Simplify AdaptiveClassifier

src/python/miplearn/classifiers/adaptive.py

@@ -5,106 +5,57 @@
 import logging
 from copy import deepcopy
 
-import numpy as np
-
 from miplearn.classifiers import Classifier
-from sklearn.model_selection import cross_val_score
+from miplearn.classifiers.counting import CountingClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
 
 logger = logging.getLogger(__name__)
 
 
 class AdaptiveClassifier(Classifier):
     """
-    A classifier that automatically switches strategies based on the number of
-    samples and cross-validation scores.
+    A meta-classifier which dynamically selects what actual classifier to use
+    based on the number of samples in the training data.
+
+    By default, uses CountingClassifier for less than 30 samples and
+    LogisticRegression (with standard scaling) for 30 or more samples.
+    """
+
+    def __init__(self, classifiers=None):
+        """
+        Initializes the classifier.
+
+        The `classifiers` argument must be a list of tuples where the second element
+        of the tuple is the classifier and the first element is the number of
+        samples required. For example, if `classifiers` is set to
+        ```
+            [(100, ClassifierA()),
+             (50,  ClassifierB()),
+             (0,   ClassifierC())]
+        ``` then ClassifierA will be used if n_samples >= 100, ClassifierB will
+        be used if 100 > n_samples >= 50 and ClassifierC will be used if
+        50 > n_samples. The list must be ordered in (strictly) decreasing order.
         """
-    def __init__(self,
-                 predictor=None,
-                 min_samples_predict=1,
-                 min_samples_cv=100,
-                 thr_fix=0.999,
-                 thr_alpha=0.50,
-                 thr_balance=0.95,
-                 ):
-        self.min_samples_predict = min_samples_predict
-        self.min_samples_cv = min_samples_cv
-        self.thr_fix = thr_fix
-        self.thr_alpha = thr_alpha
-        self.thr_balance = thr_balance
-        self.predictor_factory = predictor
-        self.predictor = None
+        if classifiers is None:
+            classifiers = [
+                (30, make_pipeline(StandardScaler(), LogisticRegression())),
+                (0, CountingClassifier())
+            ]
+        self.available_classifiers = classifiers
+        self.classifier = None
 
     def fit(self, x_train, y_train):
         n_samples = x_train.shape[0]
 
-        # If number of samples is too small, don't predict anything.
-        if n_samples < self.min_samples_predict:
-            logger.debug("    Too few samples (%d); always predicting false" % n_samples)
-            self.predictor = 0
-            return
-
-        # If vast majority of observations are false, always return false.
-        y_train_avg = np.average(y_train)
-        if y_train_avg <= 1.0 - self.thr_fix:
-            logger.debug("    Most samples are negative (%.3f); always returning false" % y_train_avg)
-            self.predictor = 0
-            return
-
-        # If vast majority of observations are true, always return true.
-        if y_train_avg >= self.thr_fix:
-            logger.debug("    Most samples are positive (%.3f); always returning true" % y_train_avg)
-            self.predictor = 1
-            return
-
-        # If classes are too unbalanced, don't predict anything.
-        if y_train_avg < (1 - self.thr_balance) or y_train_avg > self.thr_balance:
-            logger.debug("    Classes are too unbalanced (%.3f); always returning false" % y_train_avg)
-            self.predictor = 0
-            return
-
-        # Select ML model if none is provided
-        if self.predictor_factory is None:
-            if n_samples < 30:
-                from sklearn.neighbors import KNeighborsClassifier
-                self.predictor_factory = KNeighborsClassifier(n_neighbors=n_samples)
-            else:
-                from sklearn.pipeline import make_pipeline
-                from sklearn.preprocessing import StandardScaler
-                from sklearn.linear_model import LogisticRegression
-                self.predictor_factory = make_pipeline(StandardScaler(), LogisticRegression())
-
-        # Create predictor
-        if callable(self.predictor_factory):
-            pred = self.predictor_factory()
-        else:
-            pred = deepcopy(self.predictor_factory)
-
-        # Skip cross-validation if number of samples is too small
-        if n_samples < self.min_samples_cv:
-            logger.debug("    Too few samples (%d); skipping cross validation" % n_samples)
-            self.predictor = pred
-            self.predictor.fit(x_train, y_train)
-            return
-
-        # Calculate cross-validation score
-        cv_score = np.mean(cross_val_score(pred, x_train, y_train, cv=5))
-        dummy_score = max(y_train_avg, 1 - y_train_avg)
-        cv_thr = 1. * self.thr_alpha + dummy_score * (1 - self.thr_alpha)
-
-        # If cross-validation score is too low, don't predict anything.
-        if cv_score < cv_thr:
-            logger.debug("    Score is too low (%.3f < %.3f); always returning false" % (cv_score, cv_thr))
-            self.predictor = 0
-        else:
-            logger.debug("    Score is acceptable (%.3f > %.3f); training classifier" % (cv_score, cv_thr))
-            self.predictor = pred
-            self.predictor.fit(x_train, y_train)
+        for (min_samples, clf_prototype) in self.available_classifiers:
+            if n_samples >= min_samples:
+                self.classifier = deepcopy(clf_prototype)
+                self.classifier.fit(x_train, y_train)
+                break
 
