def get_scores(y_true,y_pred):
brier_score = brier_score_loss(y_true,y_pred)
log_score = log_loss(y_true,y_pred)
roc_score = roc_auc_score(y_true, y_pred)
pr_score = average_precision_score(y_true,y_pred)
r2score = r2_score(y_true,y_pred)
return math.sqrt(brier_score),log_score,roc_score,pr_score,r2score
def get_scores(shots):
y_true = [shot.result for shot in shots]
y_pred = [shot.pred for shot in shots]
brier_score = brier_score_loss(y_true,y_pred)
log_score = log_loss(y_true,y_pred)
roc_score = roc_auc_score(y_true, y_pred)
pr_score = average_precision_score(y_true,y_pred)
r2score = r2_score(y_true,y_pred)
return math.sqrt(brier_score),log_score,roc_score,pr_score,r2score
def get_classification_metrics(ground_truth_labels, predicted_labels):
classification_metric_dict = dict({})
classification_metric_dict['accuracy'] = accuracy_score(
ground_truth_labels, predicted_labels)
classification_metric_dict['precision'] = precision_score(
ground_truth_labels, predicted_labels, average='weighted')
classification_metric_dict['recall'] = recall_score(ground_truth_labels,
predicted_labels,
average='weighted')
classification_metric_dict['f1_score'] = f1_score(ground_truth_labels,
predicted_labels,
average='weighted')
classification_metric_dict['brier_score_loss'] = brier_score_loss(
ground_truth_labels, predicted_labels)
classification_metric_dict['matthews_corr_coef'] = matthews_corrcoef(
ground_truth_labels, predicted_labels)
classification_metric_dict['jaccard_score'] = jaccard_score(
ground_truth_labels, predicted_labels, average='weighted')
classification_metric_dict['cohen_kappa_score'] = cohen_kappa_score(
ground_truth_labels, predicted_labels)
return classification_metric_dict
