There is not necessarily a one-to-one correspondence between models and
+        instance features: the features may encode only part of the data necessary to
+        generate the complete model. Features may also be statistics computed from
+        the original data. For example, in the knapsack problem, an implementation
+        may decide to provide as instance features only the average weights, average
+        prices, number of items and the size of the knapsack.
+
+        The returned array MUST have the same length for all relevant instances of
+        the problem. If two instances map into arrays of different lengths,
+        they cannot be solved by the same LearningSolver object.
+
+        By default, returns [0.0].
+        """
+        return np.zeros(1)
+
+    def get_variable_features(self, names: np.ndarray) -> np.ndarray:
+        """
+        Returns dictionary mapping the name of each variable to a (1-dimensional) list
+        of numerical features describing a particular decision variable.
+
+        In combination with instance features, variable features are used by
+        LearningSolver to predict, among other things, the optimal value of each
+        decision variable before the optimization takes place. In the knapsack
+        problem, for example, an implementation could provide as variable features
+        the weight and the price of a specific item.
+
+        Like instance features, the arrays returned by this method MUST have the same
+        length for all variables within the same category, for all relevant instances
+        of the problem.
+
+        If features are not provided for a given variable, MIPLearn will use a
+        default set of features.
+
+        By default, returns [[0.0], ..., [0.0]].
+        """
+        return np.zeros((len(names), 1))
+
+    def get_variable_categories(self, names: np.ndarray) -> np.ndarray:
+        """
+        Returns a dictionary mapping the name of each variable to its category.
+
+        If two variables have the same category, LearningSolver will use the same
+        internal ML model to predict the values of both variables. If a variable is not
+        listed in the dictionary, ML models will ignore the variable.
+
+        By default, returns `names`.
+        """
+        return names
+
+    def get_constraint_features(self, names: np.ndarray) -> np.ndarray:
+        return np.zeros((len(names), 1))
+
+    def get_constraint_categories(self, names: np.ndarray) -> np.ndarray:
+        return names
+
+    def has_dynamic_lazy_constraints(self) -> bool:
+        return False
+
+    def are_constraints_lazy(self, names: np.ndarray) -> np.ndarray:
+        return np.zeros(len(names), dtype=bool)
+
+    def find_violated_lazy_constraints(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+    ) -> List[ConstraintName]:
+        """
+        Returns lazy constraint violations found for the current solution.
+
+        After solving a model, LearningSolver will ask the instance to identify which
+        lazy constraints are violated by the current solution. For each identified
+        violation, LearningSolver will then call the enforce_lazy_constraint and
+        resolve the problem. The process repeats until no further lazy constraint
+        violations are found.
+
+        Each "violation" is simply a string which allows the instance to identify
+        unambiguously which lazy constraint should be generated. In the Traveling
+        Salesman Problem, for example, a subtour violation could be a string
+        containing the cities in the subtour.
+
+        The current solution can be queried with `solver.get_solution()`. If the solver
+        is configured to use lazy callbacks, this solution may be non-integer.
+
+        For a concrete example, see TravelingSalesmanInstance.
+        """
+        return []
+
+    def enforce_lazy_constraint(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+        violation: ConstraintName,
+    ) -> None:
+        """
+        Adds constraints to the model to ensure that the given violation is fixed.
+
+        This method is typically called immediately after
+        find_violated_lazy_constraints. The violation object provided to this method
+        is exactly the same object returned earlier by
+        find_violated_lazy_constraints. After some training, LearningSolver may
+        decide to proactively build some lazy constraints at the beginning of the
+        optimization process, before a solution is even available. In this case,
+        enforce_lazy_constraints will be called without a corresponding call to
+        find_violated_lazy_constraints.
+
+        Note that this method can be called either before the optimization starts or
+        from within a callback. To ensure that constraints are added correctly in
+        either case, it is recommended to use `solver.add_constraint`, instead of
+        modifying the `model` object directly.
+
+        For a concrete example, see TravelingSalesmanInstance.
+        """
+        pass
+
+    def has_user_cuts(self) -> bool:
+        return False
+
+    def find_violated_user_cuts(self, model: Any) -> List[ConstraintName]:
+        return []
+
+    def enforce_user_cut(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+        violation: ConstraintName,
+    ) -> Any:
+        return None
+
+    def load(self) -> None:
+        pass
+
+    def free(self) -> None:
+        pass
+
+    def flush(self) -> None:
+        """
+        Save any pending changes made to the instance to the underlying data store.
+        """
+        pass
+
+    def get_samples(self) -> List[Sample]:
+        return self._samples
+
+    def create_sample(self) -> Sample:
+        sample = MemorySample()
+        self._samples.append(sample)
+        return sample

miplearn/instance/file.py

@@ -0,0 +1,131 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+import gc
+import os
+from typing import Any, Optional, List, Dict, TYPE_CHECKING
+import pickle
+
+import numpy as np
+from overrides import overrides
+
+from miplearn.features.sample import Hdf5Sample, Sample
+from miplearn.instance.base import Instance
+from miplearn.types import ConstraintName, ConstraintCategory
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import InternalSolver
+
+
+class FileInstance(Instance):
+    def __init__(self, filename: str) -> None:
+        super().__init__()
+        assert os.path.exists(filename), f"File not found: {filename}"
+        self.h5 = Hdf5Sample(filename)
+        self.instance: Optional[Instance] = None
+
+    # Delegation
+    # -------------------------------------------------------------------------
+    @overrides
+    def to_model(self) -> Any:
+        assert self.instance is not None
+        return self.instance.to_model()
+
+    @overrides
+    def get_instance_features(self) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_instance_features()
+
+    @overrides
+    def get_variable_features(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_variable_features(names)
+
+    @overrides
+    def get_variable_categories(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_variable_categories(names)
+
+    @overrides
+    def get_constraint_features(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_constraint_features(names)
+
+    @overrides
+    def get_constraint_categories(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_constraint_categories(names)
+
+    @overrides
+    def has_dynamic_lazy_constraints(self) -> bool:
+        assert self.instance is not None
+        return self.instance.has_dynamic_lazy_constraints()
+
+    @overrides
+    def are_constraints_lazy(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.are_constraints_lazy(names)
+
+    @overrides
+    def find_violated_lazy_constraints(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+    ) -> List[ConstraintName]:
+        assert self.instance is not None
+        return self.instance.find_violated_lazy_constraints(solver, model)
+
+    @overrides
+    def enforce_lazy_constraint(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+        violation: ConstraintName,
+    ) -> None:
+        assert self.instance is not None
+        self.instance.enforce_lazy_constraint(solver, model, violation)
+
+    @overrides
+    def find_violated_user_cuts(self, model: Any) -> List[ConstraintName]:
+        assert self.instance is not None
+        return self.instance.find_violated_user_cuts(model)
+
+    @overrides
+    def enforce_user_cut(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+        violation: ConstraintName,
+    ) -> None:
+        assert self.instance is not None
+        self.instance.enforce_user_cut(solver, model, violation)
+
+    # Input & Output
+    # -------------------------------------------------------------------------
+    @overrides
+    def free(self) -> None:
+        self.instance = None
+        gc.collect()
+
+    @overrides
+    def load(self) -> None:
+        if self.instance is not None:
+            return
+        pkl = self.h5.get_bytes("pickled")
+        assert pkl is not None
+        self.instance = pickle.loads(pkl)
+        assert isinstance(self.instance, Instance)
+
+    @classmethod
+    def save(cls, instance: Instance, filename: str) -> None:
+        h5 = Hdf5Sample(filename, mode="w")
+        instance_pkl = pickle.dumps(instance)
+        h5.put_bytes("pickled", instance_pkl)
+
+    @overrides
+    def create_sample(self) -> Sample:
+        return self.h5
+
+    @overrides
+    def get_samples(self) -> List[Sample]:
+        return [self.h5]

miplearn/instance/picklegz.py

@@ -0,0 +1,155 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import gc
+import gzip
+import os
+import pickle
+from typing import Optional, Any, List, cast, IO, TYPE_CHECKING, Dict
+
+import numpy as np
+from overrides import overrides
+
+from miplearn.features.sample import Sample
+from miplearn.instance.base import Instance
+from miplearn.types import ConstraintName, ConstraintCategory
+
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import InternalSolver
+
+
+class PickleGzInstance(Instance):
+    """
+    An instance backed by a gzipped pickle file.
+
+    The instance is only loaded to memory after an operation is called (for example,
+    `to_model`).
+
+    Parameters
+    ----------
+    filename: str
+        Path of the gzipped pickle file that should be loaded.
+    """
+
+    # noinspection PyMissingConstructor
+    def __init__(self, filename: str) -> None:
+        assert os.path.exists(filename), f"File not found: {filename}"
+        self.instance: Optional[Instance] = None
+        self.filename: str = filename
+
+    @overrides
+    def to_model(self) -> Any:
+        assert self.instance is not None
+        return self.instance.to_model()
+
+    @overrides
+    def get_instance_features(self) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_instance_features()
+
+    @overrides
+    def get_variable_features(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_variable_features(names)
+
+    @overrides
+    def get_variable_categories(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_variable_categories(names)
+
+    @overrides
+    def get_constraint_features(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_constraint_features(names)
+
+    @overrides
+    def get_constraint_categories(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.get_constraint_categories(names)
+
+    @overrides
+    def has_dynamic_lazy_constraints(self) -> bool:
+        assert self.instance is not None
+        return self.instance.has_dynamic_lazy_constraints()
+
+    @overrides
+    def are_constraints_lazy(self, names: np.ndarray) -> np.ndarray:
+        assert self.instance is not None
+        return self.instance.are_constraints_lazy(names)
+
+    @overrides
+    def find_violated_lazy_constraints(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+    ) -> List[ConstraintName]:
+        assert self.instance is not None
+        return self.instance.find_violated_lazy_constraints(solver, model)
+
+    @overrides
+    def enforce_lazy_constraint(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+        violation: ConstraintName,
+    ) -> None:
+        assert self.instance is not None
+        self.instance.enforce_lazy_constraint(solver, model, violation)
+
+    @overrides
+    def find_violated_user_cuts(self, model: Any) -> List[ConstraintName]:
+        assert self.instance is not None
+        return self.instance.find_violated_user_cuts(model)
+
+    @overrides
+    def enforce_user_cut(
+        self,
+        solver: "InternalSolver",
+        model: Any,
+        violation: ConstraintName,
+    ) -> None:
+        assert self.instance is not None
+        self.instance.enforce_user_cut(solver, model, violation)
+
+    @overrides
+    def load(self) -> None:
+        if self.instance is None:
+            obj = read_pickle_gz(self.filename)
+            assert isinstance(obj, Instance)
+            self.instance = obj
+
+    @overrides
+    def free(self) -> None:
+        self.instance = None  # type: ignore
+        gc.collect()
+
+    @overrides
+    def flush(self) -> None:
+        write_pickle_gz(self.instance, self.filename)
+
+    @overrides
+    def get_samples(self) -> List[Sample]:
+        assert self.instance is not None
+        return self.instance.get_samples()
+
+    @overrides
+    def create_sample(self) -> Sample:
+        assert self.instance is not None
+        return self.instance.create_sample()
+
+
+def write_pickle_gz(obj: Any, filename: str) -> None:
+    os.makedirs(os.path.dirname(filename), exist_ok=True)
+    with gzip.GzipFile(filename, "wb") as file:
+        pickle.dump(obj, cast(IO[bytes], file))
+
+
+def read_pickle_gz(filename: str) -> Any:
+    with gzip.GzipFile(filename, "rb") as file:
+        return pickle.load(cast(IO[bytes], file))
+
+
+def write_pickle_gz_multiple(objs: List[Any], dirname: str) -> None:
+    for (i, obj) in enumerate(objs):
+        write_pickle_gz(obj, f"{dirname}/{i:05d}.pkl.gz")

miplearn/log.py

@@ -1,39 +1,61 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from datetime import timedelta
 import logging
-import time
 import sys
+import time
+import traceback
+import warnings
+from typing import Dict, Any, Optional
 
-class TimeFormatter():
-    def __init__(self, start_time, log_colors):
+_formatwarning = warnings.formatwarning
+
+
+class TimeFormatter(logging.Formatter):
+    def __init__(
+        self,
+        start_time: float,
+        log_colors: Dict[str, str],
+    ) -> None:
+        super().__init__()
         self.start_time = start_time
         self.log_colors = log_colors
 
-    def format(self, record):
+    def format(self, record: logging.LogRecord) -> str:
         if record.levelno >= logging.ERROR:
             color = self.log_colors["red"]
         elif record.levelno >= logging.WARNING:
             color = self.log_colors["yellow"]
         else:
             color = self.log_colors["green"]
-        return "%s[%12.3f]%s %s" % (color,
-                                    record.created - self.start_time,
-                                    self.log_colors["reset"],
-                                    record.getMessage())
+        return "%s[%12.3f]%s %s" % (
+            color,
+            record.created - self.start_time,
+            self.log_colors["reset"],
+            record.getMessage(),
+        )
 
-def setup_logger(start_time=None,
-                 force_color=False):
+
+def formatwarning_tb(*args: Any, **kwargs: Any) -> str:
+    s = _formatwarning(*args, **kwargs)
+    tb = traceback.format_stack()
+    s += "".join(tb[:-1])
+    return s
+
+
+def setup_logger(
+    start_time: Optional[float] = None,
+    force_color: bool = False,
+) -> None:
     if start_time is None:
         start_time = time.time()
     if sys.stdout.isatty() or force_color:
         log_colors = {
-            "green":  '\033[92m',
-            "yellow": '\033[93m',
-            "red":    '\033[91m',
-            "reset":  '\033[0m',
+            "green": "\033[92m",
+            "yellow": "\033[93m",
+            "red": "\033[91m",
+            "reset": "\033[0m",
         }
     else:
         log_colors = {
@@ -41,9 +63,10 @@ def setup_logger(start_time=None,
             "yellow": "",
             "red": "",
             "reset": "",
-        }        
+        }
     handler = logging.StreamHandler()
     handler.setFormatter(TimeFormatter(start_time, log_colors))
     logging.getLogger().addHandler(handler)
     logging.getLogger("miplearn").setLevel(logging.INFO)
-    lg = logging.getLogger("miplearn")
+    warnings.formatwarning = formatwarning_tb
+    logging.captureWarnings(True)

miplearn/problems/__init__.py

@@ -1,3 +1,3 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.

miplearn/problems/knapsack.py

@@ -1,14 +1,17 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-import miplearn
-from miplearn import Instance
+from typing import List, Dict, Optional
+
 import numpy as np
 import pyomo.environ as pe
-from scipy.stats import uniform, randint, bernoulli
+from overrides import overrides
+from scipy.stats import uniform, randint, rv_discrete
 from scipy.stats.distributions import rv_frozen
 
+from miplearn.instance.base import Instance
+
 
 class ChallengeA:
     """
@@ -17,44 +20,51 @@ class ChallengeA:
     - K = 500, u ~ U(0., 1.)
     - alpha = 0.25
     """
-    def __init__(self,
-                 seed=42,
-                 n_training_instances=500,
-                 n_test_instances=50):
 
+    def __init__(
+        self,
+        seed: int = 42,
+        n_training_instances: int = 500,
+        n_test_instances: int = 50,
+    ) -> None:
         np.random.seed(seed)
-        self.gen = 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),
-                                         )
+        self.gen = 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),
+        )
         np.random.seed(seed + 1)
         self.training_instances = self.gen.generate(n_training_instances)
-        
+
         np.random.seed(seed + 2)
         self.test_instances = self.gen.generate(n_test_instances)
 
 
 class MultiKnapsackInstance(Instance):
     """Representation of the Multidimensional 0-1 Knapsack Problem.
-    
-    Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
-    sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
-    each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].
-    
-    This implementation assigns a different category for each decision variable, and therefore
-    trains one ML model per variable. It is only suitable when training and test instances have
-    same size and items don't shuffle around.
+
+    Given a set of n items and m knapsacks, the problem is to find a subset of items
+    S maximizing sum(prices[i] for i in S). If selected, each item i occupies
+    weights[i,j] units of space in each knapsack j. Furthermore, each knapsack j has
+    limited storage space, given by capacities[j].
+
+    This implementation assigns a different category for each decision variable,
+    and therefore trains one ML model per variable. It is only suitable when training
+    and test instances have same size and items don't shuffle around.
     """
-    
-    def __init__(self,
-                 prices,
-                 capacities,
-                 weights):
+
+    def __init__(
+        self,
+        prices: np.ndarray,
+        capacities: np.ndarray,
+        weights: np.ndarray,
+    ) -> None:
+        super().__init__()
         assert isinstance(prices, np.ndarray)
         assert isinstance(capacities, np.ndarray)
         assert isinstance(weights, np.ndarray)
@@ -65,83 +75,90 @@ class MultiKnapsackInstance(Instance):
         self.prices = prices
         self.capacities = capacities
         self.weights = weights
-        
-    def to_model(self):
+
+    @overrides
+    def to_model(self) -> pe.ConcreteModel:
         model = pe.ConcreteModel()
         model.x = pe.Var(range(self.n), domain=pe.Binary)
-        model.OBJ = pe.Objective(rule=lambda model: sum(model.x[j] * self.prices[j]
-                                                        for j in range(self.n)),
-                                 sense=pe.maximize)
+        model.OBJ = pe.Objective(
+            expr=sum(model.x[j] * self.prices[j] for j in range(self.n)),
+            sense=pe.maximize,
+        )
         model.eq_capacity = pe.ConstraintList()
         for i in range(self.m):
-            model.eq_capacity.add(sum(model.x[j] * self.weights[i,j]
-                                      for j in range(self.n)) <= self.capacities[i])
-        
+            model.eq_capacity.add(
+                sum(model.x[j] * self.weights[i, j] for j in range(self.n))
+                <= self.capacities[i]
+            )
+
         return model
-        
-    def get_instance_features(self):
-        return np.hstack([
-            np.mean(self.prices),
-            self.capacities,
-        ])
 
-    def get_variable_features(self, var, index):
-        return np.hstack([
-            self.prices[index],
-            self.weights[:, index],
-        ])
+    @overrides
+    def get_instance_features(self) -> np.ndarray:
+        return np.array([float(np.mean(self.prices))] + list(self.capacities))
 
-#     def get_variable_category(self, var, index):
-#         return index
+    @overrides
+    def get_variable_features(self, names: np.ndarray) -> np.ndarray:
+        features = []
+        for i in range(len(self.weights)):
+            f = [self.prices[i]]
+            f.extend(self.weights[:, i])
+            features.append(f)
+        return np.array(features)
 
 
+# noinspection PyPep8Naming
 class MultiKnapsackGenerator:
-    def __init__(self,
-                 n=randint(low=100, high=101),
-                 m=randint(low=30, high=31),
-                 w=randint(low=0, high=1000),
-                 K=randint(low=500, high=500),
-                 u=uniform(loc=0.0, scale=1.0),
-                 alpha=uniform(loc=0.25, scale=0.0),
-                 fix_w=False,
-                 w_jitter=uniform(loc=1.0, scale=0.0),
-                 round=True,
-                ):
+    def __init__(
+        self,
+        n: rv_frozen = randint(low=100, high=101),
+        m: rv_frozen = randint(low=30, high=31),
+        w: rv_frozen = randint(low=0, high=1000),
+        K: rv_frozen = randint(low=500, high=501),
+        u: rv_frozen = uniform(loc=0.0, scale=1.0),
+        alpha: rv_frozen = uniform(loc=0.25, scale=0.0),
+        fix_w: bool = False,
+        w_jitter: rv_frozen = uniform(loc=1.0, scale=0.0),
+        round: bool = True,
+    ):
         """Initialize the problem generator.
-        
-        Instances have a random number of items (or variables) and a random number of knapsacks
-        (or constraints), as specified by the provided probability distributions `n` and `m`,
-        respectively. The weight of each item `i` on knapsack `j` is sampled independently from
-        the provided distribution `w`. The capacity of knapsack `j` is set to:
-        
-            alpha_j * sum(w[i,j] for i in range(n)),
-            
-        where `alpha_j`, 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 weight of each
-        item `i` is set to:
-        
-            sum(w[i,j]/m for j in range(m)) + K * u_i,
-            
-        where `K`, the correlation coefficient, and `u_i`, the correlation multiplier, are sampled
-        from the provided probability distributions. Note that `K` is only sample once for the
-        entire instance.
-        
-        If fix_w=True is provided, then w[i,j] 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[i,j], 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[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
-        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.
-        
+        Instances have a random number of items (or variables) and a random number of
+        knapsacks (or constraints), as specified by the provided probability
+        distributions `n` and `m`, respectively. The weight of each item `i` on
+        knapsack `j` is sampled independently from the provided distribution `w`. The
+        capacity of knapsack `j` is set to:
+
+            alpha_j * sum(w[i,j] for i in range(n)),
+
+        where `alpha_j`, 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 weight of each item `i` is set to:
+
+            sum(w[i,j]/m for j in range(m)) + K * u_i,
+
+        where `K`, the correlation coefficient, and `u_i`, the correlation
+        multiplier, are sampled from the provided probability distributions. Note
+        that `K` is only sample once for the entire instance.
+
+        If fix_w=True is provided, then w[i,j] 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[i,j], 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[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 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.
+
         Parameters
         ----------
         n: rv_discrete
@@ -157,47 +174,55 @@ class MultiKnapsackGenerator:
         alpha: rv_continuous
             Probability distribution for the tightness ratio
         fix_w: boolean
-            If true, weights are kept the same (minus the noise from w_jitter) in all instances
+            If true, weights are kept the same (minus the noise from w_jitter) in all
+            instances
         w_jitter: rv_continuous
             Probability distribution for random noise added to the weights
         round: boolean
-            If true, all prices, weights and capacities are rounded to the nearest integer
+            If true, all prices, weights and capacities are rounded to the nearest
+            integer
         """
         assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
         assert isinstance(m, rv_frozen), "m should be a SciPy probability distribution"
         assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
         assert isinstance(K, rv_frozen), "K should be a SciPy probability distribution"
         assert isinstance(u, rv_frozen), "u should be a SciPy probability distribution"
-        assert isinstance(alpha, rv_frozen), "alpha should be a SciPy probability distribution"
+        assert isinstance(
+            alpha, rv_frozen
+        ), "alpha should be a SciPy probability distribution"
         assert isinstance(fix_w, bool), "fix_w should be boolean"
-        assert isinstance(w_jitter, rv_frozen), \
-                "w_jitter should be a SciPy probability distribution"
-        
+        assert isinstance(
+            w_jitter, rv_frozen
+        ), "w_jitter should be a SciPy probability distribution"
+
         self.n = n
         self.m = m
         self.w = w
-        self.K = K
         self.u = u
+        self.K = K
         self.alpha = alpha
         self.w_jitter = w_jitter
         self.round = round
-        
+        self.fix_n: Optional[int] = None
+        self.fix_m: Optional[int] = None
+        self.fix_w: Optional[np.ndarray] = None
+        self.fix_u: Optional[np.ndarray] = None
+        self.fix_K: Optional[float] = None
+
         if fix_w:
             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():
+
+    def generate(self, n_samples: int) -> List[MultiKnapsackInstance]:
+        def _sample() -> MultiKnapsackInstance:
             if self.fix_w is not None:
+                assert self.fix_m is not None
+                assert self.fix_n is not None
+                assert self.fix_u is not None
+                assert self.fix_K is not None
                 n = self.fix_n
                 m = self.fix_m
                 w = self.fix_w
@@ -211,66 +236,12 @@ class MultiKnapsackGenerator:
                 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)])
+            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)])
             if self.round:
                 p = p.round()
                 b = b.round()
                 w = w.round()
             return MultiKnapsackInstance(p, b, w)
+
         return [_sample() for _ in range(n_samples)]
-    
-
-class KnapsackInstance(Instance):
-    """
-    Simpler (one-dimensional) Knapsack Problem, used for testing.
-    """
-    def __init__(self, weights, prices, capacity):
-        self.weights = weights
-        self.prices = prices
-        self.capacity = capacity
-
-    def to_model(self):
-        model = pe.ConcreteModel()
-        items = range(len(self.weights))
-        model.x = pe.Var(items, domain=pe.Binary)
-        model.OBJ = pe.Objective(expr=sum(model.x[v] * self.prices[v] for v in items),
-                                 sense=pe.maximize)
-        model.eq_capacity = pe.Constraint(expr=sum(model.x[v] * self.weights[v]
-                                                   for v in items) <= self.capacity)
-        return model
-
-    def get_instance_features(self):
-        return np.array([
-            self.capacity,
-            np.average(self.weights),
-        ])
-
-    def get_variable_features(self, var, index):
-        return np.array([
-            self.weights[index],
-            self.prices[index],
-        ])
-
-
-class GurobiKnapsackInstance(KnapsackInstance):
-    """
-    Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
-    instead of Pyomo, used for testing.
-    """
-    def __init__(self, weights, prices, capacity):
-        super().__init__(weights, prices, capacity)
-
-    def to_model(self):
-        import gurobipy as gp
-        from gurobipy import GRB
-
-        model = gp.Model("Knapsack")
-        n = len(self.weights)
-        x = model.addVars(n, vtype=GRB.BINARY, name="x")
-        model.addConstr(gp.quicksum(x[i] * self.weights[i]
-                                    for i in range(n)) <= self.capacity,
-                        "eq_capacity")
-        model.setObjective(gp.quicksum(x[i] * self.prices[i]
-                                       for i in range(n)), GRB.MAXIMIZE)
-        return model

miplearn/problems/stab.py

@@ -1,55 +1,117 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+from typing import List, Dict
 
+import networkx as nx
 import numpy as np
 import pyomo.environ as pe
-import networkx as nx
-from miplearn import Instance
-import random
-from scipy.stats import uniform, randint, bernoulli
+from networkx import Graph
+from overrides import overrides
+from scipy.stats import uniform, randint
 from scipy.stats.distributions import rv_frozen
 
+from miplearn.instance.base import Instance
+
 
 class ChallengeA:
-    def __init__(self,
-                 seed=42,
-                 n_training_instances=500,
-                 n_test_instances=50,
-                ):
-        
+    def __init__(
+        self,
+        seed: int = 42,
+        n_training_instances: int = 500,
+        n_test_instances: int = 50,
+    ) -> None:
         np.random.seed(seed)
-        self.generator = MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.),
-                                                     n=randint(low=200, high=201),
-                                                     p=uniform(loc=0.05, scale=0.0),
-                                                     fix_graph=True)
-        
+        self.generator = MaxWeightStableSetGenerator(
+            w=uniform(loc=100.0, scale=50.0),
+            n=randint(low=200, high=201),
+            p=uniform(loc=0.05, scale=0.0),
+            fix_graph=True,
+        )
+
         np.random.seed(seed + 1)
         self.training_instances = self.generator.generate(n_training_instances)
-        
+
         np.random.seed(seed + 2)
         self.test_instances = self.generator.generate(n_test_instances)
 
 
+class MaxWeightStableSetInstance(Instance):
+    """An instance of the Maximum-Weight Stable Set Problem.
+
+    Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
+    set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
+    a subset of vertices, no two of which are adjacent.
+
+    This is one of Karp's 21 NP-complete problems.
+    """
+
+    def __init__(self, graph: Graph, weights: np.ndarray) -> None:
+        super().__init__()
+        self.graph = graph
+        self.weights = weights
+        self.nodes = list(self.graph.nodes)
+
+    @overrides
+    def to_model(self) -> pe.ConcreteModel:
+        model = pe.ConcreteModel()
+        model.x = pe.Var(self.nodes, domain=pe.Binary)
+        model.OBJ = pe.Objective(
+            expr=sum(model.x[v] * self.weights[v] for v in self.nodes),
+            sense=pe.maximize,
+        )
+        model.clique_eqs = pe.ConstraintList()
+        for clique in nx.find_cliques(self.graph):
+            model.clique_eqs.add(sum(model.x[v] for v in clique) <= 1)
+        return model
+
+    @overrides
+    def get_variable_features(self, names: np.ndarray) -> np.ndarray:
+        features = []
+        assert len(names) == len(self.nodes)
+        for i, v1 in enumerate(self.nodes):
+            assert names[i] == f"x[{v1}]".encode()
+            neighbor_weights = [0.0] * 15
+            neighbor_degrees = [100.0] * 15
+            for v2 in self.graph.neighbors(v1):
+                neighbor_weights += [self.weights[v2] / self.weights[v1]]
+                neighbor_degrees += [self.graph.degree(v2) / self.graph.degree(v1)]
+            neighbor_weights.sort(reverse=True)
+            neighbor_degrees.sort()
+            f = []
+            f += neighbor_weights[:5]
+            f += neighbor_degrees[:5]
+            f += [self.graph.degree(v1)]
+            features.append(f)
+        return np.array(features)
+
+    @overrides
+    def get_variable_categories(self, names: np.ndarray) -> np.ndarray:
+        return np.array(["default" for _ in names], dtype="S")
+
+
 class MaxWeightStableSetGenerator:
     """Random instance generator for the Maximum-Weight Stable Set Problem.
-    
-    The generator has two modes of operation. When `fix_graph=True` is provided, one random
-    Erdős-Rényi graph $G_{n,p}$ is generated in 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; the remaining
-    parameters are sampled in the same way.
+
+    The generator has two modes of operation. When `fix_graph=True` is provided,
+    one random Erdős-Rényi graph $G_{n,p}$ is generated in 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; the
+    remaining parameters are sampled in the same way.
     """
-    
-    def __init__(self,
-                 w=uniform(loc=10.0, scale=1.0),
-                 n=randint(low=250, high=251),
-                 p=uniform(loc=0.05, scale=0.0),
-                 fix_graph=True):
+
+    def __init__(
+        self,
+        w: rv_frozen = uniform(loc=10.0, scale=1.0),
+        n: rv_frozen = randint(low=250, high=251),
+        p: rv_frozen = uniform(loc=0.05, scale=0.0),
+        fix_graph: bool = True,
+    ):
         """Initialize the problem generator.
-        
+
         Parameters
         ----------
         w: rv_continuous
@@ -69,62 +131,17 @@ class MaxWeightStableSetGenerator:
         self.graph = None
         if fix_graph:
             self.graph = self._generate_graph()
-    
-    def generate(self, n_samples):
-        def _sample():
+
+    def generate(self, n_samples: int) -> List[MaxWeightStableSetInstance]:
+        def _sample() -> MaxWeightStableSetInstance:
             if self.graph is not None:
                 graph = self.graph
             else:
                 graph = self._generate_graph()
             weights = self.w.rvs(graph.number_of_nodes())
             return MaxWeightStableSetInstance(graph, weights)
+
         return [_sample() for _ in range(n_samples)]
-    
-    def _generate_graph(self):
+
+    def _generate_graph(self) -> Graph:
         return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())
-
-
-class MaxWeightStableSetInstance(Instance):
-    """An instance of the Maximum-Weight Stable Set Problem.
-    
-    Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
-    set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
-    a subset of vertices, no two of which are adjacent.
-    
-    This is one of Karp's 21 NP-complete problems.
-    """
-    
-    def __init__(self, graph, weights):
-        self.graph = graph
-        self.weights = weights
-
-    def to_model(self):
-        nodes = list(self.graph.nodes)
-        model = pe.ConcreteModel()
-        model.x = pe.Var(nodes, domain=pe.Binary)
-        model.OBJ = pe.Objective(expr=sum(model.x[v] * self.weights[v] for v in nodes),
-                                 sense=pe.maximize)
-        model.clique_eqs = pe.ConstraintList()
-        for clique in nx.find_cliques(self.graph):
-            model.clique_eqs.add(sum(model.x[i] for i in clique) <= 1)
-        return model
-    
-    def get_instance_features(self):
-        return np.ones(0)
-
-    def get_variable_features(self, var, index):
-        neighbor_weights = [0] * 15
-        neighbor_degrees = [100] * 15
-        for n in self.graph.neighbors(index):
-            neighbor_weights += [self.weights[n] / self.weights[index]]
-            neighbor_degrees += [self.graph.degree(n) / self.graph.degree(index)]
-        neighbor_weights.sort(reverse=True)
-        neighbor_degrees.sort()
-        features = []
-        features += neighbor_weights[:5]
-        features += neighbor_degrees[:5]
-        features += [self.graph.degree(index)]
-        return np.array(features)
-
-    def get_variable_category(self, var, index):
-        return "default"

miplearn/problems/tests/test_knapsack.py

@@ -1,25 +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 LearningSolver
-from miplearn.problems.knapsack import MultiKnapsackGenerator, MultiKnapsackInstance
-from scipy.stats import uniform, randint
-import numpy as np
-
-
-def test_knapsack_generator():
-    gen = MultiKnapsackGenerator(n=randint(low=100, high=101),
-                                 m=randint(low=30, high=31),
-                                 w=randint(low=0, high=1000),
-                                 K=randint(low=500, high=501),
-                                 u=uniform(loc=1.0, scale=1.0),
-                                 alpha=uniform(loc=0.50, scale=0.0),
-                                )
-    instances = gen.generate(100)
-    w_sum = sum(instance.weights for instance in instances) / len(instances)
-    p_sum = sum(instance.prices for instance in instances) / len(instances)
-    b_sum = sum(instance.capacities for instance in instances) / len(instances)
-    assert round(np.mean(w_sum), -1) == 500.
-    # assert round(np.mean(p_sum), -1) == 1200.  # flaky
-    assert round(np.mean(b_sum), -3) == 25000.

miplearn/problems/tests/test_tsp.py

@@ -1,74 +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 LearningSolver
-from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
-import numpy as np
-from numpy.linalg import norm
-from scipy.spatial.distance import pdist, squareform
-from scipy.stats import uniform, randint
-
-
-def test_generator():
-    instances = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
-                                           y=uniform(loc=0.0, scale=1000.0),
-                                           n=randint(low=100, high=101),
-                                           gamma=uniform(loc=0.95, scale=0.1),
-                                           fix_cities=True).generate(100)
-    assert len(instances) == 100
-    assert instances[0].n_cities == 100
-    assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
-    d = [instance.distances[0, 1] for instance in instances]
-    assert np.std(d) > 0
-
-
-def test_instance():
-    n_cities = 4
-    distances = np.array([
-        [0., 1., 2., 1.],
-        [1., 0., 1., 2.],
-        [2., 1., 0., 1.],
-        [1., 2., 1., 0.],
-    ])
-    instance = TravelingSalesmanInstance(n_cities, distances)
-    for solver_name in ['gurobi', 'cplex']:
-        solver = LearningSolver(solver=solver_name)
-        solver.solve(instance)
-        x = instance.solution["x"]
-        assert x[0, 1] == 1.0
-        assert x[0, 2] == 0.0
-        assert x[0, 3] == 1.0
-        assert x[1, 2] == 1.0
-        assert x[1, 3] == 0.0
-        assert x[2, 3] == 1.0
-        assert instance.lower_bound == 4.0
-        assert instance.upper_bound == 4.0
-
-
-def test_subtour():
-    n_cities = 6
-    cities = np.array([
-        [0., 0.],
-        [1., 0.],
-        [2., 0.],
-        [3., 0.],
-        [0., 1.],
-        [3., 1.],
-    ])
-    distances = squareform(pdist(cities))
-    instance = TravelingSalesmanInstance(n_cities, distances)
-    for solver_name in ['gurobi', 'cplex']:
-        solver = LearningSolver(solver=solver_name)
-        solver.solve(instance)
-        assert hasattr(instance, "found_violated_lazy_constraints")
-        assert hasattr(instance, "found_violated_user_cuts")
-        x = instance.solution["x"]
-        assert x[0, 1] == 1.0
-        assert x[0, 4] == 1.0
-        assert x[1, 2] == 1.0
-        assert x[2, 3] == 1.0
-        assert x[3, 5] == 1.0
-        assert x[4, 5] == 1.0
-        solver.fit([instance])
-        solver.solve(instance)

miplearn/problems/tsp.py

@@ -1,66 +1,149 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+from typing import List, Tuple, FrozenSet, Any, Optional, Dict
 
+import networkx as nx
 import numpy as np
 import pyomo.environ as pe
-from miplearn import Instance
-from scipy.stats import uniform, randint
+from overrides import overrides
 from scipy.spatial.distance import pdist, squareform
+from scipy.stats import uniform, randint
 from scipy.stats.distributions import rv_frozen
-import networkx as nx
-import random
+
+from miplearn.instance.base import Instance
+from miplearn.solvers.learning import InternalSolver
+from miplearn.solvers.pyomo.base import BasePyomoSolver
+from miplearn.types import ConstraintName
 
 
 class ChallengeA:
-    def __init__(self,
-                 seed=42,
-                 n_training_instances=500,
-                 n_test_instances=50,
-                ):
-        
+    def __init__(
+        self,
+        seed: int = 42,
+        n_training_instances: int = 500,
+        n_test_instances: int = 50,
+    ) -> None:
         np.random.seed(seed)
-        self.generator = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
-                                                    y=uniform(loc=0.0, scale=1000.0),
-                                                    n=randint(low=350, high=351),
-                                                    gamma=uniform(loc=0.95, scale=0.1),
-                                                    fix_cities=True,
-                                                    round=True,
-                                                   )
+        self.generator = TravelingSalesmanGenerator(
+            x=uniform(loc=0.0, scale=1000.0),
+            y=uniform(loc=0.0, scale=1000.0),
+            n=randint(low=350, high=351),
+            gamma=uniform(loc=0.95, scale=0.1),
+            fix_cities=True,
+            round=True,
+        )
 
         np.random.seed(seed + 1)
         self.training_instances = self.generator.generate(n_training_instances)
-        
+
         np.random.seed(seed + 2)
-        self.test_instances = self.generator.generate(n_test_instances)        
-    
-        
+        self.test_instances = self.generator.generate(n_test_instances)
+
+
+class TravelingSalesmanInstance(Instance):
+    """An instance ot the Traveling Salesman Problem.
+
+    Given a list of cities and the distance between each pair of cities, the problem
+    asks for the shortest route starting at the first city, visiting each other city
+    exactly once, then returning to the first city. This problem is a generalization
+    of the Hamiltonian path problem, one of Karp's 21 NP-complete problems.
+    """
+
+    def __init__(self, n_cities: int, distances: np.ndarray) -> None:
+        super().__init__()
+        assert isinstance(distances, np.ndarray)
+        assert distances.shape == (n_cities, n_cities)
+        self.n_cities = n_cities
+        self.distances = distances
+        self.edges = [
+            (i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)
+        ]
+
+    @overrides
+    def to_model(self) -> pe.ConcreteModel:
+        model = pe.ConcreteModel()
+        model.x = pe.Var(self.edges, domain=pe.Binary)
+        model.obj = pe.Objective(
+            expr=sum(model.x[i, j] * self.distances[i, j] for (i, j) in self.edges),
+            sense=pe.minimize,
+        )
+        model.eq_degree = pe.ConstraintList()
+        model.eq_subtour = pe.ConstraintList()
+        for i in range(self.n_cities):
+            model.eq_degree.add(
+                sum(
+                    model.x[min(i, j), max(i, j)]
+                    for j in range(self.n_cities)
+                    if i != j
+                )
+                == 2
+            )
+        return model
+
+    @overrides
+    def find_violated_lazy_constraints(
+        self,
+        solver: InternalSolver,
+        model: Any,
+    ) -> List[ConstraintName]:
+        selected_edges = [e for e in self.edges if model.x[e].value > 0.5]
+        graph = nx.Graph()
+        graph.add_edges_from(selected_edges)
+        violations = []
+        for c in list(nx.connected_components(graph)):
+            if len(c) < self.n_cities:
+                violations.append(",".join(map(str, c)).encode())
+        return violations
+
+    @overrides
+    def enforce_lazy_constraint(
+        self,
+        solver: InternalSolver,
+        model: Any,
+        violation: ConstraintName,
+    ) -> None:
+        assert isinstance(solver, BasePyomoSolver)
+        component = [int(v) for v in violation.decode().split(",")]
+        cut_edges = [
+            e
+            for e in self.edges
+            if (e[0] in component and e[1] not in component)
+            or (e[0] not in component and e[1] in component)
+        ]
+        constr = model.eq_subtour.add(expr=sum(model.x[e] for e in cut_edges) >= 2)
+        solver.add_constraint(constr)
+
+
 class TravelingSalesmanGenerator:
     """Random generator for the Traveling Salesman Problem."""
 
-    def __init__(self,
-                 x=uniform(loc=0.0, scale=1000.0),
-                 y=uniform(loc=0.0, scale=1000.0),
-                 n=randint(low=100, high=101),
-                 gamma=uniform(loc=1.0, scale=0.0),
-                 fix_cities=True,
-                 round=True,
-                ):
+    def __init__(
+        self,
+        x: rv_frozen = uniform(loc=0.0, scale=1000.0),
+        y: rv_frozen = uniform(loc=0.0, scale=1000.0),
+        n: rv_frozen = randint(low=100, high=101),
+        gamma: rv_frozen = uniform(loc=1.0, scale=0.0),
+        fix_cities: bool = True,
+        round: bool = True,
+    ) -> None:
         """Initializes the problem generator.
-        
-        Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n, x_i and y_i are
-        sampled independently from the provided probability distributions `n`, `x` and `y`. For each
-        (unordered) pair of cities (i,j), the distance d[i,j] between them is set to:
+
+        Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n,
+        x_i and y_i are sampled independently from the provided probability
+        distributions `n`, `x` and `y`. For each (unordered) pair of cities (i,j),
+        the distance d[i,j] between them is set to:
 
             d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
 
         where gamma is sampled from the provided probability distribution `gamma`.
 
-        If fix_cities=True, the list of cities is kept the same for all generated instances. The
-        gamma values, and therefore also the distances, are still different.
-        
-        By default, all distances d[i,j] are rounded to the nearest integer.  If `round=False`
-        is provided, this rounding will be disabled.
+        If fix_cities=True, the list of cities is kept the same for all generated
+        instances. The gamma values, and therefore also the distances, are still
+        different.
+
+        By default, all distances d[i,j] are rounded to the nearest integer.  If
+        `round=False` is provided, this rounding will be disabled.
 
