added feature selection on all models but lets see if it works, only worked until DT
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17 changed files with 2425 additions and 1202 deletions
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@ -25,7 +25,6 @@ X_eg, y_eg = make_classification(n_samples=1000,
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pipe = Pipeline([('scaler', StandardScaler()),
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('selector', SelectKBest(mutual_info_classif, k=9)),
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('classifier', LogisticRegression())])
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search_space = [{'selector__k': [5, 6, 7, 10]},
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@ -97,7 +96,7 @@ search_space = [{'fs__min_features_to_select': [1,2]
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gscv_fs = GridSearchCV(pipe
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, search_space
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, cv = skf_cv
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, cv = rskf_cv
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, scoring = mcc_score_fn
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, refit = 'mcc'
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, verbose = 1
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@ -33,7 +33,7 @@ bts_jaccard 0.54
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'bts_roc_auc': 0.65,
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'bts_jaccard': 0.55}
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#######################################################################
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# RF: hyperparam [~45]
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# RF: hyperparam [~45 min]
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Best model:
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{'clf__estimator': RandomForestClassifier(class_weight='balanced', max_depth=4, max_features=None,
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@ -5,60 +5,139 @@ Created on Wed May 18 06:03:24 2022
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@author: tanu
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"""
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parameters = [
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#cv = rskf_cv
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cv = skf_cv
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# AdaBoostClassifier: Feature Selelction + GridSearch CV + Pipeline
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###############################################################################
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# Define estimator
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estimator = AdaBoostClassifier(**rs)
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# Define pipleline with steps
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pipe_abc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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# , ('clf', AdaBoostClassifier(**rs))])
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_abc = [
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{
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'clf': [AdaBoostClassifier(**rs)]
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, 'clf__n_estimators': [1, 2, 5, 10]
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [AdaBoostClassifier(**rs)],
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'clf__n_estimators': [1, 2, 5, 10]
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# , 'clf__base_estimator' : ['SVC']
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# , 'clf__splitter' : ["best", "random"]
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}
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]
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# Create pipeline
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pipeline = Pipeline([
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('pre', MinMaxScaler()),
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('clf', ClfSwitcher()),
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])
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# Define GridSearch CV
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gscv_fs = GridSearchCV(pipe_abc
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, param_grid_abc
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, cv = cv
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, scoring = mcc_score_fn
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, refit = 'mcc'
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, verbose = 3
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, return_train_score = True
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, **njobs)
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###############################################################################
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#------------------------------
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# Fit gscv containing pipeline
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#------------------------------
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gscv_fs.fit(X, y)
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#Fitting 10 folds for each of 6 candidates, totalling 60 fits
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#Fitting 30 folds for each of 6 candidates, totalling 180 fits
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# QUESTION: HOW??
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gscv_fs.best_params_
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gscv_fs.best_score_
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# Grid search i.e hyperparameter tuning and refitting on mcc
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gscv_abc = GridSearchCV(pipeline
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, parameters
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#, scoring = 'matthews_corrcoef', refit = 'matthews_corrcoef'
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, scoring = mcc_score_fn, refit = 'mcc'
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, cv = skf_cv
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, **njobs
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, return_train_score = False
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, verbose = 3)
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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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round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
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# Fit
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gscv_abc_fit = gscv_abc.fit(X, y)
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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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gscv_abc_fit_be_mod = gscv_abc_fit.best_params_
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gscv_abc_fit_be_res = gscv_abc_fit.cv_results_
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# sanity check
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if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
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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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print('Best model:\n', gscv_abc_fit_be_mod)
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print('Best models score:\n', gscv_abc_fit.best_score_, ':' , round(gscv_abc_fit.best_score_, 2))
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# Blind test: REAL check!
