Update src/bin/semantic_similarity_infer.py

src/bin/semantic_similarity_infer.py

@@ -1,5 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
+import os 
+script_dir = os.path.dirname(os.path.abspath(__file__))
 
 import pandas as pd
 import numpy as np
@@ -68,10 +70,10 @@ def calculateCorrelationforOntology(aspect,matrix_type):
     proteinListNew[:] = []
     
     similarityMatrixNameDict = {}
-    similarityMatrixNameDict["All"] = "../data/preprocess/human_"+aspect+"_proteinSimilarityMatrix.csv" 
-    similarityMatrixNameDict["500"] = "../data/preprocess/human_"+aspect+"_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv"
-    similarityMatrixNameDict["Sparse"] = "../data/preprocess/human_"+aspect+"_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv" 
-    similarityMatrixNameDict["200"] = "../data/preprocess/human_"+aspect+"_proteinSimilarityMatrix_for_highest_annotated_200_proteins.csv"
+    similarityMatrixNameDict["All"] = os.path.join(script_dir, "../data/preprocess/human_"+aspect+"_proteinSimilarityMatrix.csv")
+    similarityMatrixNameDict["500"] = os.path.join(script_dir, "../data/preprocess/human_"+aspect+"_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv")
+    similarityMatrixNameDict["Sparse"] = os.path.join(script_dir, "../data/preprocess/human_"+aspect+"_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv")
+    similarityMatrixNameDict["200"] = os.path.join(script_dir, "../data/preprocess/human_"+aspect+"_proteinSimilarityMatrix_for_highest_annotated_200_proteins.csv")
 
     similarityMatrixFileName = similarityMatrixNameDict[matrix_type]
 
@@ -84,7 +86,8 @@ def calculateCorrelationforOntology(aspect,matrix_type):
         proteinListNew.append(prot)
     if matrix_type == "Sparse":
         #sparsified_similarities = np.load("SparsifiedSimilarites_for_highest_500.npy")
-        sparsified_similarity_coordinates = np.load("../data/auxilary_input/SparsifiedSimilarityCoordinates_"+aspect+"_for_highest_500.npy")
+        sparsified_path = os.path.join(script_dir, "../data/auxilary_input/SparsifiedSimilarityCoordinates_"+aspect+"_for_highest_500.npy")
+        sparsified_similarity_coordinates = np.load(sparsified_path)
         protParamList = sparsified_similarity_coordinates
     else:     
         i = range(len(proteinList))
@@ -141,13 +144,13 @@ def calculateCorrelationforOntology(aspect,matrix_type):
     return (cosineCorr,manhattanCorr,euclidianCorr)
 
 def report_detailed_distance_scores(representation_name,similarity_matrix_type,aspect,distance_lists):
-    saveFileName = "../results/Semantic_sim_inference_detailed_distance_scores"+aspect+"_"+similarity_matrix_type+"_"+representation_name+".pkl"
+    saveFileName = os.path.join(script_dir, "../results/Semantic_sim_inference_detailed_distance_scores"+aspect+"_"+similarity_matrix_type+"_"+representation_name+".pkl")
     with open(saveFileName, "wb") as f:
             pickle.dump(distance_lists, f)
 
 def calculate_all_correlations():
     for similarity_matrix_type in similarity_tasks:
-        saveFileName = "../results/Semantic_sim_inference_"+similarity_matrix_type+"_"+representation_name+".csv"
+        saveFileName = os.path.join(script_dir, "../results/Semantic_sim_inference_"+similarity_matrix_type+"_"+representation_name+".csv")
         buffer = "Semantic Aspect,CosineSim_Correlation,CosineSim_Correlation p-value, ManhattanSim_Correlation,ManhattanSim_Correlation p-value, EuclidianSim_Correlation,EuclidianSim_Correlation p-value \n"
         f = open(saveFileName,'w')
         f.write(buffer)