#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jun 20 13:05:23 2022 @author: tanu """ import re #all_featuresN = X_evolFN + X_structural_FN + X_genomicFN # X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN # X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat score_type_ordermapD = { 'mcc' : 1 , 'fscore' : 2 , 'jcc' : 3 , 'precision' : 4 , 'recall' : 5 , 'accuracy' : 6 , 'roc_auc' : 7 , 'TN' : 8 , 'FP' : 9 , 'FN' : 10 , 'TP' : 11 , 'trainingY_neg': 12 , 'trainingY_pos': 13 , 'blindY_neg' : 14 , 'blindY_pos' : 15 , 'fit_time' : 16 , 'score_time' : 17 } ############################################################################### #================== # Specify outdir #================== outdir_ml = outdir + 'ml/uq_v1/fgs/' print('\nOutput directory:', outdir_ml) outFile = outdir_ml + gene.lower() + '_baseline_FG.csv' #================== # other vars #================== tts_split_name = 'original' sampling_type_name = 'none' ############################################################################### #================ # Evolutionary # X_evolFN #================ feature_gp_nameEV = 'evolutionary' n_featuresEV = len(X_evolFN) scores_mmEV = MultModelsCl_dissected(input_df = X[X_evolFN] , target = y , var_type = 'mixed' , skf_cv = skf_cv , blind_test_input_df = X_bts[X_evolFN] , blind_test_target = y_bts , add_cm = True , add_yn = True) baseline_allEV = pd.DataFrame(scores_mmEV) baseline_EV = baseline_allEV.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) baseline_EV = baseline_EV.reset_index() baseline_EV.rename(columns = {'index': 'original_names'}, inplace = True) # Indicate whether BT or CT bt_pattern = re.compile(r'bts_.*') baseline_EV['data_source'] = baseline_EV.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) baseline_EV['score_type'] = baseline_EV['original_names'].str.replace('bts_|test_', '', regex = True) score_type_uniqueN = set(baseline_EV['score_type']) cL1 = list(score_type_ordermapD.keys()) cL2 = list(score_type_uniqueN) if set(cL1).issubset(cL2): print('\nPASS: sorting df by score that is mapped onto the order I want') baseline_EV['score_order'] = baseline_EV['score_type'].map(score_type_ordermapD) baseline_EV.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) else: sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') baseline_EV['feature_group'] = feature_gp_nameEV baseline_EV['sampling_type'] = sampling_type_name baseline_EV['tts_split'] = tts_split_name baseline_EV['n_features'] = n_featuresEV ############################################################################### #================ # Genomics # X_genomicFN #================ feature_gp_nameGN = 'genomics' n_featuresGN = len(X_genomicFN) scores_mmGN = MultModelsCl_dissected(input_df = X[X_genomicFN] , target = y , var_type = 'mixed' , skf_cv = skf_cv , blind_test_input_df = X_bts[X_genomicFN] , blind_test_target = y_bts , add_cm = True , add_yn = True) baseline_allGN = pd.DataFrame(scores_mmGN) baseline_GN = baseline_allGN.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) baseline_GN = baseline_GN.reset_index() baseline_GN.rename(columns = {'index': 'original_names'}, inplace = True) # Indicate whether BT or CT bt_pattern = re.compile(r'bts_.*') baseline_GN['data_source'] = baseline_GN.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) baseline_GN['score_type'] = baseline_GN['original_names'].str.replace('bts_|test_', '', regex = True) score_type_uniqueN = set(baseline_GN['score_type']) cL1 = list(score_type_ordermapD.keys()) cL2 = list(score_type_uniqueN) if set(cL1).issubset(cL2): print('\nPASS: sorting df by score that is mapped onto the order I want') baseline_GN['score_order'] = baseline_GN['score_type'].map(score_type_ordermapD) baseline_GN.