from typing import Dict, List, Optional
import numpy as np
import sklearn.metrics as sklearn_metrics
import pyhealth.metrics.calibration as calib
[docs]def binary_metrics_fn(
y_true: np.ndarray,
y_prob: np.ndarray,
metrics: Optional[List[str]] = None,
threshold: float = 0.5,
) -> Dict[str, float]:
"""Computes metrics for binary classification.
User can specify which metrics to compute by passing a list of metric names.
The accepted metric names are:
- pr_auc: area under the precision-recall curve
- roc_auc: area under the receiver operating characteristic curve
- accuracy: accuracy score
- balanced_accuracy: balanced accuracy score (usually used for imbalanced
datasets)
- f1: f1 score
- precision: precision score
- recall: recall score
- cohen_kappa: Cohen's kappa score
- jaccard: Jaccard similarity coefficient score
- ECE: Expected Calibration Error (with 20 equal-width bins). Check :func:`pyhealth.metrics.calibration.ece_confidence_binary`.
- ECE_adapt: adaptive ECE (with 20 equal-size bins). Check :func:`pyhealth.metrics.calibration.ece_confidence_binary`.
If no metrics are specified, pr_auc, roc_auc and f1 are computed by default.
This function calls sklearn.metrics functions to compute the metrics. For
more information on the metrics, please refer to the documentation of the
corresponding sklearn.metrics functions.
Args:
y_true: True target values of shape (n_samples,).
y_prob: Predicted probabilities of shape (n_samples,).
metrics: List of metrics to compute. Default is ["pr_auc", "roc_auc", "f1"].
threshold: Threshold for binary classification. Default is 0.5.
Returns:
Dictionary of metrics whose keys are the metric names and values are
the metric values.
Examples:
>>> from pyhealth.metrics import binary_metrics_fn
>>> y_true = np.array([0, 0, 1, 1])
>>> y_prob = np.array([0.1, 0.4, 0.35, 0.8])
>>> binary_metrics_fn(y_true, y_prob, metrics=["accuracy"])
{'accuracy': 0.75}
"""
if metrics is None:
metrics = ["pr_auc", "roc_auc", "f1"]
y_pred = y_prob.copy()
y_pred[y_pred >= threshold] = 1
y_pred[y_pred < threshold] = 0
output = {}
for metric in metrics:
if metric == "pr_auc":
pr_auc = sklearn_metrics.average_precision_score(y_true, y_prob)
output["pr_auc"] = pr_auc
elif metric == "roc_auc":
roc_auc = sklearn_metrics.roc_auc_score(y_true, y_prob)
output["roc_auc"] = roc_auc
elif metric == "accuracy":
accuracy = sklearn_metrics.accuracy_score(y_true, y_pred)
output["accuracy"] = accuracy
elif metric == "balanced_accuracy":
balanced_accuracy = sklearn_metrics.balanced_accuracy_score(y_true, y_pred)
output["balanced_accuracy"] = balanced_accuracy
elif metric == "f1":
f1 = sklearn_metrics.f1_score(y_true, y_pred)
output["f1"] = f1
elif metric == "precision":
precision = sklearn_metrics.precision_score(y_true, y_pred)
output["precision"] = precision
elif metric == "recall":
recall = sklearn_metrics.recall_score(y_true, y_pred)
output["recall"] = recall
elif metric == "cohen_kappa":
cohen_kappa = sklearn_metrics.cohen_kappa_score(y_true, y_pred)
output["cohen_kappa"] = cohen_kappa
elif metric == "jaccard":
jaccard = sklearn_metrics.jaccard_score(y_true, y_pred)
output["jaccard"] = jaccard
elif metric in {"ECE", "ECE_adapt"}:
output[metric] = calib.ece_confidence_binary(
y_prob, y_true, bins=20, adaptive=metric.endswith("_adapt")
)
else:
raise ValueError(f"Unknown metric for binary classification: {metric}")
return output
if __name__ == "__main__":
all_metrics = [
"pr_auc",
"roc_auc",
"accuracy",
"balanced_accuracy",
"f1",
"precision",
"recall",
"cohen_kappa",
"jaccard",
]
y_true = np.random.randint(2, size=100000)
y_prob = np.random.random(size=100000)
print(binary_metrics_fn(y_true, y_prob, metrics=all_metrics))