antibio resistance label extraction

dataset/dataset.py

-import random
-
 import numpy as np
 import torch
 import torchvision
@@ -57,11 +55,9 @@ def load_data(base_dir, batch_size, shuffle=True, noise_threshold=0):
     train_dataset = torchvision.datasets.ImageFolder(root=base_dir, transform=train_transform)
     val_dataset = torchvision.datasets.ImageFolder(root=base_dir, transform=val_transform)
 
-    #Same seed to avoid overlap while having different transforms
-    seed = random.randint(0,1000)
-    train_dataset, _ = train_test_split(train_dataset, test_size=None, train_size=None, random_state=seed, shuffle=True,
+    train_dataset, _ = train_test_split(train_dataset, test_size=None, train_size=None, random_state=42, shuffle=True,
                                              stratify=True)
-    _, val_dataset = train_test_split(val_dataset, test_size=None, train_size=None, random_state=seed, shuffle=True,
+    _, val_dataset = train_test_split(val_dataset, test_size=None, train_size=None, random_state=42, shuffle=True,
                                              stratify=True)
 
     data_loader_train = data.DataLoader(

image_processing/build_dataset.py

@@ -12,24 +12,129 @@ from build_image import build_image_ms1
 find . -name '*.mzML' -exec cp -prv '{}' '/home/leo/PycharmProjects/pseudo_image/data/raw_data' ';'
 copy des mzml depuis lecteur
 """
+antibiotic_tests = ['AMC (disk)','AMK (disk)','AMK (mic)','AMK (vitek)','AMP (vitek)','AMX (disk)',
+    'AMX (vitek)','ATM (disk)','ATM (vitek)','CAZ (disk)','CAZ (mic)','CAZ (vitek)','CHL (vitek)','CIP (disk)',
+    'CIP (vitek)','COL (mic)','CRO (mic)','CRO (vitek)','CTX (disk)','CTX (mic)','CTX (vitek)',
+    'CXM (vitek)','CZA (disk)','CZA (vitek)','CZT (disk)','CZT (vitek)','ETP (disk)','ETP (mic)','ETP (vitek)',
+    'FEP (disk)','FEP (mic)','FEP (vitek)','FOS (disk)','FOX (disk)','FOX (vitek)','GEN (disk)','GEN (mic)',
+    'GEN (vitek)','IPM (disk)','IPM (mic)','IPM (vitek)','LVX (disk)','LVX (vitek)','MEC (disk)',
+    'MEM (disk)','MEM (mic)','MEM (vitek)','NAL (vitek)','NET (disk)','OFX (vitek)','PIP (vitek)','PRL (disk)',
+    'SXT (disk)','SXT (vitek)','TCC (disk)','TCC (vitek)','TGC (disk)','TGC (vitek)',
+    'TIC (disk)','TIC (vitek)','TOB (disk)','TOB (vitek)','TZP (disk)','TZP (mic)','TZP (vitek)']
+
+antibiotic_enterrobacter_breakpoints = {
+    'AMC (disk)': {"S":14, "R":14 },
+    'AMK (disk)': {"S":18, "R":18 },
+    'AMK (mic)': {"S":8, "R":8 },
+    'AMK (vitek)': {"S":8, "R":8 },
+    'AMP (vitek)': {"S":8, "R":8 },
+    'AMX (disk)': {"S":14, "R":14 },
+    'AMX (vitek)': {"S":8, "R":8 },
+    'ATM (disk)': {"S":26, "R":21 },
+    'ATM (vitek)': {"S":1, "R":4 },
+    'CAZ (disk)': {"S":22, "R":22 },
+    'CAZ (mic)': {"S":1, "R":4 },
+    'CAZ (vitek)': {"S":1, "R":4 },
+    'CHL (vitek)': {"S":16, "R":16 },
+    'CIP (disk)': {"S":25, "R":22 },
+    'CIP (vitek)': {"S":0.25, "R":0.5 },
+    'COL (disk)': {"S":None, "R":None }, # : https://academic-oup-com.docelec.univ-lyon1.fr/cid/article/71/9/e523/5735218?login=true&token=eyJhbGciOiJub25lIn0.eyJleHAiOjE3NDU2NjA0NTgsImp0aSI6IjcxYzJmOWI1LTlhMWYtNGRiMy1iYmE0LTA0MGRlMTU3NjdmZSJ9.
+    #deleted since method is not accurate (DO NOT USE IT)
+    'COL (mic)': {"S":2, "R":2 },
+    'CRO (mic)': {"S":1, "R":2 },
+    'CRO (vitek)': {"S":1, "R":2 },
+    'CTX (disk)': {"S":20, "R":17 },
+    'CTX (mic)': {"S":1, "R":2 },
+    'CTX (vitek)': {"S":1, "R":2 },
+    'CXM (vitek)': {"S":0.001, "R":8 },
+    'CZA (disk)': {"S":13, "R":13 },
+    'CZA (vitek)': {"S":8, "R":8 },
+    'CZT (disk)': {"S":22, "R":22 },
+    'CZT (vitek)': {"S":2, "R":2 },
+    'ETP (disk)': {"S":23, "R":23 },
+    'ETP (mic)': {"S":0.5, "R":0.5 },
+    'ETP (vitek)': {"S":0.5, "R":0.5 },
+    'FEP (disk)': {"S":27, "R":24 },
+    'FEP (mic)': {"S":1, "R":4 },
+    'FEP (vitek)': {"S":1, "R":4 },
+    'FOS (disk)': {"S":24, "R":24 },#pas clair ?
+    'FOX (disk)': {"S":19, "R":19 },#screen only ?
+    'FOX (vitek)': {"S":8, "R":8 },#screen only ? high sensitivity but poor specificity for identification of AmpC-producing Enterobacterales
+    'GEN (disk)': {"S":17, "R":17 },
+    'GEN (mic)': {"S":2, "R":2 }, #entre parenthèse
+    'GEN (vitek)': {"S":2, "R":2 }, #entre parenthèse cf https://www.eucast.org/eucastguidancedocuments/ ?
+    'IPM (disk)': {"S":22, "R":19 },
+    'IPM (mic)': {"S":2, "R":4 },
+    'IPM (vitek)': {"S":2, "R":4 },
+    'LTM (disk)': {"S":None, "R":None }, # Lactimidomycin ?
+    'LVX (disk)': {"S":23, "R":19 },
+    'LVX (vitek)': {"S":0.5, "R":1 },
+    'MEC (disk)': {"S":15, "R":15 },
+    'MEM (disk)': {"S":22, "R":16 },
+    'MEM (mic)': {"S":2, "R":8 },
+    'MEM (vitek)': {"S":2, "R":8 },
+    'NAL (vitek)': {"S":2, "R":8 }, #pas présent dans EUCAST, trouvé dans CLSI M100 (for uninary tract only)
+    'NET (disk)': {"S":15, "R":12 }, #insuffisant evidencence for EUCAST, found in CLSI M100
+    'OFX (vitek)': {"S":0.25, "R":0.5 },
+    'PIP (vitek)': {"S":8, "R":8 },
+    'PRL (disk)': {"S":20, "R":20 },
+    'SXT (disk)': {"S":14, "R":11 },
+    'SXT (vitek)': {"S":2, "R":4 },
+    'TCC (disk)': {"S":8, "R":16 },
+    'TCC (vitek)': {"S":23, "R":20 },
+    'TEM (disk)': {"S":None, "R":None },#Abréviation non standard
+    'TEM (vitek)': {"S":None, "R":None },#Abréviation non standard
+    'TGC (disk)': {"S":18, "R":18 }, #pour E.coli et C.koseri seulement
+    'TGC (vitek)': {"S":0.5, "R":0.5 },
+    'TIC (disk)': {"S":13, "R":20 },
+    'TIC (vitek)': {"S":8, "R":16 },
+    'TOB (disk)': {"S":16, "R":16 }, #entre parenthèse cf https://www.eucast.org/eucastguidancedocuments/ ?
+    'TOB (vitek)': {"S":2, "R":2 }, #entre parenthèse cf https://www.eucast.org/eucastguidancedocuments/ ?
+    'TZP (disk)': {"S":20, "R":20 },
+    'TZP (mic)': {"S":8, "R":8 },
+    'TZP (vitek)': {"S":8, "R":8 },
+}
+
 