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tp = gscv_fs.predict(X_bts)
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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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print('\nMean test score from fit results:', round(mean(gscv_abc_fit_be_res['mean_test_mcc']),2))
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print('\nMean test score from fit results:', round(np.nanmean(gscv_abc_fit_be_res['mean_test_mcc']),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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######################################
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# Blind test
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######################################
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# gives gscv params used
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gscv_fs._get_param_names()
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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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# 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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test_predict = gscv_abc_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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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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print(accuracy_score(y_btsf, test_predict))
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print(matthews_corrcoef(y_btsf, test_predict))
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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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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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sel_features = X.columns[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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# get model name
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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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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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# create a dict with all scores
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abc_bts_dict = {#'best_model': list(gscv_abc_fit_be_mod.items())
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lr_btsD = {#'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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@ -66,41 +145,46 @@ abc_bts_dict = {#'best_model': list(gscv_abc_fit_be_mod.items())
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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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abc_bts_dict
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abc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
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abc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
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abc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
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abc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
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abc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
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abc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
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abc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
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abc_bts_dict
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# Create a df from dict with all scores
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abc_bts_df = pd.DataFrame.from_dict(abc_bts_dict,orient = 'index')
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abc_bts_df.columns = ['ABC']
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print(abc_bts_df)
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# Create df with best model params
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model_params = pd.Series(['best_model_params', list(gscv_abc_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 = ['ABC']
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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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abc_bts_df.columns
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abc_output = pd.concat([model_params_df, abc_bts_df], axis = 0)
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abc_output
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# Format the combined df
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# Drop the best_model_params row from abc_output
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abc_df = abc_output.drop([0], axis = 0)
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abc_df
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#FIXME: tidy the index of the formatted df
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###############################################################################
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lr_btsD
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lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
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lr_btsD['bts_mcc'] = round(matthews_corrcoef(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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output_modelD = {'model_name': model_name
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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': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
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, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
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, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
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, 'all_feature_names': all_features
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, 'n_sel_features': n_sf
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, 'sel_features_names': sel_features
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, 'train_score (MCC)': train_bscore}
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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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#========================================
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# Write final output file
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# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
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#========================================
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# output final dict as a json
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# outFile = 'LR_FS.json'
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# with open(outFile, 'w') as f:
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# json.dump(output_modelD, f)
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# #
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# with open(file, 'r') as f:
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# data = json.load(f)
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@ -5,63 +5,141 @@ Created on Wed May 18 06:03:24 2022
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@author: tanu
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"""
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parameters = [
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{
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'clf': [BaggingClassifier(**rs
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#cv = rskf_cv
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cv = skf_cv
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# BaggingClassifier: Feature Selelction + GridSearch CV + Pipeline
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###############################################################################
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estimator = BaggingClassifier(**rs
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, **njobs
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, bootstrap = True
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, oob_score = True)]
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, 'clf__n_estimators' : [10, 25, 50, 100, 150, 200, 500, 700, 1000]
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# If None, then the base estimator is a DecisionTreeClassifier.
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, oob_score = True)
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# Define pipleline with steps
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pipe_bc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_bc = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [BaggingClassifier(**rs, **njobs , bootstrap = True, oob_score = True)],
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'clf__n_estimators' : [10, 25, 50, 100, 150, 200, 500, 700, 1000]
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# , 'clf__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()'] # if none, DT is used
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}
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]
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# Create pipeline
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pipeline = Pipeline([
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('pre', MinMaxScaler()),
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('clf', ClfSwitcher()),
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])
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# Define GridSearch CV
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gscv_fs = GridSearchCV(pipe_bc
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, param_grid_bc
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, cv = cv
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, scoring = mcc_score_fn
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, refit = 'mcc'
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, verbose = 3
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, return_train_score = True
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, **njobs)
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################################################################################
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#------------------------------
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# Fit gscv containing pipeline
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#------------------------------
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gscv_fs.fit(X, y)
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# Fitting 10 folds for each of 11 candidates, totalling 110 fits
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#Fitting 30 folds for each of 11 candidates, totalling 330 fits
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# QUESTION: HOW??
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gscv_fs.best_params_
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gscv_fs.best_score_
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# Grid search i.e hyperparameter tuning and refitting on mcc
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gscv_bc = GridSearchCV(pipeline
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, parameters
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#, scoring = 'f1', refit = 'f1'
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, scoring = mcc_score_fn, refit = 'mcc'
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, cv = skf_cv
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, **njobs
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, return_train_score = False
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, verbose = 3)
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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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round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
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# Fit
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gscv_bc_fit = gscv_bc.fit(X, y)
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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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gscv_bc_fit_be_mod = gscv_bc_fit.best_params_
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gscv_bc_fit_be_res = gscv_bc_fit.cv_results_
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# sanity check
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if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
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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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print('Best model:\n', gscv_bc_fit_be_mod)
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print('Best models score:\n', gscv_bc_fit.best_score_, ':' , round(gscv_bc_fit.best_score_, 2))
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# Blind test: REAL check!
