various changes
This commit is contained in:
parent
f761dd4479
commit
5202be4adc
52 changed files with 1440 additions and 88 deletions
1
LR_FS.json
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1
LR_FS.json
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@ -0,0 +1 @@
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{"model_name": "GradientBoostingClassifier(n_estimators=10, random_state=42, subsample=0.7)", "model_refit_param": "mcc", "Best_model_params": {"clf__learning_rate": 0.1, "clf__max_depth": 3, "clf__n_estimators": 10, "clf__subsample": 0.7}, "n_all_features": 13, "fs_method": "RFECV(cv=RepeatedStratifiedKFold(n_repeats=3, n_splits=10, random_state=42),\n estimator=LogisticRegression(random_state=42),\n scoring='matthews_corrcoef')", "fs_res_array": "[False, False, False, False, True, False, False, True, False, False, False, False, True]", "fs_res_array_rank": [3, 5, 8, 2, 1, 10, 7, 1, 6, 4, 9, 11, 1], "all_feature_names": ["ligand_distance", "ligand_affinity_change", "duet_stability_change", "ddg_foldx", "deepddg", "ddg_dynamut2", "contacts", "rsa", "kd_values", "rd_values", "consurf_score", "snap2_score", "maf"], "n_sel_features": 2, "sel_features_names": ["ddg_foldx", "rd_values"], "bts_fscore": 0.7, "bts_precision": 0.56, "bts_recall": 0.93, "bts_accuracy": 0.61, "bts_roc_auc": 0.61, "bts_jaccard": 0.54, "train_score (MCC)": 0.23, "bts_mcc": 0.28, "train_bts_diff": -0.05}
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12
MLfeature_types.py
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12
MLfeature_types.py
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@ -9,11 +9,11 @@ Created on Sun May 29 06:46:19 2022
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#%% Build X: input for ML
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print('Strucutral features (n):'
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, len(common_cols_stabiltyN) + len(foldX_cols) + len(X_strFN)
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, len(X_ssFN)
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, '\nThese are:'
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, '\nCommon stablity features:', common_cols_stabiltyN
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, '\nFoldX columns:', foldX_cols
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, '\nOther struc columns:', X_strFN
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, '\nCommon stablity features:', X_stabilityN
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, '\nFoldX columns:', X_foldX_cols
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, '\nOther struc columns:', X_str
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, '\n================================================================\n')
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print('Evolutionary features (n):'
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@ -36,3 +36,7 @@ print('Categorical features (n):'
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, categorical_FN
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, '\n================================================================\n')
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if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ):
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print('\nPass: No. of features match')
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else:
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print('\nFail: Count of feature mismatch')
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0
MultClassPipe2.py
Normal file → Executable file
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MultClassPipe2.py
Normal file → Executable file
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MultClassPipe3.py
Normal file → Executable file
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MultClassPipe3.py
Normal file → Executable file
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MultClassPipe3_CALL.py
Normal file → Executable file
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MultClassPipe3_CALL.py
Normal file → Executable file
73
MultModelsCl_CALL.py
Normal file → Executable file
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MultModelsCl_CALL.py
Normal file → Executable file
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@ -6,36 +6,36 @@ Created on Tue Mar 15 11:09:50 2022
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@author: tanu
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"""
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from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
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# from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
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# from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
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from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
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# from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
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# from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
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from sklearn.pipeline import Pipeline, make_pipeline
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#%% GLOBALS
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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# from sklearn.pipeline import Pipeline, make_pipeline
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# #%% GLOBALS
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# rs = {'random_state': 42}
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# njobs = {'n_jobs': 10}
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scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jcc' : make_scorer(jaccard_score)
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})
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# scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
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# , 'fscore' : make_scorer(f1_score)
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# , 'mcc' : make_scorer(matthews_corrcoef)
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# , 'precision' : make_scorer(precision_score)
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# , 'recall' : make_scorer(recall_score)
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# , 'roc_auc' : make_scorer(roc_auc_score)
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# , 'jcc' : make_scorer(jaccard_score)
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# })
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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# skf_cv = StratifiedKFold(n_splits = 10
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# #, shuffle = False, random_state= None)
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# , shuffle = True,**rs)
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rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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, n_repeats = 3
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, **rs)
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# rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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# , n_repeats = 3
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# , **rs)
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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# mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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# jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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###############################################################################
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#%% MultModelsCl: function call()
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@ -55,6 +55,9 @@ baseline_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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baseline_BT = baseline_all.filter(like='bts_', axis=1)
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baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# Write csv
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baseline_BT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_BT_allF.csv')
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#%% SMOTE NC: Oversampling [Numerical + categorical]
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mm_skf_scoresD7 = MultModelsCl(input_df = X_smnc
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, target = y_smnc
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@ -70,6 +73,10 @@ smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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smnc_BT = smnc_all.filter(like='bts_', axis=1)
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smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# Write csv
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smnc_BT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_BT_allF.csv')
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#%% ROS: Numerical + categorical
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mm_skf_scoresD3 = MultModelsCl(input_df = X_ros
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, target = y_ros
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@ -85,6 +92,9 @@ ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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ros_BT = ros_all.filter(like='bts_', axis=1)
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ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# Write csv
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ros_BT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_BT_allF.csv')
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#%% RUS: Numerical + categorical
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mm_skf_scoresD4 = MultModelsCl(input_df = X_rus
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, target = y_rus
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@ -100,6 +110,9 @@ rus_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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rus_BT = rus_all.filter(like='bts_' , axis=1)
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rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# Write csv
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rus_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_BT_allF.csv')
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#%% ROS + RUS Combined: Numerical + categorical
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mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
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, target = y_rouC
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@ -116,6 +129,8 @@ rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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rouC_BT = rouC_all.filter(like='bts_', axis=1)
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rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# Write csv
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rouC_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_BT_allF.csv')
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#%% SMOTE OS: Numerical only
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# mm_skf_scoresD2 = MultModelsCl(input_df = X_sm
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# , target = y_sm
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@ -130,6 +145,8 @@ rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# sm_BT = sm_all.filter(like='bts_', axis=1)
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#sm_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# Write csv
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#sm_BT.to_csv(outdir + 'ml/' + gene.lower() + '_sm_BT_allF.csv')
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#%% SMOTE ENN: Over + Undersampling combined: Numerical ONLY
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# mm_skf_scoresD5 = MultModelsCl(input_df = X_enn
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# , target = y_enn
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@ -146,6 +163,9 @@ rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# enn_BT = enn_all.filter(like='bts_', axis=1)
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#enn_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# Write csv
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#enn_BT.to_csv(outdir + 'ml/' + gene.lower() + '_enn_BT_allF.csv')
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#%% Repeated ENN
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# mm_skf_scoresD6 = MultModelsCl(input_df = X_renn
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# , target = y_renn
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@ -161,5 +181,6 @@ rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# renn_BT = renn_all.filter(like='bts_', axis=1)
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# renn_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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###############################################################################
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# end of script
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##############################################################################
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0
UQ_FS_eg.py
Normal file → Executable file
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UQ_FS_eg.py
Normal file → Executable file
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UQ_FS_fn.py
Normal file → Executable file
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UQ_FS_fn.py
Normal file → Executable file
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UQ_FS_fn_CALL.py
Normal file → Executable file
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UQ_FS_fn_CALL.py
Normal file → Executable file
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UQ_FS_mixed_eg.py
Normal file → Executable file
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UQ_FS_mixed_eg.py
Normal file → Executable file
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UQ_Imbalance.py
Normal file → Executable file
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UQ_Imbalance.py
Normal file → Executable file
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UQ_LR_FS_p1.py
Normal file → Executable file
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UQ_LR_FS_p1.py
Normal file → Executable file
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UQ_LR_FS_p2.py
Normal file → Executable file
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UQ_LR_FS_p2.py
Normal file → Executable file
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UQ_LR_p1.py
Normal file → Executable file
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UQ_LR_p1.py
Normal file → Executable file
162
UQ_ML_data.py
Normal file → Executable file
162
UQ_ML_data.py
Normal file → Executable file
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@ -27,6 +27,37 @@ def setvars(gene,drug):
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from imblearn.under_sampling import EditedNearestNeighbours
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from imblearn.under_sampling import RepeatedEditedNearestNeighbours
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from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
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from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
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from sklearn.pipeline import Pipeline, make_pipeline
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#%% GLOBALS
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jcc' : make_scorer(jaccard_score)
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})
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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, n_repeats = 3
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, **rs)
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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#%% FOR LATER: Combine ED logo data
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#%% FOR LARER: active aa site annotations
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###########################################################################
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@ -52,31 +83,41 @@ def setvars(gene,drug):
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my_df_cols = my_df.columns
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geneL_basic = ['pnca']
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# -- CHECK script -- imports.py
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geneL_na = ['gid']
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geneL_na_ppi2 = ['rpob']
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geneL_ppi2 = ['alr', 'embb', 'katg']
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#%% get cols
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mycols = my_df.columns
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mycols
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# change from numberic to
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num_type = ['int64', 'float64']
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cat_type = ['object', 'bool']
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# # change from numberic to
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# num_type = ['int64', 'float64']
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# cat_type = ['object', 'bool']
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#TODO:
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# #Treat active site aa pos as category and not numerical: This needs to be part of merged_df3!
