def fit(self, y_prob: np.ndarray, y_true: np.ndarray):
"""
Learns the bin boundaries and calibration score for each bin.
Parameters
----------
y_prob : 1d ndarray
Raw probability/score of the positive class.
y_true : 1d ndarray
Binary true targets.
Returns
-------
self
"""
binned_y_true, binned_y_prob = create_binned_data(y_true, y_prob, self.n_bins)
self.bins_ = get_bin_boundaries(binned_y_prob)
self.bins_score_ = np.array([np.mean(value) for value in binned_y_true])
return self
def fit(self, y_prob: np.ndarray, y_true: np.ndarray):
"""
Learns the logistic regression weights and the
bin boundaries and calibration score for each bin.
Parameters
----------
y_prob : 1d ndarray
Raw probability/score of the positive class.
y_true : 1d ndarray
Binary true targets.
Returns
-------
self
"""
y_prob_platt = super().fit_predict(y_prob, y_true)
binned_y_true, binned_y_prob = create_binned_data(y_true, y_prob_platt, self.n_bins)
self.bins_ = get_bin_boundaries(binned_y_prob)
self.bins_score_ = np.array([np.mean(value) for value in binned_y_prob])
return self