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tp = gscv_fs.predict(X_bts)
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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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print('\nMean test score from fit results:', round(mean(gscv_bc_fit_be_res['mean_test_mcc']),2))
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print('\nMean test score from fit results:', round(np.nanmean(gscv_bc_fit_be_res['mean_test_mcc']),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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######################################
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# Blind test
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######################################
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# gives gscv params used
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gscv_fs._get_param_names()
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# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
test_predict = gscv_bc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
bc_bts_dict = {#'best_model': list(gscv_bc_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -69,41 +147,46 @@ bc_bts_dict = {#'best_model': list(gscv_bc_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
bc_bts_dict
|
||||
bc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
bc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
bc_bts_df = pd.DataFrame.from_dict(bc_bts_dict,orient = 'index')
|
||||
bc_bts_df.columns = ['BC']
|
||||
print(bc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_bc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['BC']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
bc_bts_df.columns
|
||||
bc_output = pd.concat([model_params_df, bc_bts_df], axis = 0)
|
||||
bc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from bc_output
|
||||
bc_df = bc_output.drop([0], axis = 0)
|
||||
bc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,61 +5,137 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# BernoulliNB: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = BernoulliNB()
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_bnb = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_bnb = [
|
||||
{'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
'clf': [BernoulliNB()]
|
||||
, 'clf__alpha': [1, 0]
|
||||
# 'clf': [BernoulliNB()],
|
||||
'clf__alpha': [1, 0]
|
||||
, 'clf__binarize':[None, 0]
|
||||
, 'clf__fit_prior': [True]
|
||||
, 'clf__class_prior': [None]
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_bnb = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
gscv_fs = GridSearchCV(pipe_bnb
|
||||
, param_grid_bnb
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, cv = cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Fit
|
||||
gscv_bnb_fit = gscv_bnb.fit(X, y)
|
||||
#Fitting 10 folds for each of 6 candidates, totalling 60 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
gscv_bnb_fit_be_mod = gscv_bnb_fit.best_params_
|
||||
gscv_bnb_fit_be_res = gscv_bnb_fit.cv_results_
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
print('Best model:\n', gscv_bnb_fit_be_mod)
|
||||
print('Best models score:\n', gscv_bnb_fit.best_score_, ':' , round(gscv_bnb_fit.best_score_, 2))
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_bnb_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_bnb_fit_be_res['mean_test_mcc']),2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
test_predict = gscv_bnb_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
bnb_bts_dict = {#'best_model': list(gscv_bnb_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -67,41 +143,46 @@ bnb_bts_dict = {#'best_model': list(gscv_bnb_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
bnb_bts_dict
|
||||
bnb_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
bnb_bts_df = pd.DataFrame.from_dict(bnb_bts_dict,orient = 'index')
|
||||
bnb_bts_df.columns = ['BNB']
|
||||
print(bnb_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_bnb_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['BNB']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
bnb_bts_df.columns
|
||||
bnb_output = pd.concat([model_params_df, bnb_bts_df], axis = 0)
|
||||
bnb_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from bnb_output
|
||||
bnb_df = bnb_output.drop([0], axis = 0)
|
||||
bnb_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,10 +5,32 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# DecisionTreeClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = DecisionTreeClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_dt = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_dt = [
|
||||
{
|
||||
'clf': [DecisionTreeClassifier(**rs)]
|
||||
, 'clf__max_depth': [None, 2, 4, 6, 8, 10, 12, 16, 20]
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [DecisionTreeClassifier(**rs)],
|
||||
'clf__max_depth': [None, 2, 4, 6, 8, 10, 12, 16, 20]
|
||||
, 'clf__class_weight':['balanced']
|
||||
, 'clf__criterion': ['gini', 'entropy', 'log_loss']
|
||||
, 'clf__max_features': [None, 'sqrt', 'log2']
|
||||
|
|
@ -17,51 +39,106 @@ parameters = [
|
|||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_dt
|
||||
, param_grid_dt
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_dt = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 1944 candidates, totalling 19440 fits# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_dt_fit = gscv_dt.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_dt_fit_be_mod = gscv_dt_fit.best_params_
|
||||
gscv_dt_fit_be_res = gscv_dt_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_dt_fit_be_mod)
|
||||