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# if my_df['active_aa_pos'].dtype in num_type:
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# my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object)
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# my_df['active_aa_pos'].dtype
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# -- CHECK script -- imports.py
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# FIXME: if this is not structural, remove from source..
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# Drop NA where numerical cols have them
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if gene.lower() in geneL_na_ppi2:
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#D1148 get rid of
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na_index = my_df['mutationinformation'].index[my_df['mcsm_na_affinity'].apply(np.isnan)]
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my_df = my_df.drop(index=na_index)
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# FIXME: either impute or remove!
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# for embb (L114M, F115L, V123L, V125I, V131M) delete for now
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if gene.lower() in ['embb']:
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na_index = my_df['mutationinformation'].index[my_df['ligand_distance'].apply(np.isnan)]
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my_df = my_df.drop(index=na_index)# RERUN embb with the 5 values now present
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###########################################################################
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#%% Add lineage calculation columns
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#FIXME: Check if this can be imported from config?
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total_mtblineage_u = 8
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total_mtblineage_uc = 8
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lineage_colnames = ['lineage_list_all', 'lineage_count_all', 'lineage_count_unique', 'lineage_list_unique', 'lineage_multimode']
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#bar = my_df[lineage_colnames]
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my_df['lineage_proportion'] = my_df['lineage_count_unique']/my_df['lineage_count_all']
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my_df['dist_lineage_proportion'] = my_df['lineage_count_unique']/total_mtblineage_u
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my_df['dist_lineage_proportion'] = my_df['lineage_count_unique']/total_mtblineage_uc
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###########################################################################
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#%% AA property change
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#--------------------
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@ -219,15 +260,6 @@ def setvars(gene,drug):
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#==========================
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my_df_ml = my_df.copy()
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#%% Masking columns (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
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my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
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my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
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my_df_ml.groupby(['mutationinformation'])['ligand_distance'].apply(lambda x: (x>10)).value_counts()
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my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
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(my_df_ml['ligand_affinity_change'] == 0).sum()
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#%%########################################################################
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#==========================
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# BLIND test set
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#==========================
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@ -254,7 +286,31 @@ def setvars(gene,drug):
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, 'mmcsm_lig'
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, 'contacts']
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foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
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# Build stability columns ~ gene
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if gene.lower() in geneL_basic:
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X_stabilityN = common_cols_stabiltyN
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cols_to_mask = ['ligand_affinity_change']
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if gene.lower() in geneL_ppi2:
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# X_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist']
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geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist']
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X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
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cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
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if gene.lower() in geneL_na:
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# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
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geneL_na_st_cols = ['mcsm_na_affinity']
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X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
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cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
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if gene.lower() in geneL_na_ppi2:
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# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
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geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
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X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
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cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
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X_foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
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, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
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, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
|
|
@ -262,11 +318,13 @@ def setvars(gene,drug):
|
|||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
|
||||
]
|
||||
|
||||
X_strFN = ['rsa'
|
||||
X_str = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
X_ssFN = X_stabilityN + X_str + X_foldX_cols
|
||||
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
|
@ -287,11 +345,13 @@ def setvars(gene,drug):
|
|||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
X_genomicFN = X_genomic_mafor+X_genomic_linegae
|
||||
X_genomicFN = X_genomic_mafor + X_genomic_linegae
|
||||
|
||||
#%% Construct numerical and categorical column names
|
||||
# numerical feature names
|
||||
numerical_FN = common_cols_stabiltyN + foldX_cols + X_strFN + X_evolFN + X_genomicFN
|
||||
# numerical_FN = common_cols_stabiltyN + foldX_cols + X_strFN + X_evolFN + X_genomicFN
|
||||
|
||||
numerical_FN = X_ssFN + X_evolFN + X_genomicFN
|
||||
|
||||
#categorical feature names
|
||||
categorical_FN = ['ss_class'
|
||||
|
|
@ -306,8 +366,33 @@ def setvars(gene,drug):
|
|||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'drtype_mode_labels' # beware then you can use it to predict
|
||||
# , 'active_aa_pos' # TODO?
|
||||
#, 'active_aa_pos' # TODO?
