datasets

alignement.py

@@ -211,45 +211,45 @@ def compare_include_df(df, sub_df, save = True, path = 'temp.png'):
 
 
 # create augmented dataset from ISA data + column invariant prosit peptides
-# df_base = pd.read_pickle('database/data_DIA_ISA_55_train.pkl')
-# df_base = df_base[['Sequence','Retention time']]
-#
-# df_1 = pd.read_pickle('database/data_prosit_threshold_1.pkl')
-# df_1['Sequence']= df_1['Sequence'].map(numerical_to_alphabetical_str)
-#
-# df_2 = pd.read_pickle('database/data_prosit_threshold_2.pkl')
-# df_2['Sequence']= df_2['Sequence'].map(numerical_to_alphabetical_str)
-#
-# df_3 = pd.read_pickle('database/data_prosit_threshold_3.pkl')
-# df_3['Sequence']= df_3['Sequence'].map(numerical_to_alphabetical_str)
-#
-# df_augmented_1 = pd.concat([df_1,df_base],axis=0).reset_index(drop=True)
-# df_augmented_1.columns=['sequence','irt']
-# df_augmented_1['state']='train'
-# df_augmented_1.to_csv('database/data_ISA_augmented_1.csv')
-#
-# df_augmented_2 = pd.concat([df_2,df_base],axis=0).reset_index(drop=True)
-# df_augmented_2.columns=['sequence','irt']
-# df_augmented_2['state']='train'
-# df_augmented_2.to_csv('database/data_ISA_augmented_2.csv')
-#
-# df_augmented_3 = pd.concat([df_3,df_base],axis=0).reset_index(drop=True)
-# df_augmented_3.columns=['sequence','irt']
-# df_augmented_3['state']='train'
-# df_augmented_3.to_csv('database/data_ISA_augmented_3.csv')
+df_base = pd.read_pickle('database/data_DIA_ISA_55_train_30_01.pkl')
+df_base = df_base[['Sequence','Retention time']]
+
+df_1 = pd.read_pickle('database/data_prosit_threshold_1.pkl')
+df_1['Sequence']= df_1['Sequence'].map(numerical_to_alphabetical_str)
+
+df_2 = pd.read_pickle('database/data_prosit_threshold_2.pkl')
+df_2['Sequence']= df_2['Sequence'].map(numerical_to_alphabetical_str)
+
+df_3 = pd.read_pickle('database/data_prosit_threshold_3.pkl')
+df_3['Sequence']= df_3['Sequence'].map(numerical_to_alphabetical_str)
+
+df_augmented_1 = pd.concat([df_1,df_base],axis=0).reset_index(drop=True)
+df_augmented_1.columns=['sequence','irt']
+df_augmented_1['state']='train'
+df_augmented_1.to_csv('database/data_ISA_augmented_1_30_01.csv')
+
+df_augmented_2 = pd.concat([df_2,df_base],axis=0).reset_index(drop=True)
+df_augmented_2.columns=['sequence','irt']
+df_augmented_2['state']='train'
+df_augmented_2.to_csv('database/data_ISA_augmented_2_30_01.csv')
+
+df_augmented_3 = pd.concat([df_3,df_base],axis=0).reset_index(drop=True)
+df_augmented_3.columns=['sequence','irt']
+df_augmented_3['state']='train'
+df_augmented_3.to_csv('database/data_ISA_augmented_3_30_01.csv')
 
 
 # testing intersection between test and augmented dataset
-# df_sup = pd.read_pickle('database/data_prosit_threshold_3.pkl')
-# df_test = pd.read_pickle('database/data_DIA_ISA_55_test.pkl')
-#
-# inter = []
-# n = 0
-# df_sup['Sequence']= df_sup['Sequence'].map(numerical_to_alphabetical_str)
-# groups_sup = df_sup.groupby('Sequence')
-#
-# groups_test = df_test.groupby('Sequence')
-# for seq, _ in groups_sup:
-#     for seq2, _ in groups_test:
-#         if seq2==seq :
-#             inter.append(seq)
+df_sup = pd.read_pickle('database/data_prosit_threshold_3.pkl')
+df_test = pd.read_pickle('database/data_DIA_ISA_55_test.pkl')
+
+inter = []
+n = 0
+df_sup['Sequence']= df_sup['Sequence'].map(numerical_to_alphabetical_str)
+groups_sup = df_sup.groupby('Sequence')
+
+groups_test = df_test.groupby('Sequence')
+for seq, _ in groups_sup:
+    for seq2, _ in groups_test:
+        if seq2==seq :
+            inter.append(seq)
\ No newline at end of file

model_custom.py

@@ -43,7 +43,7 @@ class Model_Common_Transformer(nn.Module):
                  n_head=1, seq_length=25,
                  charge_max=4, charge_frag_max=3, encoder_ff=512, encoder_num_layer=1, decoder_rt_num_layer=1,
                  decoder_int_num_layer=1, acti='relu', norm=False):
-        self.charge_max = charge_max #TODO filter charge in train to be in 1-4 0-5 atm
+        self.charge_max = charge_max
         self.seq_length = seq_length
         self.nb_aa = nb_aa
         self.charge_frag_max = charge_frag_max

prosit_data_merge.py

@@ -66,6 +66,7 @@ def alphabetical_to_numerical(seq):
             dec += 4
     return np.array(num)
 
+if __name__ == '__main__' :
 
     # sources = ('data/intensity/sequence_train.npy',
     #              'data/intensity/intensity_train.npy',
@@ -146,17 +147,17 @@ def alphabetical_to_numerical(seq):
     #
     # np.save('data/intensity/irt_holdout.npy',data_int.irt)
 
-df = pd.read_pickle('database/data_prosit_merged_holdout.pkl')
-print(len(df))
-possible_charges = [1,2,3,4]
-df = df[df['Charge'].isin(possible_charges)]
-print(len(df))
-df.to_pickle('database/data_prosit_merged_holdout.pkl')
-df = pd.read_pickle('database/data_prosit_merged_train.pkl')
-print(len(df))
-df=df.loc[df['Retention time']!=0]
-df = df[df['Charge'].isin(possible_charges)]
-print(len(df))
-df.to_pickle('database/data_prosit_merged_train.pkl')
-
+    # df = pd.read_pickle('database/data_prosit_merged_holdout.pkl')
+    # print(len(df))
+    # possible_charges = [1,2,3,4]
+    # df = df[df['Charge'].isin(possible_charges)]
+    # print(len(df))
+    # df.to_pickle('database/data_prosit_merged_holdout.pkl')
+    # df = pd.read_pickle('database/data_prosit_merged_train.pkl')
+    # print(len(df))
+    # df=df.loc[df['Retention time']!=0]
+    # df = df[df['Charge'].isin(possible_charges)]
+    # print(len(df))
+    # df.to_pickle('database/data_prosit_merged_train.pkl')
+    pass