def detailed_analysis(self): print_to_consol( 'Making a confusion matrix for test set classification outcomes') matrix_stats = confusion_matrix_and_stats(self.y_test, self.y_pred, 'before_cal', self.directory) logging.info(f'Detailed analysis of confusion matrix for test set. \n' f'True positives: {matrix_stats["TP"]} \n' f'True negatives: {matrix_stats["TN"]} \n' f'False positives: {matrix_stats["FP"]} \n' f'False negatives: {matrix_stats["FN"]} \n' f'Classification accuracy: {matrix_stats["acc"]} \n' f'Classification error: {matrix_stats["err"]} \n' f'Sensitivity: {matrix_stats["sensitivity"]} \n' f'Specificity: {matrix_stats["specificity"]} \n' f'False positive rate: {matrix_stats["FP-rate"]} \n' f'False negative rate: {matrix_stats["FN-rate"]} \n' f'Precision: {matrix_stats["precision"]} \n' f'F1-score: {matrix_stats["F1-score"]} \n') print_to_consol( 'Plotting precision recall curve for test set class 1 probabilities' ) logging.info( f'Plotting precision recall curve for class 1 in test set probabilities. \n' ) plot_precision_recall_vs_threshold(self.y_test, self.y_pred_proba_ones, self.directory) print_to_consol( 'Plotting ROC curve ad calculating AUC for test set class 1 probabilities' ) logging.info( f'Plotting ROC curve for class 1 in test set probabilities. \n') self.fpr, self.tpr, self.thresholds = plot_roc_curve( self.y_test, self.y_pred_proba_ones, self.directory) AUC = round( roc_auc_score(self.y_test, self.y_pred_proba_ones) * 100, 2) logging.info( f'Calculating AUC for ROC curve for class 1 in test set probabilities: {AUC} \n' ) print_to_consol('Make a radar plot for performance metrics') radar_dict = { 'Classification accuracy': matrix_stats["acc"], 'Classification error': matrix_stats["err"], 'Sensitivity': matrix_stats["sensitivity"], 'Specificity': matrix_stats["specificity"], 'False positive rate': matrix_stats["FP-rate"], 'False negative rate': matrix_stats["FN-rate"], 'Precision': matrix_stats["precision"], 'F1-score': matrix_stats["F1-score"], 'ROC AUC': AUC } plot_radar_chart(radar_dict, self.directory) print('*' * 80) print( '* Exploring probability thresholds, sensitivity, specificity for class 1 ' ) print('*' * 80) threshold_dict = evaluate_threshold(self.tpr, self.fpr, self.thresholds) logging.info( f'Exploring different probability thresholds and sensitivity-specificity trade-offs. \n' f'Threshold 0.2: {threshold_dict["0.2"]} \n' f'Threshold 0.3: {threshold_dict["0.3"]} \n' f'Threshold 0.4: {threshold_dict["0.4"]} \n' f'Threshold 0.5: {threshold_dict["0.5"]} \n' f'Threshold 0.6: {threshold_dict["0.6"]} \n' f'Threshold 0.7: {threshold_dict["0.7"]} \n' f'Threshold 0.8: {threshold_dict["0.8"]} \n' f'Threshold 0.9: {threshold_dict["0.9"]} \n') print_to_consol( 'Calibrating classifier and writing to disk; getting new accuracy') self.calibrated_clf, clf_acc = calibrate_classifier( self.model, self.X_cal_scaled, self.y_cal) date = datetime.strftime(datetime.now(), '%Y%m%d_%H%M') joblib.dump( self.calibrated_clf, os.path.join(self.directory, 'best_calibrated_predictor_' + date + '.pkl')) logging.info( f'Calibrated the best classifier with X_cal and y_cal and new accuracy {clf_acc}\n' f'Writing file to disk disk in {self.directory} \n') print_to_consol( 'Getting 95% confidence interval for calibrated classifier') alpha, upper, lower = get_confidence_interval( self.X_train_scaled, self.y_train, self.X_test_scaled, self.y_test, self.calibrated_clf, self.directory, self.bootiter, 'calibrated') logging.info(f'{alpha}% confidence interval {upper}% and {lower}% \n' f'for calibrated classifier. \n') print_to_consol('Running prediction for calibrated classifier') print_to_consol( 'Getting class predictions and