Implement MemorizingCutsComponent; STAB: switch to edge formulation

miplearn/components/cuts/mem.py

@@ -0,0 +1,105 @@
+#  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 logging
+from typing import List, Dict, Any, Hashable, Union
+
+import numpy as np
+from sklearn.preprocessing import MultiLabelBinarizer
+
+from miplearn.extractors.abstract import FeaturesExtractor
+from miplearn.h5 import H5File
+from miplearn.solvers.abstract import AbstractModel
+
+logger = logging.getLogger(__name__)
+
+
+class _BaseMemorizingConstrComponent:
+    def __init__(self, clf: Any, extractor: FeaturesExtractor, field: str) -> None:
+        self.clf = clf
+        self.extractor = extractor
+        self.constrs_: List[Hashable] = []
+        self.n_features_: int = 0
+        self.n_targets_: int = 0
+        self.field = field
+
+    def fit(
+        self,
+        train_h5: List[str],
+    ) -> None:
+        logger.info("Reading training data...")
+        n_samples = len(train_h5)
+        x, y, constrs, n_features = [], [], [], None
+        constr_to_idx: Dict[Hashable, int] = {}
+        for h5_filename in train_h5:
+            with H5File(h5_filename, "r") as h5:
+                # Store constraints
+                sample_constrs_str = h5.get_scalar(self.field)
+                assert sample_constrs_str is not None
+                assert isinstance(sample_constrs_str, str)
+                sample_constrs = eval(sample_constrs_str)
+                assert isinstance(sample_constrs, list)
+                y_sample = []
+                for c in sample_constrs:
+                    if c not in constr_to_idx:
+                        constr_to_idx[c] = len(constr_to_idx)
+                        constrs.append(c)
+                    y_sample.append(constr_to_idx[c])
+                y.append(y_sample)
+
+                # Extract features
+                x_sample = self.extractor.get_instance_features(h5)
+                assert len(x_sample.shape) == 1
+                if n_features is None:
+                    n_features = len(x_sample)
+                else:
+                    assert len(x_sample) == n_features
+                x.append(x_sample)
+        logger.info("Constructing matrices...")
+        assert n_features is not None
+        self.n_features_ = n_features
+        self.constrs_ = constrs
+        self.n_targets_ = len(constr_to_idx)
+        x_np = np.vstack(x)
+        assert x_np.shape == (n_samples, n_features)
+        y_np = MultiLabelBinarizer().fit_transform(y)
+        assert y_np.shape == (n_samples, self.n_targets_)
+        logger.info(
+            f"Dataset has {n_samples:,d} samples, "
+            f"{n_features:,d} features and {self.n_targets_:,d} targets"
+        )
+        logger.info("Training classifier...")
+        self.clf.fit(x_np, y_np)
+
+    def predict(
+        self,
+        msg: str,
+        test_h5: str,
+    ) -> List[Hashable]:
+        with H5File(test_h5, "r") as h5:
+            x_sample = self.extractor.get_instance_features(h5)
+        assert x_sample.shape == (self.n_features_,)
+        x_sample = x_sample.reshape(1, -1)
+        logger.info(msg)
+        y = self.clf.predict(x_sample)
+        assert y.shape == (1, self.n_targets_)
+        y = y.reshape(-1)
+        return [self.constrs_[i] for (i, yi) in enumerate(y) if yi > 0.5]
+
+
+class MemorizingCutsComponent(_BaseMemorizingConstrComponent):
+    def __init__(self, clf: Any, extractor: FeaturesExtractor) -> None:
+        super().__init__(clf, extractor, "mip_cuts")
+
+    def before_mip(
+        self,
+        test_h5: str,
+        model: AbstractModel,
+        stats: Dict[str, Any],
+    ) -> None:
+        if model.cuts_enforce is None:
+            return
+        assert self.constrs_ is not None
+        model.cuts_aot_ = self.predict("Predicting cutting planes...", test_h5)
+        stats["Cuts: AOT"] = len(model.cuts_aot_)

miplearn/components/lazy/mem.py

@@ -1,74 +1,22 @@
 #  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 logging
 from typing import List, Dict, Any, Hashable
 
