debugging confusion matrix

config/config.py

@@ -4,14 +4,14 @@ import argparse
 def load_args():
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('--epoches', type=int, default=10)
+    parser.add_argument('--epoches', type=int, default=3)
     parser.add_argument('--save_inter', type=int, default=50)
     parser.add_argument('--eval_inter', type=int, default=1)
     parser.add_argument('--noise_threshold', type=int, default=0)
     parser.add_argument('--lr', type=float, default=0.001)
     parser.add_argument('--batch_size', type=int, default=64)
     parser.add_argument('--model', type=str, default='ResNet18')
-    parser.add_argument('--model_type', type=str, default='solo')
+    parser.add_argument('--model_type', type=str, default='duo')
     parser.add_argument('--dataset_dir', type=str, default='data/processed_data/png_image/data_training')
     parser.add_argument('--output', type=str, default='output/out.csv')
     parser.add_argument('--save_path', type=str, default='output/best_model.pt')

main.py

@@ -90,10 +90,10 @@ def run(args):
     plt.plot(train_acc)
     plt.ylim(0, 1.05)
     plt.show()
-    plt.savefig('output/training_plot_noise_{}_lr_{}_model_{}.png'.format(args.noise_threshold,args.lr,args.model))
+    plt.savefig('output/training_plot_noise_{}_lr_{}_model_{}_{}.png'.format(args.noise_threshold,args.lr,args.model,args.model_type))
 
     load_model(model, args.save_path)
-    make_prediction(model,data_test, 'output/confusion_matrix_noise_{}_lr_{}_model_{}.png'.format(args.noise_threshold,args.lr,args.model))
+    make_prediction(model,data_test, 'output/confusion_matrix_noise_{}_lr_{}_model_{}_{}.png'.format(args.noise_threshold,args.lr,args.model,args.model_type))
 
 
 def make_prediction(model, data, f_name):
@@ -121,7 +121,7 @@ def make_prediction(model, data, f_name):
     df_cm = pd.DataFrame(cf_matrix / np.sum(cf_matrix, axis=1)[:, None], index=[i for i in classes],
                          columns=[i for i in classes])
     plt.figure(figsize=(12, 7))
-    sn.heatmap(df_cm, annot=True)
+    sn.heatmap(df_cm, annot=cf_matrix)
     plt.savefig(f_name)
 
 
@@ -206,6 +206,7 @@ def run_duo(args):
     plt.plot(train_acc)
     plt.ylim(0, 1.05)
     plt.show()
+
     plt.savefig('output/training_plot_noise_{}_lr_{}_model_{}_{}.png'.format(args.noise_threshold,args.lr,args.model,args.model_type))
 
     load_model(model, args.save_path)
@@ -215,7 +216,6 @@ def run_duo(args):
 def make_prediction_duo(model, data, f_name):
     y_pred = []
     y_true = []
-    print('Building confusion matrix')
     # iterate over test data
     for imaer,imana, label in data:
         label = label.long()
@@ -233,17 +233,14 @@ def make_prediction_duo(model, data, f_name):
     # constant for classes
 
     classes = data.dataset.dataset.classes
-    print('Prediction made')
     # Build confusion matrix
     print(len(y_true),len(y_pred))
     cf_matrix = confusion_matrix(y_true, y_pred)
-    print('CM made')
-    df_cm = pd.DataFrame(cf_matrix[:, None], index=[i for i in classes],
+    df_cm = pd.DataFrame(cf_matrix / np.sum(cf_matrix, axis=1)[:, None], index=[i for i in classes],
                          columns=[i for i in classes])
-
     print('Saving Confusion Matrix')
     plt.figure(figsize=(14, 9))
-    sn.heatmap(df_cm, annot=True)
+    sn.heatmap(df_cm, annot=cf_matrix)
     plt.savefig(f_name)