Move python files to root folder; remove built docs

miplearn/classifiers/threshold.py

@@ -0,0 +1,45 @@
+#  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.
+
+from abc import abstractmethod, ABC
+
+import numpy as np
+from sklearn.metrics._ranking import _binary_clf_curve
+
+
+class DynamicThreshold(ABC):
+    @abstractmethod
+    def find(self, clf, x_train, y_train):
+        """
+        Given a trained binary classifier `clf` and a training data set,
+        returns the numerical threshold (float) satisfying some criterea.
+        """
+        pass
+
+
+class MinPrecisionThreshold(DynamicThreshold):
+    """
+    The smallest possible threshold satisfying a minimum acceptable true
+    positive rate (also known as precision).
+    """
+
+    def __init__(self, min_precision):
+        self.min_precision = min_precision
+
+    def find(self, clf, x_train, y_train):
+        proba = clf.predict_proba(x_train)
+
+        assert isinstance(proba, np.ndarray), \
+            "classifier should return numpy array"
+        assert proba.shape == (x_train.shape[0], 2), \
+            "classifier should return (%d,%d)-shaped array, not %s" % (
+                x_train.shape[0], 2, str(proba.shape))
+
+        fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
+        precision = tps / (tps + fps)
+
+        for k in reversed(range(len(precision))):
+            if precision[k] >= self.min_precision:
+                return thresholds[k]
+        return 2.0