print('Best models score:\n', gscv_dt_fit.best_score_, ':' , round(gscv_dt_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_dt_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_dt_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_dt_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
dt_bts_dict = {#'best_model': list(gscv_dt_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -69,41 +146,46 @@ dt_bts_dict = {#'best_model': list(gscv_dt_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
dt_bts_dict
|
||||
dt_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
dt_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
dt_bts_df = pd.DataFrame.from_dict(dt_bts_dict,orient = 'index')
|
||||
dt_bts_df.columns = ['DT']
|
||||
print(dt_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_dt_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['DT']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
dt_bts_df.columns
|
||||
dt_output = pd.concat([model_params_df, dt_bts_df], axis = 0)
|
||||
dt_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from dt_output
|
||||
dt_df = dt_output.drop([0], axis = 0)
|
||||
dt_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,10 +5,31 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# GradientBoostingClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = GradientBoostingClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_gbc = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_gbc = [
|
||||
{
|
||||
'clf': [GradientBoostingClassifier(**rs)]
|
||||
, 'clf__n_estimators' : [10, 100, 200, 500, 1000]
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
{
|
||||
# 'clf': [GradientBoostingClassifier(**rs)],
|
||||
'clf__n_estimators' : [10, 100, 200, 500, 1000]
|
||||
, 'clf__n_estimators' : [10, 100, 1000]
|
||||
, 'clf__learning_rate': [0.001, 0.01, 0.1]
|
||||
, 'clf__subsample' : [0.5, 0.7, 1.0]
|
||||
|
|
@ -17,51 +38,107 @@ parameters = [
|
|||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_gbc
|
||||
, param_grid_gbc
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_gbc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 83 candidates, totalling 830 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_gbc_fit = gscv_gbc.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_gbc_fit_be_mod = gscv_gbc_fit.best_params_
|
||||
gscv_gbc_fit_be_res = gscv_gbc_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_gbc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_gbc_fit.best_score_, ':' , round(gscv_gbc_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_gbc_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_gbc_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_gbc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
gbc_bts_dict = {#'best_model': list(gscv_gbc_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -69,41 +146,46 @@ gbc_bts_dict = {#'best_model': list(gscv_gbc_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
gbc_bts_dict
|
||||
gbc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
gbc_bts_df = pd.DataFrame.from_dict(gbc_bts_dict,orient = 'index')
|
||||
gbc_bts_df.columns = ['GBC']
|
||||
print(gbc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_gbc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['GBC']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
gbc_bts_df.columns
|
||||
gbc_output = pd.concat([model_params_df, gbc_bts_df], axis = 0)
|
||||
gbc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from gbc_output
|
||||
gbc_df = gbc_output.drop([0], axis = 0)
|
||||
gbc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
|
|||
|
|
@ -5,59 +5,136 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# GaussianNB: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = GaussianNB()
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_gnb = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_gnb = [
|
||||
{
|
||||
'clf': [GaussianNB()]
|
||||
, 'clf__priors': [None]
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
{
|
||||
# 'clf': [GaussianNB()],
|
||||
'clf__priors': [None]
|
||||
, 'clf__var_smoothing': np.logspace(0,-9, num=100)
|
||||
}
|
||||
]
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_gnb
|
||||
, param_grid_gnb
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_gnb = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
# Fit
|
||||
gscv_gnb_fit = gscv_gnb.fit(X, y)
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
gscv_gnb_fit_be_mod = gscv_gnb_fit.best_params_
|
||||
gscv_gnb_fit_be_res = gscv_gnb_fit.cv_results_
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('Best model:\n', gscv_gnb_fit_be_mod)
|
||||
print('Best models score:\n', gscv_gnb_fit.best_score_, ':' , round(gscv_gnb_fit.best_score_, 2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_gnb_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_gnb_fit_be_res['mean_test_mcc']),2))
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
test_predict = gscv_gnb_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
gnb_bts_dict = {#'best_model': list(gscv_gnb_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -65,41 +142,46 @@ gnb_bts_dict = {#'best_model': list(gscv_gnb_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
gnb_bts_dict
|
||||
gnb_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
gnb_bts_df = pd.DataFrame.from_dict(gnb_bts_dict,orient = 'index')
|
||||
gnb_bts_df.columns = ['GNB']
|
||||
print(gnb_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_gnb_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['GNB']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
gnb_bts_df.columns
|
||||
gnb_output = pd.concat([model_params_df, gnb_bts_df], axis = 0)
|
||||