probabilities for test set with calibrated classifier' ) test_stats_cal, self.y_pred_cal, self.y_pred_proba_cal = testing_predict_stats( self.calibrated_clf, self.X_test_scaled, self.y_test) logging.info( f'Predicting on the test set with calibrated classifier. \n' f'Storing classes for calibrated classifier in y_pred and probabilities in y_pred_proba. \n' ) print_to_consol( 'Calculate prediction stats for y_pred and y_pred_proba of test set with calibrated classifier' ) logging.info( f'Basic stats on the test set woth calibrated classifier. \n' f'Prediction accuracy on the test set: {test_stats_cal["predict_acc"]} \n' f'Class distributio in the test set: {test_stats_cal["class_distribution"]} \n' f'Matthews Correlation Coefficient: {test_stats_cal["mcc"]} \n' f'Average number of class 1 samples: {test_stats_cal["class_one"]} \n' f'Average number of class 0 samples: {test_stats_cal["class_zero"]} \n' f'Null accuracy: {test_stats_cal["null_acc"]} \n') print_to_consol( 'Plotting histogram for class 1 prediction probabilities for test set' ) #store the predicted probabilities for class 1 of test set self.y_pred_proba_cal_ones = self.y_pred_proba_cal[:, 1] plot_hist_pred_proba(self.y_pred_proba_cal_ones, self.directory) logging.info( f'Plotting prediction probabilities for class 1 in test set in histogram for calibrated classifier. \n' ) print_to_consol( 'Making a confusion matrix for test set classification outcomes with calibrated classifier' ) matrix_stats_cal = confusion_matrix_and_stats(self.y_test, self.y_pred_cal, 'after_cal', self.directory) logging.info( f'Detailed analysis of confusion matrix for test set with calibrated classifier. \n' f'True positives: {matrix_stats_cal["TP"]} \n' f'True negatives: {matrix_stats_cal["TN"]} \n' f'False positives: {matrix_stats_cal["FP"]} \n' f'False negatives: {matrix_stats_cal["FN"]} \n' f'Classification accuracy: {matrix_stats_cal["acc"]} \n' f'Classification error: {matrix_stats_cal["err"]} \n' f'Sensitivity: {matrix_stats_cal["sensitivity"]} \n' f'Specificity: {matrix_stats_cal["specificity"]} \n' f'False positive rate: {matrix_stats_cal["FP-rate"]} \n' f'False negative rate: {matrix_stats_cal["FN-rate"]} \n' f'Precision: {matrix_stats_cal["precision"]} \n' f'F1-score: {matrix_stats_cal["F1-score"]} \n') print_to_consol( 'Plotting precision recall curve for test set class 1 probabilities with calibrated classifier' ) logging.info( f'Plotting precision recall curve for class 1 in test set probabilities with calibrated classifier. \n' ) plot_precision_recall_vs_threshold(self.y_test, self.y_pred_proba_cal_ones, self.directory) print_to_consol( 'Plotting ROC curve ad calculating AUC for test set class 1 probabilities with calibrated classifier' ) logging.info( f'Plotting ROC curve for class 1 in test set probabilities with calibrated classifier. \n' ) self.fpr_cal, self.tpr_cal, self.thresholds_cal = plot_roc_curve( self.y_test, self.y_pred_proba_cal_ones, self.directory) AUC_cal = round( roc_auc_score(self.y_test, self.y_pred_proba_cal_ones) * 100, 2) logging.info( f'Calculating AUC for ROC curve for class 1 in test set probabilities with calibrated classifier: {AUC_cal} \n' ) print_to_consol( 'Make a radar plot for performance metrics with calibrated classifier' ) radar_dict_cal = { 'Classification accuracy': matrix_stats_cal["acc"], 'Classification error': matrix_stats_cal["err"], 'Sensitivity': matrix_stats_cal["sensitivity"], 'Specificity': matrix_stats_cal["specificity"], 'False positive rate': matrix_stats_cal["FP-rate"], 'False negative rate': matrix_stats_cal["FN-rate"], 'Precision': matrix_stats_cal["precision"], 'F1-score': matrix_stats_cal["F1-score"], 'ROC AUC': AUC_cal } plot_radar_chart(radar_dict_cal, self.directory) print_to_consol( 'Exploring probability thresholds, sensitivity, specificity for class 1 with calibrated classifier' ) threshold_dict_cal = evaluate_threshold(self.tpr_cal, self.fpr_cal, self.thresholds_cal) logging.info( f'Exploring different probability thresholds and sensitivity-specificity trade-offs \n' f'for calibrated classifier. \n' f'Threshold 0.2: {threshold_dict_cal["0.2"]} \n' f'Threshold 0.3: {threshold_dict_cal["0.3"]} \n' f'Threshold 0.4: {threshold_dict_cal["0.4"]} \n' f'Threshold 0.5: {threshold_dict_cal["0.5"]} \n' f'Threshold 0.6: {threshold_dict_cal["0.6"]} \n' f'Threshold 0.7: {threshold_dict_cal["0.7"]} \n' f'Threshold 0.8: {threshold_dict_cal["0.8"]} \n' f'Threshold 0.9: {threshold_dict_cal["0.9"]} \n') end = datetime.now() duration = end - self.start logging.info(f'Training lasted for {duration} minutes \n') logging.info(f'Training completed \n') print_to_consol('Training completed')
def detailed_analysis(self): print_to_consol( 'Making a confusion matrix for test set classification outcomes') matrix_stats = confusion_matrix_and_stats(self.y_test, self.y_pred, self.directory) logging.info(f'Detailed analysis of confusion matrix for test set. \n' f'True positives: {matrix_stats["TP"]} \n' f'True negatives: {matrix_stats["TN"]} \n' f'False positives: {matrix_stats["FP"]} \n' f'False negatives: {matrix_stats["FN"]} \n' f'Classification accuracy: {matrix_stats["acc"]} \n' f'Classification error: {matrix_stats["err"]} \n' f'Sensitivity: {matrix_stats["sensitivity"]} \n' f'Specificity: {matrix_stats["specificity"]} \n' f'False positive rate: {matrix_stats["FP-rate"]} \n' f'False negative rate: {matrix_stats["FN-rate"]} \n' f'Precision: {matrix_stats["precision"]} \n' f'F1-score: {matrix_stats["F1-score"]} \n') print_to_consol( 'Plotting precision recall curve for test set class 1 probabilities' ) logging.info( f'Plotting precision recall curve for class 1 in test set probabilities. \n' ) plot_precision_recall_vs_threshold(self.y_test, self.y_pred_proba_ones, self.directory) print_to_consol( 'Plotting ROC curve ad calculating AUC for test set class 1 probabilities' ) logging.info( f'Plotting ROC curve for class 1 in test set probabilities. \n') self.fpr, self.tpr, self.thresholds = plot_roc_curve( self.y_test, self.y_pred_proba_ones, self.directory) AUC = round( roc_auc_score(self.y_test, self.y_pred_proba_ones) * 100, 2) logging.info( f'Calculating AUC for ROC curve for class 1 in test set probabilities: {AUC} \n' ) print_to_consol('Make a radar plot for performance metrics') radar_dict = { 'Classification accuracy': matrix_stats["acc"], 'Classification error': matrix_stats["err"], 'Sensitivity': matrix_stats["sensitivity"], 'Specificity': matrix_stats["specificity"], 'False positive rate': matrix_stats["FP-rate"], 'False negative rate': matrix_stats["FN-rate"], 'Precision': matrix_stats["precision"], 'F1-score': matrix_stats["F1-score"], 'ROC AUC': AUC } plot_radar_chart(radar_dict, self.directory) print_to_consol( 'Exploring probability thresholds, sensitivity, specificity for class 1' ) threshold_dict = evaluate_threshold(self.tpr, self.fpr, self.thresholds) logging.info( f'Exploring different probability thresholds and sensitivity-specificity trade-offs. \n' f'Threshold 0.2: {threshold_dict["0.2"]} \n' f'Threshold 0.3: {threshold_dict["0.3"]} \n' f'Threshold 0.4: {threshold_dict["0.4"]} \n' f'Threshold 0.5: {threshold_dict["0.5"]} \n' f'Threshold 0.6: {threshold_dict["0.6"]} \n' f'Threshold 0.7: {threshold_dict["0.7"]} \n' f'Threshold 0.8: {threshold_dict["0.8"]} \n' f'Threshold 0.9: {threshold_dict["0.9"]} \n') end = datetime.now() duration = end - self.start logging.info( f'Prediction and analysis lasted for {duration} minutes \n') logging.info(f'Prediction and analysis completed \n') print_to_consol('Prediction and analysis completed')
