Sourcery Starbot ⭐ refactored curenosm/MeIA

modulo_2/sesion_1/src/exercise-S1-3-01.py

@@ -34,7 +34,7 @@
 def test(model, test_loader):
     start_test = True
     with torch.no_grad():
-        for batch_idx, data in enumerate(test_loader):
+        for data in test_loader:
             # get batch data
             samples = data[0].float().cuda()
             labels = data[1].long().cuda()
@@ -93,8 +93,7 @@ def kfcv(X, Y, subjects, args):
 
         list_metrics_clsf = []
 
-        for i, (train_index, test_index) in enumerate(skf.split(x, y)):
-
+        for train_index, test_index in skf.split(x, y):
             x_train, y_train = x[train_index], y[train_index]
             x_test, y_test = x[test_index], y[test_index]
 
@@ -139,7 +138,7 @@ def kfcv(X, Y, subjects, args):
                 iter_train = iter(source_loader)
                 list_loss = []
 
-                for c in range(len(source_loader)):
+                for _ in range(len(source_loader)):
                     # get batch
                     samples, labels = iter_train.next()
                     samples = samples.float().cuda()
@@ -179,10 +178,9 @@ def kfcv(X, Y, subjects, args):
         list_metrics_clsf = np.array(list_metrics_clsf)
 
         # Save Classification Metrics
-        save_file = args.dir_resume + "/kfcv-results.csv"
-        f = open(save_file, 'ab')
-        np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
-        f.close()
+        save_file = f"{args.dir_resume}/kfcv-results.csv"
+        with open(save_file, 'ab') as f:
+            np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
 
 
 def main(args):

modulo_2/sesion_1/src/exercise-S1-3-02.py

@@ -35,7 +35,7 @@
 def test(model, test_loader):
     start_test = True
     with torch.no_grad():
-        for batch_idx, data in enumerate(test_loader):
+        for data in test_loader:
             # get batch data
             samples = data[0].float().cuda()
             labels = data[1].long().cuda()
@@ -79,7 +79,7 @@ def losocv(X, Y, subjects, args):
 
     # variable used to save accuracy results
     list_metrics_clsf = []
-        
+
     # Extract pairs between indexes and subjects
     fold_pairs = get_subject_indices(subjects)
 
@@ -88,7 +88,7 @@ def losocv(X, Y, subjects, args):
         print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))
 
         # Only Subjects 1, 2 are executed
-        if foldNum + 1 >= 3:
+        if foldNum >= 2:
             continue
 
         # Divide dataset into training, validation and testing sets
@@ -130,7 +130,7 @@ def losocv(X, Y, subjects, args):
             iter_train = iter(source_loader)
             list_loss = []
 
-            for c in range(len(source_loader)):
+            for _ in range(len(source_loader)):
                 # get batch
                 samples, labels = iter_train.next()
                 samples = samples.float().cuda()
@@ -164,20 +164,19 @@ def losocv(X, Y, subjects, args):
             print('Epoch: %d loss: %4f Acc: %.4f  F1-score: %.4f  AUC: %.4f' % (epoch+1, avg_loss, acc_test, f1_test, auc_test))
 
         print('Saving model...')
-        torch.save(model.state_dict(), 'trained_model/source' + str(foldNum+1) + '.pt')
+        torch.save(model.state_dict(), f'trained_model/source{str(foldNum + 1)}.pt')
 
         print("\n")
         # add to list
         list_metrics_clsf.append([acc_test, f1_test, auc_test, foldNum+1])
-    
+
     # To np array
     list_metrics_clsf = np.array(list_metrics_clsf)
 
     # Save Classification Metrics
-    save_file = args.dir_resume+"/losocv-results.csv"
-    f=open(save_file, 'ab')
-    np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
-    f.close()
+    save_file = f"{args.dir_resume}/losocv-results.csv"
+    with open(save_file, 'ab') as f:
+        np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
 
 
 def main(args):

modulo_2/sesion_1/src/utils/modules_pbashivan.py

@@ -31,16 +31,16 @@ def load_data(data_file):
     -------
     data: array_like
     """
-    print("Loading data from %s" % (data_file))
+    print(f"Loading data from {data_file}")
 
     dataMat = scipy.io.loadmat(data_file, mat_dtype=True)
     all_data = np.array(dataMat['features'])
-    
+
     data = np.array(all_data[:,:len(all_data[0])-1])
     labels = np.array(all_data[:,len(all_data[0])-1])
-    
+
     print("Data loading complete. Shape is %r" % (dataMat['features'].shape,))
-    
+
     return data, labels.T - 1   # Sequential indices
 
 
@@ -278,36 +278,34 @@ def get_subject_indices_kfcv(id_subjects):
 
 
 def load_bashivan_data(data_dir, n_channels = 64, n_windows=7, n_bands=3, generate_images=False, size_image=32, visualize=False):
-    
+
     """
         Module used to load dataset of bashivan et al. 2014.
     """
 