 def create_antibio_dataset(path='../data/label_raw/230804_strain_peptides_antibiogram_Enterobacterales.xlsx',suffix='-d200'):
     """
-    Extract and organise labels from raw excel file
+    Extract and build file name corresponding to each sample
     :param path: excel path
     :return: dataframe
     """
     df = pd.read_excel(path, header=1)
     df = df[['sample_name','species','AMC (disk)','AMK (disk)','AMK (mic)','AMK (vitek)','AMP (vitek)','AMX (disk)',
     'AMX (vitek)','ATM (disk)','ATM (vitek)','CAZ (disk)','CAZ (mic)','CAZ (vitek)','CHL (vitek)','CIP (disk)',
-    'CIP (vitek)','COL (disk)','COL (mic)','CRO (mic)','CRO (vitek)','CTX (disk)','CTX (mic)','CTX (vitek)',
+    'CIP (vitek)','COL (mic)','CRO (mic)','CRO (vitek)','CTX (disk)','CTX (mic)','CTX (vitek)',
     'CXM (vitek)','CZA (disk)','CZA (vitek)','CZT (disk)','CZT (vitek)','ETP (disk)','ETP (mic)','ETP (vitek)',
     'FEP (disk)','FEP (mic)','FEP (vitek)','FOS (disk)','FOX (disk)','FOX (vitek)','GEN (disk)','GEN (mic)',
-    'GEN (vitek)','IPM (disk)','IPM (mic)','IPM (vitek)','LTM (disk)','LVX (disk)','LVX (vitek)','MEC (disk)',
+    'GEN (vitek)','IPM (disk)','IPM (mic)','IPM (vitek)','LVX (disk)','LVX (vitek)','MEC (disk)',
     'MEM (disk)','MEM (mic)','MEM (vitek)','NAL (vitek)','NET (disk)','OFX (vitek)','PIP (vitek)','PRL (disk)',
-    'SXT (disk)','SXT (vitek)','TCC (disk)','TCC (vitek)','TEM (disk)','TEM (vitek)','TGC (disk)','TGC (vitek)',
+    'SXT (disk)','SXT (vitek)','TCC (disk)','TCC (vitek)','TGC (disk)','TGC (vitek)',
     'TIC (disk)','TIC (vitek)','TOB (disk)','TOB (vitek)','TZP (disk)','TZP (mic)','TZP (vitek)']]
 