-import numpy as np
-from sklearn.preprocessing import MultiLabelBinarizer
-
+from miplearn.components.cuts.mem import (
+    _BaseMemorizingConstrComponent,
+)
 from miplearn.extractors.abstract import FeaturesExtractor
-from miplearn.h5 import H5File
 from miplearn.solvers.abstract import AbstractModel
 
 logger = logging.getLogger(__name__)
 
 
-class MemorizingLazyConstrComponent:
+class MemorizingLazyComponent(_BaseMemorizingConstrComponent):
     def __init__(self, clf: Any, extractor: FeaturesExtractor) -> None:
-        self.clf = clf
-        self.extractor = extractor
-        self.constrs_: List[Hashable] = []
-        self.n_features_: int = 0
-        self.n_targets_: int = 0
-
-    def fit(self, train_h5: List[str]) -> None:
-        logger.info("Reading training data...")
-        n_samples = len(train_h5)
-        x, y, constrs, n_features = [], [], [], None
-        constr_to_idx: Dict[Hashable, int] = {}
-        for h5_filename in train_h5:
-            with H5File(h5_filename, "r") as h5:
-
-                # Store lazy constraints
-                sample_constrs_str = h5.get_scalar("mip_lazy")
-                assert sample_constrs_str is not None
-                assert isinstance(sample_constrs_str, str)
-                sample_constrs = eval(sample_constrs_str)
-                assert isinstance(sample_constrs, list)
-                y_sample = []
-                for c in sample_constrs:
-                    if c not in constr_to_idx:
-                        constr_to_idx[c] = len(constr_to_idx)
-                        constrs.append(c)
-                    y_sample.append(constr_to_idx[c])
-                y.append(y_sample)
-
-                # Extract features
-                x_sample = self.extractor.get_instance_features(h5)
-                assert len(x_sample.shape) == 1
-                if n_features is None:
-                    n_features = len(x_sample)
-                else:
-                    assert len(x_sample) == n_features
-                x.append(x_sample)
-
-        logger.info("Constructing matrices...")
-        assert n_features is not None
-        self.n_features_ = n_features
-        self.constrs_ = constrs
-        self.n_targets_ = len(constr_to_idx)
-        x_np = np.vstack(x)
-        assert x_np.shape == (n_samples, n_features)
-        y_np = MultiLabelBinarizer().fit_transform(y)
-        assert y_np.shape == (n_samples, self.n_targets_)
-        logger.info(
-            f"Dataset has {n_samples:,d} samples, "
-            f"{n_features:,d} features and {self.n_targets_:,d} targets"
-        )
-
-        logger.info("Training classifier...")
-        self.clf.fit(x_np, y_np)
+        super().__init__(clf, extractor, "mip_lazy")
 
     def before_mip(
         self,
@@ -78,23 +26,8 @@ class MemorizingLazyConstrComponent:
     ) -> None:
         if model.lazy_enforce is None:
             return
-
         assert self.constrs_ is not None
-
-        # Read features
-        with H5File(test_h5, "r") as h5:
-            x_sample = self.extractor.get_instance_features(h5)
-        assert x_sample.shape == (self.n_features_,)
-        x_sample = x_sample.reshape(1, -1)
-
-        # Predict violated constraints
-        logger.info("Predicting violated lazy constraints...")
-        y = self.clf.predict(x_sample)
-        assert y.shape == (1, self.n_targets_)
-        y = y.reshape(-1)
-
-        # Enforce constraints
-        violations = [self.constrs_[i] for (i, yi) in enumerate(y) if yi > 0.5]
+        violations = self.predict("Predicting violated lazy constraints...", test_h5)
         logger.info(f"Enforcing {len(violations)} constraints ahead-of-time...")
         model.lazy_enforce(model, violations)
         stats["Lazy Constraints: AOT"] = len(violations)