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) else: sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') baseline_GN['feature_group'] = feature_gp_nameGN baseline_GN['sampling_type'] = sampling_type_name baseline_GN['tts_split'] = tts_split_name baseline_GN['n_features'] = n_featuresGN ############################################################################### #all_featuresN = X_evolFN + X_structural_FN + X_genomicFN # X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN # X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat #================ # Structural cols # X_structural_FN #================ feature_gp_nameSTR = 'structural' n_featuresSTR = len(X_structural_FN) scores_mmSTR = MultModelsCl_dissected(input_df = X[X_structural_FN] , target = y , var_type = 'mixed' , skf_cv = skf_cv , blind_test_input_df = X_bts[X_structural_FN] , blind_test_target = y_bts , add_cm = True , add_yn = True) baseline_allSTR = pd.DataFrame(scores_mmSTR) baseline_STR = baseline_allSTR.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) baseline_STR = baseline_STR.reset_index() baseline_STR.rename(columns = {'index': 'original_names'}, inplace = True) # Indicate whether BT or CT bt_pattern = re.compile(r'bts_.*') baseline_STR['data_source'] = baseline_STR.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) baseline_STR['score_type'] = baseline_STR['original_names'].str.replace('bts_|test_', '', regex = True) score_type_uniqueN = set(baseline_STR['score_type']) cL1 = list(score_type_ordermapD.keys()) cL2 = list(score_type_uniqueN) if set(cL1).issubset(cL2): print('\nPASS: sorting df by score that is mapped onto the order I want') baseline_STR['score_order'] = baseline_STR['score_type'].map(score_type_ordermapD) baseline_STR.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) else: sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') baseline_STR['feature_group'] = feature_gp_nameSTR baseline_STR['sampling_type'] = sampling_type_name baseline_STR['tts_split'] = tts_split_name baseline_STR['n_features'] = n_featuresSTR ############################################################################## #================ # Stability cols # X_stability_FN #================ feature_gp_nameSTB = 'stability' n_featuresSTB = len(X_stability_FN) scores_mmSTB = MultModelsCl_dissected(input_df = X[X_stability_FN] , target = y , var_type = 'mixed' , skf_cv = skf_cv , blind_test_input_df = X_bts[X_stability_FN] , blind_test_target = y_bts , add_cm = True , add_yn = True) baseline_allSTB = pd.DataFrame(scores_mmSTB) baseline_STB = baseline_allSTB.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) baseline_STB = baseline_STB.reset_index() baseline_STB.rename(columns = {'index': 'original_names'}, inplace = True) # Indicate whether BT or CT bt_pattern = re.compile(r'bts_.*') baseline_STB['data_source'] = baseline_STB.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) baseline_STB['score_type'] = baseline_STB['original_names'].str.replace('bts_|test_', '', regex = True) score_type_uniqueN = set(baseline_STB['score_type']) cL1 = list(score_type_ordermapD.keys()) cL2 = list(score_type_uniqueN) if set(cL1).issubset(cL2): print('\nPASS: sorting df by score that is mapped onto the order I want') baseline_STB['score_order'] = baseline_STB['score_type'].map(score_type_ordermapD) baseline_STB.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) else: sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') baseline_STB['feature_group'] = feature_gp_nameSTB baseline_STB['sampling_type'] = sampling_type_name baseline_STB['tts_split'] = tts_split_name baseline_STB['n_features'] = n_featuresSTB ############################################################################### #================ # Affinity cols # X_affinityFN #================ feature_gp_nameAFF = 'affinity' n_featuresAFF = len(X_affinityFN) scores_mmAFF = MultModelsCl_dissected(input_df = X[X_affinityFN] , target = y , var_type = 'mixed' , skf_cv = skf_cv , blind_test_input_df = X_bts[X_affinityFN] , blind_test_target = y_bts , add_cm = True , add_yn = True) baseline_allAFF = pd.DataFrame(scores_mmAFF) baseline_AFF = baseline_allAFF.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) baseline_AFF = baseline_AFF.reset_index() baseline_AFF.rename(columns = {'index': 'original_names'}, inplace = True) # Indicate whether BT or CT bt_pattern = re.compile(r'bts_.