From dae614165a6868e8932e23708619021d0d38ce6b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=BChle=2C=20Laura=20Christine=20=28lakue103=29?=
<laura.kuehle@uni-duesseldorf.de>
Date: Tue, 7 Dec 2021 23:01:16 +0100
Subject: [PATCH] Removed unnecessary imports.
---
ANN_Training.py | 20 ++++++++++++++------
1 file changed, 14 insertions(+), 6 deletions(-)
diff --git a/ANN_Training.py b/ANN_Training.py
index ecd95b8..892503a 100644
--- a/ANN_Training.py
+++ b/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
@@ -156,7 +164,7 @@ def read_training_data(directory):
return TensorDataset(*map(torch.tensor, (np.load(input_file), np.load(output_file))))
-def evaluate_models(models, directory, num_iterations=100, colors = None):
+def evaluate_models(models, directory, num_iterations=100, colors=None):
if colors is None:
colors = {'Accuracy': 'red', 'Precision': 'yellow', 'Recall': 'blue',
'F-Score': 'green', 'AUROC': 'purple'}
--
GitLab