|
||||
]
|
||||
###########################################################################
|
||||
#=======================
|
||||
# Masking columns:
|
||||
# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
|
||||
#=======================
|
||||
#%% Masking columns
|
||||
# my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
|
||||
# my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
|
||||
# my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
|
||||
# (my_df_ml['ligand_affinity_change'] == 0).sum()
|
||||
|
||||
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
|
||||
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
|
||||
|
||||
# mask the column ligand distance > 10
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask] = 0
|
||||
(my_df_ml['ligand_affinity_change'] == 0).sum()
|
||||
|
||||
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
|
||||
|
||||
# write file for check
|
||||
mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
|
||||
mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
|
||||
|
||||
#%% extracting dfs based on numerical, categorical column names
|
||||
#----------------------------------
|
||||
|
|
@ -335,12 +420,12 @@ def setvars(gene,drug):
|
|||
|
||||
all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
|
||||
all_df_wtgt.shape
|
||||
#%%================================================================
|
||||
#%% Apply ML
|
||||
|
||||
#%% Data
|
||||
#%%########################################################################
|
||||
#============
|
||||
# ML data
|
||||
#============
|
||||
#------
|
||||
# X
|
||||
# X: Training and Blind test (BTS)
|
||||
#------
|
||||
X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
|
||||
X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
|
||||
|
|
@ -353,16 +438,17 @@ def setvars(gene,drug):
|
|||
y = all_df_wtgt['dst_mode'] # training data y
|
||||
y_bts = blind_test_df['dst_mode'] # blind data test y
|
||||
|
||||
#Blind test data {same format}
|
||||
#X_bts = blind_test_df[numerical_FN]
|
||||
#X_bts = blind_test_df[numerical_FN + categorical_FN]
|
||||
#y_bts = blind_test_df['dst_mode']
|
||||
|
||||
X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
|
||||
#X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
|
||||
|
||||
# Quick check
|
||||
(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
|
||||
##############################################################################
|
||||
#(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
|
||||
for i in range(len(cols_to_mask)):
|
||||
ind = i+1
|
||||
print('\nindex:', i, '\nind:', ind)
|
||||
print('\nMask count check:'
|
||||
, (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
||||
)
|
||||
|
||||
print('Original Data\n', Counter(y)
|
||||
, 'Data dim:', X.shape)
|
||||
|
||||
|
|
|
|||
0
UQ_MultClassPipe4.py
Normal file → Executable file
0
UQ_MultClassPipe4.py
Normal file → Executable file
4
UQ_MultModelsCl.py
Normal file → Executable file
4
UQ_MultModelsCl.py
Normal file → Executable file
|
|
@ -74,9 +74,9 @@ import json
|
|||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
|
||||
scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
|
||||
, 'accuracy' : make_scorer(accuracy_score)
|
||||
, 'fscore' : make_scorer(f1_score)
|
||||
, 'mcc' : make_scorer(matthews_corrcoef)
|
||||
, 'precision' : make_scorer(precision_score)
|
||||
, 'recall' : make_scorer(recall_score)
|
||||
, 'roc_auc' : make_scorer(roc_auc_score)
|
||||
|
|
|
|||
0
UQ_RF.py
Normal file → Executable file
0
UQ_RF.py
Normal file → Executable file
0
UQ_TODO_categorical_classification_columns.py
Normal file → Executable file
0
UQ_TODO_categorical_classification_columns.py
Normal file → Executable file
0
UQ_imbalance.py
Normal file → Executable file
0
UQ_imbalance.py
Normal file → Executable file
0
UQ_or_impute.py
Normal file → Executable file
0
UQ_or_impute.py
Normal file → Executable file
0
UQ_pnca_ML.py
Normal file → Executable file
0
UQ_pnca_ML.py
Normal file → Executable file
0
UQ_practice.py
Normal file → Executable file
0
UQ_practice.py
Normal file → Executable file
0
UQ_yc_RunAllClfs.py
Normal file → Executable file
0
UQ_yc_RunAllClfs.py
Normal file → Executable file
101
UQ_yc_RunAllClfs_CALL.py
Executable file
101
UQ_yc_RunAllClfs_CALL.py
Executable file
|
|
@ -0,0 +1,101 @@
|
|||
|
||||
from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
#%% CALL function
|
||||
#run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
# Baseline_data
|
||||
|
||||
YC_resD2 = run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_baseline = YC_resD2['CrossValResultsDF']
|
||||
CVResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF']
|
||||
BTSResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# from sklearn.utils import all_estimators
|
||||
# for name, algorithm in all_estimators(type_filter="classifier"):
|
||||
# clf = algorithm()
|
||||
# print('Name:', name, '\nAlgo:', clf)
|
||||
|
||||
# Random Oversampling
|
||||
YC_resD_ros = run_all_ML(input_pd=X_ros, target_label=y_ros, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_ros = YC_resD_ros['CrossValResultsDF']
|
||||
CVResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF']
|
||||
BTSResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# Random Undersampling
|
||||
YC_resD_rus = run_all_ML(input_pd=X_rus, target_label=y_rus, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_rus = YC_resD_rus['CrossValResultsDF']
|
||||
CVResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_rus = YC_resD_rus['BlindTestResultsDF']
|
||||
BTSResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# Random Oversampling+Undersampling
|
||||
YC_resD_rouC = run_all_ML(input_pd=X_rouC, target_label=y_rouC, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_rouC = YC_resD_rouC['CrossValResultsDF']
|
||||
CVResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_rouC = YC_resD_rouC['BlindTestResultsDF']
|
||||
BTSResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# SMOTE NC
|
||||
YC_resD_smnc = run_all_ML(input_pd=X_smnc, target_label=y_smnc, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_smnc = YC_resD_smnc['CrossValResultsDF']
|
||||
CVResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_smnc = YC_resD_smnc['BlindTestResultsDF']
|
||||
BTSResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
##############################################################################
|
||||
#============================================
|
||||
# BASELINE models with dissected featues
|
||||
#============================================
|
||||
# Genomics
|
||||
yC_gf = run_all_ML(input_pd=X[X_genomicFN], target_label=y, blind_test_input_df=X_bts[X_genomicFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
yc_gfCT_baseline= yC_gf['CrossValResultsDF']
|
||||
yc_gfCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
yc_gfBT_baseline = yC_gf['BlindTestResultsDF']
|
||||
yc_gfBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# Evolutionary
|
||||
yC_ev = run_all_ML(input_pd=X[X_evolFN], target_label=y, blind_test_input_df=X_bts[X_evolFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
yc_evCT_baseline= yC_ev['CrossValResultsDF']
|
||||
yc_evCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
yc_evBT_baseline = yC_ev['BlindTestResultsDF']
|
||||
yc_evBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# strucF:All
|
||||
yC_sfall = run_all_ML(input_pd=X[X_strFN], target_label=y, blind_test_input_df=X_bts[X_strFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
yc_sfallCT_baseline= yC_sfall['CrossValResultsDF']
|
||||
yc_sfallCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
yc_sfallBT_baseline = yC_sfall['BlindTestResultsDF']
|
||||
yc_sfallBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# strucF:Common ONLY
|
||||
c = [x for x in X_ssFN if x not in X_foldX_cols]
|
||||
yC_sfco= run_all_ML(input_pd=X[X_stabilityN], target_label=y
|
||||
, blind_test_input_df=X_bts[x_stabilityN]
|
||||
, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
yc_sfcoCT_baseline= yC_sfco['CrossValResultsDF']
|
||||
yc_sfcoCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
yc_sfcoBT_baseline = yC_sfco['BlindTestResultsDF']
|
||||
yc_sfcoBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# strucF:common_stability + foldX_cols i.e interaction
