preds_df.to_csv(results_folder + "rf_pred.csv")
"""
1. Classification report and summary statistics
"""
# creating classification report
class_report = classification_report(y_test, y_pred_class)
print(class_report)
# write classification report
print(class_report,
file=open(results_folder + "rf_classification_report.txt", "w"))
# define confusion matrix
cm = confusion_matrix(y_test, y_pred_class)
# run accuracy summary on confuxion matrix
dx_summary = dx_accuracy(cm)
print(dx_summary)
# save summary metrics
dx_summary.to_csv(results_folder + "rf_dx_summary.csv")
"""
2. Brier score
"""
brier_score = np.round(brier_score_loss(y_test, y_pred[:, 1]), 3)
print('RF Clinical + Labs Features Benchmark',
'\nBrier Score:',
brier_score,
file=open(results_folder + 'brier_score.txt', 'w'))
"""
3. ROC
"""
preds_df.to_csv(results_folder + "rf_mice_pred.csv")
"""
1. Classification report and summary statistics
"""
# creating classification report
class_report = classification_report(y_test, y_pred_class)
print(class_report)
# write classification report
print(class_report,
file=open(results_folder + "rf_mice_classification_report.txt", "w"))
# define confusion matrix
cm = confusion_matrix(y_test, y_pred_class)
# run accuracy summary on confuxion matrix
dx_summary = dx_accuracy(cm)
print(dx_summary)
# save summary metrics
dx_summary.to_csv(results_folder + "rf_mice_dx_summary.csv")
"""
2. Brier score
"""
brier_score = np.round(brier_score_loss(y_test, y_pred[:, 1]), 3)
print('RF Clinical + MICE Imputed Labs',
'\nBrier Score:',
brier_score,
file=open(results_folder + 'brier_score.txt', 'w'))
"""
3. ROC
"""