gnb_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from gnb_output
|
||||
gnb_df = gnb_output.drop([0], axis = 0)
|
||||
gnb_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,59 +5,136 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
{
|
||||
'clf': [GaussianProcessClassifier(**rs)]
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
, 'clf__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
|
||||
# GaussianProcessClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = GaussianProcessClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_gbc = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_gbc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [GaussianProcessClassifier(**rs)],
|
||||
'clf__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_gbc
|
||||
, param_grid_gbc
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_gpc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_gpc_fit = gscv_gpc.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_gpc_fit_be_mod = gscv_gpc_fit.best_params_
|
||||
gscv_gpc_fit_be_res = gscv_gpc_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_gpc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_gpc_fit.best_score_, ':' , round(gscv_gpc_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_gpc_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_gpc_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_gpc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
gpc_bts_dict = {#'best_model': list(gscv_gpc_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -65,41 +142,46 @@ gpc_bts_dict = {#'best_model': list(gscv_gpc_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
gpc_bts_dict
|
||||
gpc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
gpc_bts_df = pd.DataFrame.from_dict(gpc_bts_dict,orient = 'index')
|
||||
gpc_bts_df.columns = ['GPC']
|
||||
print(gpc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_gpc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['GPC']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
gpc_bts_df.columns
|
||||
gpc_output = pd.concat([model_params_df, gpc_bts_df], axis = 0)
|
||||
gpc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from gpc_output
|
||||
gpc_df = gpc_output.drop([0], axis = 0)
|
||||
gpc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,10 +5,32 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# KNeighborsClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = KNeighborsClassifier(**njobs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_knn = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_knn = [
|
||||
{
|
||||
'clf': [KNeighborsClassifier(**njobs)]
|
||||
, 'clf__n_neighbors': range(21, 51, 2)
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [KNeighborsClassifier(**njobs)],
|
||||
'clf__n_neighbors': range(21, 51, 2)
|
||||
#, 'clf__n_neighbors': [5, 7, 11]
|
||||
, 'clf__metric' : ['euclidean', 'manhattan', 'minkowski']
|
||||
, 'clf__weights' : ['uniform', 'distance']
|
||||
|
|
@ -16,51 +38,107 @@ parameters = [
|
|||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_knn
|
||||
, param_grid_knn
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_knn = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_knn_fit = gscv_knn.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_knn_fit_be_mod = gscv_knn_fit.best_params_
|
||||
gscv_knn_fit_be_res = gscv_knn_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_knn_fit_be_mod)
|
||||
print('Best models score:\n', gscv_knn_fit.best_score_, ':' , round(gscv_knn_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_knn_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_knn_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_knn_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
knn_bts_dict = {#'best_model': list(gscv_knn_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -68,41 +146,46 @@ knn_bts_dict = {#'best_model': list(gscv_knn_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
knn_bts_dict
|
||||
knn_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
knn_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
knn_bts_df = pd.DataFrame.from_dict(knn_bts_dict,orient = 'index')
|
||||
knn_bts_df.columns = ['KNN']
|
||||
print(knn_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_knn_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['KNN']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
knn_bts_df.columns
|
||||
knn_output = pd.concat([model_params_df, knn_bts_df], axis = 0)
|
||||
knn_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from knn_output
|
||||
knn_df = knn_output.drop([0], axis = 0)
|
||||
knn_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -12,26 +12,45 @@ Created on Tue Mar 15 11:09:50 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# LogisticRegression: Feature Selelction + GridSearch CV + Pipeline
|
||||
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = LogisticRegression(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_lr = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(LogisticRegression(**rs), cv = rskf_cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_lr = [
|
||||
|
||||
{'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [rskf_cv]
|
||||
},
|
||||
|
||||
{
|
||||
'clf': [LogisticRegression(**rs)],
|
||||
#'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
|
||||
# 'clf': [LogisticRegression(**rs)],
|
||||
'clf__C': np.logspace(0, 4, 10),
|
||||
'clf__penalty': ['none', 'l1', 'l2', 'elasticnet'],
|
||||
'clf__max_iter': list(range(100,800,100)),
|
||||
'clf__solver': ['saga']
|
||||
},
|
||||
{
|
||||
'clf': [LogisticRegression(**rs)],
|
||||
#'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
|
||||
