Merge branch 'main' of https://huggingface.co/spaces/mgyigit/probe3

src/about.py

@@ -93,7 +93,7 @@ Submit your own representation models and compare their performance across these
 
 If you find PROBE useful, please consider citing our work."""
 
-similarity_tasks_options = ["Sparse", "200", "500"]
+similarity_tasks_options = ["sparse", "200", "500"]
 function_prediction_aspect_options = ["MF", "BP", "CC", "All_Aspects"]
 function_prediction_dataset_options = ["High", "Middle", "Low", "All_Data_Sets"]
 family_prediction_dataset_options = ["nc", "uc50", "uc30", "mm15"]

src/bin/semantic_similarity_infer.py

@@ -52,7 +52,7 @@ def calculateCorrelationforOntology(aspect, matrix_type):
     similarityMatrixNameDict = {
         "All": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix.csv"),
         "500": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv"),
-        "Sparse": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv"),
+        "sparse": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv"),
         "200": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix_for_highest_annotated_200_proteins.csv")
     }
 
@@ -64,7 +64,7 @@ def calculateCorrelationforOntology(aspect, matrix_type):
     for prot in proteinList:
         proteinListNew.append(prot)
 
-    if matrix_type == "Sparse":
+    if matrix_type == "sparse":
         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
@@ -77,7 +77,7 @@ def calculateCorrelationforOntology(aspect, matrix_type):
     for tup in tqdm(protParamList):
         i = tup[0]
         j = tup[1]
-        if matrix_type == "Sparse":
+        if matrix_type == "sparse":
             protein1 = proteinListNew[i]
             protein2 = proteinListNew[j]
             real = human_proteinSimilarityMatrix.loc[protein1, protein2]
@@ -113,7 +113,7 @@ def calculate_all_correlations():
             corr, p_value = calculateCorrelationforOntology(aspect, similarity_matrix_type)
             
             corr_key = f"{similarity_matrix_type}_{aspect}_correlation"
-            p_value_key = f"{similarity_matrix_type}_{aspect}_p_value"
+            p_value_key = f"{similarity_matrix_type}_{aspect}_pvalue"
             
             results[corr_key] = corr
             results[p_value_key] = p_value