     # load data pbashivan
-    data, labels = load_data(data_dir + "FeatureMat_timeWin.mat")
+    data, labels = load_data(f"{data_dir}FeatureMat_timeWin.mat")
     print("Original data:",data.shape, labels.shape)
-        
+
     if generate_images:
 
         # NOTE: Only a 3D projection is proporcionated, then it is not avaliable other
         # records with positions.
-        
+
         #Load locations in 3D
-        locs_orig = scipy.io.loadmat(data_dir+'Neuroscan_locs_orig.mat', mat_dtype=True)
+        locs_orig = scipy.io.loadmat(
+            f'{data_dir}Neuroscan_locs_orig.mat', mat_dtype=True
+        )
         locs3D = locs_orig['A']
-
-        #Convert to 2D
-        locs2D =[]
-        for e in locs3D:
-            locs2D.append(azim_proj(e))
-
+
+        locs2D = [azim_proj(e) for e in locs3D]
         #save in numpy array
         locs2D = np.array(locs2D)
-        
+
         # visualize projection
         if visualize:
             print("No. channels:",locs3D.shape)
-            
+
             # Plot in 3D
             fig = plt.figure()
             ax = fig.add_subplot(111, projection='3d')
@@ -316,7 +314,7 @@ def load_bashivan_data(data_dir, n_channels = 64, n_windows=7, n_bands=3, genera
             ax.set_xlabel('X Label')
             ax.set_ylabel('Y Label')
             ax.set_zlabel('Z Label')
-            
+
             # Plot in 2D
             fig = plt.figure()
             ax = fig.add_subplot(111)
@@ -325,7 +323,7 @@ def load_bashivan_data(data_dir, n_channels = 64, n_windows=7, n_bands=3, genera
             ax.set_xlabel('X Label')
             ax.set_ylabel('Y Label')
             plt.show()
-        
+
         # NOTE: database is defined with 7 time windows
         # FFT power values extracted for three frequency bands (theta, alpha, beta). 
         # Features are arranged in band and electrodes order (theta_1, theta_2..., 
@@ -347,18 +345,20 @@ def load_bashivan_data(data_dir, n_channels = 64, n_windows=7, n_bands=3, genera
         images = np.array(images)
         # transpose
         images = images.transpose((1, 2, 0, 3, 4))
-        scipy.io.savemat(data_dir+'images.mat', mdict={'images': images})
-    
+        scipy.io.savemat(f'{data_dir}images.mat', mdict={'images': images})
+
     else:
         #Load locations in 3D
-        files_mat = scipy.io.loadmat(data_dir+'images.mat', mat_dtype=True)
+        files_mat = scipy.io.loadmat(f'{data_dir}images.mat', mat_dtype=True)
         images = files_mat['images']
-    
+
     #Load info subjects associated with trials. List of patiens for 2670 trials
-    subjects_trials= scipy.io.loadmat(data_dir+'trials_subNums.mat', mat_dtype=True)
+    subjects_trials = scipy.io.loadmat(
+        f'{data_dir}trials_subNums.mat', mat_dtype=True
+    )
     subjNumbers = np.squeeze(subjects_trials['subjectNum'])
-    
-    
+
+
     print("Shape images", images.shape)
-    
+
     return images, labels, subjNumbers

modulo_2/sesion_2/src/exercise-S1-3-01.py

@@ -34,7 +34,7 @@
 def test(model, test_loader):
     start_test = True
     with torch.no_grad():
-        for batch_idx, data in enumerate(test_loader):
+        for data in test_loader:
             # get batch data
             samples = data[0].float().cuda()
             labels = data[1].long().cuda()
@@ -93,8 +93,7 @@ def kfcv(X, Y, subjects, args):
 
         list_metrics_clsf = []
 
-        for i, (train_index, test_index) in enumerate(skf.split(x, y)):
-
+        for train_index, test_index in skf.split(x, y):
             x_train, y_train = x[train_index], y[train_index]
             x_test, y_test = x[test_index], y[test_index]
 
@@ -139,7 +138,7 @@ def kfcv(X, Y, subjects, args):
                 iter_train = iter(source_loader)
                 list_loss = []
 
-                for c in range(len(source_loader)):
+                for _ in range(len(source_loader)):
                     # get batch
                     samples, labels = iter_train.next()
                     samples = samples.float().cuda()
@@ -179,10 +178,9 @@ def kfcv(X, Y, subjects, args):
         list_metrics_clsf = np.array(list_metrics_clsf)
 