# 'clf': [LogisticRegression(**rs)],
|
||||
'clf__C': np.logspace(0, 4, 10),
|
||||
'clf__penalty': ['l2', 'none'],
|
||||
'clf__max_iter': list(range(100,800,100)),
|
||||
'clf__solver': ['newton-cg', 'lbfgs', 'sag']
|
||||
},
|
||||
{
|
||||
'clf': [LogisticRegression(**rs)],
|
||||
#'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
|
||||
# 'clf': [LogisticRegression(**rs)],
|
||||
'clf__C': np.logspace(0, 4, 10),
|
||||
'clf__penalty': ['l1', 'l2'],
|
||||
'clf__max_iter': list(range(100,800,100)),
|
||||
|
|
@ -40,50 +59,107 @@ parameters = [
|
|||
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_lr
|
||||
, param_grid_lr
|
||||
, cv = rskf_cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 1
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_lr = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_lr_fit = gscv_lr.fit(X, y)
|
||||
gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
|
||||
gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
print('Best model:\n', gscv_lr_fit_be_mod)
|
||||
print('Best models score:\n', gscv_lr_fit.best_score_, ':' , round(gscv_lr_fit.best_score_, 2))
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', ))
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
test_predict = gscv_lr_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
print(accuracy_score(y_bts, test_predict))
|
||||
print(matthews_corrcoef(y_bts, test_predict))
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -91,46 +167,47 @@ lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
lr_bts_dict
|
||||
lr_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
lr_bts_dict
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
# Create a df from dict with all scores
|
||||
lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
|
||||
lr_bts_df.columns = ['Logistic_Regression']
|
||||
print(lr_bts_df)
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
|
||||
# d2
|
||||
# def Merge(dict1, dict2):
|
||||
# res = {**dict1, **dict2}
|
||||
# return res
|
||||
# d3 = Merge(d2, lr_bts_dict)
|
||||
# d3
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_lr_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
lr_bts_df.columns
|
||||
lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
|
||||
lr_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from lr_output
|
||||
lr_df = lr_output.drop([0], axis = 0)
|
||||
lr_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
|
|
|||
|
|
@ -5,10 +5,30 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# MLPClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = MLPClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_mlp = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
param_grid_mlp = [ {
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
'clf': [MLPClassifier(**rs
|
||||
, max_iter = 1000)]
|
||||
# 'clf': [MLPClassifier(**rs, max_iter = 1000)],
|
||||
'clf__max_iter': [1000, 2000]
|
||||
, 'clf__hidden_layer_sizes': [(1), (2), (3), (5), (10)]
|
||||
, 'clf__solver': ['lbfgs', 'sgd', 'adam']
|
||||
, 'clf__learning_rate': ['constant', 'invscaling', 'adaptive']
|
||||
|
|
@ -17,51 +37,107 @@ parameters = [
|
|||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_mlp
|
||||
, param_grid_mlp
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_mlp = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_mlp_fit = gscv_mlp.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_mlp_fit_be_mod = gscv_mlp_fit.best_params_
|
||||
gscv_mlp_fit_be_res = gscv_mlp_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_mlp_fit_be_mod)
|
||||
print('Best models score:\n', gscv_mlp_fit.best_score_, ':' , round(gscv_mlp_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_mlp_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_mlp_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_mlp_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
mlp_bts_dict = {#'best_model': list(gscv_mlp_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -69,41 +145,46 @@ mlp_bts_dict = {#'best_model': list(gscv_mlp_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
mlp_bts_dict
|
||||
mlp_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
mlp_bts_df = pd.DataFrame.from_dict(mlp_bts_dict,orient = 'index')
|
||||
mlp_bts_df.columns = ['MLP']
|
||||
print(mlp_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_mlp_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['MLP']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
mlp_bts_df.columns
|
||||
mlp_output = pd.concat([model_params_df, mlp_bts_df], axis = 0)
|
||||
mlp_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from mlp_output
|
||||
mlp_df = mlp_output.drop([0], axis = 0)
|
||||
mlp_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,58 +5,137 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# QuadraticDiscriminantAnalysis: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = QuadraticDiscriminantAnalysis(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_qda = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_qda = [
|
||||
{
|
||||
'clf': [QuadraticDiscriminantAnalysis()]
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [QuadraticDiscriminantAnalysis()],
|
||||
'clf__priors': [None]
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_qda
|
||||
, param_grid_qda
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_qda = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_qda_fit = gscv_qda.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_qda_fit_be_mod = gscv_qda_fit.best_params_
|
||||
gscv_qda_fit_be_res = gscv_qda_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_qda_fit_be_mod)
|
||||
