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=train_set_metadata["idx2str"])
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()
return overall_stats
def calculate_overall_stats(predictions, targets, train_set_metadata):
overall_stats = {}
sequences = targets
last_elem_sequence = sequences[np.arange(sequences.shape[0]), (sequences != 0).cumsum(1).argmax(1)]
confusion_matrix = ConfusionMatrix(
last_elem_sequence, predictions[LAST_PREDICTIONS], labels=train_set_metadata["idx2str"]
)
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()
return overall_stats