adding formatting to get all output from ML for feature grpups starting with genomics
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4 changed files with 98 additions and 54 deletions
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@ -305,7 +305,7 @@ def MultModelsCl_dissected(input_df, target, skf_cv
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mm_skf_scoresD[model_name]['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_jaccard'] = round(jaccard_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
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#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
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return(mm_skf_scoresD)
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@ -5,21 +5,47 @@ Created on Mon Jun 20 13:05:23 2022
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@author: tanu
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"""
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import re
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#all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
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# X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
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# X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
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score_type_ordermapD = { 'mcc' : 1
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, 'fscore' : 2
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, 'jcc' : 3
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, 'precision' : 4
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, 'recall' : 5
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, 'accuracy' : 6
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, 'roc_auc' : 7
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, 'TN' : 8
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, 'FP' : 9
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, 'FN' : 10
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, 'TP' : 11
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, 'trainingY_neg': 12
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, 'trainingY_pos': 13
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, 'blindY_neg' : 14
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, 'blindY_pos' : 15
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, 'fit_time' : 16
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, 'score_time' : 17
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}
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#==================
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# Baseline models
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#==================
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cm_di2 = MultModelsCl_dissected(input_df = X
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, target = y
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, var_type = 'mixed'
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, skf_cv = skf_cv
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, blind_test_input_df = X_bts
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, blind_test_target = y_bts
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, add_cm = True
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, add_yn = True)
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# cm_di2 = MultModelsCl_dissected(input_df = X
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# , target = y
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# , var_type = 'mixed'
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# , skf_cv = skf_cv
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# , blind_test_input_df = X_bts
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# , blind_test_target = y_bts
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# , add_cm = True
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# , add_yn = True)
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baseline_all2 = pd.DataFrame(cm_di2)
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baseline_all2 = baseline_all2.T
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baseline_CTBT2 = baseline_all2.filter(regex = 'test_.*|bts_.*|TN|FP|FN|TP|.*_neg|.*_pos' , axis = 1)
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# baseline_all2 = pd.DataFrame(cm_di2)
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# baseline_all2T = baseline_all2.T
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# baseline_CTBT2 = baseline_all2T.filter(regex = 'test_.*|bts_.*|TN|FP|FN|TP|.*_neg|.*_pos' , axis = 1)
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#================
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# Stability cols
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@ -52,35 +78,68 @@ scores_mm_gn = MultModelsCl_dissected(input_df = X[X_genomicFN]
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, add_yn = True)
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baseline_all_gn = pd.DataFrame(scores_mm_gn)
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baseline_CTBT_gn = baseline_all_gn.filter(regex = '.*_time|test_.*|bts_.*|TN|FP|FN|TP|.*_neg|.*_pos' , axis = 0)
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baseline_CTBT_gn['feature_group'] = feature_gp_name
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baseline_GN = baseline_all_gn.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0)
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baseline_GN = baseline_GN.reset_index()
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baseline_GN.rename(columns = {'index': 'original_names'}, inplace = True)
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# Indicate whether BT or CT
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bt_pattern = re.compile(r'bts_.*')
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baseline_GN['data_source'] = baseline_GN.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1)
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baseline_CT = baseline_CTBT_gn.filter(regex = '.*_time|test_.*|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0)
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baseline_GN['score_type'] = baseline_GN['original_names'].str.replace('bts_|test_', '', regex = True)
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baseline_CT = baseline_CT.reset_index()
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baseline_CT.rename(columns = {'index': 'original_index'}, inplace = True)
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baseline_CT['score_type'] = baseline_CT['original_index']
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baseline_CT['score_type'] = baseline_CT['score_type'].str.replace('test_*', '', regex = True)
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baseline_CT['data_source'] = 'CT_score'
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score_type_uniqueN = set(baseline_GN['score_type'])
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cL1 = list(score_type_ordermapD.keys())
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cL2 = list(score_type_uniqueN)
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if set(cL1).issubset(cL2):
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print('\nPASS: sorting df by score that is mapped onto the order I want')
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baseline_GN['score_order'] = baseline_GN['score_type'].map(score_type_ordermapD)
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baseline_GN.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True)
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else:
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sys.exit('\nFAIL: could not sort df as score mapping for ordering failed')
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baseline_BT = baseline_CTBT_gn.filter(regex = 'bts_', axis = 0)
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baseline_GN['feature_group'] = feature_gp_name
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#-------------
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# Blind test
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#-------------
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baseline_BT = baseline_all_gn.filter(regex = 'bts_', axis = 0)