*') baseline_AFF['data_source'] = baseline_AFF.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) baseline_AFF['score_type'] = baseline_AFF['original_names'].str.replace('bts_|test_', '', regex = True) score_type_uniqueN = set(baseline_AFF['score_type']) cL1 = list(score_type_ordermapD.keys()) cL2 = list(score_type_uniqueN) if set(cL1).issubset(cL2): print('\nPASS: sorting df by score that is mapped onto the order I want') baseline_AFF['score_order'] = baseline_AFF['score_type'].map(score_type_ordermapD) baseline_AFF.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) else: sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') baseline_AFF['feature_group'] = feature_gp_nameAFF baseline_AFF['sampling_type'] = sampling_type_name baseline_AFF['tts_split'] = tts_split_name baseline_AFF['n_features'] = n_featuresAFF ############################################################################### #================ # Residue level # X_resprop_FN #================ feature_gp_nameRES = 'residue_prop' n_featuresRES = len(X_resprop_FN) scores_mmRES = MultModelsCl_dissected(input_df = X[X_resprop_FN] , target = y , var_type = 'mixed' , skf_cv = skf_cv , blind_test_input_df = X_bts[X_resprop_FN] , blind_test_target = y_bts , add_cm = True , add_yn = True) baseline_allRES = pd.DataFrame(scores_mmRES) baseline_RES = baseline_allRES.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) baseline_RES = baseline_RES.reset_index() baseline_RES.rename(columns = {'index': 'original_names'}, inplace = True) # Indicate whether BT or CT bt_pattern = re.compile(r'bts_.*') baseline_RES['data_source'] = baseline_RES.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) baseline_RES['score_type'] = baseline_RES['original_names'].str.replace('bts_|test_', '', regex = True) score_type_uniqueN = set(baseline_RES['score_type']) cL1 = list(score_type_ordermapD.keys()) cL2 = list(score_type_uniqueN) if set(cL1).issubset(cL2): print('\nPASS: sorting df by score that is mapped onto the order I want') baseline_RES['score_order'] = baseline_RES['score_type'].map(score_type_ordermapD) baseline_RES.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) else: sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') baseline_RES['feature_group'] = feature_gp_nameRES baseline_RES['sampling_type'] = sampling_type_name baseline_RES['tts_split'] = tts_split_name baseline_RES['n_features'] = n_featuresRES ############################################################################### #================ # Residue level-AAindex #X_resprop_FN - X_aaindex_Fnum #================ X_respropNOaaFN = list(set(X_resprop_FN) - set(X_aaindex_Fnum)) feature_gp_nameRNAA = 'ResPropNoAA' n_featuresRNAA = len(X_respropNOaaFN) scores_mmRNAA = MultModelsCl_dissected(input_df = X[X_respropNOaaFN] , target = y , var_type = 'mixed' , skf_cv = skf_cv , blind_test_input_df = X_bts[X_respropNOaaFN] , blind_test_target = y_bts , add_cm = True , add_yn = True) baseline_allRNAA = pd.DataFrame(scores_mmRNAA) baseline_RNAA = baseline_allRNAA.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) baseline_RNAA = baseline_RNAA.reset_index() baseline_RNAA.rename(columns = {'index': 'original_names'}, inplace = True) # Indicate whether BT or CT bt_pattern = re.compile(r'bts_.*') baseline_RNAA['data_source'] = baseline_RNAA.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) baseline_RNAA['score_type'] = baseline_RNAA['original_names'].str.replace('bts_|test_', '', regex = True) score_type_uniqueN = set(baseline_RNAA['score_type']) cL1 = list(score_type_ordermapD.keys()) cL2 = list(score_type_uniqueN) if set(cL1).issubset(cL2): print('\nPASS: sorting df by score that is mapped onto the order I want') baseline_RNAA['score_order'] = baseline_RNAA['score_type'].map(score_type_ordermapD) baseline_RNAA.