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swanlab.pr_curve ​

Github Source Code

python
pr_curve(  y_true: Union[List, np.ndarray],  y_pred_proba: Union[List, np.ndarray],  title: Optional[str, bool] = None, ) -> None
ParameterDescription
y_true(Union[List, np.ndarray]) True labels, the actual class labels (0 or 1) in binary classification problems
y_pred_proba(Union[List, np.ndarray]) Prediction probabilities, the model's predicted probability values for the positive class (range 0-1)
title(Optional[str, bool]) Whether to display chart title, defaults to None

Introduction ​

Draw a PR (Precision-Recall) curve to evaluate the performance of binary classification models. The PR curve shows the relationship between precision and recall at different thresholds.

PR curves are particularly suitable for handling imbalanced datasets and can better evaluate model performance on minority classes.

Basic Usage ​

python
from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split import xgboost as xgb import swanlab  # Generate sample data X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)  # Train model model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss') model.fit(X_train, y_train)  # Get prediction probabilities y_pred_proba = model.predict_proba(X_test)[:, 1]  # Initialize SwanLab swanlab.init(project="PR-Curve-Demo", experiment_name="PR-Curve-Example")  # Log PR curve swanlab.log({  "pr_curve": swanlab.pr_curve(y_test, y_pred_proba, title=True) })  swanlab.finish()

Custom Title ​

python
# Don't show title (default) pr_curve = swanlab.pr_curve(y_test, y_pred_proba, title=False) swanlab.log({"pr_curve_no_title": pr_curve})  # Show title pr_curve = swanlab.pr_curve(y_test, y_pred_proba, title=True) swanlab.log({"pr_curve_with_title": pr_curve})  # Custom title pr_curve = swanlab.pr_curve(y_test, y_pred_proba, title="demo") swanlab.log({"pr_curve_with_custom_title": pr_curve})

Notes ​

  1. Data Format: y_true and y_pred_proba can be lists or numpy arrays
  2. Binary Classification: This function is specifically for binary classification problems
  3. Probability Values: y_pred_proba should be the model's predicted probability for the positive class, ranging from 0-1
  4. Dependencies: Requires installation of scikit-learn and pyecharts packages
  5. AUC Calculation: The function automatically calculates the area under the PR curve (AUC), but does not display it in the title by default