|
||||
yC_fxss= run_all_ML(input_pd=X[common_cols_stabiltyN+foldX_cols], target_label=y
|
||||
, blind_test_input_df=X_bts[common_cols_stabiltyN+foldX_cols]
|
||||
, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
yc_fxssCT_baseline= yC_fxss['CrossValResultsDF']
|
||||
yc_fxssCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
yc_fxssBT_baseline = yC_fxss['BlindTestResultsDF']
|
||||
yc_fxssBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# categorical
|
||||
yC_cat= run_all_ML(input_pd=X[categorical_FN], target_label=y
|
||||
, blind_test_input_df=X_bts[categorical_FN]
|
||||
, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
yc_catCT_baseline= yC_cat['CrossValResultsDF']
|
||||
yc_catCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
yc_catBT_baseline = yC_cat['BlindTestResultsDF']
|
||||
yc_catBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
#=================================================
|
||||
# Dissected features with Over and Undersampling
|
||||
#=================================================
|
||||
176
alr_config.py
Executable file
176
alr_config.py
Executable file
|
|
@ -0,0 +1,176 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sat May 28 05:25:30 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
gene = 'alr'
|
||||
drug = 'cycloserine'
|
||||
#total_mtblineage_u = 8
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
os.chdir( homedir + '/git/ML_AI_training/')
|
||||
|
||||
from UQ_ML_data import *
|
||||
setvars(gene,drug)
|
||||
from UQ_ML_data import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
# TT run all ML clfs: baseline mode
|
||||
from UQ_MultModelsCl import MultModelsCl
|
||||
|
||||
#%%###########################################################################
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
print('TESTING cmd:'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\nTotal input features:', X.shape
|
||||
, '\n', Counter(y))
|
||||
|
||||
print('Strucutral features (n):'
|
||||
, len(X_ssFN)
|
||||
, '\nThese are:'
|
||||
, '\nCommon stablity features:', X_stabilityN
|
||||
, '\nFoldX columns:', X_foldX_cols
|
||||
, '\nOther struc columns:', X_str
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Evolutionary features (n):'
|
||||
, len(X_evolFN)
|
||||
, '\nThese are:\n'
|
||||
, X_evolFN
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Genomic features (n):'
|
||||
, len(X_genomicFN)
|
||||
, '\nThese are:\n'
|
||||
, X_genomic_mafor, '\n'
|
||||
, X_genomic_linegae
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Categorical features (n):'
|
||||
, len(categorical_FN)
|
||||
, '\nThese are:\n'
|
||||
, categorical_FN
|
||||
, '\n================================================================\n')
|
||||
|
||||
if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ):
|
||||
print('\nPass: No. of features match')
|
||||
else:
|
||||
print('\nFail: Count of feature mismatch')
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
|
||||
################################################################################
|
||||
#==================
|
||||
# Baseline models
|
||||
#==================
|
||||
mm_skf_scoresD = MultModelsCl(input_df = X
|
||||
, target = y
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
|
||||
baseline_all = pd.DataFrame(mm_skf_scoresD)
|
||||
baseline_all = baseline_all.T
|
||||
#baseline_train = baseline_all.filter(like='train_', axis=1)
|
||||
baseline_CT = baseline_all.filter(like='test_', axis=1)
|
||||
baseline_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
baseline_BT = baseline_all.filter(like='bts_', axis=1)
|
||||
baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
baseline_CT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_CT_allF.csv')
|
||||
baseline_BT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_BT_allF.csv')
|
||||
|
||||
|
||||
#%% SMOTE NC: Oversampling [Numerical + categorical]
|
||||
mm_skf_scoresD7 = MultModelsCl(input_df = X_smnc
|
||||
, target = y_smnc
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
smnc_all = pd.DataFrame(mm_skf_scoresD7)
|
||||
smnc_all = smnc_all.T
|
||||
|
||||
smnc_CT = smnc_all.filter(like='test_', axis=1)
|
||||
smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
smnc_BT = smnc_all.filter(like='bts_', axis=1)
|
||||
smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
smnc_CT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_CT_allF.csv')
|
||||
smnc_BT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_BT_allF.csv')
|
||||
|
||||
#%% ROS: Numerical + categorical
|
||||
mm_skf_scoresD3 = MultModelsCl(input_df = X_ros
|
||||
, target = y_ros
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
ros_all = pd.DataFrame(mm_skf_scoresD3)
|
||||
ros_all = ros_all.T
|
||||
|
||||
ros_CT = ros_all.filter(like='test_', axis=1)
|
||||
ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
ros_BT = ros_all.filter(like='bts_', axis=1)
|
||||
ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
ros_CT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_CT_allF.csv')
|
||||
ros_BT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_BT_allF.csv')
|
||||
|
||||
#%% RUS: Numerical + categorical
|
||||
mm_skf_scoresD4 = MultModelsCl(input_df = X_rus
|
||||
, target = y_rus
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rus_all = pd.DataFrame(mm_skf_scoresD4)
|
||||
rus_all = rus_all.T
|
||||
|
||||
rus_CT = rus_all.filter(like='test_', axis=1)
|
||||
rus_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rus_BT = rus_all.filter(like='bts_' , axis=1)
|
||||
rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rus_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_CT_allF.csv')
|
||||
rus_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_BT_allF.csv')
|
||||
|
||||
#%% ROS + RUS Combined: Numerical + categorical
|
||||
mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
|
||||
, target = y_rouC
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rouC_all = pd.DataFrame(mm_skf_scoresD8)
|
||||
rouC_all = rouC_all.T
|
||||
|
||||
rouC_CT = rouC_all.filter(like='test_', axis=1)
|
||||
rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rouC_BT = rouC_all.filter(like='bts_', axis=1)
|
||||
rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rouC_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_CT_allF.csv')
|
||||
rouC_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_BT_allF.csv')
|
||||
0
base_estimator.py
Normal file → Executable file
0
base_estimator.py
Normal file → Executable file
0
base_estimator2.py
Normal file → Executable file
0
base_estimator2.py
Normal file → Executable file
0
base_estimator3.py
Normal file → Executable file
0
base_estimator3.py
Normal file → Executable file
0
classification_params_FS.py
Normal file → Executable file
0
classification_params_FS.py
Normal file → Executable file
0
cross_validate_vs_loopity_loop.py
Normal file → Executable file
0
cross_validate_vs_loopity_loop.py
Normal file → Executable file
176
embb_config.py
Executable file
176
embb_config.py
Executable file
|
|
@ -0,0 +1,176 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sat May 28 05:25:30 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
gene = 'embB'
|
||||
drug = 'ethambutol'
|
||||
#total_mtblineage_u = 8
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
os.chdir( homedir + '/git/ML_AI_training/')
|
||||
|
||||
from UQ_ML_data import *
|
||||
setvars(gene,drug)
|
||||
from UQ_ML_data import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
# TT run all ML clfs: baseline mode
|
||||
from UQ_MultModelsCl import MultModelsCl
|
||||
|
||||
#%%###########################################################################
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
print('TESTING cmd:'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\nTotal input features:', X.shape
|
||||
, '\n', Counter(y))
|
||||
|
||||
print('Strucutral features (n):'
|
||||
, len(X_ssFN)
|
||||
, '\nThese are:'
|
||||
, '\nCommon stablity features:', X_stabilityN
|
||||
, '\nFoldX columns:', X_foldX_cols
|
||||
, '\nOther struc columns:', X_str
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Evolutionary features (n):'
|
||||
, len(X_evolFN)
|
||||
, '\nThese are:\n'
|