print('Best models score:\n', gscv_qda_fit.best_score_, ':' , round(gscv_qda_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_qda_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_qda_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_qda_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
qda_bts_dict = {#'best_model': list(gscv_qda_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -64,41 +143,46 @@ qda_bts_dict = {#'best_model': list(gscv_qda_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
qda_bts_dict
|
||||
qda_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
qda_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
qda_bts_df = pd.DataFrame.from_dict(qda_bts_dict,orient = 'index')
|
||||
qda_bts_df.columns = ['QDA']
|
||||
print(qda_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_qda_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['QDA']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
qda_bts_df.columns
|
||||
qda_output = pd.concat([model_params_df, qda_bts_df], axis = 0)
|
||||
qda_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from qda_output
|
||||
qda_df = qda_output.drop([0], axis = 0)
|
||||
qda_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,57 +5,135 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
{'clf' : [RidgeClassifier(**rs)]
|
||||
, 'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# RidgeClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = RidgeClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_abc = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
param_grid_rc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
#'clf' : [RidgeClassifier(**rs)],
|
||||
'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_rc
|
||||
, param_grid_rc
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_rc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_rc_fit = gscv_rc.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_rc_fit_be_mod = gscv_rc_fit.best_params_
|
||||
gscv_rc_fit_be_res = gscv_rc_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_rc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_rc_fit.best_score_, ':' , round(gscv_rc_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_rc_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_rc_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_rc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
rc_bts_dict = {#'best_model': list(gscv_rc_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -63,41 +141,46 @@ rc_bts_dict = {#'best_model': list(gscv_rc_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
rc_bts_dict
|
||||
rc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
rc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
rc_bts_df = pd.DataFrame.from_dict(rc_bts_dict,orient = 'index')
|
||||
rc_bts_df.columns = ['Ridge Classifier']
|
||||
print(rc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_rc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Ridge Classifier']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
rc_bts_df.columns
|
||||
rc_output = pd.concat([model_params_df, rc_bts_df], axis = 0)
|
||||
rc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from rc_output
|
||||
rc_df = rc_output.drop([0], axis = 0)
|
||||
rc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,12 +5,31 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# AdaBoostClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)](**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_rf = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_rf = [
|
||||
{
|
||||
'clf': [RandomForestClassifier(**rs
|
||||
, **njobs
|
||||
, bootstrap = True
|
||||
, oob_score = True)],
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)],
|
||||
'clf__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
|
||||
, 'clf__class_weight':['balanced','balanced_subsample']
|
||||
, 'clf__n_estimators': [10, 25, 50, 100]
|
||||
|
|
@ -21,51 +40,107 @@ parameters = [
|
|||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_rf
|
||||
, param_grid_rf
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_rf = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_rf_fit = gscv_rf.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_rf_fit_be_mod = gscv_rf_fit.best_params_
|
||||
gscv_rf_fit_be_res = gscv_rf_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_rf_fit_be_mod)
|
||||
print('Best models score:\n', gscv_rf_fit.best_score_, ':' , round(gscv_rf_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_rf_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_rf_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_rf_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
rf_bts_dict = {#'best_model': list(gscv_rf_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -73,41 +148,46 @@ rf_bts_dict = {#'best_model': list(gscv_rf_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
rf_bts_dict
|
||||
rf_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
rf_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
rf_bts_df = pd.DataFrame.from_dict(rf_bts_dict,orient = 'index')
|
||||
rf_bts_df.columns = ['Logistic_Regression']
|
||||
print(rf_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_rf_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
rf_bts_df.columns
|
||||
rf_output = pd.concat([model_params_df, rf_bts_df], axis = 0)
|
||||
rf_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from rf_output
|
||||
rf_df = rf_output.drop([0], axis = 0)
|
||||
rf_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,63 +5,140 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
parameters = [
|
||||
{
|
||||
'clf': [SVC(**rs)]
|
||||
, 'clf__kernel': ['poly', 'rbf', 'sigmoid']
|
||||
#, 'clf__kernel': ['linear']
