Improved runtime of maximum output selection.

ANN_Data_Generator.py

@@ -203,9 +203,9 @@ class TrainingDataGenerator(object):
         print('Calculation time:', toc-tic, '\n')
 
         # Set output data
+        # output_data = np.zeros(num_samples) if is_smooth else np.ones(num_samples)
         output_data = np.zeros((num_samples, 2))
-        output_index = 1 if is_smooth else 0
-        output_data[:, output_index] = np.ones(num_samples)
+        output_data[:, int(is_smooth)] = np.ones(num_samples)
 
         return input_data, output_data
 

ANN_Training.py

@@ -5,8 +5,8 @@
 TODO: Add log to pipeline
 TODO: Remove object set-up
 TODO: Optimize Snakefile-vs-config relation
-TODO: Improve maximum selection runtime
-TODO: Change output to binary
+TODO: Improve maximum selection runtime -> Done
+TODO: Change model output to binary -> Do? (changes training when applied in ANN_Model)
 TODO: Adapt TCD file to new classification
 TODO: Add evaluation for all classes (recall, precision, fscore)
 TODO: Add documentation
@@ -106,11 +106,14 @@ class ModelTrainer(object):
         x_test, y_test = test_set
         # print(self._model(x_test.float()))
         model_score = self._model(x_test.float())
-        model_output = torch.tensor([[1.0, 0.0] if value == 0 else [0.0, 1.0]
-                                     for value in torch.max(model_score, 1)[1]])
-
-        y_true = y_test.detach().numpy()[:, 0]
-        y_pred = model_output.detach().numpy()[:, 0]
+        # model_output = torch.tensor([[1.0, 0.0] if value == 0 else [0.0, 1.0]
+        #                              for value in torch.argmax(model_score, dim=1)])
+        # print(model_output)
+        model_output = torch.argmax(model_score, dim=1)
+        # print(model_output)
+
+        y_true = y_test.detach().numpy()[:, 1]
+        y_pred = model_output.detach().numpy()
         # y_score = model_score.detach().numpy()[:, 0]
         accuracy = accuracy_score(y_true, y_pred)
         # print('sklearn', accuracy)