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added and ran hyperparam script for all different classifiers, but couldn't successfully run the feature selection and hyperparam together

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Tanushree Tunstall 2022-05-20 08:09:24 +01:00
parent 74af5ef890
commit 37bda41f44
18 changed files with 131 additions and 142 deletions
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uq_ml_models/UQ_RF.py
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@ -71,7 +71,7 @@ gscv_rf_fit_be_res = gscv_rf_fit.cv_results_
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))
print('\nMean test score from fit results:', round(mean(gscv_rf_fit_be_re['mean_test_mcc']),2))
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))
######################################
@ -109,8 +109,6 @@ rf_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
rf_bts_dict
# Create a df from dict with all scores
pd.DataFrame.from_dict(rf_bts_dict, orient = 'index', columns = 'best_model')
rf_bts_df = pd.DataFrame.from_dict(rf_bts_dict,orient = 'index')
rf_bts_df.columns = ['Logistic_Regression']
print(rf_bts_df)