MIPLearn v0.3

miplearn/components/primal/joint.py

@@ -0,0 +1,88 @@
+#  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, Optional
+
+import numpy as np
+
+from miplearn.components.primal import _extract_bin_var_names_values
+from miplearn.components.primal.actions import PrimalComponentAction
+from miplearn.extractors.abstract import FeaturesExtractor
+from miplearn.solvers.abstract import AbstractModel
+from miplearn.h5 import H5File
+
+logger = logging.getLogger(__name__)
+
+
+class JointVarsPrimalComponent:
+    def __init__(
+        self, clf: Any, extractor: FeaturesExtractor, action: PrimalComponentAction
+    ):
+        self.clf = clf
+        self.extractor = extractor
+        self.bin_var_names_: Optional[np.ndarray] = None
+        self.action = action
+
+    def fit(self, train_h5: List[str]) -> None:
+        logger.info("Reading training data...")
+        self.bin_var_names_ = None
+        x, y, n_samples, n_features = [], [], len(train_h5), None
+        for h5_filename in train_h5:
+            with H5File(h5_filename, "r") as h5:
+                bin_var_names, bin_var_values, _ = _extract_bin_var_names_values(h5)
+
+                # Store/check variable names
+                if self.bin_var_names_ is None:
+                    self.bin_var_names_ = bin_var_names
+                else:
+                    assert np.all(bin_var_names == self.bin_var_names_)
+
+                # Build x and y vectors
+                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)
+                y.append(bin_var_values)
+        assert self.bin_var_names_ is not None
+
+        logger.info("Constructing matrices...")
+        x_np = np.vstack(x)
+        y_np = np.array(y)
+        assert len(x_np.shape) == 2
+        assert x_np.shape[0] == n_samples
+        assert x_np.shape[1] == n_features
+        assert y_np.shape == (n_samples, len(self.bin_var_names_))
+        logger.info(
+            f"Dataset has {n_samples:,d} samples, "
+            f"{n_features:,d} features and {y_np.shape[1]:,d} targets"
+        )
+
+        logger.info("Training classifier...")
+        self.clf.fit(x_np, y_np)
+
+        logger.info("Done fitting.")
+
+    def before_mip(
+        self, test_h5: str, model: AbstractModel, stats: Dict[str, Any]
+    ) -> None:
+        assert self.bin_var_names_ is not None
+
+        # Read features
+        with H5File(test_h5, "r") as h5:
+            x_sample = self.extractor.get_instance_features(h5)
+        assert len(x_sample.shape) == 1
+        x_sample = x_sample.reshape(1, -1)
+
+        # Predict optimal solution
+        logger.info("Predicting warm starts...")
+        y_pred = self.clf.predict(x_sample)
+        assert len(y_pred.shape) == 2
+        assert y_pred.shape[0] == 1
+        assert y_pred.shape[1] == len(self.bin_var_names_)
+
+        # Construct warm starts, based on prediction
+        self.action.perform(model, self.bin_var_names_, y_pred, stats)