||||
, X_evolFN
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Genomic features (n):'
|
||||
, len(X_genomicFN)
|
||||
, '\nThese are:\n'
|
||||
, X_genomic_mafor, '\n'
|
||||
, X_genomic_linegae
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Categorical features (n):'
|
||||
, len(categorical_FN)
|
||||
, '\nThese are:\n'
|
||||
, categorical_FN
|
||||
, '\n================================================================\n')
|
||||
|
||||
if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ):
|
||||
print('\nPass: No. of features match')
|
||||
else:
|
||||
print('\nFail: Count of feature mismatch')
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
|
||||
################################################################################
|
||||
#==================
|
||||
# Baseline models
|
||||
#==================
|
||||
mm_skf_scoresD = MultModelsCl(input_df = X
|
||||
, target = y
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
|
||||
baseline_all = pd.DataFrame(mm_skf_scoresD)
|
||||
baseline_all = baseline_all.T
|
||||
#baseline_train = baseline_all.filter(like='train_', axis=1)
|
||||
baseline_CT = baseline_all.filter(like='test_', axis=1)
|
||||
baseline_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
baseline_BT = baseline_all.filter(like='bts_', axis=1)
|
||||
baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
baseline_CT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_CT_allF.csv')
|
||||
baseline_BT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_BT_allF.csv')
|
||||
|
||||
|
||||
#%% SMOTE NC: Oversampling [Numerical + categorical]
|
||||
mm_skf_scoresD7 = MultModelsCl(input_df = X_smnc
|
||||
, target = y_smnc
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
smnc_all = pd.DataFrame(mm_skf_scoresD7)
|
||||
smnc_all = smnc_all.T
|
||||
|
||||
smnc_CT = smnc_all.filter(like='test_', axis=1)
|
||||
smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
smnc_BT = smnc_all.filter(like='bts_', axis=1)
|
||||
smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
smnc_CT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_CT_allF.csv')
|
||||
smnc_BT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_BT_allF.csv')
|
||||
|
||||
#%% ROS: Numerical + categorical
|
||||
mm_skf_scoresD3 = MultModelsCl(input_df = X_ros
|
||||
, target = y_ros
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
ros_all = pd.DataFrame(mm_skf_scoresD3)
|
||||
ros_all = ros_all.T
|
||||
|
||||
ros_CT = ros_all.filter(like='test_', axis=1)
|
||||
ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
ros_BT = ros_all.filter(like='bts_', axis=1)
|
||||
ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
ros_CT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_CT_allF.csv')
|
||||
ros_BT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_BT_allF.csv')
|
||||
|
||||
#%% RUS: Numerical + categorical
|
||||
mm_skf_scoresD4 = MultModelsCl(input_df = X_rus
|
||||
, target = y_rus
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rus_all = pd.DataFrame(mm_skf_scoresD4)
|
||||
rus_all = rus_all.T
|
||||
|
||||
rus_CT = rus_all.filter(like='test_', axis=1)
|
||||
rus_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rus_BT = rus_all.filter(like='bts_' , axis=1)
|
||||
rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rus_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_CT_allF.csv')
|
||||
rus_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_BT_allF.csv')
|
||||
|
||||
#%% ROS + RUS Combined: Numerical + categorical
|
||||
mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
|
||||
, target = y_rouC
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rouC_all = pd.DataFrame(mm_skf_scoresD8)
|
||||
rouC_all = rouC_all.T
|
||||
|
||||
rouC_CT = rouC_all.filter(like='test_', axis=1)
|
||||
rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rouC_BT = rouC_all.filter(like='bts_', axis=1)
|
||||
rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rouC_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_CT_allF.csv')
|
||||
rouC_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_BT_allF.csv')
|
||||
176
gid_config.py
Executable file
176
gid_config.py
Executable file
|
|
@ -0,0 +1,176 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sat May 28 05:25:30 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
gene = 'gid'
|
||||
drug = 'streptomycin'
|
||||
#total_mtblineage_u = 8
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
os.chdir( homedir + '/git/ML_AI_training/')
|
||||
|
||||
from UQ_ML_data import *
|
||||
setvars(gene,drug)
|
||||
from UQ_ML_data import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
# TT run all ML clfs: baseline mode
|
||||
from UQ_MultModelsCl import MultModelsCl
|
||||
|
||||
#%%###########################################################################
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
print('TESTING cmd:'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\nTotal input features:', X.shape
|
||||
, '\n', Counter(y))
|
||||
|
||||
print('Strucutral features (n):'
|
||||
, len(X_ssFN)
|
||||
, '\nThese are:'
|
||||
, '\nCommon stablity features:', X_stabilityN
|
||||
, '\nFoldX columns:', X_foldX_cols
|
||||
, '\nOther struc columns:', X_str
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Evolutionary features (n):'
|
||||
, len(X_evolFN)
|
||||
, '\nThese are:\n'
|
||||
, X_evolFN
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Genomic features (n):'
|
||||
, len(X_genomicFN)
|
||||
, '\nThese are:\n'
|
||||
, X_genomic_mafor, '\n'
|
||||
, X_genomic_linegae
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Categorical features (n):'
|
||||
, len(categorical_FN)
|
||||
, '\nThese are:\n'
|
||||
, categorical_FN
|
||||
, '\n================================================================\n')
|
||||
|
||||
if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ):
|
||||
print('\nPass: No. of features match')
|
||||
else:
|
||||
print('\nFail: Count of feature mismatch')
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
################################################################################
|
||||
|
||||
#==================
|
||||
# Baseline models
|
||||
#==================
|
||||
mm_skf_scoresD = MultModelsCl(input_df = X
|
||||
, target = y
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
|
||||
baseline_all = pd.DataFrame(mm_skf_scoresD)
|
||||
baseline_all = baseline_all.T
|
||||
#baseline_train = baseline_all.filter(like='train_', axis=1)
|
||||
baseline_CT = baseline_all.filter(like='test_', axis=1)
|
||||
baseline_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
baseline_BT = baseline_all.filter(like='bts_', axis=1)
|
||||
baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
baseline_CT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_CT_allF.csv')
|
||||
baseline_BT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_BT_allF.csv')
|
||||
|
||||
|
||||
#%% SMOTE NC: Oversampling [Numerical + categorical]
|
||||
mm_skf_scoresD7 = MultModelsCl(input_df = X_smnc
|
||||
, target = y_smnc
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
smnc_all = pd.DataFrame(mm_skf_scoresD7)
|
||||
smnc_all = smnc_all.T
|
||||
|
||||
smnc_CT = smnc_all.filter(like='test_', axis=1)
|
||||
smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
smnc_BT = smnc_all.filter(like='bts_', axis=1)
|
||||
smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
smnc_CT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_CT_allF.csv')
|
||||
smnc_BT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_BT_allF.csv')
|
||||
|
||||
#%% ROS: Numerical + categorical
|
||||
mm_skf_scoresD3 = MultModelsCl(input_df = X_ros
|
||||
, target = y_ros
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
ros_all = pd.DataFrame(mm_skf_scoresD3)
|
||||
ros_all = ros_all.T
|
||||
|
||||
ros_CT = ros_all.filter(like='test_', axis=1)
|
||||
ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
ros_BT = ros_all.filter(like='bts_', axis=1)
|
||||
ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
ros_CT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_CT_allF.csv')
|
||||
ros_BT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_BT_allF.csv')
|
||||
|
||||
#%% RUS: Numerical + categorical
|
||||
