MIPLearn v0.3

miplearn/extractors/fields.py

@@ -0,0 +1,69 @@
+#  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.
+from typing import Optional, List
+
+import numpy as np
+
+from miplearn.extractors.abstract import FeaturesExtractor
+from miplearn.h5 import H5File
+
+
+class H5FieldsExtractor(FeaturesExtractor):
+    def __init__(
+        self,
+        instance_fields: Optional[List[str]] = None,
+        var_fields: Optional[List[str]] = None,
+        constr_fields: Optional[List[str]] = None,
+    ):
+        self.instance_fields = instance_fields
+        self.var_fields = var_fields
+        self.constr_fields = constr_fields
+
+    def get_instance_features(self, h5: H5File) -> np.ndarray:
+        if self.instance_fields is None:
+            raise Exception("No instance fields provided")
+        x = []
+        for field in self.instance_fields:
+            try:
+                data = h5.get_array(field)
+            except ValueError:
+                data = h5.get_scalar(field)
+            assert data is not None
+            x.append(data)
+        x = np.hstack(x)
+        assert len(x.shape) == 1
+        return x
+
+    def get_var_features(self, h5: H5File) -> np.ndarray:
+        var_types = h5.get_array("static_var_types")
+        assert var_types is not None
+        n_vars = len(var_types)
+        if self.var_fields is None:
+            raise Exception("No var fields provided")
+        return self._extract(h5, self.var_fields, n_vars)
+
+    def get_constr_features(self, h5: H5File) -> np.ndarray:
+        constr_sense = h5.get_array("static_constr_sense")
+        assert constr_sense is not None
+        n_constr = len(constr_sense)
+        if self.constr_fields is None:
+            raise Exception("No constr fields provided")
+        return self._extract(h5, self.constr_fields, n_constr)
+
+    def _extract(self, h5, fields, n_expected):
+        x = []
+        for field in fields:
+            try:
+                data = h5.get_array(field)
+            except ValueError:
+                v = h5.get_scalar(field)
+                data = np.repeat(v, n_expected)
+            assert data is not None
+            assert len(data.shape) == 1
+            assert data.shape[0] == n_expected
+            x.append(data)
+        features = np.vstack(x).T
+        assert len(features.shape) == 2
+        assert features.shape[0] == n_expected
+        return features