Move python files to root folder; remove built docs

miplearn/classifiers/adaptive.py

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+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+from copy import deepcopy
+
+from miplearn.classifiers import Classifier
+from miplearn.classifiers.counting import CountingClassifier
+from miplearn.classifiers.evaluator import ClassifierEvaluator
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+logger = logging.getLogger(__name__)
+
+
+class AdaptiveClassifier(Classifier):
+    """
+    A meta-classifier which dynamically selects what actual classifier to use
+    based on its cross-validation score on a particular training data set.
+    """
+
+    def __init__(self,
+                 candidates=None,
+                 evaluator=ClassifierEvaluator()):
+        """
+        Initializes the meta-classifier.
+        """
+        if candidates is None:
+            candidates = {
+                "knn(100)": {
+                    "classifier": KNeighborsClassifier(n_neighbors=100),
+                    "min samples": 100,
+                },
+                "logistic": {
+                    "classifier": make_pipeline(StandardScaler(),
+                                                LogisticRegression()),
+                    "min samples": 30,
+                },
+                "counting": {
+                    "classifier": CountingClassifier(),
+                    "min samples": 0,
+                }
+            }
+        self.candidates = candidates
+        self.evaluator = evaluator
+        self.classifier = None
+
+    def fit(self, x_train, y_train):
+        best_name, best_clf, best_score = None, None, -float("inf")
+        n_samples = x_train.shape[0]
+        for (name, clf_dict) in self.candidates.items():
+            if n_samples < clf_dict["min samples"]:
+                continue
+            clf = deepcopy(clf_dict["classifier"])
+            clf.fit(x_train, y_train)
+            score = self.evaluator.evaluate(clf, x_train, y_train)
+            if score > best_score:
+                best_name, best_clf, best_score = name, clf, score
+        logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
+        self.classifier = best_clf
+
+    def predict_proba(self, x_test):
+        return self.classifier.predict_proba(x_test)