Update src/bin/function_predictor.py

src/bin/function_predictor.py

@@ -1,4 +1,7 @@
 # -*- coding: utf-8 -*-
+import os 
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
 import pandas as pd
 import numpy as np
 from datetime import datetime
@@ -63,7 +66,8 @@ def MultiLabelSVC_cross_val_predict(representation_name, dataset, X, y, classifi
     y_pred = cross_val_predict(clf, Xn, y, cv=kf)
 
     if detailed_output:
-        with open(r"../results/Ontology_based_function_prediction_{1}_{0}_model.pkl".format(representation_name,dataset.split(".")[0]),"wb") as file:
+        ont_path = r"../results/Ontology_based_function_prediction_{1}_{0}_model.pkl".format(representation_name,dataset.split(".")[0])
+        with open(os.path.join(script_dir, ont_path),"wb") as file:
             pickle.dump(clf,file)
         
     acc_cv = []
@@ -114,7 +118,7 @@ def MultiLabelSVC_cross_val_predict(representation_name, dataset, X, y, classifi
    
 def ProtDescModel():   
     #desc_file = pd.read_csv(r"protein_representations\final\{0}_dim{1}.tsv".format(representation_name,desc_dim),sep="\t")    
-    datasets = os.listdir(r"../data/auxilary_input/GO_datasets") 
+    datasets = os.listdir(os.path.join(script_dir, r"../data/auxilary_input/GO_datasets"))
     if  dataset_type == "All_Data_Sets" and aspect_type == "All_Aspects":
         filtered_datasets = datasets
     elif dataset_type == "All_Data_Sets":
@@ -129,7 +133,7 @@ def ProtDescModel():
 
     for dt in tqdm(filtered_datasets,total=len(filtered_datasets)):
         print(r"Protein function prediction is started for the dataset: {0}".format(dt.split(".")[0]))
-        dt_file = pd.read_csv(r"../data/auxilary_input/GO_datasets/{0}".format(dt),sep="\t")
+        dt_file = pd.read_csv(os.path.join(script_dir, r"../data/auxilary_input/GO_datasets/{0}".format(dt)),sep="\t")
         dt_merge = dt_file.merge(representation_dataframe,left_on="Protein_Id",right_on="Entry")
 
         dt_X = dt_merge['Vector']
@@ -149,7 +153,7 @@ def ProtDescModel():
         predictions = dt_merge.iloc[:,:6]
         predictions["predicted_values"] = list(model[3].toarray())
         if detailed_output:
-            predictions.to_csv(r"../results/Ontology_based_function_prediction_{1}_{0}_predictions.tsv".format(representation_name,dt.split(".")[0]),sep="\t",index=None)
+            predictions.to_csv(os.path.join(script_dir, r"../results/Ontology_based_function_prediction_{1}_{0}_predictions.tsv".format(representation_name,dt.split(".")[0])),sep="\t",index=None)
 
     return (cv_results, cv_mean_results,cv_std_results)             
 
@@ -164,7 +168,7 @@ def pred_output():
     for i in cv_result:
         df_cv_result.loc[len(df_cv_result)] = i
     if detailed_output:
-        df_cv_result.to_csv(r"../results/Ontology_based_function_prediction_5cv_{0}.tsv".format(representation_name),sep="\t",index=None)
+        df_cv_result.to_csv(os.path.join(script_dir, r"../results/Ontology_based_function_prediction_5cv_{0}.tsv".format(representation_name)),sep="\t",index=None)
 
     cv_mean_result = model[1]
     df_cv_mean_result =  pd.DataFrame({"Model": pd.Series([], dtype='str') ,"Accuracy": pd.Series([], dtype='float'),"F1_Micro": pd.Series([], dtype='float'),\
@@ -178,7 +182,7 @@ def pred_output():
 
     for j in cv_mean_result:
         df_cv_mean_result.loc[len(df_cv_mean_result)] = j
-    df_cv_mean_result.to_csv(r"../results/Ontology_based_function_prediction_5cv_mean_{0}.tsv".format(representation_name),sep="\t",index=None)
+    df_cv_mean_result.to_csv(os.path.join(script_dir, r"../results/Ontology_based_function_prediction_5cv_mean_{0}.tsv".format(representation_name)),sep="\t",index=None)
 
 #save std deviation of scores to file
     cv_std_result = model[2]
@@ -193,7 +197,7 @@ def pred_output():
 
     for k in cv_std_result:
         df_cv_std_result.loc[len(df_cv_std_result)] = k
-    df_cv_std_result.to_csv(r"../results/Ontology_based_function_prediction_5cv_std_{0}.tsv".format(representation_name),sep="\t",index=None)
+    df_cv_std_result.to_csv(os.path.join(script_dir, r"../results/Ontology_based_function_prediction_5cv_std_{0}.tsv".format(representation_name)),sep="\t",index=None)
 
 print(datetime.now())