added UQ_LR_FS.py scrip for LR feature selection. SO far this is manual. cannot get it to be part of pipeline
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uq_ml_models/UQ_LR_FS.py
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uq_ml_models/UQ_LR_FS.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon May 16 05:59:12 2022
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@author: tanu
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"""
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 15 11:09:50 2022
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@author: tanu
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"""
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#%% Import libs
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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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, 'jaccard' : make_scorer(jaccard_score)
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})
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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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#%% Get data
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y.to_frame().value_counts().plot(kind = 'bar')
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blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
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# %% Logistic Regression + FS + hyperparameter
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# https://www.tomasbeuzen.com/post/scikit-learn-gridsearch-pipelines/
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# from sklearn.feature_selection import SelectKBest, mutual_info_classif
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# # Create pipeline
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# pipe = Pipeline([
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# ('pre', MinMaxScaler())
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# , ('fs', RFECV( LogisticRegression(**rs), cv = skf_cv, scoring = 'matthews_corrcoef', **njobs,verbose = 3))
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# #, ('fs', SelectKBest(mutual_info_classif, k=5))
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# , ('clf', LogisticRegression(**rs))
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# ])
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# # Create search space
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# param_grid = [{'fs__step': [1]},
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# {
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# 'clf': [LogisticRegression(**rs)],
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# #'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__C': np.logspace(0, 4, 10),
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# 'clf__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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# 'clf__max_iter': list(range(100,800,100)),
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# 'clf__solver': ['saga']
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# },
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# {
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# 'clf': [LogisticRegression(**rs)],
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# #'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__C': np.logspace(0, 4, 10),
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# 'clf__penalty': ['l2', 'none'],
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# 'clf__max_iter': list(range(100,800,100)),
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# 'clf__solver': ['newton-cg', 'lbfgs', 'sag']
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# },
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# {
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# 'clf': [LogisticRegression(**rs)],
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# #'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__C': np.logspace(0, 4, 10),
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# 'clf__penalty': ['l1', 'l2'],
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# 'clf__max_iter': list(range(100,800,100)),
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# 'clf__solver': ['liblinear']
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# }]
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# # Run Grid search
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# gscv_fs_lr = GridSearchCV(pipe
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# , param_grid
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# , cv = skf_cv
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# , scoring = mcc_score_fn, refit = 'mcc'
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# , verbose = 3)
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# gscv_fs_lr_fit = gscv_fs_lr.fit(X, y)
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# gscv_fs_lr_fit_be_mod = gscv_fs_lr_fit.best_params_
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# gscv_fs_lr_fit_be_res = gscv_fs_lr_fit.cv_results_
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# print('Best model:\n', gscv_fs_lr_fit_be_mod)
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# print('Best models score:\n', gscv_fs_lr_fit.best_score_, ':' , round(gscv_fs_lr_fit.best_score_, 2))
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# #print('\nMean test score from fit results:', round(mean(gscv_fs_lr_fit_be_res['mean_test_mcc']),2))
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# print('\nMean test score from fit results:', round(np.nanmean(gscv_fs_lr_fit_be_res['mean_test_mcc']),2))
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##############################################################################
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#MANUAL
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#%% Logistic Regression + hyperparam + FS: BaseEstimator: ClfSwitcher()
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model_lr = LogisticRegression(**rs)
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model_rfecv = RFECV(estimator = model_lr
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, cv = rskf_cv
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#, cv = 10
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, scoring = 'matthews_corrcoef'
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)
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# model_rfecv = SequentialFeatureSelector(estimator = model_lr
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# , n_features_to_select = 'auto'
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# , tol = None
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# # , cv = 10
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# , cv = rskf_cv
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# # , direction ='backward'
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# , direction ='forward'
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# , **njobs)
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param_grid2 = [
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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#'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'C': np.logspace(0, 4, 10),
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'penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'max_iter': list(range(100,800,100)),
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'solver': ['saga']
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},
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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#'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'C': np.logspace(0, 4, 10),
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'penalty': ['l2', 'none'],
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'max_iter': list(range(100,800,100)),
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'solver': ['newton-cg', 'lbfgs', 'sag']
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},
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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#'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'C': np.logspace(0, 4, 10),
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'penalty': ['l1', 'l2'],
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'max_iter': list(range(100,800,100)),
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'solver': ['liblinear']
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}
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]
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#-------------------------------------------------------------------------------
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# Grid search CV + FS
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gscv_lr = GridSearchCV(model_lr
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, param_grid2
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, scoring = mcc_score_fn, refit = 'mcc'
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, cv = skf_cv
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, return_train_score = False
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, verbose = 3
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, **njobs)
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#------------------------------------------------------------------------------
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# Create pipeline
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pipeline = Pipeline([('pre', MinMaxScaler())
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#, ('feature_selection', sfs_selector)
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, ('feature_selection', model_rfecv )
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, ('clf', gscv_lr)])
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# Fit
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lr_fs_fit = pipeline.fit(X,y)
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lr_fs_fit_be_mod = lr_fs_fit.best_params_
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lr_fs_fit_be_res = lr_fs_fit.cv_results_
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print('Best model:\n', lr_fs_fit_be_mod)
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print('Best models score:\n', lr_fs_fit.best_score_, ':' , round(lr_fs_fit.best_score_, 2))
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pipeline.predict(X_bts)
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lr_fs.predict(X_bts)
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test_predict = pipeline.predict(X_bts)
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print(test_predict)
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print(np.array(y_bts))
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#y_btsf = np.array(y_bts)
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print(accuracy_score(y_bts, test_predict))
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print(matthews_corrcoef(y_bts, test_predict))
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######################################
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# Blind test
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######################################
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# See how it does on the BLIND test
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#print('\nBlind test score, mcc:', ))
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test_predict = lr_fs_fit.predict(X_bts)
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print(test_predict)
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print(np.array(y_bts))
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y_btsf = np.array(y_bts)
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print(accuracy_score(y_bts, test_predict))
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print(matthews_corrcoef(y_bts, test_predict))
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# create a dict with all scores
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lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
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'bts_fscore':None
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, 'bts_mcc':None
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, 'bts_precision':None
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, 'bts_recall':None
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, 'bts_accuracy':None
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, 'bts_roc_auc':None
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, 'bts_jaccard':None }
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lr_bts_dict
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lr_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
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lr_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
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lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
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lr_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
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lr_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
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lr_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
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lr_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
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lr_bts_dict
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# Create a df from dict with all scores
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lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
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lr_bts_df.columns = ['Logistic_Regression']
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print(lr_bts_df)
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# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
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# d2
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# def Merge(dict1, dict2):
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# res = {**dict1, **dict2}
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# return res
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# d3 = Merge(d2, lr_bts_dict)
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# d3
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# Create df with best model params
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model_params = pd.Series(['best_model_params', list(lr_fs_fit_be_mod.items() )])
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model_params_df = model_params.to_frame()
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model_params_df
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model_params_df.columns = ['Logistic_Regression']
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model_params_df.columns
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# Combine the df of scores and the best model params
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lr_bts_df.columns
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lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
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lr_output
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# Format the combined df
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# Drop the best_model_params row from lr_output
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lr_df = lr_output.drop([0], axis = 0)
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lr_df
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#FIXME: tidy the index of the formatted df
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###############################################################################
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