         Arguments
         ---------
@@ -71,30 +154,36 @@ class TravelingSalesmanGenerator:
         n: rv_discrete
             Probability distribution for the number of cities.
         fix_cities: bool
-            If False, cities will be resampled for every generated instance. Otherwise, list of
-            cities will be computed once, during the constructor.
+            If False, cities will be resampled for every generated instance. Otherwise, list
+            of cities will be computed once, during the constructor.
         round: bool
             If True, distances are rounded to the nearest integer.
         """
         assert isinstance(x, rv_frozen), "x should be a SciPy probability distribution"
         assert isinstance(y, rv_frozen), "y should be a SciPy probability distribution"
         assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
-        assert isinstance(gamma, rv_frozen), "gamma should be a SciPy probability distribution"
+        assert isinstance(
+            gamma,
+            rv_frozen,
+        ), "gamma should be a SciPy probability distribution"
         self.x = x
         self.y = y
         self.n = n
         self.gamma = gamma
         self.round = round
-        
+
         if fix_cities:
+            self.fixed_n: Optional[int]
+            self.fixed_cities: Optional[np.ndarray]
             self.fixed_n, self.fixed_cities = self._generate_cities()
         else:
             self.fixed_n = None
             self.fixed_cities = None
-    
-    def generate(self, n_samples):
-        def _sample():
+
+    def generate(self, n_samples: int) -> List[TravelingSalesmanInstance]:
+        def _sample() -> TravelingSalesmanInstance:
             if self.fixed_cities is not None:
+                assert self.fixed_n is not None
                 n, cities = self.fixed_n, self.fixed_cities
             else:
                 n, cities = self._generate_cities()
@@ -103,73 +192,10 @@ class TravelingSalesmanGenerator:
             if self.round:
                 distances = distances.round()
             return TravelingSalesmanInstance(n, distances)
+
         return [_sample() for _ in range(n_samples)]
-            
-    def _generate_cities(self):
+
+    def _generate_cities(self) -> Tuple[int, np.ndarray]:
         n = self.n.rvs()
         cities = np.array([(self.x.rvs(), self.y.rvs()) for _ in range(n)])
         return n, cities
-    
-
-class TravelingSalesmanInstance(Instance):
-    """An instance ot the Traveling Salesman Problem.
-    
-    Given a list of cities and the distance between each pair of cities, the problem asks for the
-    shortest route starting at the first city, visiting each other city exactly once, then
-    returning to the first city. This problem is a generalization of the Hamiltonian path problem,
-    one of Karp's 21 NP-complete problems.
-    """
-    
-    def __init__(self, n_cities, distances):
-        assert isinstance(distances, np.ndarray)
-        assert distances.shape == (n_cities, n_cities)
-        self.n_cities = n_cities
-        self.distances = distances
-    
-    def to_model(self):
-        model = pe.ConcreteModel()
-        model.edges = edges = [(i,j)
-                              for i in range(self.n_cities)
-                              for j in range(i+1, self.n_cities)]
-        model.x = pe.Var(edges, domain=pe.Binary)
-        model.obj = pe.Objective(expr=sum(model.x[i,j] * self.distances[i,j]
-                                          for (i,j) in edges),
-                                 sense=pe.minimize)
-        model.eq_degree = pe.ConstraintList()
-        model.eq_subtour = pe.ConstraintList()
-        for i in range(self.n_cities):
-            model.eq_degree.add(sum(model.x[min(i,j), max(i,j)]
-                                    for j in range(self.n_cities) if i != j) == 2)
-        return model
-    
-    def get_instance_features(self):
-        return np.array([1])
-    
-    def get_variable_features(self, var_name, index):
-        return np.array([1])
-    
-    def get_variable_category(self, var_name, index):
-        return index
-    
-    def find_violated_lazy_constraints(self, model):
-        selected_edges = [e for e in model.edges if model.x[e].value > 0.5]
-        graph = nx.Graph()
-        graph.add_edges_from(selected_edges)
-        components = [frozenset(c) for c in list(nx.connected_components(graph))]
-        violations = []
-        for c in components:
-            if len(c) < self.n_cities:
-                violations += [c]
-        return violations
-    
-    def build_lazy_constraint(self, model, component):
-        cut_edges = [e for e in model.edges
-                     if (e[0] in component and e[1] not in component) or
-                        (e[0] not in component and e[1] in component)]
-        return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) >= 2)
-
-    def find_violated_user_cuts(self, model):
-        return self.find_violated_lazy_constraints(model)
-
-    def build_user_cut(self, model, violation):
-        return self.build_lazy_constraint(model, violation)

miplearn/solvers/__init__.py

@@ -1,32 +1,48 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
 import sys
+from typing import Any, List, TextIO, cast, TypeVar, Optional, Sized
 
 logger = logging.getLogger(__name__)
 
 
-class RedirectOutput:
-    def __init__(self, streams):
+class _RedirectOutput:
+    def __init__(self, streams: List[Any]) -> None:
         self.streams = streams
 
-    def write(self, data):
+    def write(self, data: Any) -> None:
         for stream in self.streams:
             stream.write(data)
 
-    def flush(self):
+    def flush(self) -> None:
         for stream in self.streams:
             stream.flush()
 
-    def __enter__(self):
+    def __enter__(self) -> Any:
         self._original_stdout = sys.stdout
         self._original_stderr = sys.stderr
-        sys.stdout = self
-        sys.stderr = self
+        sys.stdout = cast(TextIO, self)
+        sys.stderr = cast(TextIO, self)
         return self
 
-    def __exit__(self, _type, _value, _traceback):
+    def __exit__(
+        self,
+        _type: Any,
+        _value: Any,
+        _traceback: Any,
+    ) -> None:
         sys.stdout = self._original_stdout
         sys.stderr = self._original_stderr
+
+
+T = TypeVar("T", bound=Sized)
+
+
+def _none_if_empty(obj: T) -> Optional[T]:
+    if len(obj) == 0:
+        return None
+    else:
+        return obj

miplearn/solvers/gurobi.py

@@ -1,313 +1,558 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+import logging
 import re
 import sys
-import logging
 from io import StringIO
+from random import randint
+from typing import List, Any, Dict, Optional, TYPE_CHECKING
 
-from . import RedirectOutput
-from .internal import InternalSolver
+import numpy as np
+from overrides import overrides
+
+from miplearn.instance.base import Instance
+from miplearn.solvers import _RedirectOutput
+from miplearn.solvers.internal import (
+    InternalSolver,
+    LPSolveStats,
+    IterationCallback,
+    LazyCallback,
+    MIPSolveStats,
+    Variables,
+    Constraints,
+)
+from miplearn.solvers.pyomo.base import PyomoTestInstanceKnapsack
+from miplearn.types import (
+    SolverParams,
+    UserCutCallback,
+    Solution,
+)
+
+if TYPE_CHECKING:
+    import gurobipy
 
 logger = logging.getLogger(__name__)
 
 
 class GurobiSolver(InternalSolver):
-    def __init__(self,
-                 params=None,
-                 lazy_cb_frequency=1,
-                 ):
-        """
-        An InternalSolver backed by Gurobi's Python API (without Pyomo).
+    """
+    An InternalSolver backed by Gurobi's Python API (without Pyomo).
 
-        Parameters
-        ----------
-        params
-            Parameters to pass to Gurobi. For example, params={"MIPGap": 1e-3}
-            sets the gap tolerance to 1e-3.
-        lazy_cb_frequency
-            If 1, calls lazy constraint callbacks whenever an integer solution
-            is found. If 2, calls it also at every node, after solving the
-            LP relaxation of that node.
-        """
+    Parameters
+    ----------
+    params: Optional[SolverParams]
+        Parameters to pass to Gurobi. For example, `params={"MIPGap": 1e-3}`
+        sets the gap tolerance to 1e-3.
+    lazy_cb_frequency: int
+        If 1, calls lazy constraint callbacks whenever an integer solution
+        is found. If 2, calls it also at every node, after solving the
+        LP relaxation of that node.
+    """
+
+    def __init__(
+        self,
+        params: Optional[SolverParams] = None,
+        lazy_cb_frequency: int = 1,
+    ) -> None:
+        import gurobipy
+
+        assert lazy_cb_frequency in [1, 2]
         if params is None:
             params = {}
-        from gurobipy import GRB
-        self.GRB = GRB
-        self.instance = None
-        self.model = None
-        self.params = params
-        self._all_vars = None
-        self._bin_vars = None
-        self.cb_where = None
-        assert lazy_cb_frequency in [1, 2]
-        if lazy_cb_frequency == 1:
-            self.lazy_cb_where = [self.GRB.Callback.MIPSOL]
-        else:
-            self.lazy_cb_where = [self.GRB.Callback.MIPSOL,
-                                  self.GRB.Callback.MIPNODE]
+        params["InfUnbdInfo"] = True
+        params["Seed"] = randint(0, 1_000_000)
 
-    def set_instance(self, instance, model=None):
+        self.gp = gurobipy
+        self.instance: Optional[Instance] = None
+        self.model: Optional["gurobipy.Model"] = None
+        self.params: SolverParams = params
+        self.cb_where: Optional[int] = None
+        self.lazy_cb_frequency = lazy_cb_frequency
+        self._dirty = True
+        self._has_lp_solution = False
+        self._has_mip_solution = False
+
+        self._varname_to_var: Dict[bytes, "gurobipy.Var"] = {}
+        self._cname_to_constr: Dict[str, "gurobipy.Constr"] = {}
+        self._gp_vars: List["gurobipy.Var"] = []
+        self._gp_constrs: List["gurobipy.Constr"] = []
+        self._var_names: np.ndarray = np.empty(0)
+        self._constr_names: List[str] = []
+        self._var_types: np.ndarray = np.empty(0)
+        self._var_lbs: np.ndarray = np.empty(0)
+        self._var_ubs: np.ndarray = np.empty(0)
+        self._var_obj_coeffs: np.ndarray = np.empty(0)
+
+        if self.lazy_cb_frequency == 1:
+            self.lazy_cb_where = [self.gp.GRB.Callback.MIPSOL]
+        else:
+            self.lazy_cb_where = [
+                self.gp.GRB.Callback.MIPSOL,
+                self.gp.GRB.Callback.MIPNODE,
+            ]
+
+    @overrides
+    def add_constraints(self, cf: Constraints) -> None:
+        assert cf.names is not None
+        assert cf.senses is not None
+        assert cf.lhs is not None
+        assert cf.rhs is not None
+        assert self.model is not None
+        for i in range(len(cf.names)):
+            sense = cf.senses[i]
+            lhs = self.gp.quicksum(
+                self._varname_to_var[varname] * coeff for (varname, coeff) in cf.lhs[i]
+            )
+            if sense == b"=":
+                self.model.addConstr(lhs == cf.rhs[i], name=cf.names[i])
+            elif sense == b"<":
+                self.model.addConstr(lhs <= cf.rhs[i], name=cf.names[i])
+            elif sense == b">":
+                self.model.addConstr(lhs >= cf.rhs[i], name=cf.names[i])
+            else:
+                raise Exception(f"Unknown sense: {sense}")
+        self.model.update()
+        self._dirty = True
+        self._has_lp_solution = False
+        self._has_mip_solution = False
+
+    @overrides
+    def are_callbacks_supported(self) -> bool:
+        return True
+
+    @overrides
+    def are_constraints_satisfied(
+        self,
+        cf: Constraints,
+        tol: float = 1e-5,
+    ) -> List[bool]:
+        assert cf.names is not None
+        assert cf.senses is not None
+        assert cf.lhs is not None
+        assert cf.rhs is not None
+        assert self.model is not None
+        result = []
+        for i in range(len(cf.names)):
+            sense = cf.senses[i]
+            lhs = sum(
+                self._varname_to_var[varname].x * coeff
+                for (varname, coeff) in cf.lhs[i]
+            )
+            if sense == "<":
+                result.append(lhs <= cf.rhs[i] + tol)
+            elif sense == ">":
+                result.append(lhs >= cf.rhs[i] - tol)
+            else:
+                result.append(abs(cf.rhs[i] - lhs) <= tol)
+        return result
+
+    @overrides
+    def build_test_instance_infeasible(self) -> Instance:
+        return GurobiTestInstanceInfeasible()
+
+    @overrides
+    def build_test_instance_knapsack(self) -> Instance:
+        return GurobiTestInstanceKnapsack(
+            weights=[23.0, 26.0, 20.0, 18.0],
+            prices=[505.0, 352.0, 458.0, 220.0],
+            capacity=67.0,
+        )
+
+    @overrides
+    def clone(self) -> "GurobiSolver":
+        return GurobiSolver(
+            params=self.params,
+            lazy_cb_frequency=self.lazy_cb_frequency,
+        )
+
+    @overrides
+    def fix(self, solution: Solution) -> None:
+        self._raise_if_callback()
+        for (varname, value) in solution.items():
+            if value is None:
+                continue
+            var = self._varname_to_var[varname]
+            var.vtype = self.gp.GRB.CONTINUOUS
+            var.lb = value
+            var.ub = value
+
+    @overrides
+    def get_constraint_attrs(self) -> List[str]:
+        return [
+            "basis_status",
+            "categories",
+            "dual_values",
+            "lazy",
+            "lhs",
+            "names",
+            "rhs",
+            "sa_rhs_down",
+            "sa_rhs_up",
+            "senses",
+            "slacks",
+            "user_features",
+        ]
+
+    @overrides
+    def get_constraints(
+        self,
+        with_static: bool = True,
+        with_sa: bool = True,
+        with_lhs: bool = True,
+    ) -> Constraints:
+        model = self.model
+        assert model is not None
+        assert model.numVars == len(self._gp_vars)
+
+        def _parse_gurobi_cbasis(v: int) -> str:
+            if v == 0:
+                return "B"
+            if v == -1:
+                return "N"
+            raise Exception(f"unknown cbasis: {v}")
+
+        gp_constrs = model.getConstrs()
+        constr_names = np.array(model.getAttr("constrName", gp_constrs), dtype="S")
+        lhs: Optional[List] = None
+        rhs, senses, slacks, basis_status = None, None, None, None
+        dual_value, basis_status, sa_rhs_up, sa_rhs_down = None, None, None, None
+
+        if with_static:
+            rhs = np.array(model.getAttr("rhs", gp_constrs), dtype=float)
+            senses = np.array(model.getAttr("sense", gp_constrs), dtype="S")
+            if with_lhs:
+                lhs = [None for _ in gp_constrs]
+                for (i, gp_constr) in enumerate(gp_constrs):
+                    expr = model.getRow(gp_constr)
+                    lhs[i] = [
+                        (self._var_names[expr.getVar(j).index], expr.getCoeff(j))
+                        for j in range(expr.size())
+                    ]
+
+        if self._has_lp_solution:
+            dual_value = np.array(model.getAttr("pi", gp_constrs), dtype=float)
+            basis_status = np.array(
+                [_parse_gurobi_cbasis(c) for c in model.getAttr("cbasis", gp_constrs)],
+                dtype="S",
+            )
+            if with_sa:
+                sa_rhs_up = np.array(model.getAttr("saRhsUp", gp_constrs), dtype=float)
+                sa_rhs_down = np.array(
+                    model.getAttr("saRhsLow", gp_constrs), dtype=float
+                )
+
+        if self._has_lp_solution or self._has_mip_solution:
+            slacks = np.array(model.getAttr("slack", gp_constrs), dtype=float)
+
+        return Constraints(
+            basis_status=basis_status,
+            dual_values=dual_value,
+            lhs=lhs,
+            names=constr_names,
+            rhs=rhs,
+            sa_rhs_down=sa_rhs_down,
+            sa_rhs_up=sa_rhs_up,
+            senses=senses,
+            slacks=slacks,
+        )
+
+    @overrides
+    def get_solution(self) -> Optional[Solution]:
+        assert self.model is not None
+        if self.cb_where is not None:
+            if self.cb_where == self.gp.GRB.Callback.MIPNODE:
+                return {
+                    v.varName.encode(): self.model.cbGetNodeRel(v)
+                    for v in self.model.getVars()
+                }
+            elif self.cb_where == self.gp.GRB.Callback.MIPSOL:
+                return {
+                    v.varName.encode(): self.model.cbGetSolution(v)
+                    for v in self.model.getVars()
+                }
+            else:
+                raise Exception(
+                    f"get_solution can only be called from a callback "
+                    f"when cb_where is either MIPNODE or MIPSOL"
+                )
+        if self.model.solCount == 0:
+            return None
+        return {v.varName.encode(): v.x for v in self.model.getVars()}
+
+    @overrides
+    def get_variable_attrs(self) -> List[str]:
+        return [
+            "names",
+            "basis_status",
+            "categories",
+            "lower_bounds",
+            "obj_coeffs",
+            "reduced_costs",
+            "sa_lb_down",
+            "sa_lb_up",
+            "sa_obj_down",
+            "sa_obj_up",
+            "sa_ub_down",
+            "sa_ub_up",
+            "types",
+            "upper_bounds",
+            "user_features",
+            "values",
+        ]
+
+    @overrides
+    def get_variables(
+        self,
+        with_static: bool = True,
+        with_sa: bool = True,
+    ) -> Variables:
+        model = self.model
+        assert model is not None
+
+        def _parse_gurobi_vbasis(b: int) -> str:
+            if b == 0:
+                return "B"
+            elif b == -1:
+                return "L"
+            elif b == -2:
+                return "U"
+            elif b == -3:
+                return "S"
+            else:
+                raise Exception(f"unknown vbasis: {basis_status}")
+
+        basis_status: Optional[np.ndarray] = None
+        upper_bounds, lower_bounds, types, values = None, None, None, None
+        obj_coeffs, reduced_costs = None, None
+        sa_obj_up, sa_ub_up, sa_lb_up = None, None, None
+        sa_obj_down, sa_ub_down, sa_lb_down = None, None, None
+
+        if with_static:
+            upper_bounds = self._var_ubs
+            lower_bounds = self._var_lbs
+            types = self._var_types
+            obj_coeffs = self._var_obj_coeffs
+
+        if self._has_lp_solution:
+            reduced_costs = np.array(model.getAttr("rc", self._gp_vars), dtype=float)
+            basis_status = np.array(
+                [
+                    _parse_gurobi_vbasis(b)
+                    for b in model.getAttr("vbasis", self._gp_vars)
+                ],
+                dtype="S",
+            )
+
+            if with_sa:
+                sa_obj_up = np.array(
+                    model.getAttr("saobjUp", self._gp_vars),
+                    dtype=float,
+                )
+                sa_obj_down = np.array(
+                    model.getAttr("saobjLow", self._gp_vars),
+                    dtype=float,
+                )
+                sa_ub_up = np.array(
+                    model.getAttr("saubUp", self._gp_vars),
+                    dtype=float,
+                )
+                sa_ub_down = np.array(
+                    model.getAttr("saubLow", self._gp_vars),
+                    dtype=float,
+                )
+                sa_lb_up = np.array(
+                    model.getAttr("salbUp", self._gp_vars),
+                    dtype=float,
+                )
+                sa_lb_down = np.array(
+                    model.getAttr("salbLow", self._gp_vars),
+                    dtype=float,
+                )
+
+        if model.solCount > 0:
+            values = np.array(model.getAttr("x", self._gp_vars), dtype=float)
+
+        return Variables(
+            names=self._var_names,
+            upper_bounds=upper_bounds,
+            lower_bounds=lower_bounds,
+            types=types,
+            obj_coeffs=obj_coeffs,
+            reduced_costs=reduced_costs,
+            basis_status=basis_status,
+            sa_obj_up=sa_obj_up,
+            sa_obj_down=sa_obj_down,
+            sa_ub_up=sa_ub_up,
+            sa_ub_down=sa_ub_down,
+            sa_lb_up=sa_lb_up,
+            sa_lb_down=sa_lb_down,
+            values=values,
+        )
+
+    @overrides
+    def is_infeasible(self) -> bool:
+        assert self.model is not None
+        return self.model.status in [self.gp.GRB.INFEASIBLE, self.gp.GRB.INF_OR_UNBD]
+
+    @overrides
+    def remove_constraints(self, names: List[str]) -> None:
+        assert self.model is not None
+        constrs = [self.model.getConstrByName(n) for n in names]
+        self.model.remove(constrs)
+        self.model.update()
+
+    @overrides
+    def set_instance(
+        self,
+        instance: Instance,
+        model: Any = None,
+    ) -> None:
         self._raise_if_callback()
         if model is None:
             model = instance.to_model()
+        assert isinstance(model, self.gp.Model)
         self.instance = instance
         self.model = model
         self.model.update()
-        self._update_vars()
+        self._update()
 
-    def _raise_if_callback(self):
-        if self.cb_where is not None:
-            raise Exception("method cannot be called from a callback")
-
-    def _update_vars(self):
-        self._all_vars = {}
-        self._bin_vars = {}
-        for var in self.model.getVars():
-            m = re.search(r"([^[]*)\[(.*)\]", var.varName)
-            if m is None:
-                name = var.varName
-                idx = [0]
-            else:
-                name = m.group(1)
-                idx = tuple(int(k) if k.isdecimal() else k
-                            for k in m.group(2).split(","))
-            if len(idx) == 1:
-                idx = idx[0]
-            if name not in self._all_vars:
-                self._all_vars[name] = {}
-            self._all_vars[name][idx] = var
-            if var.vtype != 'C':
-                if name not in self._bin_vars:
-                    self._bin_vars[name] = {}
-                self._bin_vars[name][idx] = var
-
-    def _apply_params(self):
-        for (name, value) in self.params.items():
-            self.model.setParam(name, value)
-
-    def solve_lp(self, tee=False):
+    @overrides
+    def set_warm_start(self, solution: Solution) -> None:
         self._raise_if_callback()
-        self._apply_params()
-        streams = [StringIO()]
-        if tee:
-            streams += [sys.stdout]
-        for (varname, vardict) in self._bin_vars.items():
-            for (idx, var) in vardict.items():
-                var.vtype = self.GRB.CONTINUOUS
-                var.lb = 0.0
-                var.ub = 1.0
-        with RedirectOutput(streams):
-            self.model.optimize()
-        for (varname, vardict) in self._bin_vars.items():
-            for (idx, var) in vardict.items():
-                var.vtype = self.GRB.BINARY
-        log = streams[0].getvalue()
-        return {
-            "Optimal value": self.model.objVal,
-            "Log": log
-        }
+        self._clear_warm_start()
+        for (var_name, value) in solution.items():
+            var = self._varname_to_var[var_name]
+            if value is not None:
+                var.start = value
 
-    def solve(self,
-              tee=False,
-              iteration_cb=None,
-              lazy_cb=None):
+    @overrides
+    def solve(
+        self,
+        tee: bool = False,
+        iteration_cb: Optional[IterationCallback] = None,
+        lazy_cb: Optional[LazyCallback] = None,
+        user_cut_cb: Optional[UserCutCallback] = None,
+    ) -> MIPSolveStats:
         self._raise_if_callback()
+        assert self.model is not None
+        if iteration_cb is None:
+            iteration_cb = lambda: False
+        callback_exceptions = []
 
-        def cb_wrapper(cb_model, cb_where):
+        # Create callback wrapper
+        def cb_wrapper(cb_model: Any, cb_where: int) -> None:
             try:
                 self.cb_where = cb_where
-                if cb_where in self.lazy_cb_where:
+                if lazy_cb is not None and cb_where in self.lazy_cb_where:
                     lazy_cb(self, self.model)
-            except:
+                if user_cut_cb is not None and cb_where == self.gp.GRB.Callback.MIPNODE:
+                    user_cut_cb(self, self.model)
+            except Exception as e:
                 logger.exception("callback error")
+                callback_exceptions.append(e)
             finally:
                 self.cb_where = None
 
