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Update src/bin/PROBE.py
Browse files- src/bin/PROBE.py +5 -8
src/bin/PROBE.py
CHANGED
@@ -17,14 +17,7 @@ def load_representation(multi_col_representation_vector_file_path):
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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):
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print("\n\nPROBE (Protein RepresentatiOn Benchmark) run is started...\n\n")
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print(type(benchmarks))
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print(benchmarks)
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if any(item in ['similarity', 'function', 'family', 'all'] for item in benchmarks):
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print("AAAAAAAAA")
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else:
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print("BBBBBBBB")
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if any(item in ['similarity', 'function', 'family', 'all'] for item in benchmarks):
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print("\nRepresentation vectors are loading...\n")
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human_representation_dataframe = load_representation(representation_file_human)
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@@ -36,7 +29,11 @@ def run_probe(benchmarks, representation_name, representation_file_human, repres
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ssi.protein_names = ssi.representation_dataframe['Entry'].tolist()
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ssi.similarity_tasks = similarity_tasks
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ssi.detailed_output = detailed_output
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if "function" in benchmarks:
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print("\n\nOntology-based protein function prediction benchmark is running...\n")
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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):
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print("\n\nPROBE (Protein RepresentatiOn Benchmark) run is started...\n\n")
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if any(item in ['similarity', 'function', 'family', 'all'] for item in benchmarks):
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print("\nRepresentation vectors are loading...\n")
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human_representation_dataframe = load_representation(representation_file_human)
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ssi.protein_names = ssi.representation_dataframe['Entry'].tolist()
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ssi.similarity_tasks = similarity_tasks
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ssi.detailed_output = detailed_output
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print("IN SIMILARITY CALC")
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similarity_result = ssi.calculate_all_correlations()
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print("OUT SIMILARITY CALC")
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print(similarity_result)
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if "function" in benchmarks:
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print("\n\nOntology-based protein function prediction benchmark is running...\n")
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