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baseline_BT = baseline_BT.reset_index()
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baseline_BT.rename(columns = {'index': 'original_index'}, inplace = True)
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baseline_BT['score_type'] = baseline_BT['original_index']
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baseline_BT.rename(columns = {'index': 'original_names'}, inplace = True)
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baseline_BT['score_type'] = baseline_BT['original_names']
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baseline_BT['score_type'] = baseline_BT['score_type'].str.replace('bts_*', '', regex = True)
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baseline_BT['data_source'] = 'BT_score'
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# rpow bind
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if all(baseline_CT.columns == baseline_BT.columns):
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print('\nPASS:colnames match, proceeding to rowbind')
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comb_df = pd.concat([baseline_BT,baseline_CT], axis = 0, ignore_index = True )
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#--------
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# CV
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#--------
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baseline_CT = baseline_all_gn.filter(regex = '.*_time|test_.*|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0)
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baseline_CT = baseline_CT.reset_index()
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baseline_CT.rename(columns = {'index': 'original_names'}, inplace = True)
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baseline_CT['score_type'] = baseline_CT['original_names']
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baseline_CT['score_type'] = baseline_CT['score_type'].str.replace('test_*', '', regex = True)
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baseline_CT['data_source'] = 'CT_score'
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baseline_CT
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baseline_CT
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#----------------------
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# rpow bind: CT and BT
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#----------------------
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if all(baseline_BT.columns == baseline_CT.columns):
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print('\nPASS: Colnames match, proceeding to row bind for data:', feature_gp_name
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, '\nDim of df1 (BT):', baseline_BT.shape
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, '\nDim of df2 (CT):', baseline_CT.shape)
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comb_df_gn = pd.concat([baseline_BT, baseline_CT], axis = 0, ignore_index = True)
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comb_df_gn['feature_group'] = feature_gp_name
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print('\nDim of combined df:', comb_df_gn.shape)
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else:
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print('\nFAIL: colnames mismatch, cannot combine')
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# good way but I don't like to have to rearrange the columns later
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#frames_tocombine = [baseline_BT, baseline_CT]
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#common_cols = list(set.intersection(*(set(df.columns) for df in frames_tocombine)))
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#a = pd.concat([df[common_cols] for df in frames_tocombine], ignore_index=True)
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###############################################################################
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#================
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# Evolution
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#================
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@ -578,9 +578,15 @@ X_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [US
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X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat
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###############################################################################
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# Feature groups further collaps:
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#========================
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# FG6 collapsed: Structural : Atability + Affinity + ResidueProp
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#========================
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X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
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###############################################################################
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#========================
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# BUILDING all features
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#========================
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all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
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###############################################################################
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@ -49,32 +49,11 @@ print('\nOutput directory:', outdir_ml)
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#%%###########################################################################
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print('\n================================================================\n')
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, '\n\nTotal no. of evolutionary features:' , len(X_evolFN)
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, '\n\nTotal no. of stability features:' , len(X_stability_FN)
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, '\n--------Common stabilty cols:' , len(X_common_stability_Fnum)
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, '\n--------Foldx cols:' , len(X_foldX_Fnum)
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, '\n\nTotal no. of affinity features:' , len(X_affinityFN)
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, '\n--------Common affinity cols:' , len(common_affinity_Fnum)
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, '\n--------Gene specific affinity cols:' , len(gene_affinity_colnames)
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, '\n\nTotal no. of residue level features:', len(X_resprop_FN)
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, '\n--------AA index cols:' , len(X_aaindex_Fnum)
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, '\n--------Residue Prop cols:' , len(X_str_Fnum)
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, '\n--------AA change Prop cols:' , len(X_aap_Fcat)
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, '\n\nTotal no. of genomic features:' , len(X_genomicFN)
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, '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum)
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, '\n--------Lineage cols:' , len(X_gn_linegae_Fnum)
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, '\n--------Other cols:' , len(X_gn_Fcat)
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X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
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X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
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all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
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###############################################################################
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print('\n================================================================'
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, '\nTotal Evolutionary features (n):' , len(X_evolFN)
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