add fit_predict

combo/models/base.py

@@ -66,6 +66,7 @@ def fit(self, X, y=None):
         """
         pass
 
+    # todo: make sure fit then predict is equivalent to fit_predict
     @abstractmethod
     def fit_predict(self, X, y=None):
         """Fit estimator and predict on X. y is optional for unsupervised

combo/models/classifier_comb.py

@@ -168,3 +168,22 @@ def predict_proba(self, X):
             Warning('average method is invoked for predict_proba as'
                     'probability is not continuous')
             return np.mean(all_scores * self.weights, axis=2)
+
+    def fit_predict(self, X, y):
+        """Fit estimator and predict on X
+
+        Parameters
+        ----------
+        X : numpy array of shape (n_samples, n_features)
+            The input samples.
+
+        y : numpy array of shape (n_samples,), optional (default=None)
+            The ground truth of the input samples (labels).
+
+        Returns
+        -------
+        labels : numpy array of shape (n_samples,)
+            Class labels for each data sample.
+        """
+        self.fit(X, y)
+        return self.predict(X)

combo/models/classifier_dcs.py

@@ -200,3 +200,22 @@ def _predict_internal(self, X, predict_proba):
                     test_sample)
 
         return y_predicted
+
+    def fit_predict(self, X, y):
+        """Fit estimator and predict on X
+
+        Parameters
+        ----------
+        X : numpy array of shape (n_samples, n_features)
+            The input samples.
+
+        y : numpy array of shape (n_samples,), optional (default=None)
+            The ground truth of the input samples (labels).
+
+        Returns
+        -------
+        labels : numpy array of shape (n_samples,)
+            Class labels for each data sample.
+        """
+        self.fit(X, y)
+        return self.predict(X)

combo/models/classifier_des.py

@@ -254,3 +254,22 @@ def _predict_internal(self, X, predict_proba):
             return score_to_proba(y_predicted)
         else:
             return y_predicted
+
+    def fit_predict(self, X, y):
+        """Fit estimator and predict on X
+
+        Parameters
+        ----------
+        X : numpy array of shape (n_samples, n_features)
+            The input samples.
+
+        y : numpy array of shape (n_samples,), optional (default=None)
+            The ground truth of the input samples (labels).
+
+        Returns
+        -------
+        labels : numpy array of shape (n_samples,)
+            Class labels for each data sample.
+        """
+        self.fit(X, y)
+        return self.predict(X)

combo/models/classifier_stacking.py

@@ -291,3 +291,22 @@ def predict_proba(self, X):
         """
         X_new_comb = self._process_data(X)
         return self.meta_clf.predict_proba(X_new_comb)
+
+    def fit_predict(self, X, y):
+        """Fit estimator and predict on X
+
+        Parameters
+        ----------
+        X : numpy array of shape (n_samples, n_features)
+            The input samples.
+
+        y : numpy array of shape (n_samples,), optional (default=None)
+            The ground truth of the input samples (labels).
+
+        Returns
+        -------
+        labels : numpy array of shape (n_samples,)
+            Class labels for each data sample.
+        """
+        self.fit(X, y)
+        return self.predict(X)

combo/models/cluster_comb.py

@@ -156,6 +156,26 @@ def predict_proba(self, X):
         raise NotImplemented("predict_proba function is currently disabled for"
                              "clustering due to inconsistent behaviours.")
 
+    def fit_predict(self, X, y=None):
+        """Fit estimator and predict on X. y is optional for unsupervised
+        methods.
+
+        Parameters
+        ----------
+        X : numpy array of shape (n_samples, n_features)
+            The input samples.
+
+        y : numpy array of shape (n_samples,), optional (default=None)
+            The ground truth of the input samples (labels).
+
+        Returns
+        -------
+        labels : numpy array of shape (n_samples,)
+            Cluster labels for each data sample.
+        """
+        self.fit(X)
+        return self.labels_
+
 
 def clusterer_ensemble_scores(original_labels, n_estimators, n_clusters,
                               weights=None, return_results=False,

combo/models/detector_comb.py

@@ -235,3 +235,23 @@ def predict_proba(self, X, proba_method='linear'):
         check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
         X = check_array(X)
         return self._detector_predict_proba(X, proba_method)
+
+    def fit_predict(self, X, y=None):
+        """Fit estimator and predict on X. y is optional for unsupervised
+        methods.
+
+        Parameters
+        ----------
+        X : numpy array of shape (n_samples, n_features)
+            The input samples.
+
+        y : numpy array of shape (n_samples,), optional (default=None)
+            The ground truth of the input samples (labels).
+
+        Returns
+        -------
+        labels : numpy array of shape (n_samples,)
+            Class labels for each data sample.
+        """
+        self.fit(X)
+        return self.predict(X)

