add docstring for metrics (#38)

pyhealth/metrics/binary.py

@@ -1,4 +1,5 @@
 from typing import List, Optional, Dict
+
 import numpy as np
 import sklearn.metrics as sklearn_metrics
 
@@ -9,6 +10,43 @@ def binary_metrics_fn(
  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
+ 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"]
 

pyhealth/metrics/multiclass.py

@@ -9,6 +9,55 @@ def multiclass_metrics_fn(
  y_prob: np.ndarray,
  metrics: Optional[List[str]] = None,
 ) -> Dict[str, float]:
+ """Computes metrics for multiclass classification.
+
+ User can specify which metrics to compute by passing a list of metric names.
+ The accepted metric names are:
+ - roc_auc_macro_ovo: area under the receiver operating characteristic curve,
+ macro averaged over one-vs-one multiclass classification
+ - roc_auc_macro_ovr: area under the receiver operating characteristic curve,
+ macro averaged over one-vs-rest multiclass classification
+ - roc_auc_weighted_ovo: area under the receiver operating characteristic curve,
+ weighted averaged over one-vs-one multiclass classification
+ - roc_auc_weighted_ovr: area under the receiver operating characteristic curve,
+ weighted averaged over one-vs-rest multiclass classification
+ - accuracy: accuracy score
+ - balanced_accuracy: balanced accuracy score (usually used for imbalanced
+ datasets)
+ - f1_micro: f1 score, micro averaged
+ - f1_macro: f1 score, macro averaged
+ - f1_weighted: f1 score, weighted averaged
+ - jaccard_micro: Jaccard similarity coefficient score, micro averaged
+ - jaccard_macro: Jaccard similarity coefficient score, macro averaged
+ - jaccard_weighted: Jaccard similarity coefficient score, weighted averaged
+ - cohen_kappa: Cohen's kappa score
+ If no metrics are specified, accuracy, f1_macro, and f1_micro 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, n_classes).
+ metrics: List of metrics to compute. Default is ["accuracy", "f1_macro",
+ "f1_micro"].
+
+ Returns:
+ Dictionary of metrics whose keys are the metric names and values are
+ the metric values.
+
+ Examples:
+ >>> from pyhealth.metrics import multiclass_metrics_fn
+ >>> y_true = np.array([0, 1, 2, 2])
+ >>> y_prob = np.array([[0.9, 0.05, 0.05],
+ ... [0.05, 0.9, 0.05],
+ ... [0.05, 0.05, 0.9],
+ ... [0.6, 0.2, 0.2]])
+ >>> multiclass_metrics_fn(y_true, y_prob, metrics=["accuracy"])
+ {'accuracy': 0.75}
+ """
  if metrics is None:
  metrics = ["accuracy", "f1_macro", "f1_micro"]
 

pyhealth/metrics/multilabel.py

@@ -10,6 +10,63 @@ def multilabel_metrics_fn(
  metrics: Optional[List[str]] = None,
  threshold: float = 0.5,
 ) -> Dict[str, float]:
+ """Computes metrics for multilabel classification.
+
+ User can specify which metrics to compute by passing a list of metric names.
+ The accepted metric names are:
+ - roc_auc_micro: area under the receiver operating characteristic curve,
+ micro averaged
+ - roc_auc_macro: area under the receiver operating characteristic curve,
+ macro averaged
+ - roc_auc_weighted: area under the receiver operating characteristic curve,
+ weighted averaged
+ - roc_auc_samples: area under the receiver operating characteristic curve,
+ samples averaged
+ - pr_auc_micro: area under the precision recall curve, micro averaged
+ - pr_auc_macro: area under the precision recall curve, macro averaged
+ - pr_auc_weighted: area under the precision recall curve, weighted averaged
+ - pr_auc_samples: area under the precision recall curve, samples averaged
+ - accuracy: accuracy score
+ - f1_micro: f1 score, micro averaged
+ - f1_macro: f1 score, macro averaged
+ - f1_weighted: f1 score, weighted averaged
+ - f1_samples: f1 score, samples averaged
+ - precision_micro: precision score, micro averaged
+ - precision_macro: precision score, macro averaged
+ - precision_weighted: precision score, weighted averaged
+ - precision_samples: precision score, samples averaged
+ - recall_micro: recall score, micro averaged
+ - recall_macro: recall score, macro averaged
+ - recall_weighted: recall score, weighted averaged
+ - recall_samples: recall score, samples averaged
+ - jaccard_micro: Jaccard similarity coefficient score, micro averaged
+ - jaccard_macro: Jaccard similarity coefficient score, macro averaged
+ - jaccard_weighted: Jaccard similarity coefficient score, weighted averaged
+ - jaccard_samples: Jaccard similarity coefficient score, samples averaged
+ - hamming_loss: Hamming loss
+ If no metrics are specified, pr_auc_samples is 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, n_labels).
+ y_prob: Predicted probabilities of shape (n_samples, n_labels).
+ metrics: List of metrics to compute. Default is ["pr_auc_samples"].
+ threshold: Threshold to binarize the predicted probabilities. 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 multilabel_metrics_fn
+ >>> y_true = np.array([[0, 1, 1], [1, 0, 1]])
+ >>> y_prob = np.array([[0.1, 0.9, 0.8], [0.05, 0.95, 0.6]])
+ >>> multilabel_metrics_fn(y_true, y_prob, metrics=["accuracy"])
+ {'accuracy': 0.5}
+ """
  if metrics is None:
  metrics = ["pr_auc_samples"]