typo

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.9 (LC-MS-RT-prediction)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (LC-MS-RT-prediction)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (LC-MS-RT-prediction)" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file

common_dataset.py

@@ -95,7 +95,7 @@ def alphabetical_to_numerical(seq, vocab):
     else :
         for i in range(len(seq) - 2 * seq.count('-')):
             if seq[i + dec] != '-':
-                num.append(ALPHABET_UNMOD[seq[i + dec]])
+                num.append(IUPAC_VOCAB[seq[i + dec]])
             else:
                 if seq[i + dec + 1:i + dec + 4] == 'CaC':
                     num.append(21)

data_viz.py

-from unittest.mock import inplace
 
 import matplotlib.pyplot as plt
 import numpy as np
 import random
 import pandas as pd
-from sympy.utilities.misc import replace
+
 
 from mass_prediction import compute_frag_mz_ration
 
@@ -230,9 +229,9 @@ def add_length(dataframe):
     dataframe['length']=dataframe['seq'].map(fonc)
 
 
-df = pd.read_csv('output/out_ISA.csv')
+df = pd.read_csv('output/out_ISA_no_tape.csv')
 add_length(df)
 df['abs_error'] =  np.abs(df['rt pred']-df['true rt'])
-# histo_abs_error(df, display=False, save=True, path='temp.png')
-scatter_rt(df, display=False, save=True, path='temp.png')
+histo_abs_error(df, display=False, save=True, path='temp.png')
+# scatter_rt(df, display=False, save=True, path='temp.png')
 # histo_length_by_error(df, 10, save=True, path='temp.png')
\ No newline at end of file

main_custom.py

@@ -202,7 +202,7 @@ def main(args):
                                                                    path_test=args.dataset_test,
                                                                    batch_size=args.batch_size, length=25, pad = False, convert=True, vocab='iapuc')
     elif args.forward == 'rt':
-        data_train, data_val, data_test = dataloader.load_data(data_source=args.dataset_train,
+        data_train, data_val, data_test = dataloader.load_data(data_sources=args.dataset_train,
                                                                batch_size=args.batch_size, length=25)
 
     print('\nData loaded')

requirements.txt

@@ -13,7 +13,7 @@ scikit-learn~=1.4.1.post1
 Levenshtein~=0.25.0
 keras~=2.15.0
 tensorflow~=2.15.0.post1
-pillow~=10.1.0
+pillow~=10.3.0
 nibabel~=5.2.1
 nilearn~=0.10.4
 scipy~=1.12.0