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# SVC: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = SVC(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_svc = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_svc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [SVC(**rs)],
|
||||
'clf__kernel': ['poly', 'rbf', 'sigmoid']
|
||||
#, 'clf__kernel': ['linear']
|
||||
, 'clf__C' : [50, 10, 1.0, 0.1, 0.01]
|
||||
, 'clf__gamma': ['scale', 'auto']
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_svc
|
||||
, param_grid_svc
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_svc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_svc_fit = gscv_svc.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_svc_fit_be_mod = gscv_svc_fit.best_params_
|
||||
gscv_svc_fit_be_res = gscv_svc_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_svc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_svc_fit.best_score_, ':' , round(gscv_svc_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_svc_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_svc_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_svc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
svc_bts_dict = {#'best_model': list(gscv_svc_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -69,41 +146,46 @@ svc_bts_dict = {#'best_model': list(gscv_svc_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
svc_bts_dict
|
||||
svc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
svc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
svc_bts_df = pd.DataFrame.from_dict(svc_bts_dict,orient = 'index')
|
||||
svc_bts_df.columns = ['SVC']
|
||||
print(svc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_svc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['SVC']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
svc_bts_df.columns
|
||||
svc_output = pd.concat([model_params_df, svc_bts_df], axis = 0)
|
||||
svc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from svc_output
|
||||
svc_df = svc_output.drop([0], axis = 0)
|
||||
svc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -5,8 +5,6 @@ Created on Wed May 18 06:03:24 2022
|
|||
|
||||
@author: tanu
|
||||
"""
|
||||
|
||||
#%%
|
||||
#https://www.datatechnotes.com/2019/07/classification-example-with.html
|
||||
# XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
|
||||
# colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,
|
||||
|
|
@ -15,62 +13,137 @@ Created on Wed May 18 06:03:24 2022
|
|||
# objective='multi:softprob', random_state=0, reg_alpha=0,
|
||||
# reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,
|
||||
# subsample=1, verbosity=1)
|
||||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
parameters = [
|
||||
# XGBClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = XGBClassifier(**rs, **njobs, verbose = 3)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_xgb = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
param_grid_xgb = [
|
||||
{
|
||||
'clf': [XGBClassifier(**rs , **njobs, verbose = 3)]
|
||||
, 'clf__learning_rate': [0.01, 0.05, 0.1, 0.2]
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
{
|
||||
# 'clf': [XGBClassifier(**rs , **njobs, verbose = 3)],
|
||||
'clf__learning_rate': [0.01, 0.05, 0.1, 0.2]
|
||||
, 'clf__max_depth': [4, 6, 8, 10, 12, 16, 20]
|
||||
#, 'clf__min_samples_leaf': [4, 8, 12, 16, 20]
|
||||
#, 'clf__max_features': ['auto', 'sqrt']
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
# Define GridSearch CV
|
||||
gscv_fs = GridSearchCV(pipe_xgb
|
||||
, param_grid_xgb
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 3
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
###############################################################################
|
||||
#------------------------------
|
||||
# Fit gscv containing pipeline
|
||||
#------------------------------
|
||||
gscv_fs.fit(X, y)
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_xgb = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
#Fitting 10 folds for each of 4 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
# Fit
|
||||
gscv_xgb_fit = gscv_xgb.fit(X, y)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
gscv_xgb_fit_be_mod = gscv_xgb_fit.best_params_
|
||||
gscv_xgb_fit_be_res = gscv_xgb_fit.cv_results_
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
print('Best model:\n', gscv_xgb_fit_be_mod)
|
||||
print('Best models score:\n', gscv_xgb_fit.best_score_, ':' , round(gscv_xgb_fit.best_score_, 2))
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_xgb_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_xgb_fit_be_res['mean_test_mcc']),2))
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
test_predict = gscv_xgb_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
xgb_bts_dict = {#'best_model': list(gscv_xgb_fit_be_mod.items())
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
|
|
@ -78,41 +151,46 @@ xgb_bts_dict = {#'best_model': list(gscv_xgb_fit_be_mod.items())
|
|||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
xgb_bts_dict
|
||||
xgb_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
xgb_bts_df = pd.DataFrame.from_dict(xgb_bts_dict,orient = 'index')
|
||||
xgb_bts_df.columns = ['XGBoost']
|
||||
print(xgb_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_xgb_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['XGBoost']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
xgb_bts_df.columns
|
||||
xgb_output = pd.concat([model_params_df, xgb_bts_df], axis = 0)
|
||||
xgb_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from xgb_output
|
||||
xgb_df = xgb_output.drop([0], axis = 0)
|
||||
xgb_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
Loading…
Add table
Add a link
Reference in a new issue