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)