mm_skf_scoresD4 = MultModelsCl(input_df = X_rus
|
||||
, target = y_rus
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rus_all = pd.DataFrame(mm_skf_scoresD4)
|
||||
rus_all = rus_all.T
|
||||
|
||||
rus_CT = rus_all.filter(like='test_', axis=1)
|
||||
rus_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rus_BT = rus_all.filter(like='bts_' , axis=1)
|
||||
rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rus_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_CT_allF.csv')
|
||||
rus_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_BT_allF.csv')
|
||||
|
||||
#%% ROS + RUS Combined: Numerical + categorical
|
||||
mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
|
||||
, target = y_rouC
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rouC_all = pd.DataFrame(mm_skf_scoresD8)
|
||||
rouC_all = rouC_all.T
|
||||
|
||||
rouC_CT = rouC_all.filter(like='test_', axis=1)
|
||||
rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rouC_BT = rouC_all.filter(like='bts_', axis=1)
|
||||
rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rouC_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_CT_allF.csv')
|
||||
rouC_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_BT_allF.csv')
|
||||
0
grid_search_vs_base_estimator.py
Normal file → Executable file
0
grid_search_vs_base_estimator.py
Normal file → Executable file
0
gscv.py
Normal file → Executable file
0
gscv.py
Normal file → Executable file
0
gscv_eg.py
Normal file → Executable file
0
gscv_eg.py
Normal file → Executable file
0
imbalance_p1.py
Normal file → Executable file
0
imbalance_p1.py
Normal file → Executable file
0
imbalance_p2.py
Normal file → Executable file
0
imbalance_p2.py
Normal file → Executable file
10
imports.py
Normal file → Executable file
10
imports.py
Normal file → Executable file
|
|
@ -99,8 +99,8 @@ from MultClassPipe2 import MultClassPipeline2
|
|||
from loopity_loop import MultClassPipeSKFLoop
|
||||
from MultClassPipe3 import MultClassPipeSKFCV
|
||||
|
||||
gene = 'pncA'
|
||||
drug = 'pyrazinamide'
|
||||
#gene = 'pncA'
|
||||
#drug = 'pyrazinamide'
|
||||
|
||||
#==============
|
||||
# directories
|
||||
|
|
@ -119,10 +119,10 @@ my_df = pd.read_csv(infile_ml1)
|
|||
my_df.dtypes
|
||||
my_df_cols = my_df.columns
|
||||
|
||||
geneL_basic = ['pnca']
|
||||
geneL_basic = ['pncA']
|
||||
geneL_na = ['gid']
|
||||
geneL_na_ppi2 = ['rpob']
|
||||
geneL_ppi2 = ['alr', 'embb', 'katg']
|
||||
geneL_na_ppi2 = ['rpoB']
|
||||
geneL_ppi2 = ['alr', 'embB', 'katG']
|
||||
#%% get cols
|
||||
mycols = my_df.columns
|
||||
|
||||
|
|
|
|||
0
imports_unsup.py
Normal file → Executable file
0
imports_unsup.py
Normal file → Executable file
0
intra_model_gscv.py
Normal file → Executable file
0
intra_model_gscv.py
Normal file → Executable file
0
itertools.py
Normal file → Executable file
0
itertools.py
Normal file → Executable file
174
katg_config.py
Executable file
174
katg_config.py
Executable file
|
|
@ -0,0 +1,174 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sat May 28 05:25:30 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
gene = 'katG'
|
||||
drug = 'isoniazid'
|
||||
#total_mtblineage_u = 8
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
os.chdir( homedir + '/git/ML_AI_training/')
|
||||
|
||||
from UQ_ML_data import *
|
||||
setvars(gene,drug)
|
||||
from UQ_ML_data import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
# TT run all ML clfs: baseline mode
|
||||
from UQ_MultModelsCl import MultModelsCl
|
||||
|
||||
#%%###########################################################################
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
print('TESTING cmd:'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\nTotal input features:', X.shape
|
||||
, '\n', Counter(y))
|
||||
|
||||
print('Strucutral features (n):'
|
||||
, len(X_ssFN)
|
||||
, '\nThese are:'
|
||||
, '\nCommon stablity features:', X_stabilityN
|
||||
, '\nFoldX columns:', X_foldX_cols
|
||||
, '\nOther struc columns:', X_str
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Evolutionary features (n):'
|
||||
, len(X_evolFN)
|
||||
, '\nThese are:\n'
|
||||
, X_evolFN
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Genomic features (n):'
|
||||
, len(X_genomicFN)
|
||||
, '\nThese are:\n'
|
||||
, X_genomic_mafor, '\n'
|
||||
, X_genomic_linegae
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Categorical features (n):'
|
||||
, len(categorical_FN)
|
||||
, '\nThese are:\n'
|
||||
, categorical_FN
|
||||
, '\n================================================================\n')
|
||||
|
||||
if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ):
|
||||
print('\nPass: No. of features match')
|
||||
else:
|
||||
print('\nFail: Count of feature mismatch')
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
#==================
|
||||
# Baseline models
|
||||
#==================
|
||||
mm_skf_scoresD = MultModelsCl(input_df = X
|
||||
, target = y
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
|
||||
baseline_all = pd.DataFrame(mm_skf_scoresD)
|
||||
baseline_all = baseline_all.T
|
||||
#baseline_train = baseline_all.filter(like='train_', axis=1)
|
||||
baseline_CT = baseline_all.filter(like='test_', axis=1)
|
||||
baseline_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
baseline_BT = baseline_all.filter(like='bts_', axis=1)
|
||||
baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
baseline_CT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_CT_allF.csv')
|
||||
baseline_BT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_BT_allF.csv')
|
||||
|
||||
|
||||
#%% SMOTE NC: Oversampling [Numerical + categorical]
|
||||
mm_skf_scoresD7 = MultModelsCl(input_df = X_smnc
|
||||
, target = y_smnc
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
smnc_all = pd.DataFrame(mm_skf_scoresD7)
|
||||
smnc_all = smnc_all.T
|
||||
|
||||
smnc_CT = smnc_all.filter(like='test_', axis=1)
|
||||
smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
smnc_BT = smnc_all.filter(like='bts_', axis=1)
|
||||
smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
smnc_CT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_CT_allF.csv')
|
||||
smnc_BT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_BT_allF.csv')
|
||||
|
||||
#%% ROS: Numerical + categorical
|
||||
mm_skf_scoresD3 = MultModelsCl(input_df = X_ros
|
||||
, target = y_ros
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
ros_all = pd.DataFrame(mm_skf_scoresD3)
|
||||
ros_all = ros_all.T
|
||||
|
||||
ros_CT = ros_all.filter(like='test_', axis=1)
|
||||
ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
ros_BT = ros_all.filter(like='bts_', axis=1)
|
||||
ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
ros_CT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_CT_allF.csv')
|
||||
ros_BT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_BT_allF.csv')
|
||||
|
||||
#%% RUS: Numerical + categorical
|
||||
mm_skf_scoresD4 = MultModelsCl(input_df = X_rus
|
||||
, target = y_rus
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rus_all = pd.DataFrame(mm_skf_scoresD4)
|
||||
rus_all = rus_all.T
|
||||
|
||||
rus_CT = rus_all.filter(like='test_', axis=1)
|
||||
rus_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rus_BT = rus_all.filter(like='bts_' , axis=1)
|
||||
rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rus_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_CT_allF.csv')
|
||||
rus_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_BT_allF.csv')
|
||||
|
||||
#%% ROS + RUS Combined: Numerical + categorical
|
||||
mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
|
||||
, target = y_rouC
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rouC_all = pd.DataFrame(mm_skf_scoresD8)
|
||||
rouC_all = rouC_all.T
|
||||
|
||||
rouC_CT = rouC_all.filter(like='test_', axis=1)
|
||||
rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rouC_BT = rouC_all.filter(like='bts_', axis=1)
|
||||
rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rouC_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_CT_allF.csv')
|
||||
rouC_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_BT_allF.csv')
|
||||
0
loopity_loop.py
Normal file → Executable file
0
loopity_loop.py
Normal file → Executable file
0
loopity_loop_CALL.py
Normal file → Executable file
0