-        if lazy_cb:
+        # Configure Gurobi
+        if lazy_cb is not None:
             self.params["LazyConstraints"] = 1
-        self._apply_params()
+        if user_cut_cb is not None:
+            self.params["PreCrush"] = 1
+
+        # Solve problem
         total_wallclock_time = 0
         total_nodes = 0
-        streams = [StringIO()]
+        streams: List[Any] = [StringIO()]
         if tee:
             streams += [sys.stdout]
-        if iteration_cb is None:
-            iteration_cb = lambda : False
+        self._apply_params(streams)
         while True:
-            logger.debug("Solving MIP...")
-            with RedirectOutput(streams):
-                if lazy_cb is None:
-                    self.model.optimize()
-                else:
-                    self.model.optimize(cb_wrapper)
+            with _RedirectOutput(streams):
+                self.model.optimize(cb_wrapper)
+                self._dirty = False
+            if len(callback_exceptions) > 0:
+                raise callback_exceptions[0]
             total_wallclock_time += self.model.runtime
             total_nodes += int(self.model.nodeCount)
             should_repeat = iteration_cb()
             if not should_repeat:
                 break
+        self._has_lp_solution = False
+        self._has_mip_solution = self.model.solCount > 0
+
+        # Fetch results and stats
         log = streams[0].getvalue()
-        if self.model.modelSense == 1:
-            sense = "min"
-            lb = self.model.objBound
-            ub = self.model.objVal
-        else:
-            sense = "max"
-            lb = self.model.objVal
-            ub = self.model.objBound
-        return {
-            "Lower bound": lb,
-            "Upper bound": ub,
-            "Wallclock time": total_wallclock_time,
-            "Nodes": total_nodes,
-            "Sense": sense,
-            "Log": log,
-            "Warm start value": self._extract_warm_start_value(log),
-        }
-
-    def get_solution(self):
-        self._raise_if_callback()
-
-        solution = {}
-        for (varname, vardict) in self._all_vars.items():
-            solution[varname] = {}
-            for (idx, var) in vardict.items():
-                solution[varname][idx] = var.x
-        return solution
-
-    def get_value(self, var_name, index):
-        var = self._all_vars[var_name][index]
-        return self._get_value(var)
-
-    def _get_value(self, var):
-        if self.cb_where == self.GRB.Callback.MIPSOL:
-            return self.model.cbGetSolution(var)
-        elif self.cb_where == self.GRB.Callback.MIPNODE:
-            return self.model.cbGetNodeRel(var)
-        elif self.cb_where is None:
-            return var.x
-        else:
-            raise Exception("get_value cannot be called from cb_where=%s" % self.cb_where)
-
-    def get_variables(self):
-        self._raise_if_callback()
-        variables = {}
-        for (varname, vardict) in self._all_vars.items():
-            variables[varname] = []
-            for (idx, var) in vardict.items():
-                variables[varname] += [idx]
-        return variables
-
-    def add_constraint(self, constraint, name=""):
-        if type(constraint) is tuple:
-            lhs, sense, rhs, name = constraint
-            if self.cb_where in [self.GRB.Callback.MIPSOL, self.GRB.Callback.MIPNODE]:
-                self.model.cbLazy(lhs, sense, rhs)
+        ub, lb = None, None
+        sense = "min" if self.model.modelSense == 1 else "max"
+        if self.model.solCount > 0:
+            if self.model.modelSense == 1:
+                lb = self.model.objBound
+                ub = self.model.objVal
             else:
-                self.model.addConstr(lhs, sense, rhs, name)
-        else:
-            if self.cb_where in [self.GRB.Callback.MIPSOL, self.GRB.Callback.MIPNODE]:
-                self.model.cbLazy(constraint)
-            else:
-                self.model.addConstr(constraint, name=name)
+                lb = self.model.objVal
+                ub = self.model.objBound
+        ws_value = self._extract_warm_start_value(log)
+        return MIPSolveStats(
+            mip_lower_bound=lb,
+            mip_upper_bound=ub,
+            mip_wallclock_time=total_wallclock_time,
+            mip_nodes=total_nodes,
+            mip_sense=sense,
+            mip_log=log,
+            mip_warm_start_value=ws_value,
+        )
 
-    def set_warm_start(self, solution):
+    @overrides
+    def solve_lp(
+        self,
+        tee: bool = False,
+    ) -> LPSolveStats:
         self._raise_if_callback()
-        count_fixed, count_total = 0, 0
-        for (varname, vardict) in solution.items():
-            for (idx, value) in vardict.items():
-                count_total += 1
-                if value is not None:
-                    count_fixed += 1
-                    self._all_vars[varname][idx].start = value
-        logger.info("Setting start values for %d variables (out of %d)" %
-                    (count_fixed, count_total))
+        streams: List[Any] = [StringIO()]
+        if tee:
+            streams += [sys.stdout]
+        self._apply_params(streams)
+        assert self.model is not None
+        for (i, var) in enumerate(self._gp_vars):
+            if self._var_types[i] == b"B":
+                var.vtype = self.gp.GRB.CONTINUOUS
+                var.lb = 0.0
+                var.ub = 1.0
+        with _RedirectOutput(streams):
+            self.model.optimize()
+            self._dirty = False
+        for (i, var) in enumerate(self._gp_vars):
+            if self._var_types[i] == b"B":
+                var.vtype = self.gp.GRB.BINARY
+        log = streams[0].getvalue()
+        self._has_lp_solution = self.model.solCount > 0
+        self._has_mip_solution = False
+        opt_value = None
+        if not self.is_infeasible():
+            opt_value = self.model.objVal
+        return LPSolveStats(
+            lp_value=opt_value,
+            lp_log=log,
+            lp_wallclock_time=self.model.runtime,
+        )
 
-    def clear_warm_start(self):
-        self._raise_if_callback()
-        for (varname, vardict) in self._all_vars:
-            for (idx, var) in vardict.items():
-                var[idx].start = self.GRB.UNDEFINED
+    def _apply_params(self, streams: List[Any]) -> None:
+        assert self.model is not None
+        with _RedirectOutput(streams):
+            for (name, value) in self.params.items():
+                self.model.setParam(name, value)
 
-    def fix(self, solution):
-        self._raise_if_callback()
-        for (varname, vardict) in solution.items():
-            for (idx, value) in vardict.items():
-                if value is None:
-                    continue
-                var = self._all_vars[varname][idx]
-                var.vtype = self.GRB.CONTINUOUS
-                var.lb = value
-                var.ub = value
+    def _clear_warm_start(self) -> None:
+        for var in self._varname_to_var.values():
+            var.start = self.gp.GRB.UNDEFINED
 
-    def get_constraint_ids(self):
-        self._raise_if_callback()
-        self.model.update()
-        return [c.ConstrName for c in self.model.getConstrs()]
-
-    def extract_constraint(self, cid):
-        self._raise_if_callback()
-        constr = self.model.getConstrByName(cid)
-        cobj = (self.model.getRow(constr),
-                constr.sense,
-                constr.RHS,
-                constr.ConstrName)
-        self.model.remove(constr)
-        return cobj
-
-    def is_constraint_satisfied(self, cobj, tol=1e-5):
-        lhs, sense, rhs, name = cobj
-        if self.cb_where is not None:
-            lhs_value = lhs.getConstant()
-            for i in range(lhs.size()):
-                var = lhs.getVar(i)
-                coeff = lhs.getCoeff(i)
-                lhs_value += self._get_value(var) * coeff
-        else:
-            lhs_value = lhs.getValue()
-        if sense == "<":
-            return lhs_value <= rhs + tol
-        elif sense == ">":
-            return lhs_value >= rhs - tol
-        elif sense == "=":
-            return abs(rhs - lhs_value) < abs(tol)
-        else:
-            raise Exception("Unknown sense: %s" % sense)
-
-    def get_constraint_slacks(self):
-        return {c.ConstrName: c.Slack for c in self.model.getConstrs()}
-
-    def relax(self):
-        self.model = self.model.relax()
-        self._update_vars()
-
-    def set_branching_priorities(self, priorities):
-        self._raise_if_callback()
-        logger.warning("set_branching_priorities not implemented")
-
-    def set_threads(self, threads):
-        self._raise_if_callback()
-        self.params["Threads"] = threads
-
-    def set_time_limit(self, time_limit):
-        self._raise_if_callback()
-        self.params["TimeLimit"] = time_limit
-
-    def set_node_limit(self, node_limit):
-        self._raise_if_callback()
-        self.params["NodeLimit"] = node_limit
-
-    def set_gap_tolerance(self, gap_tolerance):
-        self._raise_if_callback()
-        self.params["MIPGap"] = gap_tolerance
-
-    def _extract_warm_start_value(self, log):
-        ws = self.__extract(log, "MIP start with objective ([0-9.e+-]*)")
-        if ws is not None:
-            ws = float(ws)
-        return ws
-
-    def __extract(self, log, regexp, default=None):
+    @staticmethod
+    def _extract(
+        log: str,
+        regexp: str,
+        default: Optional[str] = None,
+    ) -> Optional[str]:
         value = default
         for line in log.splitlines():
             matches = re.findall(regexp, line)
@@ -316,19 +561,158 @@ class GurobiSolver(InternalSolver):
             value = matches[0]
         return value
 
-    def __getstate__(self):     
+    def _extract_warm_start_value(self, log: str) -> Optional[float]:
+        ws = self._extract(log, "MIP start with objective ([0-9.e+-]*)")
+        if ws is None:
+            return None
+        return float(ws)
+
+    def _get_value(self, var: Any) -> float:
+        assert self.model is not None
+        if self.cb_where == self.gp.GRB.Callback.MIPSOL:
+            return self.model.cbGetSolution(var)
+        elif self.cb_where == self.gp.GRB.Callback.MIPNODE:
+            return self.model.cbGetNodeRel(var)
+        elif self.cb_where is None:
+            return var.x
+        else:
+            raise Exception(
+                "get_value cannot be called from cb_where=%s" % self.cb_where
+            )
+
+    def _raise_if_callback(self) -> None:
+        if self.cb_where is not None:
+            raise Exception("method cannot be called from a callback")
+
+    def _update(self) -> None:
+        assert self.model is not None
+        gp_vars: List["gurobipy.Var"] = self.model.getVars()
+        gp_constrs: List["gurobipy.Constr"] = self.model.getConstrs()
+        var_names: np.ndarray = np.array(
+            self.model.getAttr("varName", gp_vars),
+            dtype="S",
+        )
+        var_types: np.ndarray = np.array(
+            self.model.getAttr("vtype", gp_vars),
+            dtype="S",
+        )
+        var_ubs: np.ndarray = np.array(
+            self.model.getAttr("ub", gp_vars),
+            dtype=float,
+        )
+        var_lbs: np.ndarray = np.array(
+            self.model.getAttr("lb", gp_vars),
+            dtype=float,
+        )
+        var_obj_coeffs: np.ndarray = np.array(
+            self.model.getAttr("obj", gp_vars),
+            dtype=float,
+        )
+        constr_names: List[str] = self.model.getAttr("constrName", gp_constrs)
+        varname_to_var: Dict[bytes, "gurobipy.Var"] = {}
+        cname_to_constr: Dict = {}
+        for (i, gp_var) in enumerate(gp_vars):
+            assert var_names[i] not in varname_to_var, (
+                f"Duplicated variable name detected: {var_names[i]}. "
+                f"Unique variable names are currently required."
+            )
+            if var_types[i] == b"I":
+                assert var_ubs[i] == 1.0, (
+                    "Only binary and continuous variables are currently supported. "
+                    f"Integer variable {var_names[i]} has upper bound {var_ubs[i]}."
+                )
+                assert var_lbs[i] == 0.0, (
+                    "Only binary and continuous variables are currently supported. "
+                    f"Integer variable {var_names[i]} has lower bound {var_ubs[i]}."
+                )
+                var_types[i] = b"B"
+            assert var_types[i] in [b"B", b"C"], (
+                "Only binary and continuous variables are currently supported. "
+                f"Variable {var_names[i]} has type {var_types[i]}."
+            )
+            varname_to_var[var_names[i]] = gp_var
+        for (i, gp_constr) in enumerate(gp_constrs):
+            assert constr_names[i] not in cname_to_constr, (
+                f"Duplicated constraint name detected: {constr_names[i]}. "
+                f"Unique constraint names are currently required."
+            )
+            cname_to_constr[constr_names[i]] = gp_constr
+        self._varname_to_var = varname_to_var
+        self._cname_to_constr = cname_to_constr
+        self._gp_vars = gp_vars
+        self._gp_constrs = gp_constrs
+        self._var_names = var_names
+        self._constr_names = constr_names
+        self._var_types = var_types
+        self._var_lbs = var_lbs
+        self._var_ubs = var_ubs
+        self._var_obj_coeffs = var_obj_coeffs
+
+    def __getstate__(self) -> Dict:
         return {
             "params": self.params,
             "lazy_cb_where": self.lazy_cb_where,
         }
 
-    def __setstate__(self, state):
-        from gurobipy import GRB
+    def __setstate__(self, state: Dict) -> None:
         self.params = state["params"]
         self.lazy_cb_where = state["lazy_cb_where"]
-        self.GRB = GRB
         self.instance = None
         self.model = None
-        self._all_vars = None
-        self._bin_vars = None
-        self.cb_where = None        
+        self.cb_where = None
+
+
+class GurobiTestInstanceInfeasible(Instance):
+    @overrides
+    def to_model(self) -> Any:
+        import gurobipy as gp
+        from gurobipy import GRB
+
+        model = gp.Model()
+        x = model.addVars(1, vtype=GRB.BINARY, name="x")
+        model.addConstr(x[0] >= 2)
+        model.setObjective(x[0])
+        return model
+
+
+class GurobiTestInstanceKnapsack(PyomoTestInstanceKnapsack):
+    """
+    Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
+    instead of Pyomo, used for testing.
+    """
+
+    def __init__(
+        self,
+        weights: List[float],
+        prices: List[float],
+        capacity: float,
+    ) -> None:
+        super().__init__(weights, prices, capacity)
+
+    @overrides
+    def to_model(self) -> Any:
+        import gurobipy as gp
+        from gurobipy import GRB
+
+        model = gp.Model("Knapsack")
+        n = len(self.weights)
+        x = model.addVars(n, vtype=GRB.BINARY, name="x")
+        z = model.addVar(vtype=GRB.CONTINUOUS, name="z", ub=self.capacity)
+        model.addConstr(
+            gp.quicksum(x[i] * self.weights[i] for i in range(n)) == z,
+            "eq_capacity",
+        )
+        model.setObjective(
+            gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
+        )
+        return model
+
+    @overrides
+    def enforce_lazy_constraint(
+        self,
+        solver: InternalSolver,
+        model: Any,
+        violation: str,
+    ) -> None:
+        x0 = model.getVarByName("x[0]")
+        model.cbLazy(x0 <= 0)

miplearn/solvers/internal.py

@@ -1,15 +1,127 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
 from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Optional, List, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from miplearn.instance.base import Instance
+from miplearn.types import (
+    IterationCallback,
+    LazyCallback,
+    UserCutCallback,
+    Solution,
+)
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from miplearn.features.sample import Sample
 
-class ExtractedConstraint(ABC):
-    pass
+
+@dataclass
+class LPSolveStats:
+    lp_log: Optional[str] = None
+    lp_value: Optional[float] = None
+    lp_wallclock_time: Optional[float] = None
+
+    def to_list(self) -> List[float]:
+        features: List[float] = []
+        for attr in ["lp_value", "lp_wallclock_time"]:
+            if getattr(self, attr) is not None:
+                features.append(getattr(self, attr))
+        return features
+
+
+@dataclass
+class MIPSolveStats:
+    mip_lower_bound: Optional[float] = None
+    mip_log: Optional[str] = None
+    mip_nodes: Optional[int] = None
+    mip_sense: Optional[str] = None
+    mip_upper_bound: Optional[float] = None
+    mip_wallclock_time: Optional[float] = None
+    mip_warm_start_value: Optional[float] = None
+
+
+@dataclass
+class Variables:
+    names: Optional[np.ndarray] = None
+    basis_status: Optional[np.ndarray] = None
+    lower_bounds: Optional[np.ndarray] = None
+    obj_coeffs: Optional[np.ndarray] = None
+    reduced_costs: Optional[np.ndarray] = None
+    sa_lb_down: Optional[np.ndarray] = None
+    sa_lb_up: Optional[np.ndarray] = None
+    sa_obj_down: Optional[np.ndarray] = None
+    sa_obj_up: Optional[np.ndarray] = None
+    sa_ub_down: Optional[np.ndarray] = None
+    sa_ub_up: Optional[np.ndarray] = None
+    types: Optional[np.ndarray] = None
+    upper_bounds: Optional[np.ndarray] = None
+    values: Optional[np.ndarray] = None
+
+
+@dataclass
+class Constraints:
+    basis_status: Optional[np.ndarray] = None
+    dual_values: Optional[np.ndarray] = None
+    lazy: Optional[np.ndarray] = None
+    lhs: Optional[List[List[Tuple[bytes, float]]]] = None
+    names: Optional[np.ndarray] = None
+    rhs: Optional[np.ndarray] = None
+    sa_rhs_down: Optional[np.ndarray] = None
+    sa_rhs_up: Optional[np.ndarray] = None
+    senses: Optional[np.ndarray] = None
+    slacks: Optional[np.ndarray] = None
+
+    @staticmethod
+    def from_sample(sample: "Sample") -> "Constraints":
+        return Constraints(
+            basis_status=sample.get_array("lp_constr_basis_status"),
+            dual_values=sample.get_array("lp_constr_dual_values"),
+            lazy=sample.get_array("static_constr_lazy"),
+            # lhs=sample.get_vector("static_constr_lhs"),
+            names=sample.get_array("static_constr_names"),
+            rhs=sample.get_array("static_constr_rhs"),
+            sa_rhs_down=sample.get_array("lp_constr_sa_rhs_down"),
+            sa_rhs_up=sample.get_array("lp_constr_sa_rhs_up"),
+            senses=sample.get_array("static_constr_senses"),
+            slacks=sample.get_array("lp_constr_slacks"),
+        )
+
+    def __getitem__(self, selected: List[bool]) -> "Constraints":
+        return Constraints(
+            basis_status=(
+                None if self.basis_status is None else self.basis_status[selected]
+            ),
+            dual_values=(
+                None if self.dual_values is None else self.dual_values[selected]
+            ),
+            names=(None if self.names is None else self.names[selected]),
+            lazy=(None if self.lazy is None else self.lazy[selected]),
+            lhs=self._filter(self.lhs, selected),
+            rhs=(None if self.rhs is None else self.rhs[selected]),
+            sa_rhs_down=(
+                None if self.sa_rhs_down is None else self.sa_rhs_down[selected]
+            ),
+            sa_rhs_up=(None if self.sa_rhs_up is None else self.sa_rhs_up[selected]),
+            senses=(None if self.senses is None else self.senses[selected]),
+            slacks=(None if self.slacks is None else self.slacks[selected]),
+        )
+
+    def _filter(
+        self,
+        obj: Optional[List],
+        selected: List[bool],
+    ) -> Optional[List]:
+        if obj is None:
+            return None
+        return [obj[i] for (i, selected_i) in enumerate(selected) if selected_i]
 
 
 class InternalSolver(ABC):
@@ -18,67 +130,152 @@ class InternalSolver(ABC):
     """
 
     @abstractmethod
-    def solve_lp(self, tee=False):
+    def add_constraints(self, cf: Constraints) -> None:
+        """Adds the given constraints to the model."""
+        pass
+
+    @abstractmethod
+    def are_constraints_satisfied(
+        self,
+        cf: Constraints,
+        tol: float = 1e-5,
+    ) -> List[bool]:
         """
-        Solves the LP relaxation of the currently loaded instance. After this
-        method finishes, the solution can be retrieved by calling `get_solution`.
+        Checks whether the current solution satisfies the given constraints.
+        """
+        pass
 
-        Parameters
-        ----------
-        tee: bool
-            If true, prints the solver log to the screen.
+    def are_callbacks_supported(self) -> bool:
+        """
+        Returns True if this solver supports native callbacks, such as lazy constraints
+        callback or user cuts callback.
+        """
+        return False
 
-        Returns
-        -------
-        dict
-            A dictionary of solver statistics containing the following keys:
-            "Optimal value".
+    @abstractmethod
+    def build_test_instance_infeasible(self) -> Instance:
+        """
+        Returns an infeasible instance, for testing purposes.
         """
         pass
 
     @abstractmethod
-    def get_solution(self):
+    def build_test_instance_knapsack(self) -> Instance:
+        """
+        Returns an instance corresponding to the following MIP, for testing purposes:
+
+          maximize  505 x0 + 352 x1 + 458 x2 + 220 x3
+          s.t.      eq_capacity: z = 23 x0 + 26 x1 + 20 x2 + 18 x3
+                    x0, x1, x2, x3 binary
+                    0 <= z <= 67 continuous
+        """
+        pass
+
+    @abstractmethod
+    def clone(self) -> "InternalSolver":
+        """
+        Returns a new copy of this solver with identical parameters, but otherwise
+        completely unitialized.
+        """
+        pass
+
+    @abstractmethod
+    def fix(self, solution: Solution) -> None:
+        """
+        Fixes the values of a subset of decision variables. Missing values in the
+        solution indicate variables that should be left free.
+        """
+        pass
+
+    @abstractmethod
+    def get_solution(self) -> Optional[Solution]:
         """
         Returns current solution found by the solver.
 
         If called after `solve`, returns the best primal solution found during
         the search. If called after `solve_lp`, returns the optimal solution
-        to the LP relaxation.
-
-        The solution is a dictionary `sol`, where the optimal value of `var[idx]`
-        is given by `sol[var][idx]`.
+        to the LP relaxation. If no primal solution is available, return None.
         """
         pass
 
     @abstractmethod
-    def set_warm_start(self, solution):
+    def get_constraint_attrs(self) -> List[str]:
+        """
+        Returns a list of constraint attributes supported by this solver. Used for
+        testing purposes only.
         """
-        Sets the warm start to be used by the solver.
 
