Implement MemorizingCutsComponent; STAB: switch to edge formulation

tests/components/cuts/test_mem.py

@@ -0,0 +1,80 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2023, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from typing import Any, List, Hashable, Dict
+from unittest.mock import Mock
+
+import gurobipy as gp
+import networkx as nx
+from gurobipy import GRB, quicksum
+from sklearn.dummy import DummyClassifier
+from sklearn.neighbors import KNeighborsClassifier
+
+from miplearn.components.cuts.mem import MemorizingCutsComponent
+from miplearn.extractors.abstract import FeaturesExtractor
+from miplearn.problems.stab import build_stab_model
+from miplearn.solvers.gurobi import GurobiModel
+from miplearn.solvers.learning import LearningSolver
+import numpy as np
+
+
+# def test_usage() -> None:
+#     model = _build_cut_model()
+#     solver = LearningSolver(components=[])
+#     solver.optimize(model)
+#     assert model.cuts_ is not None
+#     assert len(model.cuts_) > 0
+#     assert False
+
+
+def test_mem_component(
+    stab_h5: List[str],
+    default_extractor: FeaturesExtractor,
+) -> None:
+    clf = Mock(wraps=DummyClassifier())
+    comp = MemorizingCutsComponent(clf=clf, extractor=default_extractor)
+    comp.fit(stab_h5)
+
+    # Should call fit method with correct arguments
+    clf.fit.assert_called()
+    x, y = clf.fit.call_args.args
+    assert x.shape == (3, 50)
+    assert y.shape == (3, 388)
+    y = y.tolist()
+    assert y[0][:20] == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+    assert y[1][:20] == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]
+    assert y[2][:20] == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]
+
+    # Should store violations
+    assert comp.constrs_ is not None
+    assert comp.n_features_ == 50
+    assert comp.n_targets_ == 388
+    assert len(comp.constrs_) == 388
+
+    # Call before-mip
+    stats: Dict[str, Any] = {}
+    model = Mock()
+    comp.before_mip(stab_h5[0], model, stats)
+
+    # Should call predict with correct args
+    clf.predict.assert_called()
+    (x_test,) = clf.predict.call_args.args
+    assert x_test.shape == (1, 50)
+
+    # Should set cuts_aot_
+    assert model.cuts_aot_ is not None
+    assert len(model.cuts_aot_) == 243
+
+
+def test_usage_stab(
+    stab_h5: List[str],
+    default_extractor: FeaturesExtractor,
+) -> None:
+    data_filenames = [f.replace(".h5", ".pkl.gz") for f in stab_h5]
+    clf = KNeighborsClassifier(n_neighbors=1)
+    comp = MemorizingCutsComponent(clf=clf, extractor=default_extractor)
+    solver = LearningSolver(components=[comp])
+    solver.fit(data_filenames)
+    stats = solver.optimize(data_filenames[0], build_stab_model)
+    assert stats["Cuts: AOT"] > 0

tests/components/lazy/test_mem.py

@@ -8,7 +8,7 @@ from unittest.mock import Mock
 from sklearn.dummy import DummyClassifier
 from sklearn.neighbors import KNeighborsClassifier
 
-from miplearn.components.lazy.mem import MemorizingLazyConstrComponent
+from miplearn.components.lazy.mem import MemorizingLazyComponent
 from miplearn.extractors.abstract import FeaturesExtractor
 from miplearn.problems.tsp import build_tsp_model
 from miplearn.solvers.learning import LearningSolver
@@ -19,7 +19,7 @@ def test_mem_component(
     default_extractor: FeaturesExtractor,
 ) -> None:
     clf = Mock(wraps=DummyClassifier())
-    comp = MemorizingLazyConstrComponent(clf=clf, extractor=default_extractor)
+    comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
     comp.fit(tsp_h5)
 
     # Should call fit method with correct arguments
@@ -56,7 +56,7 @@ def test_usage_tsp(
     # Should not crash
     data_filenames = [f.replace(".h5", ".pkl.gz") for f in tsp_h5]
     clf = KNeighborsClassifier(n_neighbors=1)
-    comp = MemorizingLazyConstrComponent(clf=clf, extractor=default_extractor)
+    comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
     solver = LearningSolver(components=[comp])
     solver.fit(data_filenames)
     solver.optimize(data_filenames[0], build_tsp_model)

tests/conftest.py

@@ -1,10 +1,13 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-
+import os
+import shutil
+import tempfile
 from glob import glob
-from os.path import dirname
-from typing import List
+from os.path import dirname, basename, isfile
+from tempfile import NamedTemporaryFile
+from typing import List, Any
 
 import pytest
 
@@ -12,14 +15,45 @@ from miplearn.extractors.abstract import FeaturesExtractor
 from miplearn.extractors.fields import H5FieldsExtractor
 