def detailed_analysis(self): print('*' * 80) print( '* Making a confusion matrix for test set classification outcomes' ) print('*' * 80) matrix_stats = confusion_matrix_and_stats(self.y_test, self.y_pred, self.directory) logging.info(f'Detailed analysis of confusion matrix for test set. \n' f'True positives: {matrix_stats["TP"]} \n' f'True negatives: {matrix_stats["TN"]} \n' f'False positives: {matrix_stats["FP"]} \n' f'False negatives: {matrix_stats["FN"]} \n' f'Classification accuracy: {matrix_stats["acc"]} \n' f'Classification error: {matrix_stats["err"]} \n' f'Sensitivity: {matrix_stats["sensitivity"]} \n' f'Specificity: {matrix_stats["specificity"]} \n' f'False positive rate: {matrix_stats["FP-rate"]} \n' f'False negative rate: {matrix_stats["FN-rate"]} \n' f'Precision: {matrix_stats["precision"]} \n' f'F1-score: {matrix_stats["F1-score"]} \n') print('*' * 80) print( '* Plotting precision recall curve for test set class 1 probabilities' ) print('*' * 80) logging.info( f'Plotting precision recall curve for class 1 in test set probabilities. \n' ) plot_precision_recall_vs_threshold(self.y_test, self.y_pred_proba_ones, self.directory) print('*' * 80) print( '* Plotting ROC curve ad calculating AUC for test set class 1 probabilities' ) print('*' * 80) logging.info( f'Plotting ROC curve for class 1 in test set probabilities. \n') self.fpr, self.tpr, self.thresholds = plot_roc_curve( self.y_test, self.y_pred_proba_ones, self.directory) AUC = round( roc_auc_score(self.y_test, self.y_pred_proba_ones) * 100, 2) logging.info( f'Calculating AUC for ROC curve for class 1 in test set probabilities: {AUC} \n' ) print('*' * 80) print('* Make a radar plot for performance metrics') print('*' * 80) radar_dict = { 'Classification accuracy': matrix_stats["acc"], 'Classification error': matrix_stats["err"], 'Sensitivity': matrix_stats["sensitivity"], 'Specificity': matrix_stats["specificity"], 'False positive rate': matrix_stats["FP-rate"], 'False negative rate': matrix_stats["FN-rate"], 'Precision': matrix_stats["precision"], 'F1-score': matrix_stats["F1-score"], 'ROC AUC': AUC } plot_radar_chart(radar_dict, self.directory) print('*' * 80) print( '* Exploring probability thresholds, sensitivity, specificity for class 1 ' ) print('*' * 80) threshold_dict = evaluate_threshold(self.tpr, self.fpr, self.thresholds) logging.info( f'Exploring different probability thresholds and sensitivity-specificity trade-offs. \n' f'Threshold 0.2: {threshold_dict["0.2"]} \n' f'Threshold 0.3: {threshold_dict["0.3"]} \n' f'Threshold 0.4: {threshold_dict["0.4"]} \n' f'Threshold 0.5: {threshold_dict["0.5"]} \n' f'Threshold 0.6: {threshold_dict["0.6"]} \n' f'Threshold 0.7: {threshold_dict["0.7"]} \n' f'Threshold 0.8: {threshold_dict["0.8"]} \n' f'Threshold 0.9: {threshold_dict["0.9"]} \n') print('*' * 80) print( '* Calibrating classifier and writing to disk; getting new accuracy' ) print('*' * 80) self.calibrated_clf, clf_acc = calibrate_classifier( self.model, self.X_cal, self.y_cal) date = datetime.strftime(datetime.now(), '%Y%m%d_%H%M') joblib.dump( self.calibrated_clf, os.path.join(self.directory, 'best_calibrated_predictor_' + date + '.pkl')) logging.info( f'Calibrated the best classifier with X_cal and y_cal and new accuracy {clf_acc}\n' f'Writing file to disk disk in {self.directory} \n') end = datetime.now() duration = end - self.start logging.info(f'Training lasted for {duration} minutes \n') logging.info(f'Training completed \n') print('*' * 80) print('* Training completed') print('*' * 80)