-        The solution should be a dictionary following the same format as the
-        one produced by `get_solution`. Only one warm start is supported.
-        Calling this function when a warm start already exists will
-        remove the previous warm start.
+        pass
+
+    @abstractmethod
+    def get_constraints(
+        self,
+        with_static: bool = True,
+        with_sa: bool = True,
+        with_lhs: bool = True,
+    ) -> Constraints:
+        pass
+
+    @abstractmethod
+    def get_variable_attrs(self) -> List[str]:
+        """
+        Returns a list of variable attributes supported by this solver. Used for
+        testing purposes only.
         """
         pass
 
     @abstractmethod
-    def clear_warm_start(self):
+    def get_variables(
+        self,
+        with_static: bool = True,
+        with_sa: bool = True,
+    ) -> Variables:
         """
-        Removes any existing warm start from the solver.
+        Returns a description of the decision variables in the problem.
+
+        Parameters
+        ----------
+        with_static: bool
+            If True, include features that do not change during the solution process,
+            such as variable types and names. This parameter is used to reduce the
+            amount of duplicated data collected by LearningSolver. Features that do
+            not change are only collected once.
+        with_sa: bool
+            If True, collect sensitivity analysis information. For large models,
+            collecting this information may be expensive, so this parameter is useful
+            for reducing running times.
         """
         pass
 
     @abstractmethod
-    def set_instance(self, instance, model=None):
+    def is_infeasible(self) -> bool:
+        """
+        Returns True if the model has been proved to be infeasible.
+        Must be called after solve.
+        """
+        pass
+
+    @abstractmethod
+    def remove_constraints(self, names: np.ndarray) -> None:
+        """
+        Removes the given constraints from the model.
+        """
+        pass
+
+    @abstractmethod
+    def set_instance(
+        self,
+        instance: Instance,
+        model: Any = None,
+    ) -> None:
         """
         Loads the given instance into the solver.
 
         Parameters
         ----------
-        instance: miplearn.Instance
+        instance: Instance
             The instance to be loaded.
-        model:
+        model: Any
             The concrete optimization model corresponding to this instance
             (e.g. JuMP.Model or pyomo.core.ConcreteModel). If not provided,
             it will be generated by calling `instance.to_model()`.
@@ -86,140 +283,66 @@ class InternalSolver(ABC):
         pass
 
     @abstractmethod
-    def fix(self, solution):
+    def set_warm_start(self, solution: Solution) -> None:
         """
-        Fixes the values of a subset of decision variables.
+        Sets the warm start to be used by the solver.
 
-        The values should be provided in the dictionary format generated by
-        `get_solution`. Missing values in the solution indicate variables
-        that should be left free.
+        Only one warm start is supported. Calling this function when a warm start
+        already exists will remove the previous warm start.
         """
         pass
 
     @abstractmethod
-    def set_branching_priorities(self, priorities):
-        """
-        Sets the branching priorities for the given decision variables.
-
-        When the MIP solver needs to decide on which variable to branch, variables
-        with higher priority are picked first, given that they are fractional.
-        Ties are solved arbitrarily. By default, all variables have priority zero.
-
-        The priorities should be provided in the dictionary format generated by
-        `get_solution`. Missing values indicate variables whose priorities
-        should not be modified.
-        """
-        pass
-
-    @abstractmethod
-    def add_constraint(self, constraint):
-        """
-        Adds a single constraint to the model.
-        """
-        pass
-
-    @abstractmethod
-    def solve(self, tee=False, iteration_cb=None, lazy_cb=None):
+    def solve(
+        self,
+        tee: bool = False,
+        iteration_cb: Optional[IterationCallback] = None,
+        lazy_cb: Optional[LazyCallback] = None,
+        user_cut_cb: Optional[UserCutCallback] = None,
+    ) -> MIPSolveStats:
         """
         Solves the currently loaded instance. After this method finishes,
         the best solution found can be retrieved by calling `get_solution`.
 
         Parameters
         ----------
-        iteration_cb: () -> Bool
+        iteration_cb: IterationCallback
             By default, InternalSolver makes a single call to the native `solve`
             method and returns the result. If an iteration callback is provided
             instead, InternalSolver enters a loop, where `solve` and `iteration_cb`
-            are called alternatively. To stop the loop, `iteration_cb` should
-            return False. Any other result causes the solver to loop again.
-        lazy_cb: (internal_solver, model) -> None
+            are called alternatively. To stop the loop, `iteration_cb` should return
+            False. Any other result causes the solver to loop again.
+        lazy_cb: LazyCallback
             This function is called whenever the solver finds a new candidate
-            solution and can be used to add lazy constraints to the model. Only
-            two operations within the callback are allowed:
-                - Querying the value of a variable, through `get_value(var, idx)`
-                - Querying if a constraint is satisfied, through `is_constraint_satisfied(cobj)`
-                - Adding a new constraint to the problem, through `add_constraint`
+            solution and can be used to add lazy constraints to the model. Only the
+            following operations within the callback are allowed:
+                - Querying the value of a variable
+                - Querying if a constraint is satisfied
+                - Adding a new constraint to the problem
             Additional operations may be allowed by specific subclasses.
-        tee: Bool
+        user_cut_cb: UserCutCallback
+            This function is called whenever the solver found a new integer-infeasible
+            solution and needs to generate cutting planes to cut it off.
+        tee: bool
             If true, prints the solver log to the screen.
-
-        Returns
-        -------
-        dict
-            A dictionary of solver statistics containing the following keys:
-            "Lower bound", "Upper bound", "Wallclock time", "Nodes", "Sense",
-             "Log" and "Warm start value".
         """
         pass
 
     @abstractmethod
-    def get_value(self, var_name, index):
+    def solve_lp(
+        self,
+        tee: bool = False,
+    ) -> LPSolveStats:
         """
-        Returns the current value of a decision variable.
+        Solves the LP relaxation of the currently loaded instance. After this
+        method finishes, the solution can be retrieved by calling `get_solution`.
+
+        This method should not permanently modify the problem. That is, subsequent
+        calls to `solve` should solve the original MIP, not the LP relaxation.
+
+        Parameters
+        ----------
+        tee
+            If true, prints the solver log to the screen.
         """
         pass
-
-    @abstractmethod
-    def get_constraint_ids(self):
-        """
-        Returns a list of ids, which uniquely identify each constraint in the model.
-        """
-        pass
-
-    @abstractmethod
-    def extract_constraint(self, cid):
-        """
-        Removes a given constraint from the model and returns an object `cobj` which
-        can be used to verify if the removed constraint is still satisfied by
-        the current solution, using `is_constraint_satisfied(cobj)`, and can potentially
-        be re-added to the model using `add_constraint(cobj)`.
-        """
-        pass
-
-    @abstractmethod
-    def relax(self):
-        """
-        Drops all integrality constraints from the model.
-        """
-        pass
-
-    @abstractmethod
-    def get_constraint_slacks(self):
-        """
-        Returns a dictionary mapping constraint name to the constraint slack
-        in the current solution.
-        """
-        pass
-
-    @abstractmethod
-    def is_constraint_satisfied(self, cobj):
-        pass
-
-    @abstractmethod
-    def set_threads(self, threads):
-        pass
-
-    @abstractmethod
-    def set_time_limit(self, time_limit):
-        pass
-
-    @abstractmethod
-    def set_node_limit(self, node_limit):
-        pass
-
-    @abstractmethod
-    def set_gap_tolerance(self, gap_tolerance):
-        pass
-
-    @abstractmethod
-    def get_variables(self):
-        pass
-
-    def get_empty_solution(self):
-        solution = {}
-        for (var, indices) in self.get_variables().items():
-            solution[var] = {}
-            for idx in indices:
-                solution[var][idx] = 0.0
-        return solution
-

miplearn/solvers/learning.py

@@ -1,181 +1,337 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
-from copy import deepcopy
-from typing import Optional, List
+import time
+import traceback
+from typing import Optional, List, Any, cast, Dict, Tuple
+
 from p_tqdm import p_map
 
-from .. import (ObjectiveValueComponent,
-                PrimalSolutionComponent,
-                DynamicLazyConstraintsComponent,
-                UserCutsComponent)
-from .pyomo.cplex import CplexPyomoSolver
-from .pyomo.gurobi import GurobiPyomoSolver
+from miplearn.components.component import Component
+from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
+from miplearn.components.dynamic_user_cuts import UserCutsComponent
+from miplearn.components.objective import ObjectiveValueComponent
+from miplearn.components.primal import PrimalSolutionComponent
+from miplearn.features.extractor import FeaturesExtractor
+from miplearn.instance.base import Instance
+from miplearn.instance.picklegz import PickleGzInstance
+from miplearn.solvers import _RedirectOutput
+from miplearn.solvers.internal import InternalSolver
+from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
+from miplearn.types import LearningSolveStats
 
 logger = logging.getLogger(__name__)
 
 
-# Global memory for multiprocessing
-SOLVER = [None]  # type: List[Optional[LearningSolver]]
-INSTANCES = [None]  # type: List[Optional[dict]]
+class _GlobalVariables:
+    def __init__(self) -> None:
+        self.solver: Optional[LearningSolver] = None
+        self.instances: Optional[List[Instance]] = None
+        self.discard_outputs: bool = False
 
 
-def _parallel_solve(instance_idx):
-    solver = deepcopy(SOLVER[0])
-    instance = INSTANCES[0][instance_idx]
-    if not hasattr(instance, "found_violated_lazy_constraints"):
-        instance.found_violated_lazy_constraints = []
-    if not hasattr(instance, "found_violated_user_cuts"):
-        instance.found_violated_user_cuts = []
-    if not hasattr(instance, "slacks"):
-        instance.slacks = {}
-    solver_results = solver.solve(instance)
-    return {
-        "solver_results": solver_results,
-        "solution": instance.solution,
-        "lp_solution": instance.lp_solution,
-        "found_violated_lazy_constraints": instance.found_violated_lazy_constraints,
-        "found_violated_user_cuts": instance.found_violated_user_cuts,
-        "slacks": instance.slacks
-    }
+# Global variables used for multiprocessing. Global variables are copied by the
+# operating system when the process forks. Local variables are copied through
+# serialization, which is a much slower process.
+_GLOBAL = [_GlobalVariables()]
+
+
+def _parallel_solve(
+    idx: int,
+) -> Tuple[Optional[LearningSolveStats], Optional[Instance]]:
+    solver = _GLOBAL[0].solver
+    instances = _GLOBAL[0].instances
+    discard_outputs = _GLOBAL[0].discard_outputs
+    assert solver is not None
+    assert instances is not None
+    try:
+        stats = solver.solve(
+            instances[idx],
+            discard_output=discard_outputs,
+        )
+        instances[idx].free()
+        return stats, instances[idx]
+    except Exception as e:
+        traceback.print_exc()
+        logger.exception(f"Exception while solving {instances[idx]}. Ignoring.")
+        return None, None
 
 
 class LearningSolver:
-    def __init__(self,
-                 components=None,
-                 gap_tolerance=1e-4,
-                 mode="exact",
-                 solver="gurobi",
-                 threads=None,
-                 time_limit=None,
-                 node_limit=None,
-                 solve_lp_first=True,
-                 use_lazy_cb=False):
-        """
-        Mixed-Integer Linear Programming (MIP) solver that extracts information
-        from previous runs and uses Machine Learning methods to accelerate the
-        solution of new (yet unseen) instances.
+    """
+    Mixed-Integer Linear Programming (MIP) solver that extracts information
+    from previous runs and uses Machine Learning methods to accelerate the
+    solution of new (yet unseen) instances.
 
-        Parameters
-        ----------
-        components
-            Set of components in the solver. By default, includes:
-                - ObjectiveValueComponent
-                - PrimalSolutionComponent
-                - DynamicLazyConstraintsComponent
-                - UserCutsComponent
-        gap_tolerance
-            Relative MIP gap tolerance. By default, 1e-4.
-        mode
-            If "exact", solves problem to optimality, keeping all optimality
-            guarantees provided by the MIP solver. If "heuristic", uses machine
-            learning more agressively, and may return suboptimal solutions.
-        solver
-            The internal MIP solver to use. Can be either "cplex", "gurobi", a
-            solver class such as GurobiSolver, or a solver instance such as
-            GurobiSolver().
-        threads
-            Maximum number of threads to use. If None, uses solver default.
-        time_limit
-            Maximum running time in seconds. If None, uses solver default.
-        node_limit
-            Maximum number of branch-and-bound nodes to explore. If None, uses
-            solver default.
-        use_lazy_cb
-            If True, uses lazy callbacks to enforce lazy constraints, instead of
-            a simple solver loop. This functionality may not supported by
-            all internal MIP solvers.
-        solve_lp_first: bool
-            If true, solve LP relaxation first, then solve original MILP. This
-            option should be activated if the LP relaxation is not very
-            expensive to solve and if it provides good hints for the integer
-            solution.
-        """
-        self.components = {}
-        self.mode = mode
-        self.internal_solver = None
-        self.internal_solver_factory = solver
-        self.threads = threads
-        self.time_limit = time_limit
-        self.gap_tolerance = gap_tolerance
+    Parameters
+    ----------
+    components: List[Component]
+        Set of components in the solver. By default, includes
+        `ObjectiveValueComponent`, `PrimalSolutionComponent`,
+        `DynamicLazyConstraintsComponent` and `UserCutsComponent`.
+    mode: str
+        If "exact", solves problem to optimality, keeping all optimality
+        guarantees provided by the MIP solver. If "heuristic", uses machine
+        learning more aggressively, and may return suboptimal solutions.
+    solver: Callable[[], InternalSolver]
+        A callable that constructs the internal solver. If None is provided,
+        use GurobiPyomoSolver.
+    use_lazy_cb: bool
+        If true, use native solver callbacks for enforcing lazy constraints,
+        instead of a simple loop. May not be supported by all solvers.
+    solve_lp: bool
+        If true, solve the root LP relaxation before solving the MIP. This
+        option should be activated if the LP relaxation is not very
+        expensive to solve and if it provides good hints for the integer
+        solution.
+    simulate_perfect: bool
+        If true, each call to solve actually performs three actions: solve
+        the original problem, train the ML models on the data that was just
+        collected, and solve the problem again. This is useful for evaluating
+        the theoretical performance of perfect ML models.
+    """
+
+    def __init__(
+        self,
+        components: Optional[List[Component]] = None,
+        mode: str = "exact",
+        solver: Optional[InternalSolver] = None,
+        use_lazy_cb: bool = False,
+        solve_lp: bool = True,
+        simulate_perfect: bool = False,
+        extractor: Optional[FeaturesExtractor] = None,
+        extract_lhs: bool = True,
+        extract_sa: bool = True,
+    ) -> None:
+        if solver is None:
+            solver = GurobiPyomoSolver()
+        if extractor is None:
+            extractor = FeaturesExtractor(
+                with_sa=extract_sa,
+                with_lhs=extract_lhs,
+            )
+        assert isinstance(solver, InternalSolver)
+        self.components: Dict[str, Component] = {}
+        self.internal_solver: Optional[InternalSolver] = None
+        self.internal_solver_prototype: InternalSolver = solver
+        self.mode: str = mode
+        self.simulate_perfect: bool = simulate_perfect
+        self.solve_lp: bool = solve_lp
         self.tee = False
-        self.node_limit = node_limit
-        self.solve_lp_first = solve_lp_first
-        self.use_lazy_cb = use_lazy_cb
-
+        self.use_lazy_cb: bool = use_lazy_cb
+        self.extractor = extractor
         if components is not None:
             for comp in components:
-                self.add(comp)
+                self._add_component(comp)
         else:
-            self.add(ObjectiveValueComponent())
-            self.add(PrimalSolutionComponent())
-            self.add(DynamicLazyConstraintsComponent())
-            self.add(UserCutsComponent())
-
+            self._add_component(ObjectiveValueComponent())
+            self._add_component(PrimalSolutionComponent(mode=mode))
+            self._add_component(DynamicLazyConstraintsComponent())
+            self._add_component(UserCutsComponent())
         assert self.mode in ["exact", "heuristic"]
+
+    def _solve(
+        self,
+        instance: Instance,
+        model: Any = None,
+        discard_output: bool = False,
+        tee: bool = False,
+    ) -> LearningSolveStats:
+
+        # Generate model
+        # -------------------------------------------------------
+        instance.load()
+        if model is None:
+            with _RedirectOutput([]):
+                model = instance.to_model()
+
+        # Initialize training sample
+        # -------------------------------------------------------
+        sample = instance.create_sample()
+
+        # Initialize stats
+        # -------------------------------------------------------
+        stats: LearningSolveStats = {}
+
+        # Initialize internal solver
+        # -------------------------------------------------------
+        self.tee = tee
+        self.internal_solver = self.internal_solver_prototype.clone()
+        assert self.internal_solver is not None
+        assert isinstance(self.internal_solver, InternalSolver)
+        self.internal_solver.set_instance(instance, model)
+
+        # Extract features (after-load)
+        # -------------------------------------------------------
+        logger.info("Extracting features (after-load)...")
+        initial_time = time.time()
+        self.extractor.extract_after_load_features(
+            instance, self.internal_solver, sample
+        )
+        logger.info(
+            "Features (after-load) extracted in %.2f seconds"
+            % (time.time() - initial_time)
+        )
+
+        callback_args = (
+            self,
+            instance,
+            model,
+            stats,
+            sample,
+        )
+
+        # Solve root LP relaxation
+        # -------------------------------------------------------
+        lp_stats = None
+        if self.solve_lp:
+            logger.debug("Running before_solve_lp callbacks...")
+            for component in self.components.values():
+                component.before_solve_lp(*callback_args)
+
+            logger.info("Solving root LP relaxation...")
+            lp_stats = self.internal_solver.solve_lp(tee=tee)
+            stats.update(cast(LearningSolveStats, lp_stats.__dict__))
+            assert lp_stats.lp_wallclock_time is not None
+            logger.info(
+                "LP relaxation solved in %.2f seconds" % lp_stats.lp_wallclock_time
+            )
+
+            logger.debug("Running after_solve_lp callbacks...")
+            for component in self.components.values():
+                component.after_solve_lp(*callback_args)
+
+            # Extract features (after-lp)
+            # -------------------------------------------------------
+            logger.info("Extracting features (after-lp)...")
+            initial_time = time.time()
+            self.extractor.extract_after_lp_features(
+                self.internal_solver, sample, lp_stats
+            )
+            logger.info(
+                "Features (after-lp) extracted in %.2f seconds"
+                % (time.time() - initial_time)
+            )
+
+        # Callback wrappers
+        # -------------------------------------------------------
+        def iteration_cb_wrapper() -> bool:
+            should_repeat = False
+            for comp in self.components.values():
+                if comp.iteration_cb(self, instance, model):
+                    should_repeat = True
+            return should_repeat
+
+        def lazy_cb_wrapper(
+            cb_solver: InternalSolver,
+            cb_model: Any,
+        ) -> None:
+            for comp in self.components.values():
+                comp.lazy_cb(self, instance, model)
+
+        def user_cut_cb_wrapper(
+            cb_solver: InternalSolver,
+            cb_model: Any,
+        ) -> None:
+            for comp in self.components.values():
+                comp.user_cut_cb(self, instance, model)
+
+        lazy_cb = None
+        if self.use_lazy_cb:
+            lazy_cb = lazy_cb_wrapper
+
+        user_cut_cb = None
+        if instance.has_user_cuts():
+            user_cut_cb = user_cut_cb_wrapper
+
+        # Before-solve callbacks
+        # -------------------------------------------------------
+        logger.debug("Running before_solve_mip callbacks...")
         for component in self.components.values():
-            component.mode = self.mode
+            component.before_solve_mip(*callback_args)
 
-    def _create_internal_solver(self):
-        logger.debug("Initializing %s" % self.internal_solver_factory)
-        if self.internal_solver_factory == "cplex":
-            solver = CplexPyomoSolver()
-        elif self.internal_solver_factory == "gurobi":
-            solver = GurobiPyomoSolver()
-        elif callable(self.internal_solver_factory):
-            solver = self.internal_solver_factory()
-        else:
-            solver = self.internal_solver_factory
-        if self.threads is not None:
-            logger.info("Setting threads to %d" % self.threads)
-            solver.set_threads(self.threads)
-        if self.time_limit is not None:
-            logger.info("Setting time limit to %f" % self.time_limit)
-            solver.set_time_limit(self.time_limit)
-        if self.gap_tolerance is not None:
-            logger.info("Setting gap tolerance to %f" % self.gap_tolerance)
-            solver.set_gap_tolerance(self.gap_tolerance)
-        if self.node_limit is not None:
-            logger.info("Setting node limit to %d" % self.node_limit)
-            solver.set_node_limit(self.node_limit)
-        return solver
+        # Solve MIP
+        # -------------------------------------------------------
+        logger.info("Solving MIP...")
+        mip_stats = self.internal_solver.solve(
+            tee=tee,
+            iteration_cb=iteration_cb_wrapper,
+            user_cut_cb=user_cut_cb,
+            lazy_cb=lazy_cb,
+        )
+        assert mip_stats.mip_wallclock_time is not None
+        logger.info("MIP solved in %.2f seconds" % mip_stats.mip_wallclock_time)
+        stats.update(cast(LearningSolveStats, mip_stats.__dict__))
+        stats["Solver"] = "default"
+        stats["Gap"] = self._compute_gap(
+            ub=mip_stats.mip_upper_bound,
+            lb=mip_stats.mip_lower_bound,
+        )
+        stats["Mode"] = self.mode
 
-    def solve(self,
-              instance,
-              model=None,
-              tee=False):
+        # Extract features (after-mip)
+        # -------------------------------------------------------
+        logger.info("Extracting features (after-mip)...")
+        initial_time = time.time()
+        for (k, v) in mip_stats.__dict__.items():
+            sample.put_scalar(k, v)
+        self.extractor.extract_after_mip_features(self.internal_solver, sample)
+        logger.info(
+            "Features (after-mip) extracted in %.2f seconds"
+            % (time.time() - initial_time)
+        )
+
+        # After-solve callbacks
+        # -------------------------------------------------------
+        logger.debug("Calling after_solve_mip callbacks...")
+        for component in self.components.values():
+            component.after_solve_mip(*callback_args)
+
+        # Flush
+        # -------------------------------------------------------
+        if not discard_output:
+            instance.flush()
+
+        return stats
+
+    def solve(
+        self,
+        instance: Instance,
+        model: Any = None,
+        discard_output: bool = False,
+        tee: bool = False,
+    ) -> LearningSolveStats:
         """
         Solves the given instance. If trained machine-learning models are
         available, they will be used to accelerate the solution process.
 
