renamed function file to UQ_FS_fn.py and added new file to call this function
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2 changed files with 92 additions and 28 deletions
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UQ_FS_fn.py
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UQ_FS_fn.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 23 23:25:26 2022
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@author: tanu
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"""
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#####################################
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def fsgs(input_df
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, target
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, blind_test_df = pd.DataFrame()
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#, y_trueS = pd.Series()
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, estimator = LogisticRegression(**rs)
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, param_gridLd = {}
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, cv_method = 10
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, var_type = ['numerical'
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, 'categorical'
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, 'mixed']
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, fs_estimator = [LogisticRegression(**rs)]
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, fs = RFECV(DecisionTreeClassifier(**rs) , cv = 10, scoring = 'matthews_corrcoef')
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):
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'''
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returns
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Dict containing results from FS and hyperparam tuning
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'''
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# Determine categorical and numerical features
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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# Determine preprocessing steps ~ var_type
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if var_type == 'numerical':
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t = [('num', MinMaxScaler(), numerical_ix)]
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if var_type == 'categorical':
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'mixed':
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t = [('cat', OneHotEncoder(), categorical_ix)
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, ('num', MinMaxScaler(), numerical_ix)]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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###########################################################################
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#=================
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# Create var_type ~ column names
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# using one hot encoder with RFECV means the names internally are lost
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# Hence fit col_transformeer to my input_df and get all the column names
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# out and stored in a var to allow the 'selected features' to be subsetted
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# from the numpy boolean array
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#=================
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col_transform.fit(input_df)
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col_transform.get_feature_names_out()
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var_type_colnames = col_transform.get_feature_names_out()
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var_type_colnames = pd.Index(var_type_colnames)
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if var_type == 'mixed':
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print('\nVariable type is:', var_type
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, '\nNo. of columns in input_df:', len(input_df.columns)
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, '\nNo. of columns post one hot encoder:', len(var_type_colnames))
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else:
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print('\nNo. of columns in input_df:', len(input_df.columns))
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############################################################################
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# Create Pipeline object
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pipe = Pipeline([
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#('pre', MinMaxScaler()),
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('pre', col_transform),
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('fs', fs),
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#('clf', LogisticRegression(**rs))])
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('clf', estimator)])
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############################################################################
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# Define GridSearchCV
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gscv_fs = GridSearchCV(pipe
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, param_gridLd
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, cv = cv_method
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, scoring = mcc_score_fn
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, refit = 'mcc'
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, verbose = 1
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, return_train_score = True
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, **njobs)
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gscv_fs.fit(input_df, target)
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###########################################################################
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# Get best param and scores out
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gscv_fs.best_params_
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gscv_fs.best_score_
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# Training best score corresponds to the max of the mean_test<score>
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train_bscore = round(gscv_fs.best_score_, 2); train_bscore
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print('\nTraining best score (MCC):', train_bscore)
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gscv_fs.cv_results_['mean_test_mcc']
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round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
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round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)
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check_train_score = [round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
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, round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)]
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check_train_score = np.nanmax(check_train_score)
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# Training results
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gscv_tr_resD = gscv_fs.cv_results_
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mod_refit_param = gscv_fs.refit
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# sanity check
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if train_bscore == check_train_score:
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print('\nVerified training score (MCC):', train_bscore )
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else:
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print('\nTraining score could not be internatlly verified. Please check training results dict')
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#-------------------------
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# Blind test: REAL check!
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#-------------------------
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#tp = gscv_fs.predict(X_bts)
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tp = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
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#=================
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# info extraction
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#=================
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# gives input vals??
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gscv_fs._check_n_features
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# gives gscv params used
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gscv_fs._get_param_names()
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# gives ??
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gscv_fs.best_estimator_
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gscv_fs.best_params_ # gives best estimator params as a dict
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gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
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gscv_fs.best_estimator_.named_steps['fs'].get_support()
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gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
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gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
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gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
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#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
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############################################################################
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#============
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# FS results
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#============
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# Now get the features out
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#--------------
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# All features
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#--------------
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all_features = gscv_fs.feature_names_in_
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n_all_features = gscv_fs.n_features_in_
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#all_features = gsfit.feature_names_in_
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#--------------
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# Selected features by the classifier
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# Important to have var_type_colnames here
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#----------------
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#sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()] 3 only for numerical df
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sel_features = var_type_colnames[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
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n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
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#--------------
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# Get model name
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#--------------
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model_name = gscv_fs.best_estimator_.named_steps['clf']
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b_model_params = gscv_fs.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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###############################################################################
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############################## OUTPUT #########################################
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###############################################################################
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#=========================
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# Blind test: BTS results
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#=========================
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# Build the final results with all scores for a feature selected model
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#bts_predict = gscv_fs.predict(X_bts)
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bts_predict = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
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bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
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# Diff b/w train and bts test scores
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train_test_diff = train_bscore - bts_mcc_score
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print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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# create a dict with all scores
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lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
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#'bts_mcc':None
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'bts_fscore':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_btsD
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#lr_btsD['bts_mcc'] = bts_mcc_score
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lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
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lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
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lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
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lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
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lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
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lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
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lr_btsD
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#===========================
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# Add FS related model info
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#===========================
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model_namef = str(model_name)
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# FIXME: doesn't tell you which it has chosen
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fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
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all_featuresL = list(all_features)
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fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
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fs_res_array_rankf = list( gscv_fs.best_estimator_.named_steps['fs'].ranking_)
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sel_featuresf = list(sel_features)
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n_sf = int(n_sf)
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output_modelD = {'model_name': model_namef
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, 'model_refit_param': mod_refit_param
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, 'Best_model_params': b_model_params
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, 'n_all_features': n_all_features
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, 'fs_method': fs_methodf
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, 'fs_res_array': fs_res_arrayf
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, 'fs_res_array_rank': fs_res_array_rankf
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, 'all_feature_names': all_featuresL
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, 'n_sel_features': n_sf
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, 'sel_features_names': sel_featuresf}
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output_modelD
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#========================================
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# Update output_modelD with bts_results
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#========================================
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output_modelD.update(lr_btsD)
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output_modelD
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output_modelD['train_score (MCC)'] = train_bscore
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output_modelD['bts_mcc'] = bts_mcc_score
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output_modelD['train_bts_diff'] = round(train_test_diff,2)
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print(output_modelD)
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return(output_modelD)
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