saving and organising work to call form cmd line
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5 changed files with 232 additions and 106 deletions
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@ -6,6 +6,38 @@ 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.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 = ({'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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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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###############################################################################
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#%% MultModelsCl: function call()
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mm_skf_scoresD = MultModelsCl(input_df = X
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, target = y
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@ -22,35 +54,7 @@ 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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#%% 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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# , var_type = 'numerical'
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# , skf_cv = skf_cv)
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# sm_all = pd.DataFrame(mm_skf_scoresD2)
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# sm_all = sm_all.T
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# sm_CT = sm_all.filter(like='test_', axis=1)
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#sm_CT.sort_values(by = ['test_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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#%% 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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# , var_type = 'numerical'
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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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# enn_all = pd.DataFrame(mm_skf_scoresD5)
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# enn_all = enn_all.T
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# enn_CT = enn_all.filter(like='test_', axis=1)
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#enn_CT.sort_values(by = ['test_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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#%% 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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@ -97,7 +101,7 @@ 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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#%% ROS + RUS Combined: Numerical + categorical
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mm_skf_scoresD8= MultModelsCl(input_df = X_rouC
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mm_skf_scoresD8 = MultModelsCl(input_df = X_rouC
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, target = y_rouC
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, var_type = 'mixed'
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, skf_cv = skf_cv
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@ -106,12 +110,43 @@ mm_skf_scoresD8= MultModelsCl(input_df = X_rouC
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rouC_all = pd.DataFrame(mm_skf_scoresD8)
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rouC_all = rouC_all.T
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rouC_CT = ros_all.filter(like='test_', axis=1)
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rouC_CT = rouC_all.filter(like='test_', axis=1)
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rouC_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True)
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rouC_BT = ros_all.filter(like='bts_', axis=1)
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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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#%%
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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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# , var_type = 'numerical'
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# , skf_cv = skf_cv)
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# sm_all = pd.DataFrame(mm_skf_scoresD2)
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# sm_all = sm_all.T
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# sm_CT = sm_all.filter(like='test_', axis=1)
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#sm_CT.sort_values(by = ['test_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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#%% 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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# , var_type = 'numerical'
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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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# enn_all = pd.DataFrame(mm_skf_scoresD5)
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# enn_all = enn_all.T
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# enn_CT = enn_all.filter(like='test_', axis=1)
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#enn_CT.sort_values(by = ['test_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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#%% 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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# , var_type = 'numerical'
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