-        This method modifies the instance object. Specifically, the following
-        properties are set:
-            - instance.lp_solution
-            - instance.lp_value
-            - instance.lower_bound
-            - instance.upper_bound
-            - instance.solution
-            - instance.solver_log
-        Additional solver components may set additional properties. Please
-        see their documentation for more details.
+        The argument `instance` may be either an Instance object or a
+        filename pointing to a pickled Instance object.
+
+        This method adds a new training sample to `instance.training_sample`.
+        If a filename is provided, then the file is modified in-place. That is,
+        the original file is overwritten.
 
         If `solver.solve_lp_first` is False, the properties lp_solution and
         lp_value will be set to dummy values.
 
         Parameters
         ----------
-        instance: miplearn.Instance
-            The instance to be solved
-        model: pyomo.core.ConcreteModel
+        instance: Instance
+            The instance to be solved.
+        model: Any
             The corresponding Pyomo model. If not provided, it will be created.
+        discard_output: bool
+            If True, do not write the modified instances anywhere; simply discard
+            them. Useful during benchmarking.
         tee: bool
             If true, prints solver log to screen.
 
         Returns
         -------
-        dict
+        LearningSolveStats
             A dictionary of solver statistics containing at least the following
             keys: "Lower bound", "Upper bound", "Wallclock time", "Nodes",
             "Sense", "Log", "Warm start value" and "LP value".
@@ -185,106 +341,118 @@ class LearningSolver:
             "Predicted UB". See the documentation of each component for more
             details.
         """
+        if self.simulate_perfect:
+            if not isinstance(instance, PickleGzInstance):
+                raise Exception("Not implemented")
+            self._solve(
+                instance=instance,
+                model=model,
+                tee=tee,
+            )
+            self.fit([instance])
+            instance.instance = None
+        return self._solve(
+            instance=instance,
+            model=model,
+            discard_output=discard_output,
+            tee=tee,
+        )
 
-        if model is None:
-            model = instance.to_model()
+    def parallel_solve(
+        self,
+        instances: List[Instance],
+        n_jobs: int = 4,
+        label: str = "Solve",
+        discard_outputs: bool = False,
+    ) -> List[LearningSolveStats]:
+        """
+        Solves multiple instances in parallel.
 
-        self.tee = tee
-        self.internal_solver = self._create_internal_solver()
-        self.internal_solver.set_instance(instance, model)
+        This method is equivalent to calling `solve` for each item on the list,
+        but it processes multiple instances at the same time. Like `solve`, this
+        method modifies each instance in place. Also like `solve`, a list of
+        filenames may be provided.
 
-        if self.solve_lp_first:
-            logger.info("Solving LP relaxation...")
-            results = self.internal_solver.solve_lp(tee=tee)
-            instance.lp_solution = self.internal_solver.get_solution()
-            instance.lp_value = results["Optimal value"]
+        Parameters
+        ----------
+        discard_outputs: bool
+            If True, do not write the modified instances anywhere; simply discard
+            them instead. Useful during benchmarking.
+        label: str
+            Label to show in the progress bar.
+        instances: List[Instance]
+            The instances to be solved.
+        n_jobs: int
+            Number of instances to solve in parallel at a time.
+
+        Returns
+        -------
+        List[LearningSolveStats]
+            List of solver statistics, with one entry for each provided instance.
+            The list is the same you would obtain by calling
+            `[solver.solve(p) for p in instances]`
+        """
+        if n_jobs == 1:
+            return [self.solve(p) for p in instances]
         else:
-            instance.lp_solution = self.internal_solver.get_empty_solution()
-            instance.lp_value = 0.0
+            self.internal_solver = None
+            self._silence_miplearn_logger()
+            _GLOBAL[0].solver = self
+            _GLOBAL[0].instances = instances
+            _GLOBAL[0].discard_outputs = discard_outputs
+            results = p_map(
+                _parallel_solve,
+                list(range(len(instances))),
+                num_cpus=n_jobs,
+                desc=label,
+            )
+            results = [r for r in results if r[0]]
+            stats = []
+            for (idx, (s, instance)) in enumerate(results):
+                stats.append(s)
+                instances[idx] = instance
+            self._restore_miplearn_logger()
+            return stats
 
-        logger.debug("Running before_solve callbacks...")
-        for component in self.components.values():
-            component.before_solve(self, instance, model)
-
-        def iteration_cb():
-            should_repeat = False
-            for comp in self.components.values():
-                if comp.after_iteration(self, instance, model):
-                    should_repeat = True
-            return should_repeat
-
-        def lazy_cb_wrapper(cb_solver, cb_model):
-            for comp in self.components.values():
-                comp.on_lazy_callback(self, instance, model)
-
-        lazy_cb = None
-        if self.use_lazy_cb:
-            lazy_cb = lazy_cb_wrapper
-
-        logger.info("Solving MILP...")
-        results = self.internal_solver.solve(tee=tee,
-                                             iteration_cb=iteration_cb,
-                                             lazy_cb=lazy_cb)
-        results["LP value"] = instance.lp_value
-
-        # Read MIP solution and bounds
-        instance.lower_bound = results["Lower bound"]
-        instance.upper_bound = results["Upper bound"]
-        instance.solver_log = results["Log"]
-        instance.solution = self.internal_solver.get_solution()
-
-        logger.debug("Calling after_solve callbacks...")
-        for component in self.components.values():
-            component.after_solve(self, instance, model, results)
-
-        return results
-
-    def parallel_solve(self,
-                       instances,
-                       n_jobs=4,
-                       label="Solve"):
-
-        self.internal_solver = None
-        self._silence_miplearn_logger()
-        SOLVER[0] = self
-        INSTANCES[0] = instances
-        p_map_results = p_map(_parallel_solve,
-                              list(range(len(instances))),
-                              num_cpus=n_jobs,
-                              desc=label)
-        results = [p["solver_results"] for p in p_map_results]
-        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["solver_results"]["LP value"]
-            instances[idx].lower_bound = r["solver_results"]["Lower bound"]
-            instances[idx].upper_bound = r["solver_results"]["Upper bound"]
-            instances[idx].found_violated_lazy_constraints = r["found_violated_lazy_constraints"]
-            instances[idx].found_violated_user_cuts = r["found_violated_user_cuts"]
-            instances[idx].slacks = r["slacks"]
-            instances[idx].solver_log = r["solver_results"]["Log"]
-        self._restore_miplearn_logger()
-        return results
-
-    def fit(self, training_instances):
+    def fit(
+        self,
+        training_instances: List[Instance],
+        n_jobs: int = 1,
+    ) -> None:
         if len(training_instances) == 0:
+            logger.warning("Empty list of training instances provided. Skipping.")
             return
-        for component in self.components.values():
-            component.fit(training_instances)
+        Component.fit_multiple(
+            list(self.components.values()),
+            training_instances,
+            n_jobs=n_jobs,
+        )
 
-    def add(self, component):
+    def _add_component(self, component: Component) -> None:
         name = component.__class__.__name__
         self.components[name] = component
 
-    def _silence_miplearn_logger(self):
+    def _silence_miplearn_logger(self) -> None:
         miplearn_logger = logging.getLogger("miplearn")
         self.prev_log_level = miplearn_logger.getEffectiveLevel()
-        miplearn_logger.setLevel(logging.WARNING)    
-        
-    def _restore_miplearn_logger(self):
+        miplearn_logger.setLevel(logging.WARNING)
+
+    def _restore_miplearn_logger(self) -> None:
         miplearn_logger = logging.getLogger("miplearn")
-        miplearn_logger.setLevel(self.prev_log_level)    
-        
-    def __getstate__(self):
+        miplearn_logger.setLevel(self.prev_log_level)
+
+    def __getstate__(self) -> Dict:
         self.internal_solver = None
         return self.__dict__
+
+    @staticmethod
+    def _compute_gap(ub: Optional[float], lb: Optional[float]) -> Optional[float]:
+        if lb is None or ub is None or lb * ub < 0:
+            # solver did not find a solution and/or bound
+            return None
+        elif abs(ub - lb) < 1e-6:
+            # avoid division by zero when ub = lb = 0
+            return 0.0
+        else:
+            # divide by max(abs(ub),abs(lb)) to ensure gap <= 1
+            return (ub - lb) / max(abs(ub), abs(lb))

miplearn/solvers/pyomo/__init__.py

@@ -1,3 +1,3 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.

miplearn/solvers/pyomo/base.py

@@ -1,19 +1,40 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import logging
 import re
 import sys
-import logging
-import pyomo
-from abc import abstractmethod
 from io import StringIO
-from pyomo import environ as pe
-from pyomo.core import Var, Constraint
+from typing import Any, List, Dict, Optional, Tuple
 
-from .. import RedirectOutput
-from ..internal import InternalSolver
-from ...instance import Instance
+import numpy as np
+import pyomo
+from overrides import overrides
+from pyomo import environ as pe
+from pyomo.core import Var, Suffix, Objective
+from pyomo.core.base import _GeneralVarData
+from pyomo.core.base.constraint import ConstraintList
+from pyomo.core.expr.numeric_expr import SumExpression, MonomialTermExpression
+from pyomo.opt import TerminationCondition
+from pyomo.opt.base.solvers import SolverFactory
+
+from miplearn.instance.base import Instance
+from miplearn.solvers import _RedirectOutput, _none_if_empty
+from miplearn.solvers.internal import (
+    InternalSolver,
+    LPSolveStats,
+    IterationCallback,
+    LazyCallback,
+    MIPSolveStats,
+    Variables,
+    Constraints,
+)
+from miplearn.types import (
+    SolverParams,
+    UserCutCallback,
+    Solution,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -23,160 +44,453 @@ class BasePyomoSolver(InternalSolver):
     Base class for all Pyomo solvers.
     """
 
-    def __init__(self):
-        self.instance = None
-        self.model = None
-        self._all_vars = None
-        self._bin_vars = None
-        self._is_warm_start_available = False
-        self._pyomo_solver = None
-        self._obj_sense = None
-        self._varname_to_var = {}
-        self._cname_to_constr = {}
+    def __init__(
+        self,
+        solver_factory: SolverFactory,
+        params: SolverParams,
+    ) -> None:
+        self.instance: Optional[Instance] = None
+        self.model: Optional[pe.ConcreteModel] = None
+        self.params = params
+        self._all_vars: List[pe.Var] = []
+        self._bin_vars: List[pe.Var] = []
+        self._is_warm_start_available: bool = False
+        self._pyomo_solver: SolverFactory = solver_factory
+        self._obj_sense: str = "min"
+        self._varname_to_var: Dict[bytes, pe.Var] = {}
+        self._cname_to_constr: Dict[str, pe.Constraint] = {}
+        self._termination_condition: str = ""
+        self._has_lp_solution = False
+        self._has_mip_solution = False
+        self._obj: Dict[str, float] = {}
 
-    def solve_lp(self, tee=False):
-        for var in self._bin_vars:
-            lb, ub = var.bounds
-            var.setlb(lb)
-            var.setub(ub)
-            var.domain = pyomo.core.base.set_types.Reals
-            self._pyomo_solver.update_var(var)
-        results = self._pyomo_solver.solve(tee=tee)
-        for var in self._bin_vars:
-            var.domain = pyomo.core.base.set_types.Binary
-            self._pyomo_solver.update_var(var)
-        return {
-            "Optimal value": results["Problem"][0]["Lower bound"],
-        }
+        for (key, value) in params.items():
+            self._pyomo_solver.options[key] = value
 
-    def get_solution(self):
-        solution = {}
+    def add_constraint(
+        self,
+        constr: Any,
+    ) -> None:
+        assert self.model is not None
+        self._pyomo_solver.add_constraint(constr)
+        self._termination_condition = ""
+        self._has_lp_solution = False
+        self._has_mip_solution = False
+
+    @overrides
+    def add_constraints(self, cf: Constraints) -> None:
+        assert cf.names is not None
+        assert cf.senses is not None
+        assert cf.lhs is not None
+        assert cf.rhs is not None
+        assert self.model is not None
+        for (i, name) in enumerate(cf.names):
+            lhs = 0.0
+            for (varname, coeff) in cf.lhs[i]:
+                var = self._varname_to_var[varname]
+                lhs += var * coeff
+            if cf.senses[i] == b"=":
+                expr = lhs == cf.rhs[i]
+            elif cf.senses[i] == b"<":
+                expr = lhs <= cf.rhs[i]
+            elif cf.senses[i] == b">":
+                expr = lhs >= cf.rhs[i]
+            else:
+                raise Exception(f"Unknown sense: {cf.senses[i]}")
+            cl = pe.Constraint(expr=expr, name=name)
+            self.model.add_component(name.decode(), cl)
+            self._pyomo_solver.add_constraint(cl)
+            self._cname_to_constr[name] = cl
+        self._termination_condition = ""
+        self._has_lp_solution = False
+        self._has_mip_solution = False
+
+    @overrides
+    def are_callbacks_supported(self) -> bool:
+        return False
+
+    @overrides
+    def are_constraints_satisfied(
+        self,
+        cf: Constraints,
+        tol: float = 1e-5,
+    ) -> List[bool]:
+        assert cf.names is not None
+        assert cf.lhs is not None
+        assert cf.rhs is not None
+        assert cf.senses is not None
+        result = []
+        for (i, name) in enumerate(cf.names):
+            lhs = 0.0
+            for (varname, coeff) in cf.lhs[i]:
+                var = self._varname_to_var[varname]
+                lhs += var.value * coeff
+            if cf.senses[i] == "<":
+                result.append(lhs <= cf.rhs[i] + tol)
+            elif cf.senses[i] == ">":
+                result.append(lhs >= cf.rhs[i] - tol)
+            else:
+                result.append(abs(cf.rhs[i] - lhs) < tol)
+        return result
+
+    @overrides
+    def build_test_instance_infeasible(self) -> Instance:
+        return PyomoTestInstanceInfeasible()
+
+    @overrides
+    def build_test_instance_knapsack(self) -> Instance:
+        return PyomoTestInstanceKnapsack(
+            weights=[23.0, 26.0, 20.0, 18.0],
+            prices=[505.0, 352.0, 458.0, 220.0],
+            capacity=67.0,
+        )
+
+    @overrides
+    def fix(self, solution: Solution) -> None:
+        for (varname, value) in solution.items():
+            if value is None:
+                continue
+            var = self._varname_to_var[varname]
+            var.fix(value)
+            self._pyomo_solver.update_var(var)
+
+    @overrides
+    def get_constraints(
+        self,
+        with_static: bool = True,
+        with_sa: bool = True,
+        with_lhs: bool = True,
+    ) -> Constraints:
+        model = self.model
+        assert model is not None
+
+        names: List[str] = []
+        rhs: List[float] = []
+        lhs: List[List[Tuple[bytes, float]]] = []
+        senses: List[str] = []
+        dual_values: List[float] = []
+        slacks: List[float] = []
+
+        def _parse_constraint(c: pe.Constraint) -> None:
+            assert model is not None
+            if with_static:
+                # Extract RHS and sense
+                has_ub = c.has_ub()
+                has_lb = c.has_lb()
+                assert (
+                    (not has_lb) or (not has_ub) or c.upper() == c.lower()
+                ), "range constraints not supported"
+                if not has_ub:
+                    senses.append(">")
+                    rhs.append(float(c.lower()))
+                elif not has_lb:
+                    senses.append("<")
+                    rhs.append(float(c.upper()))
+                else:
+                    senses.append("=")
+                    rhs.append(float(c.upper()))
+
+                if with_lhs:
+                    # Extract LHS
+                    lhsc = []
+                    expr = c.body
+                    if isinstance(expr, SumExpression):
+                        for term in expr._args_:
+                            if isinstance(term, MonomialTermExpression):
+                                lhsc.append(
+                                    (
+                                        term._args_[1].name.encode(),
+                                        float(term._args_[0]),
+                                    )
+                                )
+                            elif isinstance(term, _GeneralVarData):
+                                lhsc.append((term.name.encode(), 1.0))
+                            else:
+                                raise Exception(
+                                    f"Unknown term type: {term.__class__.__name__}"
+                                )
+                    elif isinstance(expr, _GeneralVarData):
+                        lhsc.append((expr.name.encode(), 1.0))
+                    else:
+                        raise Exception(
+                            f"Unknown expression type: {expr.__class__.__name__}"
+                        )
+                    lhs.append(lhsc)
+
+            # Extract dual values
+            if self._has_lp_solution:
+                dual_values.append(model.dual[c])
+
+            # Extract slacks
+            if self._has_mip_solution or self._has_lp_solution:
+                slacks.append(model.slack[c])
+
+        for constr in model.component_objects(pyomo.core.Constraint):
+            if isinstance(constr, pe.ConstraintList):
+                for idx in constr:
+                    names.append(f"{constr.name}[{idx}]")
+                    _parse_constraint(constr[idx])
+            else:
+                names.append(constr.name)
+                _parse_constraint(constr)
+
+        return Constraints(
+            names=_none_if_empty(np.array(names, dtype="S")),
+            rhs=_none_if_empty(np.array(rhs, dtype=float)),
+            senses=_none_if_empty(np.array(senses, dtype="S")),
+            lhs=_none_if_empty(lhs),
+            slacks=_none_if_empty(np.array(slacks, dtype=float)),
+            dual_values=_none_if_empty(np.array(dual_values, dtype=float)),
+        )
+
+    @overrides
+    def get_constraint_attrs(self) -> List[str]:
+        return [
+            "dual_values",
+            "lhs",
+            "names",
+            "rhs",
+            "senses",
+            "slacks",
+        ]
+
+    @overrides
+    def get_solution(self) -> Optional[Solution]:
+        assert self.model is not None
+        if self.is_infeasible():
+            return None
+        solution: Solution = {}
         for var in self.model.component_objects(Var):
-            solution[str(var)] = {}
             for index in var:
-                solution[str(var)][index] = var[index].value
+                if var[index].fixed:
+                    continue
+                solution[f"{var}[{index}]".encode()] = var[index].value
         return solution
 
-    def get_value(self, var_name, index):
-        var = self._varname_to_var[var_name]
-        return var[index].value
+    @overrides
+    def get_variables(
+        self,
+        with_static: bool = True,
+        with_sa: bool = True,
+    ) -> Variables:
+        assert self.model is not None
 
-    def get_variables(self):
-        variables = {}
-        for var in self.model.component_objects(Var):
-            variables[str(var)] = []
-            for index in var:
-                variables[str(var)] += [index]
-        return variables
+        names: List[str] = []
+        types: List[str] = []
+        upper_bounds: List[float] = []
+        lower_bounds: List[float] = []
+        obj_coeffs: List[float] = []
+        reduced_costs: List[float] = []
+        values: List[float] = []
 
-    def set_warm_start(self, solution):
-        self.clear_warm_start()
-        count_total, count_fixed = 0, 0
-        for var_name in solution:
-            var = self._varname_to_var[var_name]
-            for index in solution[var_name]:
-                count_total += 1
-                var[index].value = solution[var_name][index]
-                if solution[var_name][index] is not None:
-                    count_fixed += 1
-        if count_fixed > 0:
-            self._is_warm_start_available = True
-        logger.info("Setting start values for %d variables (out of %d)" %
-                    (count_fixed, count_total))
+        for (i, var) in enumerate(self.model.component_objects(pyomo.core.Var)):
+            for idx in var:
+                v = var[idx]
 
-    def clear_warm_start(self):
-        for var in self._all_vars:
-            if not var.fixed:
-                var.value = None
-        self._is_warm_start_available = False
+                # Variable name
+                if idx is None:
+                    names.append(str(var))
+                else:
+                    names.append(f"{var}[{idx}]")
 
-    def set_instance(self, instance, model=None):
+                if with_static:
+                    # Variable type
+                    if v.domain == pyomo.core.Binary:
+                        types.append("B")
+                    elif v.domain in [
+                        pyomo.core.Reals,
+                        pyomo.core.NonNegativeReals,
+                        pyomo.core.NonPositiveReals,
+                        pyomo.core.NegativeReals,
+                        pyomo.core.PositiveReals,
+                    ]:
+                        types.append("C")
+                    else:
+                        raise Exception(f"unknown variable domain: {v.domain}")
+
+                    # Bounds
+                    lb, ub = v.bounds
+                    upper_bounds.append(float(ub))
+                    lower_bounds.append(float(lb))
+
+                    # Objective coefficient
+                    if v.name in self._obj:
+                        obj_coeffs.append(self._obj[v.name])
+                    else:
+                        obj_coeffs.append(0.0)
+
+                # Reduced costs
+                if self._has_lp_solution:
+                    reduced_costs.append(self.model.rc[v])
+
+                # Values
+                if self._has_lp_solution or self._has_mip_solution:
+                    values.append(v.value)
+
+        return Variables(
+            names=_none_if_empty(np.array(names, dtype="S")),
+            types=_none_if_empty(np.array(types, dtype="S")),
+            upper_bounds=_none_if_empty(np.array(upper_bounds, dtype=float)),
+            lower_bounds=_none_if_empty(np.array(lower_bounds, dtype=float)),
+            obj_coeffs=_none_if_empty(np.array(obj_coeffs, dtype=float)),
+            reduced_costs=_none_if_empty(np.array(reduced_costs, dtype=float)),
+            values=_none_if_empty(np.array(values, dtype=float)),
+        )
+
+    @overrides
+    def get_variable_attrs(self) -> List[str]:
+        return [
+            "names",
+            # "basis_status",
+            "categories",
+            "lower_bounds",
+            "obj_coeffs",
+            "reduced_costs",
+            # "sa_lb_down",
+            # "sa_lb_up",
+            # "sa_obj_down",
+            # "sa_obj_up",
+            # "sa_ub_down",
+            # "sa_ub_up",
+            "types",
+            "upper_bounds",
+            "user_features",
+            "values",
+        ]
+
+    @overrides
+    def is_infeasible(self) -> bool:
+        return self._termination_condition == TerminationCondition.infeasible
+
+    @overrides
+    def remove_constraints(self, names: List[str]) -> None:
+        assert self.model is not None
+        for name in names:
+            constr = self._cname_to_constr[name]
+            del self._cname_to_constr[name]
+            self.model.del_component(constr)
+            self._pyomo_solver.remove_constraint(constr)
+
+    @overrides
+    def set_instance(
+        self,
+        instance: Instance,
+        model: Any = None,
+    ) -> None:
         if model is None:
             model = instance.to_model()
-        assert isinstance(instance, Instance)
         assert isinstance(model, pe.ConcreteModel)
         self.instance = instance
         self.model = model
+        self.model.extra_constraints = ConstraintList()
+        self.model.dual = Suffix(direction=Suffix.IMPORT)
+        self.model.rc = Suffix(direction=Suffix.IMPORT)
+        self.model.slack = Suffix(direction=Suffix.IMPORT)
         self._pyomo_solver.set_instance(model)
         self._update_obj()
         self._update_vars()
         self._update_constrs()
 
