Removed unnecessary comments.

ANN_Training.py

@@ -7,6 +7,7 @@ TODO: Remove object set-up (for more flexibility)
 TODO: Adapt TCD file to new classification
 TODO: Add documentation
 TODO: Improve log output
+TODO: Remove unnecessary comments -> Done
 
 """
 import numpy as np
@@ -101,30 +102,14 @@ class ModelTrainer(object):
         self._model.eval()
 
         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.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)
         precision, recall, f_score, support = precision_recall_fscore_support(y_true, y_pred)
-        # print(precision, recall, f_score)
-        # print()
-        # auroc = roc_auc_score(y_true, y_score)
-        # print('auroc raw', auroc)
         auroc = roc_auc_score(y_true, y_pred)
-        print('auroc true', auroc)
-        # fpr, tpr, thresholds = roc_curve(y_true, y_score)
-        # roc = [tpr, fpr, thresholds]
-        # print(roc)
-        # plt.plot(fpr, tpr, label="AUC="+str(auroc))
 
         return {'Precision_Smooth': precision[0], 'Precision_Troubled': precision[1],
                 'Recall_Smooth': recall[0], 'Recall_Troubled': recall[1],
@@ -143,9 +128,6 @@ class ModelTrainer(object):
         torch.save(self._model.state_dict(), model_dir + '/model__' + name + '.pt')
         torch.save(self._validation_loss, model_dir + '/loss__' + name + '.pt')
 
-    # def _classify(self):
-    #     pass
-
 
 def read_training_data(directory, normalized=True):
     # Get training dataset from saved file and map to Torch tensor and dataset
@@ -170,7 +152,6 @@ def evaluate_models(models, directory, num_iterations=100, colors=None,
     for iteration in range(num_iterations):
         for train_index, test_index in KFold(
                 n_splits=5, shuffle=True).split(datasets['normalized']):
-            # print("TRAIN:", train_index, "TEST:", test_index)
             for dataset in datasets.keys():
                 training_set = TensorDataset(*datasets[dataset][train_index])
                 test_set = datasets[dataset][test_index]
@@ -200,12 +181,3 @@ def evaluate_models(models, directory, num_iterations=100, colors=None,
 
         plt.figure(identifier)
         plt.savefig(plot_dir + '/' + identifier + '/' + '_'.join(models.keys()) + '.pdf')
-
-
-# Loss Functions: BCELoss, BCEWithLogitsLoss,
-# CrossEntropyLoss (not working), MSELoss (with reduction='sum')
-# Optimizer: Adam, SGD
-# trainer = ModelTrainer({'num_epochs': 1000})
-# trainer.epoch_training()
-# trainer.test_model()
-# trainer.save_model()

Plotting.py

@@ -156,7 +156,7 @@ def plot_details(fine_projection, fine_mesh, coarse_projection, basis, wavelet,
 
 
 def calculate_approximate_solution(projection, points, polynomial_degree, basis):
-    """"Calculates approximate solution.
+    """Calculates approximate solution.
 
     Parameters
     ----------
@@ -273,7 +273,6 @@ def plot_classification_accuracy(evaluation_dict, colors):
     ax.set_ylim(top=1.02)
     ax.set_title('Classification Evaluation (Barplot)')
     ax.legend(loc='upper right')
-    # fig.tight_layout()
 
 
 def plot_boxplot(evaluation_dict, colors):