 
-@pytest.fixture()
-def multiknapsack_h5() -> List[str]:
-    return sorted(glob(f"{dirname(__file__)}/fixtures/multiknapsack-n100*.h5"))
+def _h5_fixture(pattern: str, request: Any) -> List[str]:
+    """
+    Create a temporary copy of the provided .h5 files, along with the companion
+    .pkl.gz files, and return the path to the copy. Also register a finalizer,
+    so that the temporary folder is removed after the tests.
+    """
+    filenames = glob(f"{dirname(__file__)}/fixtures/{pattern}")
+    print(filenames)
+    tmpdir = tempfile.mkdtemp()
+
+    def cleanup() -> None:
+        shutil.rmtree(tmpdir)
+
+    request.addfinalizer(cleanup)
+
+    print(tmpdir)
+    for f in filenames:
+        fbase, _ = os.path.splitext(f)
+        for ext in [".h5", ".pkl.gz"]:
+            dest = os.path.join(tmpdir, f"{basename(fbase)}{ext}")
+            print(dest)
+            shutil.copy(f"{fbase}{ext}", dest)
+            assert isfile(dest)
+    return sorted(glob(f"{tmpdir}/*.h5"))
 
 
 @pytest.fixture()
-def tsp_h5() -> List[str]:
-    return sorted(glob(f"{dirname(__file__)}/fixtures/tsp-n20*.h5"))
+def multiknapsack_h5(request: Any) -> List[str]:
+    return _h5_fixture("multiknapsack*.h5", request)
+
+
+@pytest.fixture()
+def tsp_h5(request: Any) -> List[str]:
+    return _h5_fixture("tsp*.h5", request)
+
+
+@pytest.fixture()
+def stab_h5(request: Any) -> List[str]:
+    return _h5_fixture("stab*.h5", request)
 
 
 @pytest.fixture()

tests/fixtures/gen_stab.py

@@ -0,0 +1,23 @@
+from os.path import dirname
+
+import numpy as np
+from scipy.stats import uniform, randint
+
+from miplearn.collectors.basic import BasicCollector
+from miplearn.io import write_pkl_gz
+from miplearn.problems.stab import (
+    MaxWeightStableSetGenerator,
+    build_stab_model,
+)
+
+np.random.seed(42)
+gen = MaxWeightStableSetGenerator(
+    w=uniform(10.0, scale=1.0),
+    n=randint(low=50, high=51),
+    p=uniform(loc=0.5, scale=0.0),
+    fix_graph=True,
+)
+data = gen.generate(3)
+data_filenames = write_pkl_gz(data, dirname(__file__), prefix="stab-n50-")
+collector = BasicCollector()
+collector.collect(data_filenames, build_stab_model)

tests/problems/test_stab.py

@@ -9,8 +9,7 @@ import numpy as np
 from miplearn.h5 import H5File
 from miplearn.problems.stab import (
     MaxWeightStableSetData,
-    build_stab_model_pyomo,
-    build_stab_model_gurobipy,
+    build_stab_model,
 )
 from miplearn.solvers.abstract import AbstractModel
 
@@ -21,8 +20,7 @@ def test_stab() -> None:
         weights=np.array([1.0, 1.0, 1.0, 1.0, 1.0]),
     )
     for model in [
-        build_stab_model_pyomo(data),
-        build_stab_model_gurobipy(data),
+        build_stab_model(data),
     ]:
         assert isinstance(model, AbstractModel)
         with NamedTemporaryFile() as tempfile:

tests/test_lazy_pyomo.py

@@ -39,6 +39,6 @@ def _build_model() -> PyomoModel:
 def test_pyomo_callback() -> None:
     model = _build_model()
     model.optimize()
-    assert model.lazy_constrs_ is not None
-    assert len(model.lazy_constrs_) > 0
+    assert model.lazy_ is not None
+    assert len(model.lazy_) > 0
     assert model.inner.x.value == 0.0