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) else: sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') baseline_RNAA['feature_group'] = feature_gp_nameRNAA baseline_RNAA['sampling_type'] = sampling_type_name baseline_RNAA['tts_split'] = tts_split_name baseline_RNAA['n_features'] = n_featuresRNAA ############################################################################### #================ # Structural cols-AAindex #X_structural_FN - X_aaindex_Fnum #================ X_strNOaaFN = list(set(X_structural_FN) - set(X_aaindex_Fnum)) feature_gp_nameSNAA = 'StrNoAA' n_featuresSNAA = len(X_strNOaaFN) scores_mmSNAA = MultModelsCl_dissected(input_df = X[X_strNOaaFN] , target = y , var_type = 'mixed' , skf_cv = skf_cv , blind_test_input_df = X_bts[X_strNOaaFN] , blind_test_target = y_bts , add_cm = True , add_yn = True) baseline_allSNAA = pd.DataFrame(scores_mmSNAA) baseline_SNAA = baseline_allSNAA.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) baseline_SNAA = baseline_SNAA.reset_index() baseline_SNAA.rename(columns = {'index': 'original_names'}, inplace = True) # Indicate whether BT or CT bt_pattern = re.compile(r'bts_.*') baseline_SNAA['data_source'] = baseline_SNAA.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) baseline_SNAA['score_type'] = baseline_SNAA['original_names'].str.replace('bts_|test_', '', regex = True) score_type_uniqueN = set(baseline_SNAA['score_type']) cL1 = list(score_type_ordermapD.keys()) cL2 = list(score_type_uniqueN) if set(cL1).issubset(cL2): print('\nPASS: sorting df by score that is mapped onto the order I want') baseline_SNAA['score_order'] = baseline_SNAA['score_type'].map(score_type_ordermapD) baseline_SNAA.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) else: sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') baseline_SNAA['feature_group'] = feature_gp_nameSNAA baseline_SNAA['sampling_type'] = sampling_type_name baseline_SNAA['tts_split'] = tts_split_name baseline_SNAA['n_features'] = n_featuresSNAA ############################################################################### #%% COMBINING all FG dfs #================ # Combine all # https://stackoverflow.com/questions/39862654/pandas-concat-of-multiple-data-frames-using-only-common-columns #================ dfs_combine = [baseline_EV, baseline_GN, baseline_STR, baseline_STB, baseline_AFF, baseline_RES , baseline_RNAA , baseline_SNAA] dfs_nrows = [] for df in dfs_combine: dfs_nrows = dfs_nrows + [len(df)] dfs_nrows = max(dfs_nrows) dfs_ncols = [] for df in dfs_combine: dfs_ncols = dfs_ncols + [len(df.columns)] dfs_ncols = max(dfs_ncols) # dfs_ncols = [] # dfs_ncols2 = mode(dfs_ncols.append(len(df.columns) for df in dfs_combine) # dfs_ncols2 expected_nrows = len(dfs_combine) * dfs_nrows expected_ncols = dfs_ncols common_cols = list(set.intersection(*(set(df.columns) for df in dfs_combine))) if len(common_cols) == dfs_ncols : combined_FG_baseline = pd.concat([df[common_cols] for df in dfs_combine], ignore_index=True) fgs = combined_FG_baseline[['feature_group', 'n_features']] fgs = fgs.drop_duplicates() print('\nConcatenating dfs with feature groups after ML analysis (sampling type):' , '\nNo. of dfs combining:', len(dfs_combine) , '\nSampling type:', sampling_type , '\nThe feature groups are:' , '\n', fgs) if len(combined_FG_baseline) == expected_nrows and len(combined_FG_baseline.columns) == expected_ncols: print('\nPASS:', len(dfs_combine), 'dfs successfully combined' , '\nnrows in combined_df:', len(combined_FG_baseline) , '\nncols in combined_df:', len(combined_FG_baseline.columns)) else: print('\nFAIL: concatenating failed' , '\nExpected nrows:', expected_nrows , '\nGot:', len(combined_FG_baseline) , '\nExpected ncols:', expected_ncols , '\nGot:', len(combined_FG_baseline.columns)) sys.exit() else: sys.exit('\nConcatenting dfs not possible,check numbers ') # # rpow bind # if all(ll((baseline_EV.columns == baseline_GN.columns == baseline_STR.columns)): # print('\nPASS:colnames match, proceeding to rowbind') # comb_df = pd.concat()], axis = 0, ignore_index = True ) ############################################################################### #==================== # Write output file #==================== combined_FG_baseline.to_csv(outFile) print('\nFile successfully written:', outFile) ###############################################################################