fix LSTM unit_test

tods/detection_algorithm/core/CollectiveCommonTest.py

@@ -94,7 +94,6 @@ class CollectiveCommonTest:
 
     def test_prediction_scores(self):
         pred_scores, _, _ = self.clf.decision_function(self.X_test)
-
         # check score shapes
         assert_equal(pred_scores.shape[0], self.y_test.shape[0])
 

tods/detection_algorithm/core/LSTMOD.py

@@ -141,33 +141,7 @@ class LSTMOutlierDetector(CollectiveBaseDetector):
         relative_error = (np.linalg.norm(y_predict - y_buf, axis=1) / np.linalg.norm(y_buf + 1e-6, axis=1)).ravel()
 
         return relative_error
-
-    def predict(self, X): # pragma: no cover
-        """Predict if a particular sample is an outlier or not.
-
-        Parameters
-        ----------
-        X : numpy array of shape (n_samples, n_features)
-            The input samples.
-
-        Returns
-        -------
-        outlier_labels : numpy array of shape (n_samples,)
-            For each observation, tells whether or not
-            it should be considered as an outlier according to the
-            fitted model. 0 stands for inliers and 1 for outliers.
-        """
-
-        check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
-
-        pred_score, X_left_inds, X_right_inds = self.decision_function(X)
-
-        pred_score = np.concatenate((np.zeros((self.window_size,)), pred_score))
-        X_left_inds = np.concatenate((np.zeros((self.window_size,)), X_left_inds))
-        X_right_inds = np.concatenate((np.zeros((self.window_size,)), X_right_inds))
-
-        return (pred_score > self.threshold_).astype(
-            'int').ravel(), X_left_inds.ravel(), X_right_inds.ravel()
+    
 
     def decision_function(self, X: np.array):
         """Predict raw anomaly scores of X using the fitted detector.
@@ -259,6 +233,10 @@ class LSTMOutlierDetector(CollectiveBaseDetector):
         # print(relative_error_right_inds)
         pred_score = danger_coefficient * self.danger_coefficient_weight + averaged_relative_error * (1 - self.danger_coefficient_weight)
 
+        pred_score = np.concatenate((np.zeros((self.window_size,)), pred_score))
+        relative_error_left_inds = np.concatenate((np.arange(self.window_size), relative_error_left_inds+self.window_size))
+        relative_error_right_inds = np.concatenate((np.arange(self.window_size)+self.window_size, relative_error_right_inds+self.window_size))
+
         return pred_score, relative_error_left_inds, relative_error_right_inds
 
 

tods/tests/detection_algorithm/test_LSTMODetector.py

@@ -27,7 +27,7 @@ class LSTMODTestCase(unittest.TestCase):
 
         self.X_train = d3m_dataframe({'data': [3., 4., 8., 16, 18, 13., 22., 36., 59., 128, 62, 67, 78, 100]},
                                       columns=['data'], generate_metadata=True)
-        self.y_train = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+        self.y_train = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
         self.X_test = d3m_dataframe({'data': [3., 4., 8.6, 13.4, 22.5, 17, 19.2, 36.1, 127, -23, 59.2]},
                                      columns=['data'], generate_metadata=True)
         self.y_test = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0])