-    def _update_obj(self):
-        self._obj_sense = "max"
-        if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
-            self._obj_sense = "min"
+    @overrides
+    def set_warm_start(self, solution: Solution) -> None:
+        self._clear_warm_start()
+        count_fixed = 0
+        for (var_name, value) in solution.items():
+            if value is None:
+                continue
+            var = self._varname_to_var[var_name]
+            var.value = solution[var_name]
+            count_fixed += 1
+        if count_fixed > 0:
+            self._is_warm_start_available = True
 
-    def _update_vars(self):
-        self._all_vars = []
-        self._bin_vars = []
-        self._varname_to_var = {}
-        for var in self.model.component_objects(Var):
-            self._varname_to_var[var.name] = var
-            for idx in var:
-                self._all_vars += [var[idx]]
-                if var[idx].domain == pyomo.core.base.set_types.Binary:
-                    self._bin_vars += [var[idx]]
-
-    def _update_constrs(self):
-        self._cname_to_constr = {}
-        for constr in self.model.component_objects(Constraint):
-            self._cname_to_constr[constr.name] = constr
-
-    def fix(self, solution):
-        count_total, count_fixed = 0, 0
-        for varname in solution:
-            for index in solution[varname]:
-                var = self._varname_to_var[varname]
-                count_total += 1
-                if solution[varname][index] is None:
-                    continue
-                count_fixed += 1
-                var[index].fix(solution[varname][index])
-                self._pyomo_solver.update_var(var[index])
-        logger.info("Fixing values for %d variables (out of %d)" %
-                    (count_fixed, count_total))
-
-    def add_constraint(self, constraint):
-        self._pyomo_solver.add_constraint(constraint)
-        self._update_constrs()
-
-    def solve(self,
-              tee=False,
-              iteration_cb=None,
-              lazy_cb=None):
-        if lazy_cb is not None:
-            raise Exception("lazy callback not supported")
+    @overrides
+    def solve(
+        self,
+        tee: bool = False,
+        iteration_cb: Optional[IterationCallback] = None,
+        lazy_cb: Optional[LazyCallback] = None,
+        user_cut_cb: Optional[UserCutCallback] = None,
+    ) -> MIPSolveStats:
+        assert lazy_cb is None, "callbacks are not currently supported"
+        assert user_cut_cb is None, "callbacks are not currently supported"
         total_wallclock_time = 0
-        streams = [StringIO()]
+        streams: List[Any] = [StringIO()]
         if tee:
             streams += [sys.stdout]
         if iteration_cb is None:
             iteration_cb = lambda: False
-        self.instance.found_violated_lazy_constraints = []
-        self.instance.found_violated_user_cuts = []
         while True:
             logger.debug("Solving MIP...")
-            with RedirectOutput(streams):
-                results = self._pyomo_solver.solve(tee=True,
-                                                   warmstart=self._is_warm_start_available)
+            with _RedirectOutput(streams):
+                results = self._pyomo_solver.solve(
+                    tee=True,
+                    warmstart=self._is_warm_start_available,
+                )
             total_wallclock_time += results["Solver"][0]["Wallclock time"]
             should_repeat = iteration_cb()
             if not should_repeat:
                 break
         log = streams[0].getvalue()
-        return {
-            "Lower bound": results["Problem"][0]["Lower bound"],
-            "Upper bound": results["Problem"][0]["Upper bound"],
-            "Wallclock time": total_wallclock_time,
-            "Nodes": self._extract_node_count(log),
-            "Sense": self._obj_sense,
-            "Log": log,
-            "Warm start value": self._extract_warm_start_value(log),
-        }
+        node_count = self._extract_node_count(log)
+        ws_value = self._extract_warm_start_value(log)
+        self._termination_condition = results["Solver"][0]["Termination condition"]
+        lb, ub = None, None
+        self._has_mip_solution = False
+        self._has_lp_solution = False
+        if not self.is_infeasible():
+            self._has_mip_solution = True
+            lb = results["Problem"][0]["Lower bound"]
+            ub = results["Problem"][0]["Upper bound"]
+        return MIPSolveStats(
+            mip_lower_bound=lb,
+            mip_upper_bound=ub,
+            mip_wallclock_time=total_wallclock_time,
+            mip_sense=self._obj_sense,
+            mip_log=log,
+            mip_nodes=node_count,
+            mip_warm_start_value=ws_value,
+        )
+
+    @overrides
+    def solve_lp(
+        self,
+        tee: bool = False,
+    ) -> LPSolveStats:
+        self._relax()
+        streams: List[Any] = [StringIO()]
+        if tee:
+            streams += [sys.stdout]
+        with _RedirectOutput(streams):
+            results = self._pyomo_solver.solve(tee=True)
+        self._termination_condition = results["Solver"][0]["Termination condition"]
+        self._restore_integrality()
+        opt_value = None
+        self._has_lp_solution = False
+        self._has_mip_solution = False
+        if not self.is_infeasible():
+            opt_value = results["Problem"][0]["Lower bound"]
+            self._has_lp_solution = True
+        return LPSolveStats(
+            lp_value=opt_value,
+            lp_log=streams[0].getvalue(),
+            lp_wallclock_time=results["Solver"][0]["Wallclock time"],
+        )
+
+    def _clear_warm_start(self) -> None:
+        for var in self._all_vars:
+            if not var.fixed:
+                var.value = None
+        self._is_warm_start_available = False
 
     @staticmethod
-    def __extract(log, regexp, default=None):
+    def _extract(
+        log: str,
+        regexp: Optional[str],
+        default: Optional[str] = None,
+    ) -> Optional[str]:
+        if regexp is None:
+            return default
         value = default
         for line in log.splitlines():
             matches = re.findall(regexp, line)
@@ -185,68 +499,150 @@ class BasePyomoSolver(InternalSolver):
             value = matches[0]
         return value
 
-    def _extract_warm_start_value(self, log):
-        value = self.__extract(log, self._get_warm_start_regexp())
-        if value is not None:
-            value = float(value)
-        return value
+    def _extract_node_count(self, log: str) -> Optional[int]:
+        value = self._extract(log, self._get_node_count_regexp())
+        if value is None:
+            return None
+        return int(value)
 
-    def _extract_node_count(self, log):
-        return int(self.__extract(log,
-                                  self._get_node_count_regexp(),
-                                  default=1))
+    def _extract_warm_start_value(self, log: str) -> Optional[float]:
+        value = self._extract(log, self._get_warm_start_regexp())
+        if value is None:
+            return None
+        return float(value)
 
-    def set_threads(self, threads):
-        key = self._get_threads_option_name()
-        self._pyomo_solver.options[key] = threads
+    def _get_node_count_regexp(self) -> Optional[str]:
+        return None
 
-    def set_time_limit(self, time_limit):
-        key = self._get_time_limit_option_name()
-        self._pyomo_solver.options[key] = time_limit
+    def _get_warm_start_regexp(self) -> Optional[str]:
+        return None
 
-    def set_node_limit(self, node_limit):
-        key = self._get_node_limit_option_name()
-        self._pyomo_solver.options[key] = node_limit
+    def _parse_pyomo_expr(self, expr: Any) -> Dict[str, float]:
+        lhs = {}
+        if isinstance(expr, SumExpression):
+            for term in expr._args_:
+                if isinstance(term, MonomialTermExpression):
+                    lhs[term._args_[1].name] = float(term._args_[0])
+                elif isinstance(term, _GeneralVarData):
+                    lhs[term.name] = 1.0
+                else:
+                    raise Exception(f"Unknown term type: {term.__class__.__name__}")
+        elif isinstance(expr, _GeneralVarData):
+            lhs[expr.name] = 1.0
+        else:
+            raise Exception(f"Unknown expression type: {expr.__class__.__name__}")
+        return lhs
 
-    def set_gap_tolerance(self, gap_tolerance):
-        key = self._get_gap_tolerance_option_name()
-        self._pyomo_solver.options[key] = gap_tolerance
+    def _relax(self) -> None:
+        for var in self._bin_vars:
+            lb, ub = var.bounds
+            var.setlb(lb)
+            var.setub(ub)
+            var.domain = pyomo.core.base.set_types.Reals
+            self._pyomo_solver.update_var(var)
 
-    def get_constraint_ids(self):
-        return list(self._cname_to_constr.keys())
+    def _restore_integrality(self) -> None:
+        for var in self._bin_vars:
+            var.domain = pyomo.core.base.set_types.Binary
+            self._pyomo_solver.update_var(var)
 
-    def extract_constraint(self, cid):
-        raise Exception("Not implemented")
+    def _update_obj(self) -> None:
+        self._obj_sense = "max"
+        if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
+            self._obj_sense = "min"
 
-    def is_constraint_satisfied(self, cobj):
-        raise Exception("Not implemented")
+    def _update_vars(self) -> None:
+        assert self.model is not None
+        self._all_vars = []
+        self._bin_vars = []
+        self._varname_to_var = {}
+        for var in self.model.component_objects(Var):
+            for idx in var:
+                varname = f"{var.name}[{idx}]".encode()
+                if idx is None:
+                    varname = var.name.encode()
+                self._varname_to_var[varname] = var[idx]
+                self._all_vars += [var[idx]]
+                if var[idx].domain == pyomo.core.base.set_types.Binary:
+                    self._bin_vars += [var[idx]]
+        for obj in self.model.component_objects(Objective):
+            self._obj = self._parse_pyomo_expr(obj.expr)
+            break
 
-    @abstractmethod
-    def _get_warm_start_regexp(self):
-        pass
+    def _update_constrs(self) -> None:
+        assert self.model is not None
+        self._cname_to_constr.clear()
+        for constr in self.model.component_objects(pyomo.core.Constraint):
+            if isinstance(constr, pe.ConstraintList):
+                for idx in constr:
+                    self._cname_to_constr[f"{constr.name}[{idx}]"] = constr[idx]
+            else:
+                self._cname_to_constr[constr.name] = constr
 
-    @abstractmethod
-    def _get_node_count_regexp(self):
-        pass
 
-    @abstractmethod
-    def _get_threads_option_name(self):
-        pass
+class PyomoTestInstanceInfeasible(Instance):
+    @overrides
+    def to_model(self) -> pe.ConcreteModel:
+        model = pe.ConcreteModel()
+        model.x = pe.Var([0], domain=pe.Binary)
+        model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
+        model.eq = pe.Constraint(expr=model.x[0] >= 2)
+        return model
 
-    @abstractmethod
-    def _get_time_limit_option_name(self):
-        pass
 
-    @abstractmethod
-    def _get_node_limit_option_name(self):
-        pass
+class PyomoTestInstanceKnapsack(Instance):
+    """
+    Simpler (one-dimensional) Knapsack Problem, used for testing.
+    """
 
-    @abstractmethod
-    def _get_gap_tolerance_option_name(self):
-        pass
+    def __init__(
+        self,
+        weights: List[float],
+        prices: List[float],
+        capacity: float,
+    ) -> None:
+        super().__init__()
+        self.weights = weights
+        self.prices = prices
+        self.capacity = capacity
+        self.n = len(weights)
 
-    def relax(self):
-        raise Exception("not implemented")
+    @overrides
+    def to_model(self) -> pe.ConcreteModel:
+        model = pe.ConcreteModel()
+        items = range(len(self.weights))
+        model.x = pe.Var(items, domain=pe.Binary)
+        model.z = pe.Var(domain=pe.Reals, bounds=(0, self.capacity))
+        model.OBJ = pe.Objective(
+            expr=sum(model.x[v] * self.prices[v] for v in items),
+            sense=pe.maximize,
+        )
+        model.eq_capacity = pe.Constraint(
+            expr=sum(model.x[v] * self.weights[v] for v in items) == model.z
+        )
+        return model
 
-    def get_constraint_slacks(self):
-        raise Exception("not implemented")
+    @overrides
+    def get_instance_features(self) -> np.ndarray:
+        return np.array(
+            [
+                self.capacity,
+                np.average(self.weights),
+            ]
+        )
+
+    @overrides
+    def get_variable_features(self, names: np.ndarray) -> np.ndarray:
+        return np.vstack(
+            [
+                [[self.weights[i], self.prices[i]] for i in range(self.n)],
+                [0.0, 0.0],
+            ]
+        )
+
+    @overrides
+    def get_variable_categories(self, names: np.ndarray) -> np.ndarray:
+        return np.array(
+            ["default" if n.decode().startswith("x") else "" for n in names],
+            dtype="S",
+        )

miplearn/solvers/pyomo/cplex.py

@@ -1,49 +1,49 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+from typing import Optional
 
+from overrides import overrides
 from pyomo import environ as pe
 from scipy.stats import randint
 
-from .base import BasePyomoSolver
+from miplearn.solvers.pyomo.base import BasePyomoSolver
+from miplearn.types import SolverParams
 
 
 class CplexPyomoSolver(BasePyomoSolver):
-    def __init__(self, options=None):
-        """
-        Creates a new CPLEX solver, accessed through Pyomo.
+    """
+    An InternalSolver that uses CPLEX and the Pyomo modeling language.
 
-        Parameters
-        ----------
-        options: dict
-            Dictionary of options to pass to the Pyomo solver. For example,
-            {"mip_display": 5} to increase the log verbosity.
-        """
-        super().__init__()
-        self._pyomo_solver = pe.SolverFactory('cplex_persistent')
-        self._pyomo_solver.options["randomseed"] = randint(low=0, high=1000).rvs()
-        self._pyomo_solver.options["mip_display"] = 4
-        if options is not None:
-            for (key, value) in options.items():
-                self._pyomo_solver.options[key] = value
+    Parameters
+    ----------
+    params: dict
+        Dictionary of options to pass to the Pyomo solver. For example,
+        {"mip_display": 5} to increase the log verbosity.
+    """
 
-    def _get_warm_start_regexp(self):
+    def __init__(
+        self,
+        params: Optional[SolverParams] = None,
+    ) -> None:
+        if params is None:
+            params = {}
+        params["randomseed"] = randint(low=0, high=1000).rvs()
+        if "mip_display" not in params.keys():
+            params["mip_display"] = 4
+        super().__init__(
+            solver_factory=pe.SolverFactory("cplex_persistent"),
+            params=params,
+        )
+
+    @overrides
+    def _get_warm_start_regexp(self) -> str:
         return "MIP start .* with objective ([0-9.e+-]*)\\."
 
-    def _get_node_count_regexp(self):
+    @overrides
+    def _get_node_count_regexp(self) -> str:
         return "^[ *] *([0-9]+)"
 
-    def _get_threads_option_name(self):
-        return "threads"
-
-    def _get_time_limit_option_name(self):
-        return "timelimit"
-
-    def _get_node_limit_option_name(self):
-        return "mip_limits_nodes"
-
-    def _get_gap_tolerance_option_name(self):
-        return "mip_tolerances_mipgap"
-
-    def set_branching_priorities(self, priorities):
-        raise NotImplementedError
+    @overrides
+    def clone(self) -> "CplexPyomoSolver":
+        return CplexPyomoSolver(params=self.params)

miplearn/solvers/pyomo/gurobi.py

@@ -1,63 +1,55 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-import sys
 import logging
-from io import StringIO
+from typing import Optional
+
+from overrides import overrides
 from pyomo import environ as pe
 from scipy.stats import randint
 
-from .base import BasePyomoSolver
-from .. import RedirectOutput
+from miplearn.solvers.pyomo.base import BasePyomoSolver
+from miplearn.types import SolverParams
 
 logger = logging.getLogger(__name__)
 
 
 class GurobiPyomoSolver(BasePyomoSolver):
-    def __init__(self,
-                 options=None):
-        """
-        Creates a new Gurobi solver, accessed through Pyomo.
+    """
+    An InternalSolver that uses Gurobi and the Pyomo modeling language.
 
-        Parameters
-        ----------
-        options: dict
-            Dictionary of options to pass to the Pyomo solver. For example,
-            {"Threads": 4} to set the number of threads.
-        """
-        super().__init__()
-        self._pyomo_solver = pe.SolverFactory('gurobi_persistent')
-        self._pyomo_solver.options["Seed"] = randint(low=0, high=1000).rvs()
-        if options is not None:
-            for (key, value) in options.items():
-                self._pyomo_solver.options[key] = value
+    Parameters
+    ----------
+    params: dict
+        Dictionary of options to pass to the Pyomo solver. For example,
+        {"Threads": 4} to set the number of threads.
+    """
 
-    def _extract_node_count(self, log):
+    def __init__(
+        self,
+        params: Optional[SolverParams] = None,
+    ) -> None:
+        if params is None:
+            params = {}
+        params["seed"] = randint(low=0, high=1000).rvs()
+        super().__init__(
+            solver_factory=pe.SolverFactory("gurobi_persistent"),
+            params=params,
+        )
+
+    @overrides
+    def clone(self) -> "GurobiPyomoSolver":
+        return GurobiPyomoSolver(params=self.params)
+
+    @overrides
+    def _extract_node_count(self, log: str) -> int:
         return max(1, int(self._pyomo_solver._solver_model.getAttr("NodeCount")))
 
-    def _get_warm_start_regexp(self):
+    @overrides
+    def _get_warm_start_regexp(self) -> str:
         return "MIP start with objective ([0-9.e+-]*)"
 
-    def _get_node_count_regexp(self):
+    @overrides
+    def _get_node_count_regexp(self) -> Optional[str]:
         return None
-
-    def _get_threads_option_name(self):
-        return "Threads"
-
-    def _get_time_limit_option_name(self):
-        return "TimeLimit"
-
-    def _get_node_limit_option_name(self):
-        return "NodeLimit"
-
-    def _get_gap_tolerance_option_name(self):
-        return "MIPGap"
-
-    def set_branching_priorities(self, priorities):
-        from gurobipy import GRB
-        for varname in priorities.keys():
-            var = self._varname_to_var[varname]
-            for (index, priority) in priorities[varname].items():
-                gvar = self._pyomo_solver._pyomo_var_to_solver_var_map[var[index]]
-                gvar.setAttr(GRB.Attr.BranchPriority, int(round(priority)))

miplearn/solvers/pyomo/xpress.py

@@ -0,0 +1,43 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+from typing import Optional
+
+from overrides import overrides
+from pyomo import environ as pe
+from scipy.stats import randint
+
+from miplearn.solvers.pyomo.base import BasePyomoSolver
+from miplearn.types import SolverParams
+
+logger = logging.getLogger(__name__)
+
+
+class XpressPyomoSolver(BasePyomoSolver):
+    """
+    An InternalSolver that uses XPRESS and the Pyomo modeling language.
+
+    Parameters
+    ----------
+    params: dict
+        Dictionary of options to pass to the Pyomo solver. For example,
+        {"Threads": 4} to set the number of threads.
+    """
+
+    def __init__(
+        self,
+        params: Optional[SolverParams] = None,
+    ) -> None:
+        if params is None:
+            params = {}
+        params["randomseed"] = randint(low=0, high=1000).rvs()
+        super().__init__(
+            solver_factory=pe.SolverFactory("xpress_persistent"),
+            params=params,
+        )
+
+    @overrides
+    def clone(self) -> "XpressPyomoSolver":
+        return XpressPyomoSolver(params=self.params)

miplearn/solvers/tests/__init__.py

@@ -1,26 +1,299 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from miplearn import BasePyomoSolver, GurobiSolver, GurobiPyomoSolver, CplexPyomoSolver
-from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
+from typing import Any, List
+
+import numpy as np
+
+from miplearn.solvers.internal import InternalSolver, Variables, Constraints
+
+inf = float("inf")
 
 
-def _get_instance(solver):
-    if issubclass(solver, BasePyomoSolver) or isinstance(solver, BasePyomoSolver):
-        return KnapsackInstance(
-            weights=[23., 26., 20., 18.],
-            prices=[505., 352., 458., 220.],
-            capacity=67.,
-        )
-    if issubclass(solver, GurobiSolver) or isinstance(solver, GurobiSolver):
-        return GurobiKnapsackInstance(
-            weights=[23., 26., 20., 18.],
-            prices=[505., 352., 458., 220.],
-            capacity=67.,
-        )
-    assert False
+# NOTE:
+# This file is in the main source folder, so that it can be called from Julia.
 
