def fit(self, X, y):
"""Fit one regressor for each quantile.
Parameters
----------
* `X` [array-like, shape=(n_samples, n_features)]:
Training vectors, where `n_samples` is the number of samples
and `n_features` is the number of features.
* `y` [array-like, shape=(n_samples,)]:
Target values (real numbers in regression)
"""
rng = check_random_state(self.random_state)
if self.base_estimator is None:
base_estimator = GradientBoostingRegressor(loss='quantile')
else:
base_estimator = self.base_estimator
if not isinstance(base_estimator, GradientBoostingRegressor):
raise ValueError('base_estimator has to be of type'
' GradientBoostingRegressor.')
if not base_estimator.loss == 'quantile':
raise ValueError('base_estimator has to use quantile'
' loss not %s' % base_estimator.loss)
# The predictions for different quantiles should be sorted.
# Therefore each of the regressors need the same seed.
base_estimator.set_params(random_state=rng)
regressors = []
for q in self.quantiles:
regressor = clone(base_estimator)
regressor.set_params(alpha=q)
regressors.append(regressor)
self.regressors_ = Parallel(n_jobs=self.n_jobs, backend='threading')(
delayed(_parallel_fit)(regressor, X, y)
for regressor in regressors)
return self
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