Removed unnecessary imports.

ANN_Training.py

@@ -3,16 +3,25 @@
 @author: Laura C. Kühle, Soraya Terrab (sorayaterrab)
 
 TODO: Give option to compare multiple models -> Done
-TODO: Add more evaluation measures (AUROC, ROC, F1, training accuracy, boxplot over CVF, etc.) -> Done
+TODO: Add more evaluation measures (AUROC, ROC, F1, training accuracy, boxplot over CVF, etc.)
+    -> Done
 TODO: Add log to pipeline
 TODO: Remove object set-up
 TODO: Optimize Snakefile-vs-config relation
 TODO: Improve maximum selection runtime
-TODO: Discuss if we want training accuracy/ROC in addition to CFV
-TODO: Discuss whether to change output to binary
+TODO: Discuss if we want training accuracy/ROC in addition to CFV -> Done (No)
+TODO: Discuss whether to change output to binary -> Done (Yes)
+TODO: Change output to binary
 TODO: Adapt TCD file to new classification
 TODO: Improve classification stat handling -> Done
 TODO: Discuss automatic comparison between (non-)normalized data
+    -> Done (Flag for comparison)
+TODO: Add flag for evaluation of non-normalized data as well -> Next!
+TODO: Add evaluation for all classes (recall, precision, fscore)
+TODO: Add documentation
+TODO: Separate model training in Snakefile by using wildcards -> Done
+TODO: Correct import statements -> Done (Installed new version)
+TODO: Remove unnecessary imports -> Done
 
 """
 import numpy as np
@@ -21,8 +30,7 @@ import os
 import torch
 from torch.utils.data import TensorDataset, DataLoader, random_split
 from sklearn.model_selection import KFold
-# from sklearn.metrics import accuracy_score
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support, precision_score, roc_auc_score, roc_curve
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_auc_score
 
 import ANN_Model
 from Plotting import plot_classification_accuracy, plot_boxplot