 
-def _get_internal_solvers():
-    return [GurobiPyomoSolver, CplexPyomoSolver, GurobiSolver]
+def _filter_attrs(allowed_keys: List[str], obj: Any) -> Any:
+    for key in obj.__dict__.keys():
+        if key not in allowed_keys:
+            setattr(obj, key, None)
+    return obj
+
+
+def run_internal_solver_tests(solver: InternalSolver) -> None:
+    run_basic_usage_tests(solver.clone())
+    run_warm_start_tests(solver.clone())
+    run_infeasibility_tests(solver.clone())
+    run_iteration_cb_tests(solver.clone())
+    if solver.are_callbacks_supported():
+        run_lazy_cb_tests(solver.clone())
+
+
+def run_basic_usage_tests(solver: InternalSolver) -> None:
+    # Create and set instance
+    instance = solver.build_test_instance_knapsack()
+    model = instance.to_model()
+    solver.set_instance(instance, model)
+
+    # Fetch variables (after-load)
+    assert_equals(
+        solver.get_variables(),
+        Variables(
+            names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"),
+            lower_bounds=np.array([0.0, 0.0, 0.0, 0.0, 0.0]),
+            upper_bounds=np.array([1.0, 1.0, 1.0, 1.0, 67.0]),
+            types=np.array(["B", "B", "B", "B", "C"], dtype="S"),
+            obj_coeffs=np.array([505.0, 352.0, 458.0, 220.0, 0.0]),
+        ),
+    )
+
+    # Fetch constraints (after-load)
+    assert_equals(
+        solver.get_constraints(),
+        Constraints(
+            names=np.array(["eq_capacity"], dtype="S"),
+            rhs=np.array([0.0]),
+            lhs=[
+                [
+                    (b"x[0]", 23.0),
+                    (b"x[1]", 26.0),
+                    (b"x[2]", 20.0),
+                    (b"x[3]", 18.0),
+                    (b"z", -1.0),
+                ],
+            ],
+            senses=np.array(["="], dtype="S"),
+        ),
+    )
+
+    # Solve linear programming relaxation
+    lp_stats = solver.solve_lp()
+    assert not solver.is_infeasible()
+    assert lp_stats.lp_value is not None
+    assert_equals(round(lp_stats.lp_value, 3), 1287.923)
+    assert lp_stats.lp_log is not None
+    assert len(lp_stats.lp_log) > 100
+    assert lp_stats.lp_wallclock_time is not None
+    assert lp_stats.lp_wallclock_time > 0
+
+    # Fetch variables (after-lp)
+    assert_equals(
+        solver.get_variables(with_static=False),
+        _filter_attrs(
+            solver.get_variable_attrs(),
+            Variables(
+                names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"),
+                basis_status=np.array(["U", "B", "U", "L", "U"], dtype="S"),
+                reduced_costs=np.array(
+                    [193.615385, 0.0, 187.230769, -23.692308, 13.538462]
+                ),
+                sa_lb_down=np.array([-inf, -inf, -inf, -0.111111, -inf]),
+                sa_lb_up=np.array([1.0, 0.923077, 1.0, 1.0, 67.0]),
+                sa_obj_down=np.array(
+                    [311.384615, 317.777778, 270.769231, -inf, -13.538462]
+                ),
+                sa_obj_up=np.array([inf, 570.869565, inf, 243.692308, inf]),
+                sa_ub_down=np.array([0.913043, 0.923077, 0.9, 0.0, 43.0]),
+                sa_ub_up=np.array([2.043478, inf, 2.2, inf, 69.0]),
+                values=np.array([1.0, 0.923077, 1.0, 0.0, 67.0]),
+            ),
+        ),
+    )
+
+    # Fetch constraints (after-lp)
+    assert_equals(
+        solver.get_constraints(with_static=False),
+        _filter_attrs(
+            solver.get_constraint_attrs(),
+            Constraints(
+                basis_status=np.array(["N"], dtype="S"),
+                dual_values=np.array([13.538462]),
+                names=np.array(["eq_capacity"], dtype="S"),
+                sa_rhs_down=np.array([-24.0]),
+                sa_rhs_up=np.array([2.0]),
+                slacks=np.array([0.0]),
+            ),
+        ),
+    )
+
+    # Solve MIP
+    mip_stats = solver.solve(
+        tee=True,
+    )
+    assert not solver.is_infeasible()
+    assert mip_stats.mip_log is not None
+    assert len(mip_stats.mip_log) > 100
+    assert mip_stats.mip_lower_bound is not None
+    assert_equals(mip_stats.mip_lower_bound, 1183.0)
+    assert mip_stats.mip_upper_bound is not None
+    assert_equals(mip_stats.mip_upper_bound, 1183.0)
+    assert mip_stats.mip_sense is not None
+    assert_equals(mip_stats.mip_sense, "max")
+    assert mip_stats.mip_wallclock_time is not None
+    assert isinstance(mip_stats.mip_wallclock_time, float)
+    assert mip_stats.mip_wallclock_time > 0
+
+    # Fetch variables (after-mip)
+    assert_equals(
+        solver.get_variables(with_static=False),
+        _filter_attrs(
+            solver.get_variable_attrs(),
+            Variables(
+                names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"),
+                values=np.array([1.0, 0.0, 1.0, 1.0, 61.0]),
+            ),
+        ),
+    )
+
+    # Fetch constraints (after-mip)
+    assert_equals(
+        solver.get_constraints(with_static=False),
+        _filter_attrs(
+            solver.get_constraint_attrs(),
+            Constraints(
+                names=np.array(["eq_capacity"], dtype="S"),
+                slacks=np.array([0.0]),
+            ),
+        ),
+    )
+
+    # Build new constraint and verify that it is violated
+    cf = Constraints(
+        names=np.array(["cut"], dtype="S"),
+        lhs=[[(b"x[0]", 1.0)]],
+        rhs=np.array([0.0]),
+        senses=np.array(["<"], dtype="S"),
+    )
+    assert_equals(solver.are_constraints_satisfied(cf), [False])
+
+    # Add constraint and verify it affects solution
+    solver.add_constraints(cf)
+    assert_equals(
+        solver.get_constraints(with_static=True),
+        _filter_attrs(
+            solver.get_constraint_attrs(),
+            Constraints(
+                names=np.array(["eq_capacity", "cut"], dtype="S"),
+                rhs=np.array([0.0, 0.0]),
+                lhs=[
+                    [
+                        (b"x[0]", 23.0),
+                        (b"x[1]", 26.0),
+                        (b"x[2]", 20.0),
+                        (b"x[3]", 18.0),
+                        (b"z", -1.0),
+                    ],
+                    [
+                        (b"x[0]", 1.0),
+                    ],
+                ],
+                senses=np.array(["=", "<"], dtype="S"),
+            ),
+        ),
+    )
+    stats = solver.solve()
+    assert_equals(stats.mip_lower_bound, 1030.0)
+    assert_equals(solver.are_constraints_satisfied(cf), [True])
+
+    # Remove the new constraint
+    solver.remove_constraints(np.array(["cut"], dtype="S"))
+
+    # New constraint should no longer affect solution
+    stats = solver.solve()
+    assert_equals(stats.mip_lower_bound, 1183.0)
+
+
+def run_warm_start_tests(solver: InternalSolver) -> None:
+    instance = solver.build_test_instance_knapsack()
+    model = instance.to_model()
+    solver.set_instance(instance, model)
+    solver.set_warm_start({b"x[0]": 1.0, b"x[1]": 0.0, b"x[2]": 0.0, b"x[3]": 1.0})
+    stats = solver.solve(tee=True)
+    if stats.mip_warm_start_value is not None:
+        assert_equals(stats.mip_warm_start_value, 725.0)
+
+    solver.set_warm_start({b"x[0]": 1.0, b"x[1]": 1.0, b"x[2]": 1.0, b"x[3]": 1.0})
+    stats = solver.solve(tee=True)
+    assert stats.mip_warm_start_value is None
+
+    solver.fix({b"x[0]": 1.0, b"x[1]": 0.0, b"x[2]": 0.0, b"x[3]": 1.0})
+    stats = solver.solve(tee=True)
+    assert_equals(stats.mip_lower_bound, 725.0)
+    assert_equals(stats.mip_upper_bound, 725.0)
+
+
+def run_infeasibility_tests(solver: InternalSolver) -> None:
+    instance = solver.build_test_instance_infeasible()
+    solver.set_instance(instance)
+    mip_stats = solver.solve()
+    assert solver.is_infeasible()
+    assert solver.get_solution() is None
+    assert mip_stats.mip_upper_bound is None
+    assert mip_stats.mip_lower_bound is None
+    lp_stats = solver.solve_lp()
+    assert solver.get_solution() is None
+    assert lp_stats.lp_value is None
+
+
+def run_iteration_cb_tests(solver: InternalSolver) -> None:
+    instance = solver.build_test_instance_knapsack()
+    solver.set_instance(instance)
+    count = 0
+
+    def custom_iteration_cb() -> bool:
+        nonlocal count
+        count += 1
+        return count < 5
+
+    solver.solve(iteration_cb=custom_iteration_cb)
+    assert_equals(count, 5)
+
+
+def run_lazy_cb_tests(solver: InternalSolver) -> None:
+    instance = solver.build_test_instance_knapsack()
+    model = instance.to_model()
+
+    def lazy_cb(cb_solver: InternalSolver, cb_model: Any) -> None:
+        relsol = cb_solver.get_solution()
+        assert relsol is not None
+        assert relsol[b"x[0]"] is not None
+        if relsol[b"x[0]"] > 0:
+            instance.enforce_lazy_constraint(cb_solver, cb_model, b"cut")
+
+    solver.set_instance(instance, model)
+    solver.solve(lazy_cb=lazy_cb)
+    solution = solver.get_solution()
+    assert solution is not None
+    assert_equals(solution[b"x[0]"], 0.0)
+
+
+def _equals_preprocess(obj: Any) -> Any:
+    if isinstance(obj, np.ndarray):
+        if obj.dtype == "float64":
+            return np.round(obj, decimals=6).tolist()
+        else:
+            return obj.tolist()
+    elif isinstance(obj, (int, str, bool, np.bool_, np.bytes_, bytes, bytearray)):
+        return obj
+    elif isinstance(obj, float):
+        return round(obj, 6)
+    elif isinstance(obj, list):
+        return [_equals_preprocess(i) for i in obj]
+    elif isinstance(obj, tuple):
+        return tuple(_equals_preprocess(i) for i in obj)
+    elif obj is None:
+        return None
+    elif isinstance(obj, dict):
+        return {k: _equals_preprocess(v) for (k, v) in obj.items()}
+    else:
+        for key in obj.__dict__.keys():
+            obj.__dict__[key] = _equals_preprocess(obj.__dict__[key])
+        return obj
+
+
+def assert_equals(left: Any, right: Any) -> None:
+    left = _equals_preprocess(left)
+    right = _equals_preprocess(right)
+    assert left == right, f"left:\n{left}\nright:\n{right}"

miplearn/solvers/tests/test_internal_solver.py

@@ -1,156 +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.
-
-import logging
-from io import StringIO
-
-import pyomo.environ as pe
-
-from miplearn import BasePyomoSolver, GurobiSolver
-from miplearn.solvers import RedirectOutput
-from . import _get_instance, _get_internal_solvers
-
-logger = logging.getLogger(__name__)
-
-
-def test_redirect_output():
-    import sys
-    original_stdout = sys.stdout
-    io = StringIO()
-    with RedirectOutput([io]):
-        print("Hello world")
-    assert sys.stdout == original_stdout
-    assert io.getvalue() == "Hello world\n"
-
-
-def test_internal_solver_warm_starts():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-        instance = _get_instance(solver_class)
-        model = instance.to_model()
-        solver = solver_class()
-        solver.set_instance(instance, model)
-        solver.set_warm_start({
-            "x": {
-                0: 1.0,
-                1: 0.0,
-                2: 0.0,
-                3: 1.0,
-            }
-        })
-        stats = solver.solve(tee=True)
-        assert stats["Warm start value"] == 725.0
-
-        solver.set_warm_start({
-            "x": {
-                0: 1.0,
-                1: 1.0,
-                2: 1.0,
-                3: 1.0,
-            }
-        })
-        stats = solver.solve(tee=True)
-        assert stats["Warm start value"] is None
-
-        solver.fix({
-            "x": {
-                0: 1.0,
-                1: 0.0,
-                2: 0.0,
-                3: 1.0,
-            }
-        })
-        stats = solver.solve(tee=True)
-        assert stats["Lower bound"] == 725.0
-        assert stats["Upper bound"] == 725.0
-
-
-def test_internal_solver():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-
-        instance = _get_instance(solver_class)
-        model = instance.to_model()
-        solver = solver_class()
-        solver.set_instance(instance, model)
-
-        stats = solver.solve_lp()
-        assert round(stats["Optimal value"], 3) == 1287.923
-
-        solution = solver.get_solution()
-        assert round(solution["x"][0], 3) == 1.000
-        assert round(solution["x"][1], 3) == 0.923
-        assert round(solution["x"][2], 3) == 1.000
-        assert round(solution["x"][3], 3) == 0.000
-
-        stats = solver.solve(tee=True)
-        assert len(stats["Log"]) > 100
-        assert stats["Lower bound"] == 1183.0
-        assert stats["Upper bound"] == 1183.0
-        assert stats["Sense"] == "max"
-        assert isinstance(stats["Wallclock time"], float)
-        assert isinstance(stats["Nodes"], int)
-
-        solution = solver.get_solution()
-        assert solution["x"][0] == 1.0
-        assert solution["x"][1] == 0.0
-        assert solution["x"][2] == 1.0
-        assert solution["x"][3] == 1.0
-
-        # Add a brand new constraint
-        if isinstance(solver, BasePyomoSolver):
-            model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
-            solver.add_constraint(model.cut)
-        elif isinstance(solver, GurobiSolver):
-            x = model.getVarByName("x[0]")
-            solver.add_constraint(x <= 0.0, name="cut")
-        else:
-            raise Exception("Illegal state")
-
-        # New constraint should affect solution and should be listed in
-        # constraint ids
-        assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
-        stats = solver.solve()
-        assert stats["Lower bound"] == 1030.0
-
-        if isinstance(solver, GurobiSolver):
-            # Extract new constraint
-            cobj = solver.extract_constraint("cut")
-
-            # New constraint should no longer affect solution and should no longer
-            # be listed in constraint ids
-            assert solver.get_constraint_ids() == ["eq_capacity"]
-            stats = solver.solve()
-            assert stats["Lower bound"] == 1183.0
-
-            # New constraint should not be satisfied by current solution
-            assert not solver.is_constraint_satisfied(cobj)
-
-            # Re-add constraint
-            solver.add_constraint(cobj)
-
-            # Constraint should affect solution again
-            assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
-            stats = solver.solve()
-            assert stats["Lower bound"] == 1030.0
-
-            # New constraint should now be satisfied
-            assert solver.is_constraint_satisfied(cobj)
-
-
-def test_iteration_cb():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-        instance = _get_instance(solver_class)
-        solver = solver_class()
-        solver.set_instance(instance)
-        count = 0
-
-        def custom_iteration_cb():
-            nonlocal count
-            count += 1
-            return count < 5
-
-        solver.solve(iteration_cb=custom_iteration_cb)
-        assert count == 5

miplearn/solvers/tests/test_lazy_cb.py

@@ -1,27 +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.
-
-import logging
-
-from . import _get_instance
-from ... import GurobiSolver
-
-logger = logging.getLogger(__name__)
-
-
-def test_lazy_cb():
-    solver = GurobiSolver()
-    instance = _get_instance(solver)
-    model = instance.to_model()
-
-    def lazy_cb(cb_solver, cb_model):
-        logger.info("x[0] = %.f" % cb_solver.get_value("x", 0))
-        cobj = (cb_model.getVarByName("x[0]") * 1.0, "<", 0.0, "cut")
-        if not cb_solver.is_constraint_satisfied(cobj):
-            cb_solver.add_constraint(cobj)
-
-    solver.set_instance(instance, model)
-    solver.solve(lazy_cb=lazy_cb)
-    solution = solver.get_solution()
-    assert solution["x"][0] == 0.0

miplearn/solvers/tests/test_learning_solver.py

@@ -1,67 +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.
-
-import logging
-import pickle
-import tempfile
-
-from miplearn import DynamicLazyConstraintsComponent
-from miplearn import LearningSolver
-
-from . import _get_instance, _get_internal_solvers
-
-logger = logging.getLogger(__name__)
-
-
-def test_learning_solver():
-    for mode in ["exact", "heuristic"]:
-        for internal_solver in _get_internal_solvers():
-            logger.info("Solver: %s" % internal_solver)
-            instance = _get_instance(internal_solver)
-            solver = LearningSolver(time_limit=300,
-                                    gap_tolerance=1e-3,
-                                    threads=1,
-                                    solver=internal_solver,
-                                    mode=mode)
-
-            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
-            assert instance.found_violated_lazy_constraints == []
-            assert instance.found_violated_user_cuts == []
-            assert len(instance.solver_log) > 100
-
-            solver.fit([instance])
-            solver.solve(instance)
-
-            # Assert solver is picklable
-            with tempfile.TemporaryFile() as file:
-                pickle.dump(solver, file)
-
-
-def test_parallel_solve():
-    for internal_solver in _get_internal_solvers():
-        instances = [_get_instance(internal_solver) for _ in range(10)]
-        solver = LearningSolver(solver=internal_solver)
-        results = solver.parallel_solve(instances, n_jobs=3)
-        assert len(results) == 10
-        for instance in instances:
-            assert len(instance.solution["x"].keys()) == 4
-
-
-def test_add_components():
-    solver = LearningSolver(components=[])
-    solver.add(DynamicLazyConstraintsComponent())
-    solver.add(DynamicLazyConstraintsComponent())
-    assert len(solver.components) == 1
-    assert "DynamicLazyConstraintsComponent" in solver.components

miplearn/tests/__init__.py

@@ -1,25 +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 LearningSolver
-from miplearn.problems.knapsack import KnapsackInstance
-
-
-def get_test_pyomo_instances():
-    instances = [
-        KnapsackInstance(
-            weights=[23., 26., 20., 18.],
-            prices=[505., 352., 458., 220.],
-            capacity=67.,
-        ),
-        KnapsackInstance(
-            weights=[25., 30., 22., 18.],
-            prices=[500., 365., 420., 150.],
-            capacity=70.,
-        ),
-    ]
-    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

miplearn/tests/test_benchmark.py

@@ -1,36 +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.
-
-import os.path
-
-from miplearn import LearningSolver, BenchmarkRunner
-from miplearn.problems.stab import MaxWeightStableSetGenerator
-from scipy.stats import randint
-
-
-def test_benchmark():
-    # Generate training and test instances
-    train_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(5)    
-    test_instances  = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(3)
-
-    # Training phase...
-    training_solver = LearningSolver()
-    training_solver.parallel_solve(train_instances, n_jobs=10)
-
-    # Test phase...
-    test_solvers = {
-        "Strategy A": LearningSolver(),
-        "Strategy B": LearningSolver(),
-    }
-    benchmark = BenchmarkRunner(test_solvers)
-    benchmark.fit(train_instances)
-    benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
-    assert benchmark.raw_results().values.shape == (12,16)
-    
-    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 == (12,16)

miplearn/tests/test_extractors.py

@@ -1,62 +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.problems.knapsack import KnapsackInstance
-from miplearn import (LearningSolver,
-                      SolutionExtractor,
-                      InstanceFeaturesExtractor,
-                      VariableFeaturesExtractor,
-                     )
-import numpy as np
-import pyomo.environ as pe
-
-
-def _get_instances():
-    instances = [
-        KnapsackInstance(weights=[1., 2., 3.],
-                         prices=[10., 20., 30.],
-                         capacity=2.5,
-                        ),
-        KnapsackInstance(weights=[3., 4., 5.],
-                         prices=[20., 30., 40.],
-                         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_solution_extractor():
-    instances, models = _get_instances()
-    features = SolutionExtractor().extract(instances)
-    assert isinstance(features, dict)
-    assert "default" in features.keys()
-    assert isinstance(features["default"], np.ndarray)
-    assert features["default"].shape == (6, 2)
-    assert features["default"].ravel().tolist() == [
-        1., 0.,
-        0., 1.,
-        1., 0.,
-        1., 0.,
-        0., 1.,
-        1., 0.,
-    ]
-
-    
-def test_instance_features_extractor():
-    instances, models = _get_instances()
-    features = InstanceFeaturesExtractor().extract(instances)
-    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)
-    

miplearn/types.py

@@ -0,0 +1,52 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from typing import Optional, Dict, Callable, Any, Union, TYPE_CHECKING
+
+from mypy_extensions import TypedDict
+
+if TYPE_CHECKING:
+    # noinspection PyUnresolvedReferences
+    from miplearn.solvers.learning import InternalSolver
+
+Category = bytes
+ConstraintName = bytes
+ConstraintCategory = bytes
+IterationCallback = Callable[[], bool]
+LazyCallback = Callable[[Any, Any], None]
+SolverParams = Dict[str, Any]
+UserCutCallback = Callable[["InternalSolver", Any], None]
+Solution = Dict[bytes, Optional[float]]
+
+LearningSolveStats = TypedDict(
+    "LearningSolveStats",
+    {
+        "Gap": Optional[float],
+        "Instance": Union[str, int],
+        "lp_log": str,
+        "lp_value": Optional[float],
+        "lp_wallclock_time": Optional[float],
+        "mip_lower_bound": Optional[float],
+        "mip_log": str,
+        "Mode": str,
+        "mip_nodes": Optional[int],
+        "Objective: Predicted lower bound": float,
+        "Objective: Predicted upper bound": float,
+        "Primal: Free": int,
+        "Primal: One": int,
+        "Primal: Zero": int,
+        "Sense": str,
+        "Solver": str,
+        "mip_upper_bound": Optional[float],
+        "mip_wallclock_time": float,
+        "mip_warm_start_value": Optional[float],
+        "LazyStatic: Removed": int,
+        "LazyStatic: Kept": int,
+        "LazyStatic: Restored": int,
+        "LazyStatic: Iterations": int,
+        "UserCuts: Added ahead-of-time": int,
+        "UserCuts: Added in callback": int,
+    },
+    total=False,
+)