def calculate_overall_stats(predictions, targets, train_set_metadata):
overall_stats = {}
confusion_matrix = ConfusionMatrix(targets,
predictions[PREDICTIONS],
labels=['False', 'True'])
overall_stats['confusion_matrix'] = confusion_matrix.cm.tolist()
overall_stats['overall_stats'] = confusion_matrix.stats()
overall_stats['per_class_stats'] = confusion_matrix.per_class_stats()
fpr, tpr, thresholds = roc_curve(targets, predictions[PROBABILITIES])
overall_stats['roc_curve'] = {
'false_positive_rate': fpr.tolist(),
'true_positive_rate': tpr.tolist()
}
overall_stats['roc_auc_macro'] = roc_auc_score(
targets, predictions[PROBABILITIES], average='macro')
overall_stats['roc_auc_micro'] = roc_auc_score(
targets, predictions[PROBABILITIES], average='micro')
ps, rs, thresholds = precision_recall_curve(targets,
predictions[PROBABILITIES])
overall_stats['precision_recall_curve'] = {
'precisions': ps.tolist(),
'recalls': rs.tolist()
}
overall_stats['average_precision_macro'] = average_precision_score(
targets, predictions[PROBABILITIES], average='macro')
overall_stats['average_precision_micro'] = average_precision_score(
targets, predictions[PROBABILITIES], average='micro')
overall_stats['average_precision_samples'] = average_precision_score(
targets, predictions[PROBABILITIES], average='samples')
return overall_stats
def calculate_overall_stats(predictions, targets, train_set_metadata):
overall_stats = {}
confusion_matrix = ConfusionMatrix(targets,
predictions[PREDICTIONS],
labels=["False", "True"])
overall_stats["confusion_matrix"] = confusion_matrix.cm.tolist()
overall_stats["overall_stats"] = confusion_matrix.stats()
overall_stats["per_class_stats"] = confusion_matrix.per_class_stats()
fpr, tpr, thresholds = roc_curve(targets, predictions[PROBABILITIES])
overall_stats["roc_curve"] = {
"false_positive_rate": fpr.tolist(),
"true_positive_rate": tpr.tolist(),
}
overall_stats["roc_auc_macro"] = roc_auc_score(
targets, predictions[PROBABILITIES], average="macro")
overall_stats["roc_auc_micro"] = roc_auc_score(
targets, predictions[PROBABILITIES], average="micro")
ps, rs, thresholds = precision_recall_curve(targets,
predictions[PROBABILITIES])
overall_stats["precision_recall_curve"] = {
"precisions": ps.tolist(),
"recalls": rs.tolist(),
}
overall_stats["average_precision_macro"] = average_precision_score(
targets, predictions[PROBABILITIES], average="macro")
overall_stats["average_precision_micro"] = average_precision_score(
targets, predictions[PROBABILITIES], average="micro")
overall_stats["average_precision_samples"] = average_precision_score(
targets, predictions[PROBABILITIES], average="samples")
return overall_stats
def calculate_overall_stats(test_stats, output_feature, dataset,
train_set_metadata):
feature_name = output_feature['name']
stats = test_stats[feature_name]
confusion_matrix = ConfusionMatrix(dataset.get(feature_name),
stats[PREDICTIONS],
labels=['False', 'True'])
stats['confusion_matrix'] = confusion_matrix.cm.tolist()
stats['overall_stats'] = confusion_matrix.stats()
stats['per_class_stats'] = confusion_matrix.per_class_stats()
fpr, tpr, thresholds = roc_curve(dataset.get(feature_name),
stats[PROBABILITIES])
stats['roc_curve'] = {
'false_positive_rate': fpr.tolist(),
'true_positive_rate': tpr.tolist()
}
stats['roc_auc_macro'] = roc_auc_score(dataset.get(feature_name),
stats[PROBABILITIES],
average='macro')
stats['roc_auc_micro'] = roc_auc_score(dataset.get(feature_name),
stats[PROBABILITIES],
average='micro')
ps, rs, thresholds = precision_recall_curve(dataset.get(feature_name),
stats[PROBABILITIES])
stats['precision_recall_curve'] = {
'precisions': ps.tolist(),
'recalls': rs.tolist()
}
stats['average_precision_macro'] = average_precision_score(
dataset.get(feature_name), stats[PROBABILITIES], average='macro')
stats['average_precision_micro'] = average_precision_score(
dataset.get(feature_name), stats[PROBABILITIES], average='micro')
stats['average_precision_samples'] = average_precision_score(
dataset.get(feature_name), stats[PROBABILITIES], average='samples')