loopity_loop_CALL.py
Normal file → Executable file
121
pnca_config.py
121
pnca_config.py
|
|
@ -10,7 +10,7 @@ import os
|
|||
|
||||
gene = 'pncA'
|
||||
drug = 'pyrazinamide'
|
||||
#total_mtblineage_u = 8
|
||||
#total_mtblineage_uc = 8
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
os.chdir( homedir + '/git/ML_AI_training/')
|
||||
|
|
@ -20,7 +20,7 @@ setvars(gene,drug)
|
|||
from UQ_ML_data import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
from UQ_yc_RunAllClfs import run_all_ML
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
# TT run all ML clfs: baseline mode
|
||||
from UQ_MultModelsCl import MultModelsCl
|
||||
|
|
@ -28,6 +28,7 @@ from UQ_MultModelsCl import MultModelsCl
|
|||
#%%###########################################################################
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
print('TESTING cmd:'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
|
|
@ -35,11 +36,11 @@ print('TESTING cmd:'
|
|||
, '\n', Counter(y))
|
||||
|
||||
print('Strucutral features (n):'
|
||||
, len(common_cols_stabiltyN) + len(foldX_cols) + len(X_strFN)
|
||||
, len(X_ssFN)
|
||||
, '\nThese are:'
|
||||
, '\nCommon stablity features:', common_cols_stabiltyN
|
||||
, '\nFoldX columns:', foldX_cols
|
||||
, '\nOther struc columns:', X_strFN
|
||||
, '\nCommon stablity features:', X_stabilityN
|
||||
, '\nFoldX columns:', X_foldX_cols
|
||||
, '\nOther struc columns:', X_str
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Evolutionary features (n):'
|
||||
|
|
@ -60,7 +61,115 @@ print('Categorical features (n):'
|
|||
, '\nThese are:\n'
|
||||
, categorical_FN
|
||||
, '\n================================================================\n')
|
||||
|
||||
if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ):
|
||||
print('\nPass: No. of features match')
|
||||
else:
|
||||
print('\nFail: Count of feature mismatch')
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
################################################################################
|
||||
#==================
|
||||
# Baseline models
|
||||
#==================
|
||||
mm_skf_scoresD = MultModelsCl(input_df = X
|
||||
, target = y
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
|
||||
baseline_all = pd.DataFrame(mm_skf_scoresD)
|
||||
baseline_all = baseline_all.T
|
||||
#baseline_train = baseline_all.filter(like='train_', axis=1)
|
||||
baseline_CT = baseline_all.filter(like='test_', axis=1)
|
||||
baseline_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
baseline_BT = baseline_all.filter(like='bts_', axis=1)
|
||||
baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
baseline_CT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_CT_allF.csv')
|
||||
baseline_BT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_BT_allF.csv')
|
||||
|
||||
|
||||
#%% SMOTE NC: Oversampling [Numerical + categorical]
|
||||
mm_skf_scoresD7 = MultModelsCl(input_df = X_smnc
|
||||
, target = y_smnc
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
smnc_all = pd.DataFrame(mm_skf_scoresD7)
|
||||
smnc_all = smnc_all.T
|
||||
|
||||
smnc_CT = smnc_all.filter(like='test_', axis=1)
|
||||
smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
smnc_BT = smnc_all.filter(like='bts_', axis=1)
|
||||
smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
smnc_CT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_CT_allF.csv')
|
||||
smnc_BT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_BT_allF.csv')
|
||||
|
||||
#%% ROS: Numerical + categorical
|
||||
mm_skf_scoresD3 = MultModelsCl(input_df = X_ros
|
||||
, target = y_ros
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
ros_all = pd.DataFrame(mm_skf_scoresD3)
|
||||
ros_all = ros_all.T
|
||||
|
||||
ros_CT = ros_all.filter(like='test_', axis=1)
|
||||
ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
ros_BT = ros_all.filter(like='bts_', axis=1)
|
||||
ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
ros_CT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_CT_allF.csv')
|
||||
ros_BT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_BT_allF.csv')
|
||||
|
||||
#%% RUS: Numerical + categorical
|
||||
mm_skf_scoresD4 = MultModelsCl(input_df = X_rus
|
||||
, target = y_rus
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rus_all = pd.DataFrame(mm_skf_scoresD4)
|
||||
rus_all = rus_all.T
|
||||
|
||||
rus_CT = rus_all.filter(like='test_', axis=1)
|
||||
rus_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rus_BT = rus_all.filter(like='bts_' , axis=1)
|
||||
rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rus_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_CT_allF.csv')
|
||||
rus_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_BT_allF.csv')
|
||||
|
||||
#%% ROS + RUS Combined: Numerical + categorical
|
||||
mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
|
||||
, target = y_rouC
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rouC_all = pd.DataFrame(mm_skf_scoresD8)
|
||||
rouC_all = rouC_all.T
|
||||
|
||||
rouC_CT = rouC_all.filter(like='test_', axis=1)
|
||||
rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rouC_BT = rouC_all.filter(like='bts_', axis=1)
|
||||
rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rouC_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_CT_allF.csv')
|
||||
rouC_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_BT_allF.csv')
|
||||
|
|
|
|||
60
rfecv_vis.py
Normal file
60
rfecv_vis.py
Normal file
|
|
@ -0,0 +1,60 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sun May 29 12:21:34 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
from sklearn.svm import SVC
|
||||
from sklearn.datasets import make_classification
|
||||
|
||||
from yellowbrick.model_selection import RFECV
|
||||
|
||||
# Instantiate RFECV visualizer with a linear SVM classifier
|
||||
visualizer = RFECV(SVC(kernel='linear', C=1))
|
||||
|
||||
visualizer.fit(X[numerical_FN], y) # Fit the data to the visualizer
|
||||
visualizer.show()
|
||||
|
||||
|
||||
numerical_ix = X.select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_ix
|
||||
categorical_ix = X.select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_ix
|
||||
|
||||
# Determine preprocessing steps ~ var_type
|
||||
var_type = 'mixed'
|
||||
var_type = 'numerical'
|
||||
|
||||
if var_type == 'numerical':
|
||||
t = [('num', MinMaxScaler(), numerical_ix)]
|
||||
|
||||
if var_type == 'categorical':
|
||||
t = [('cat', OneHotEncoder(), categorical_ix)]
|
||||
|
||||
if var_type == 'mixed':
|
||||
t = [('cat', OneHotEncoder(), categorical_ix)
|
||||
, ('num', MinMaxScaler(), numerical_ix)]
|
||||
|
||||
t = [('num', MinMaxScaler(), numerical_ix)
|
||||
, ('cat', OneHotEncoder(), categorical_ix)]
|
||||
|
||||
col_transform = ColumnTransformer(transformers = t
|
||||
, remainder='passthrough')
|
||||
#--------------ALEX help
|
||||
# col_transform
|
||||
# col_transform.fit(X)
|
||||
# test = col_transform.transform(X)
|
||||
# print(col_transform.get_feature_names_out())
|
||||
|
||||
# foo = col_transform.fit_transform(X)
|
||||
Xm = col_transform.fit_transform(X)
|
||||
# (foo == test).all()
|
||||
#-----------------------
|
||||
|
||||
visualizer.fit(Xm, y) # Fit the data to the visualizer
|
||||
visualizer.show()
|
||||
|
||||
|
||||
visualizer.fit(X[numerical_FN], y) # Fit the data to the visualizer
|
||||
visualizer.show()
|
||||
0
rfecv_with_ohe.py
Normal file → Executable file
0
rfecv_with_ohe.py
Normal file → Executable file
176
rpob_config.py
Executable file
176
rpob_config.py
Executable file
|
|
@ -0,0 +1,176 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sat May 28 05:25:30 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
gene = 'rpoB'
|
||||
drug = 'rifampicin'
|
||||
#total_mtblineage_u = 8
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
os.chdir( homedir + '/git/ML_AI_training/')
|
||||
|
||||
from UQ_ML_data import *
|
||||
setvars(gene,drug)
|
||||
from UQ_ML_data import *
|
||||
|
||||
# from YC run_all_ML: run locally
|
||||
#from UQ_yc_RunAllClfs import run_all_ML
|
||||
|
||||
# TT run all ML clfs: baseline mode
|
||||
from UQ_MultModelsCl import MultModelsCl
|
||||
|
||||
#%%###########################################################################
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
