Implement MemorizingLazyConstrComponent

miplearn/components/lazy.py

@@ -1,43 +0,0 @@
-#  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 json
-from typing import Any, Dict, List
-
-import gurobipy as gp
-
-from ..h5 import H5File
-
-
-class ExpertLazyComponent:
-    def __init__(self) -> None:
-        pass
-
-    def fit(self, train_h5: List[str]) -> None:
-        pass
-
-    def before_mip(self, test_h5: str, model: gp.Model, stats: Dict[str, Any]) -> None:
-        with H5File(test_h5, "r") as h5:
-            constr_names = h5.get_array("static_constr_names")
-            constr_lazy = h5.get_array("mip_constr_lazy")
-            constr_violations = h5.get_scalar("mip_constr_violations")
-
-            assert constr_names is not None
-            assert constr_violations is not None
-
-            # Static lazy constraints
-            n_static_lazy = 0
-            if constr_lazy is not None:
-                for (constr_idx, constr_name) in enumerate(constr_names):
-                    if constr_lazy[constr_idx]:
-                        constr = model.getConstrByName(constr_name.decode())
-                        constr.lazy = 3
-                        n_static_lazy += 1
-            stats.update({"Static lazy constraints": n_static_lazy})
-
-            # Dynamic lazy constraints
-            if hasattr(model, "_fix_violations"):
-                violations = json.loads(constr_violations)
-                model._fix_violations(model, violations, "aot")
-                stats.update({"Dynamic lazy constraints": len(violations)})

miplearn/components/lazy/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
+
+import numpy as np
+from sklearn.preprocessing import MultiLabelBinarizer
+
+from miplearn.extractors.abstract import FeaturesExtractor
+from miplearn.h5 import H5File
+from miplearn.solvers.gurobi import GurobiModel
+
+logger = logging.getLogger(__name__)
+
+# TODO: Replace GurobiModel by AbstractModel
+# TODO: fix_violations: remove model.inner
+# TODO: fix_violations: remove `where`
+# TODO: Write documentation
+# TODO: Implement ExpertLazyConstrComponent
+
+
+class MemorizingLazyConstrComponent:
+    def __init__(self, clf: Any, extractor: FeaturesExtractor) -> None:
+        self.clf = clf
+        self.extractor = extractor
+        self.violations_: 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, violations, n_features = [], [], [], None
+        violation_to_idx: Dict[Hashable, int] = {}
+        for h5_filename in train_h5:
+            with H5File(h5_filename, "r") as h5:
+
+                # Store lazy constraints
+                sample_violations_str = h5.get_scalar("mip_constr_violations")
+                assert sample_violations_str is not None
+                assert isinstance(sample_violations_str, str)
+                sample_violations = eval(sample_violations_str)
+                assert isinstance(sample_violations, list)
+                y_sample = []
+                for v in sample_violations:
+                    if v not in violation_to_idx:
+                        violation_to_idx[v] = len(violation_to_idx)
+                        violations.append(v)
+                    y_sample.append(violation_to_idx[v])
+                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.violations_ = violations
+        self.n_targets_ = len(violation_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 before_mip(
+        self,
+        test_h5: str,
+        model: GurobiModel,
+        stats: Dict[str, Any],
+    ) -> None:
+        assert self.violations_ is not None
+        if model.fix_violations is None:
+            return
+
+        # 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.violations_[i] for (i, yi) in enumerate(y) if yi > 0.5]
+        logger.info(f"Enforcing {len(violations)} constraints ahead-of-time...")
+        model.fix_violations(model, violations, "aot")
+        stats["Lazy Constraints: AOT"] = len(violations)