         # Save Classification Metrics
-        save_file = args.dir_resume + "/kfcv-results.csv"
-        f = open(save_file, 'ab')
-        np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
-        f.close()
+        save_file = f"{args.dir_resume}/kfcv-results.csv"
+        with open(save_file, 'ab') as f:
+            np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
 
 
 def main(args):

modulo_2/sesion_2/src/exercise-S1-3-02.py

@@ -35,7 +35,7 @@
 def test(model, test_loader):
     start_test = True
     with torch.no_grad():
-        for batch_idx, data in enumerate(test_loader):
+        for data in test_loader:
             # get batch data
             samples = data[0].float().cuda()
             labels = data[1].long().cuda()
@@ -79,7 +79,7 @@ def losocv(X, Y, subjects, args):
 
     # variable used to save accuracy results
     list_metrics_clsf = []
-        
+
     # Extract pairs between indexes and subjects
     fold_pairs = get_subject_indices(subjects)
 
@@ -88,7 +88,7 @@ def losocv(X, Y, subjects, args):
         print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))
 
         # Only Subjects 1, 2 are executed
-        if foldNum + 1 >= 3:
+        if foldNum >= 2:
             continue
 
         # Divide dataset into training, validation and testing sets
@@ -130,7 +130,7 @@ def losocv(X, Y, subjects, args):
             iter_train = iter(source_loader)
             list_loss = []
 
-            for c in range(len(source_loader)):
+            for _ in range(len(source_loader)):
                 # get batch
                 samples, labels = iter_train.next()
                 samples = samples.float().cuda()
@@ -164,20 +164,19 @@ def losocv(X, Y, subjects, args):
             print('Epoch: %d loss: %4f Acc: %.4f  F1-score: %.4f  AUC: %.4f' % (epoch+1, avg_loss, acc_test, f1_test, auc_test))
 
         print('Saving model...')
-        torch.save(model.state_dict(), 'trained_model/source' + str(foldNum+1) + '.pt')
+        torch.save(model.state_dict(), f'trained_model/source{str(foldNum + 1)}.pt')
 
         print("\n")
         # add to list
         list_metrics_clsf.append([acc_test, f1_test, auc_test, foldNum+1])
-    
+
     # To np array
     list_metrics_clsf = np.array(list_metrics_clsf)
 
     # Save Classification Metrics
-    save_file = args.dir_resume+"/losocv-results.csv"
-    f=open(save_file, 'ab')
-    np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
-    f.close()
+    save_file = f"{args.dir_resume}/losocv-results.csv"
+    with open(save_file, 'ab') as f:
+        np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
 
 
 def main(args):

modulo_2/sesion_2/src/exercise-S2-2-01.py

@@ -35,7 +35,7 @@
 def test(network_f, network_h, test_loader):
     start_test = True
     with torch.no_grad():
-        for batch_idx, data in enumerate(test_loader):
+        for data in test_loader:
             # get batch data
             samples = data[0].float().cuda()
             labels = data[1].long().cuda()
@@ -80,16 +80,16 @@ def losocv(X, Y, subjects, args):
 
     # variable used to save accuracy results
     list_metrics_clsf = []
-        
+
     # Extract pairs between indexes and subjects
     fold_pairs = get_subject_indices(subjects)
-    
+
     # Iterate over fold_pairs
     for foldNum, fold in enumerate(fold_pairs):
         print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))
 
         # Only Subjects 1, 2 are executed
-        if foldNum + 1 >= 3:# and foldNum + 1 <= 11:
+        if foldNum >= 2:# and foldNum + 1 <= 11:
             continue
 
         # Get source and target datasets
@@ -144,6 +144,8 @@ def losocv(X, Y, subjects, args):
         # Almacenar pérdida
         list_loss = []
 
+        # [Total loss]
+        lambda_dis = 1.0
         for iter_num in range(0, args.max_iterations + 1):
             network_f.train()
             network_h.train()
@@ -176,8 +178,6 @@ def losocv(X, Y, subjects, args):
             # [Classification Loss]
             classifier_loss = criterion(outputs_source, labels_source)
 
-            # [Total loss]
-            lambda_dis = 1.0
             total_loss = classifier_loss + lambda_dis * transfer_loss
 
             # Reset gradients
@@ -209,15 +209,14 @@ def losocv(X, Y, subjects, args):
         print("\n")
         # add to list
         list_metrics_clsf.append([acc, f1, auc, foldNum+1])
-    
+
     # To np array
     list_metrics_clsf = np.array(list_metrics_clsf)
 
     # Save Classification Metrics
-    save_file = args.dir_resume+"/losocv-results.csv"
-    f=open(save_file, 'ab')
-    np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
-    f.close()
+    save_file = f"{args.dir_resume}/losocv-results.csv"
+    with open(save_file, 'ab') as f:
+        np.savetxt(f, list_metrics_clsf, delimiter=",", fmt='%0.4f')
 
 
 def main(args):