Update src/bin/target_family_classifier.py

src/bin/target_family_classifier.py

@@ -4,6 +4,8 @@ Created on Mon Jun  8 09:32:26 2020
 
 @author: Muammer
 """
+import os 
+script_dir = os.path.dirname(os.path.abspath(__file__))
 
 import numpy as np
 from sklearn.model_selection import cross_validate
@@ -88,7 +90,7 @@ def score_protein_rep(dataset):
 
     vecsize = 0
     #protein_list = pd.read_csv('../data/auxilary_input/entry_class.csv')
-    protein_list = pd.read_csv('../data/preprocess/entry_class_nn.csv')
+    protein_list = pd.read_csv(os.path.join(script_dir, '../data/preprocess/entry_class_nn.csv'))
     dataframe = pd.read_csv(representation_path)
     #dataframe = convert_dataframe_to_multi_col(dataframe)    
     #dataframe = pd.read_pickle(pkl_data_path)
@@ -129,8 +131,8 @@ def score_protein_rep(dataset):
     mcc_perclass = []
     sup_perclass = []
     report_list = []
-    train_index = pd.read_csv('../data/preprocess/indexes/'+dataset+'_trainindex.csv')
-    test_index = pd.read_csv('../data/preprocess/indexes/testindex_family.csv')
+    train_index = pd.read_csv(os.path.join(script_dir, '../data/preprocess/indexes/'+dataset+'_trainindex.csv'))
+    test_index = pd.read_csv(os.path.join(script_dir, '../data/preprocess/indexes/testindex_family.csv'))
     train_index = train_index.dropna(axis=1) 
     test_index = test_index.dropna(axis=1)
     #print(train_index)
@@ -187,7 +189,7 @@ def score_protein_rep(dataset):
         report_list.append(class_report)
     
     if detailed_output:
-        conf.to_csv('../results/Drug_target_protein_family_classification_confusion_'+dataset+'_'+representation_name+'.csv', index=None)
+        conf.to_csv(os.path.join(script_dir, '../results/Drug_target_protein_family_classification_confusion_'+dataset+'_'+representation_name+'.csv'), index=None)
 
     f1_perclass = pd.concat(f1_perclass, axis=1)
     ac_perclass = pd.concat(ac_perclass, axis=1)
@@ -195,7 +197,7 @@ def score_protein_rep(dataset):
     sup_perclass = pd.concat(sup_perclass, axis=1)
     
     report_list = pd.concat(report_list, axis=1)
-    report_list.to_csv('../results/Drug_target_protein_family_classification_class_based_results_'+dataset+'_'+representation_name+'.csv')
+    report_list.to_csv(os,path,join(script_dir, '../results/Drug_target_protein_family_classification_class_based_results_'+dataset+'_'+representation_name+'.csv'))
     
     report = pd.DataFrame()    
     f1mean = np.mean(f1, axis=0)
@@ -212,7 +214,7 @@ def score_protein_rep(dataset):
     report['Accuracy'] = [acmean, acstd]
     report['MCC'] = [mccmean, mccstd]
 
-    report.to_csv('../results/Drug_target_protein_family_classification_mean_results_'+dataset+'_'+representation_name+'.csv',index=False)
+    report.to_csv(os.path.join(script_dir, '../results/Drug_target_protein_family_classification_mean_results_'+dataset+'_'+representation_name+'.csv',index=False))
     #report.to_csv('scores_general.csv')
     #print(report)   
     if detailed_output: