modifié : image_processing/build_dataset.py

	modifié :         main.py

image_processing/build_dataset.py

@@ -145,7 +145,7 @@ def create_antibio_dataset(path='../data/label_raw/230804_strain_peptides_antibi
         l = split_before_number(s)
         species = l[0]
         nb = l[1]
-        return '{}-{}-{}{}.mzML'.format(species,nb,analyse,suffix)
+        return f'{species}-{nb}-{analyse}{suffix}.mzML'
 
     df['path_ana'] = df['sample_name'].map(lambda x: create_fname(x,analyse='ANA'))
     df['path_aer'] = df['sample_name'].map(lambda x: create_fname(x, analyse='AER'))
@@ -172,8 +172,8 @@ def create_dataset():
             name = label[label['path_aer'] == path.split("/")[-1]]['sample_name'].values[0]
             analyse = 'AER'
         if species is not None: #save image in species specific dir
-            directory_path_png = '../data/processed_data/png_image/{}'.format(species)
-            directory_path_npy = '../data/processed_data/npy_image/{}'.format(species)
+            directory_path_png = f'../data/processed_data/png_image/{species}'
+            directory_path_npy = f'../data/processed_data/npy_image/{species}'
             if not os.path.isdir(directory_path_png):
                 os.makedirs(directory_path_png)
             if not os.path.isdir(directory_path_npy):

main.py

@@ -11,6 +11,7 @@ from sklearn.metrics import confusion_matrix
 import seaborn as sn
 import pandas as pd
 
+#test modif git
 
 
 def train(model, data_train, optimizer, loss_function, epoch):