Update src/bin/PROBE.py

src/bin/PROBE.py

@@ -6,16 +6,6 @@ from . import target_family_classifier as tfc
 from . import function_predictor as fp
 from . import binding_affinity_estimator as bae
 
-print("\n\nPROBE (Protein RepresentatiOn Benchmark) run is started...\n\n")
-
-with open('probe_config.yaml') as f:
-	args = yaml.load(f, Loader=yaml.FullLoader)
-
-if args["benchmark"] not in ["similarity","family","function","affinity","all"]:
-        parser.error('At least one benchmark type should be selected')
-
-print(args)
-
 def load_representation(multi_col_representation_vector_file_path):
     multi_col_representation_vector = pd.read_csv(multi_col_representation_vector_file_path)
     vals = multi_col_representation_vector.iloc[:,1:(len(multi_col_representation_vector.columns))]
@@ -25,40 +15,6 @@ def load_representation(multi_col_representation_vector_file_path):
         original_values_as_df.loc[index] = [multi_col_representation_vector.iloc[index]['Entry']] + [list_of_floats]
     return original_values_as_df
 
-if args["benchmark"] in  ["similarity","function","all"]:
-    print("\nRepresentation vectors are loading...\n")
-    representation_dataframe = load_representation(args["representation_file_human"])
- 
-if args["benchmark"] in  ["similarity","all"]:
-    print("\nSemantic similarity Inference Benchmark is running...\n")
-    ssi.representation_dataframe = representation_dataframe
-    ssi.representation_name = args["representation_name"]
-    ssi.protein_names = ssi.representation_dataframe['Entry'].tolist()
-    ssi.similarity_tasks = args["similarity_tasks"]
-    ssi.detailed_output = args["detailed_output"]
-    ssi.calculate_all_correlations()
-if args["benchmark"] in  ["function","all"]:
-    print("\n\nOntology-based protein function prediction benchmark is running...\n")
-    fp.aspect_type = args["function_prediction_aspect"]
-    fp.dataset_type = args["function_prediction_dataset"]
-    fp.representation_dataframe = representation_dataframe
-    fp.representation_name = args["representation_name"]
-    fp.detailed_output = args["detailed_output"]
-    fp.pred_output()
-if args["benchmark"] in  ["family","all"]:
-    print("\n\nDrug target protein family classification benchmark is running...\n")
-    tfc.representation_path = args["representation_file_human"]
-    tfc.representation_name = args["representation_name"]
-    tfc.detailed_output = args["detailed_output"]
-    for dataset in args["family_prediction_dataset"]:
-	    tfc.score_protein_rep(dataset)
-if args["benchmark"] in  ["affinity","all"]:
-    print("\n\nProtein-protein binding affinity estimation benchmark is running...\n")
-    bae.skempi_vectors_path = args["representation_file_affinity"]
-    bae.representation_name = args["representation_name"]
-    bae.predict_affinities_and_report_results()
-print("\n\nPROBE (Protein RepresentatiOn Benchmark) run is finished...\n")
-
 def run_probe(benchmarks, representation_name, representation_file_human, representation_file_affinity, similarity_tasks=["Sparse","200","500"], function_prediction_aspec="All_Aspects", function_prediction_dataset="All_Data_Sets", family_prediction_dataset=["nc","uc50","uc30","mm15"], detailed_output=False):
     print("\n\nPROBE (Protein RepresentatiOn Benchmark) run is started...\n\n")