+    for test in antibiotic_tests :# S - Susceptible R - Resistant U- Uncertain
+        #convert to string and transform >8 to 8
+        df[test] = df[test].map(lambda x :float(str(x).replace('>','').replace('<','')))
+        df[test+' cat']= 'NA'
+        if 'mic' in test or 'vitek' in test :
+            try :
+                df.loc[df[test] <= antibiotic_enterrobacter_breakpoints[test]['S'], test+ ' cat'] = 'S'
+                df.loc[df[test] >= antibiotic_enterrobacter_breakpoints[test]['R'], test + ' cat'] = 'R'
+                df.loc[(antibiotic_enterrobacter_breakpoints[test]['S'] < df[test]) & (df[test]  < antibiotic_enterrobacter_breakpoints[test]['R']), test + ' cat'] = 'U'
+            except:
+                pass
+        elif 'disk' in test:
+            try :
+                df.loc[df[test] >= antibiotic_enterrobacter_breakpoints[test]['S'], test + ' cat'] = 'S'
+                df.loc[df[test] <= antibiotic_enterrobacter_breakpoints[test]['R'], test + ' cat'] = 'R'
+                df.loc[
+                    (antibiotic_enterrobacter_breakpoints[test]['S'] > df[test]) & (df[test] > antibiotic_enterrobacter_breakpoints[test][
+                        'R']), test + ' cat'] = 'U'
+            except:
+                pass
+
+
     def split_before_number(s):
         return re.split(r'(\d+)', s)
 
@@ -98,5 +203,26 @@ def create_dataset():
             np.save(directory_path_npy + "/" + name + '_' + analyse + '.npy', mat)
 
 
+def extract_antio_res_labels():
+    """
+    Extract and organise labels from raw excel file
+    :param
+    path: excel
+    path
+    :return: dataframe
+    """
+    path = '../data/label_raw/230804_strain_peptides_antibiogram_Enterobacterales.xlsx'
+    df = pd.read_excel(path, header=1)
+    df = df[['sample_name','species','AMC (disk)','AMK (disk)','AMK (mic)','AMK (vitek)','AMP (vitek)','AMX (disk)',
+    'AMX (vitek)','ATM (disk)','ATM (vitek)','CAZ (disk)','CAZ (mic)','CAZ (vitek)','CHL (vitek)','CIP (disk)',
+    'CIP (vitek)','COL (disk)','COL (mic)','CRO (mic)','CRO (vitek)','CTX (disk)','CTX (mic)','CTX (vitek)',
+    'CXM (vitek)','CZA (disk)','CZA (vitek)','CZT (disk)','CZT (vitek)','ETP (disk)','ETP (mic)','ETP (vitek)',
+    'FEP (disk)','FEP (mic)','FEP (vitek)','FOS (disk)','FOX (disk)','FOX (vitek)','GEN (disk)','GEN (mic)',
+    'GEN (vitek)','IPM (disk)','IPM (mic)','IPM (vitek)','LTM (disk)','LVX (disk)','LVX (vitek)','MEC (disk)',
+    'MEM (disk)','MEM (mic)','MEM (vitek)','NAL (vitek)','NET (disk)','OFX (vitek)','PIP (vitek)','PRL (disk)',
+    'SXT (disk)','SXT (vitek)','TCC (disk)','TCC (vitek)','TEM (disk)','TEM (vitek)','TGC (disk)','TGC (vitek)',
+    'TIC (disk)','TIC (vitek)','TOB (disk)','TOB (vitek)','TZP (disk)','TZP (mic)','TZP (vitek)']]
+
+
 if __name__ =='__main__' :
-    create_dataset()
+    df = create_antibio_dataset()
\ No newline at end of file

models/model.py

@@ -296,7 +296,7 @@ class Classification_model_duo(nn.Module):
         self.predictor = nn.Linear(in_features=self.n_class*2,out_features=self.n_class)
 
 
-    def forward(self, input_aer, input_ana):
+    def forward(self, input_aer, input_ana, input_ref):
         out_aer =  self.im_encoder(input_aer)
         out_ana = self.im_encoder(input_ana)
         out = torch.concat([out_aer,out_ana],dim=1)