added UQ_LR FS2.py that has the FS run with LR model as part of pipeline and gridsearch
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uq_ml_models/UQ_LR_FS2.py
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uq_ml_models/UQ_LR_FS2.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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#%% 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 = skf_cv
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#, cv = 10
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, min_features_to_select = 1 # default
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, scoring = 'matthews_corrcoef'
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)
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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(estimator = model_lr
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, param_grid = 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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################
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# NOTE: GS is going into pipeline,
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# Cannot get BEST model out
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################
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# Create pipeline
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pipeline = Pipeline([('pre', MinMaxScaler())
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#, ('fs', sfs_selector)
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, ('fs', model_rfecv )
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, ('clf', gscv_lr)])
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# Fit # dont assign fit
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#lr_fs_fit = pipeline.fit(X,y)
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pipeline.fit(X,y)
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pipeline.best_params_
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#https://github.com/scikit-learn/scikit-learn/issues/7536
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n_fs = gscv_lr.best_estimator_.n_features_in_
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n_fs
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sel_features = X.columns[pipeline.named_steps['fs'].get_support()]
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print('\nNo. of features selected with RFECV for model'
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, pipeline.named_steps['clf'].estimator
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, ':', n_fs
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, '\nThese are:', sel_features
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)
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##############################################################
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# THIS ONE
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#########
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# Make Pipeline go into GS with FS
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#########
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# step 1: specify model
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#modLR = LogisticRegression(**rs)
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# step 2: specify fs
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#model_rfecv = RFECV(estimator = model_lr
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# , cv = skf_cv
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#, min_features_to_select = 1 # default
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#, scoring = 'matthews_corrcoef'
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#)
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# step 3: specify param grid as dict
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param_grid2 = [
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{'fs__min_features_to_select': [1]
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, 'fs__cv': [skf_cv]
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#, 'fs__scoring': ['matthews_corrcoef']},
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#, 'fs__scoring': [mcc_score_fn]}
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},
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['l2'],
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'clf__max_iter': list(range(100,200,100)),
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#'clf__solver': ['newton-cg', 'lbfgs', 'sag']
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'clf__solver': ['sag']
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},
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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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(100,200,100)),
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'clf__solver': ['liblinear']
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}
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]
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# step 4: create pipeline
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pipeline = Pipeline([
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('pre', MinMaxScaler())
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#, ('fs', model_rfecv)
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, ('fs', RFECV(LogisticRegression(**rs), scoring = 'matthews_corrcoef'))
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, ('clf', LogisticRegression(**rs))])
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# step 5: Perform Gridsearch CV
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gs_final = GridSearchCV(pipeline
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, param_grid2
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, cv = skf_cv
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, scoring = mcc_score_fn, refit = 'mcc'
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, verbose = 1
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, return_train_score = False
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, **njobs)
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#fit
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gs_final.fit(X,y)
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gs_final.best_params_
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gs_final.best_score_
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# assign the fit
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gsfit = gs_final.fit(X,y)
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#gsfit.best_estimator_
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gsfit.best_params_
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gsfit.best_score_
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# Now get the features out
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all_features = gs_final.feature_names_in_
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#all_features = gsfit.feature_names_in_
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sel_features = X.columns[gs_final.best_estimator_.named_steps['fs'].get_support()]
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n_sf = gs_final.best_estimator_.named_steps['fs'].n_features_
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# get model name
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model_name = gs_final.best_estimator_.named_steps['clf']
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b_model_params = gs_final.best_params_
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print('\n========================================'
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, '\nRunning model:'
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, '\nModel name:', model_name
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, '\n==============================================='
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, '\nRunning feature selection with RFECV for model'
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, '\nTotal no. of features in model:', len(all_features)
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, '\nThese are:\n', all_features, '\n\n'
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, '\nNo of features for best model: ', n_sf
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, '\nThese are:', sel_features, '\n\n'
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, '\nBest Model hyperparams:', b_model_params
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)
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