combo/models/detector_lscp.py

@@ -208,3 +208,23 @@ def predict_proba(self, X, proba_method='linear'):
         check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
         X = check_array(X)
         return self._detector_predict_proba(X, proba_method)
+
+    def fit_predict(self, X, y=None):
+        """Fit estimator and predict on X. y is optional for unsupervised
+        methods.
+
+        Parameters
+        ----------
+        X : numpy array of shape (n_samples, n_features)
+            The input samples.
+
+        y : numpy array of shape (n_samples,), optional (default=None)
+            The ground truth of the input samples (labels).
+
+        Returns
+        -------
+        labels : numpy array of shape (n_samples,)
+            Class labels for each data sample.
+        """
+        self.fit(X)
+        return self.predict(X)

combo/test/test_classifier_comb.py

@@ -36,6 +36,34 @@
 from combo.utils.data import evaluate_print
 
 
+class TestFitPredict(unittest.TestCase):
+    def setUp(self):
+        self.roc_floor = 0.9
+        self.accuracy_floor = 0.9
+
+        random_state = 42
+        X, y = load_breast_cancer(return_X_y=True)
+
+        self.X_train, self.X_test, self.y_train, self.y_test = \
+            train_test_split(X, y, test_size=0.4, random_state=random_state)
+
+        classifiers = [DecisionTreeClassifier(random_state=random_state),
+                       LogisticRegression(random_state=random_state),
+                       KNeighborsClassifier(),
+                       RandomForestClassifier(random_state=random_state),
+                       GradientBoostingClassifier(random_state=random_state)]
+
+        self.clf = SimpleClassifierAggregator(classifiers, method='average')
+
+    def test_fit_predict(self):
+        y_train_predicted = self.clf.fit_predict(self.X_train, self.y_train)
+        assert_equal(len(y_train_predicted), self.X_train.shape[0])
+
+        # check performance
+        assert_greater(accuracy_score(self.y_train, y_train_predicted),
+                       self.accuracy_floor)
+
+
 class TestAverage(unittest.TestCase):
     def setUp(self):
         self.roc_floor = 0.9

combo/test/test_classifier_dcs.py

@@ -97,6 +97,15 @@ def test_prediction_proba(self):
         assert_allclose(np.ones([self.X_test.shape[0], ]),
                         y_test_predicted_sum)
 
+    def test_fit_predict(self):
+        y_train_predict = self.clf.fit_predict(self.X_train, self.y_train)
+
+        assert_equal(len(y_train_predict), self.X_train.shape[0])
+
+        # check performance
+        assert_greater(accuracy_score(self.y_train, y_train_predict),
+                       self.accuracy_floor)
+
     def tearDown(self):
         pass
 

combo/test/test_classifier_des.py

@@ -97,6 +97,15 @@ def test_prediction_proba(self):
         assert_allclose(np.ones([self.X_test.shape[0], ]),
                         y_test_predicted_sum)
 
+    def test_fit_predict(self):
+        y_train_predict = self.clf.fit_predict(self.X_train, self.y_train)
+
+        assert_equal(len(y_train_predict), self.X_train.shape[0])
+
+        # check performance
+        assert_greater(accuracy_score(self.y_train, y_train_predict),
+                       self.accuracy_floor)
+
     def tearDown(self):
         pass
 

combo/test/test_classifier_stacking.py

@@ -97,6 +97,15 @@ def test_prediction_proba(self):
         assert_allclose(np.ones([self.X_test.shape[0], ]),
                         y_test_predicted_sum)
 
+    def test_fit_predict(self):
+        y_train_predict = self.clf.fit_predict(self.X_train, self.y_train)
+
+        assert_equal(len(y_train_predict), self.X_train.shape[0])
+
+        # check performance
+        assert_greater(accuracy_score(self.y_train, y_train_predict),
+                       self.accuracy_floor)
+
     def tearDown(self):
         pass
 

combo/test/test_cluster_comb.py

@@ -35,7 +35,6 @@ def setUp(self):
         self.X, self.y = load_breast_cancer(return_X_y=True)
 
         n_clusters = 5
-        n_estimators = 3
 
         # Initialize a set of estimators
         estimators = [KMeans(n_clusters=n_clusters),
@@ -57,6 +56,11 @@ def test_scores(self):
         predicted_labels = self.estimator.labels_
         assert_equal(predicted_labels.shape[0], self.X.shape[0])
 
+    def test_fit_predict(self):
+        predicted_labels = self.estimator.fit_predict(self.X)
+        assert_equal(predicted_labels.shape[0], self.X.shape[0])
+
+
     def tearDown(self):
         pass
 

combo/test/test_detector_comb.py

@@ -230,6 +230,10 @@ def test_prediction_proba_parameter(self):
         with assert_raises(ValueError):
             self.clf.predict_proba(self.X_test, proba_method='something')
 
+    def test_fit_predict(self):
+        pred_labels = self.clf.fit_predict(self.X_train)
+        assert_equal(pred_labels.shape, self.y_train.shape)
+
     def tearDown(self):
         pass
 

combo/test/test_detector_lscp.py

@@ -110,6 +110,10 @@ def test_prediction_proba_parameter(self):
         with assert_raises(ValueError):
             self.clf.predict_proba(self.X_test, proba_method='something')
 
+    def test_fit_predict(self):
+        pred_labels = self.clf.fit_predict(self.X_train)
+        assert_equal(pred_labels.shape, self.y_train.shape)
+
     def tearDown(self):
         pass