print('TESTING cmd:'
|
||||
, '\nGene name:', gene
|
||||
, '\nDrug name:', drug
|
||||
, '\nTotal input features:', X.shape
|
||||
, '\n', Counter(y))
|
||||
|
||||
print('Strucutral features (n):'
|
||||
, len(X_ssFN)
|
||||
, '\nThese are:'
|
||||
, '\nCommon stablity features:', X_stabilityN
|
||||
, '\nFoldX columns:', X_foldX_cols
|
||||
, '\nOther struc columns:', X_str
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Evolutionary features (n):'
|
||||
, len(X_evolFN)
|
||||
, '\nThese are:\n'
|
||||
, X_evolFN
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Genomic features (n):'
|
||||
, len(X_genomicFN)
|
||||
, '\nThese are:\n'
|
||||
, X_genomic_mafor, '\n'
|
||||
, X_genomic_linegae
|
||||
, '\n================================================================\n')
|
||||
|
||||
print('Categorical features (n):'
|
||||
, len(categorical_FN)
|
||||
, '\nThese are:\n'
|
||||
, categorical_FN
|
||||
, '\n================================================================\n')
|
||||
|
||||
if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ):
|
||||
print('\nPass: No. of features match')
|
||||
else:
|
||||
print('\nFail: Count of feature mismatch')
|
||||
|
||||
print('\n#####################################################################\n')
|
||||
|
||||
|
||||
################################################################################
|
||||
#==================
|
||||
# Baseline models
|
||||
#==================
|
||||
mm_skf_scoresD = MultModelsCl(input_df = X
|
||||
, target = y
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
|
||||
baseline_all = pd.DataFrame(mm_skf_scoresD)
|
||||
baseline_all = baseline_all.T
|
||||
#baseline_train = baseline_all.filter(like='train_', axis=1)
|
||||
baseline_CT = baseline_all.filter(like='test_', axis=1)
|
||||
baseline_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
baseline_BT = baseline_all.filter(like='bts_', axis=1)
|
||||
baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
baseline_CT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_CT_allF.csv')
|
||||
baseline_BT.to_csv(outdir + 'ml/' + gene.lower() + '_baseline_BT_allF.csv')
|
||||
|
||||
|
||||
#%% SMOTE NC: Oversampling [Numerical + categorical]
|
||||
mm_skf_scoresD7 = MultModelsCl(input_df = X_smnc
|
||||
, target = y_smnc
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
smnc_all = pd.DataFrame(mm_skf_scoresD7)
|
||||
smnc_all = smnc_all.T
|
||||
|
||||
smnc_CT = smnc_all.filter(like='test_', axis=1)
|
||||
smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
smnc_BT = smnc_all.filter(like='bts_', axis=1)
|
||||
smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
smnc_CT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_CT_allF.csv')
|
||||
smnc_BT.to_csv(outdir + 'ml/' + gene.lower() + '_smnc_BT_allF.csv')
|
||||
|
||||
#%% ROS: Numerical + categorical
|
||||
mm_skf_scoresD3 = MultModelsCl(input_df = X_ros
|
||||
, target = y_ros
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
ros_all = pd.DataFrame(mm_skf_scoresD3)
|
||||
ros_all = ros_all.T
|
||||
|
||||
ros_CT = ros_all.filter(like='test_', axis=1)
|
||||
ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
ros_BT = ros_all.filter(like='bts_', axis=1)
|
||||
ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
ros_CT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_CT_allF.csv')
|
||||
ros_BT.to_csv(outdir + 'ml/' + gene.lower() + '_ros_BT_allF.csv')
|
||||
|
||||
#%% RUS: Numerical + categorical
|
||||
mm_skf_scoresD4 = MultModelsCl(input_df = X_rus
|
||||
, target = y_rus
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rus_all = pd.DataFrame(mm_skf_scoresD4)
|
||||
rus_all = rus_all.T
|
||||
|
||||
rus_CT = rus_all.filter(like='test_', axis=1)
|
||||
rus_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rus_BT = rus_all.filter(like='bts_' , axis=1)
|
||||
rus_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rus_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_CT_allF.csv')
|
||||
rus_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rus_BT_allF.csv')
|
||||
|
||||
#%% ROS + RUS Combined: Numerical + categorical
|
||||
mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
|
||||
, target = y_rouC
|
||||
, var_type = 'mixed'
|
||||
, skf_cv = skf_cv
|
||||
, blind_test_input_df = X_bts
|
||||
, blind_test_target = y_bts)
|
||||
rouC_all = pd.DataFrame(mm_skf_scoresD8)
|
||||
rouC_all = rouC_all.T
|
||||
|
||||
rouC_CT = rouC_all.filter(like='test_', axis=1)
|
||||
rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
|
||||
|
||||
rouC_BT = rouC_all.filter(like='bts_', axis=1)
|
||||
rouC_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
|
||||
|
||||
# Write csv
|
||||
rouC_CT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_CT_allF.csv')
|
||||
rouC_BT.to_csv(outdir + 'ml/' + gene.lower() + '_rouC_BT_allF.csv')
|
||||
92
temp.py
Executable file
92
temp.py
Executable file
|
|
@ -0,0 +1,92 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sun May 29 09:22:51 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
|
||||
geneL_basic = ['pncA']
|
||||
geneL_na = ['gid']
|
||||
geneL_na_ppi2 = ['rpoB']
|
||||
geneL_ppi2 = ['alr', 'embB', 'katG']
|
||||
#%% get cols
|
||||
mycols = my_df.columns
|
||||
|
||||
# # change from numberic to
|
||||
# num_type = ['int64', 'float64']
|
||||
# cat_type = ['object', 'bool']
|
||||
|
||||
# if my_df['active_aa_pos'].dtype in num_type:
|
||||
# my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object)
|
||||
# my_df['active_aa_pos'].dtype
|
||||
|
||||
# FIXME: if this is not structural, remove from source..
|
||||
# Drop NA where numerical cols have them
|
||||
if gene.lower() in geneL_na_ppi2:
|
||||
#D1148 get rid of
|
||||
na_index = my_df['mutationinformation'].index[my_df['mcsm_na_affinity'].apply(np.isnan)]
|
||||
my_df = my_df.drop(index=na_index)
|
||||
|
||||
# FIXME: either impute or remove!
|
||||
# for embb (L114M, F115L, V123L, V125I, V131M) delete for now
|
||||
if gene.lower() in ['embb']:
|
||||
na_index = my_df['mutationinformation'].index[my_df['ligand_distance'].apply(np.isnan)]
|
||||
#my_df = my_df.drop(index=na_index))# RERUN embb with the 5 values now present
|
||||
#%%===========================================================================
|
||||
|
||||
#%%
|
||||
# GET X
|
||||
common_cols_stabiltyN = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'contacts']
|
||||
|
||||
# Build stability columns ~ gene
|
||||
if gene.lower() in geneL_basic:
|
||||
x_stabilityN = common_cols_stabiltyN
|
||||
cols_to_mask = ['ligand_affinity_change']
|
||||
|
||||
if gene.lower() in geneL_ppi2:
|
||||
# x_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist']
|
||||
geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
x_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
|
||||
|
||||
if gene.lower() in geneL_na:
|
||||
# x_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
|
||||
geneL_na_st_cols = ['mcsm_na_affinity']
|
||||
x_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
|
||||
|
||||
if gene.lower() in geneL_na_ppi2:
|
||||
# x_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
x_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
|
||||
|
||||
|
||||
#%% Masking columns (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
|
||||
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
|
||||
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
|
||||
|
||||
# mask the column ligand distance > 10
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask] = 0
|
||||
(my_df_ml['ligand_affinity_change'] == 0).sum()
|
||||
|
||||
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
|
||||
|
||||
|
||||
for i in range(len(cols_to_mask)):
|
||||
ind = i+1
|
||||
print('\nindex:', i, '\nind:', ind)
|
||||
print('\nMask count check:'
|
||||
, (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
||||
)
|
||||
|
||||
(my_df_ml[cols_to_mask[0]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
||||
(my_df_ml[cols_to_mask[1]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
||||
0
umap_fs.py
Normal file → Executable file
0
umap_fs.py
Normal file → Executable file
0
unsup_v1.py
Normal file → Executable file
0
unsup_v1.py
Normal file → Executable file
Loading…
Add table
Add a link
Reference in a new issue