     def predict_proba(self, x_test):
-        if isinstance(self.predictor, int):
-            y_pred = np.zeros((x_test.shape[0], 2))
-            y_pred[:, self.predictor] = 1.0
-            return y_pred
-        else:
-            return self.predictor.predict_proba(x_test)
+        return self.classifier.predict_proba(x_test)
+
 

src/python/miplearn/classifiers/counting.py

@@ -21,7 +21,8 @@ class CountingClassifier(Classifier):
         self.mean = np.mean(y_train)
 
     def predict_proba(self, x_test):
-        return np.array([[1 - self.mean, self.mean]])
+        return np.array([[1 - self.mean, self.mean]
+                         for _ in range(x_test.shape[0])])
 
     def __repr__(self):
-        return "CountingClassifier(mean=%.3f)" % self.mean
+        return "CountingClassifier(mean=%s)" % self.mean

src/python/miplearn/classifiers/tests/test_counting.py

@@ -12,6 +12,7 @@ E = 0.1
 def test_counting():
     clf = CountingClassifier()
     clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
-    expected_proba = np.array([[0.375, 0.625]])
-    actual_proba = clf.predict_proba(np.zeros((1, 25)))
+    expected_proba = np.array([[0.375, 0.625],
+                               [0.375, 0.625]])
+    actual_proba = clf.predict_proba(np.zeros((2, 25)))
     assert norm(actual_proba - expected_proba) < E

src/python/miplearn/components/primal.py

@@ -67,6 +67,7 @@ class PrimalSolutionComponent(Component):
         x_test = VariableFeaturesExtractor().extract([instance])
         var_split = Extractor.split_variables(instance)
         for category in var_split.keys():
+            n = len(var_split[category])
             for (i, (var, index)) in enumerate(var_split[category]):
                 if var not in solution.keys():
                     solution[var] = {}
@@ -76,10 +77,12 @@ class PrimalSolutionComponent(Component):
                     continue
                 clf = self.classifiers[category, label]
                 if isinstance(clf, float):
-                    ws = np.array([[1-clf, clf]
-                                   for _ in range(len(var_split[category]))])
+                    ws = np.array([[1 - clf, clf] for _ in range(n)])
                 else:
                     ws = clf.predict_proba(x_test[category])
+                print("clf=", clf)
+                print("x_test=", x_test[category])
+                assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (n, ws.shape)
                 for (i, (var, index)) in enumerate(var_split[category]):
                     if ws[i, 1] >= self.threshold:
                         solution[var][index] = label