def test_bin_with_size_zero(): # Data p_cal = .5 * np.ones([100, 2]) y_cal = np.hstack([np.ones([50]), np.zeros([50])]) # Fit calibration model bbq = calm.BayesianBinningQuantiles() bbq.fit(X=p_cal, y=y_cal) # Predict p_pred = bbq.predict_proba(X=p_cal) # Check for NaNs assert not np.any(np.isnan(p_pred)), "Calibrated probabilities are NaN."
random_state=random_state), "GPcalib_approx": calm.GPCalibration(n_classes=2, maxiter=1000, n_inducing_points=10, logits=False, random_state=random_state, inf_mean_approx=True), "Platt": calm.PlattScaling(random_state=random_state), "Isotonic": calm.IsotonicRegression(), "Beta": calm.BetaCalibration(), "BBQ": calm.BayesianBinningQuantiles(), "Temp": calm.TemperatureScaling() } # Create benchmark object kitti_benchmark = pycalib.benchmark.KITTIBinaryData( run_dir=run_dir, clf_output_dir=clf_output_dir, classifier_names=classifier_names, cal_methods=list(cal_methods.values()), cal_method_names=list(cal_methods.keys()), n_splits=10, test_size=8000, train_size=1000, random_state=random_state)