QuantileRegressor
Bases: ArrayModel, ABC
Quantile regression model used by SPCI.
Trained on autoregressive residual data: given the last w residuals,
predict the quantile distribution of the next residual. Subclasses can
wrap any quantile-regression library (QRF, LightGBM quantile, neural
quantile networks, etc.).
QuantileRegressor.fit
Parameters
X : NDArray, shape (n_samples, w) Training features. Each row is a window ofw past residuals.
y : NDArray, shape (n_samples,)
Training targets (the next residual after each window).
QuantileRegressor.predict_quantile
q-quantile of the conditional distribution.
Parameters
X : NDArray, shape (n_samples, w) q : float in (0, 1)Returns
NDArray, shape (n_samples,)QuantileRegressor.predict_quantiles
2 * beta_grid_size quantiles per (series, horizon)
cell while searching for the width-minimizing beta. Most quantile
libraries answer a whole list in one traversal of the fitted model, which
is dramatically cheaper than one call per level — for
quantile-forest, 42 separate calls measured 52.2 ms against 1.23 ms
batched. That loop dominated SPCI’s interval() outright: the
measured ~53 ms per cell was essentially all quantile calls.
The default implementation loops over :meth:predict_quantile, so a
custom regressor only has to implement the scalar method. Override this
when the backend can do better.
Parameters
X : NDArray, shape (n_samples, w) qs : NDArray, shape (n_quantiles,) Levels in(0, 1).
Returns
NDArray, shape (n_samples, n_quantiles) Columnj is the qs[j]-quantile.
QRFQuantileRegressor
Bases: QuantileRegressor
Quantile Random Forest regressor (Meinshausen 2006).
Default quantile estimator for SPCI per Xu & Xie (2023). Wraps the
quantile-forest package, exposing a clean two-method interface.
Notes
Thequantile-forest package is an optional dependency. The import is
deferred to :meth:fit (not construction) so that a
:class:QRFQuantileRegressor — including one built as
:class:~conformalforecast.methods.spci.SequentialPredictiveConformalInference’s
default regressor — can be constructed, and its schema generated via
model_json_schema(), without quantile-forest installed. Calling
:meth:fit without it installed raises :class:ImportError.
QRFQuantileRegressor.fit
Parameters
X : NDArray, shape (n_samples, n_features) Training features. Each row is a window of past residuals, optionally concatenated with exogenous features. y : NDArray, shape (n_samples,) The next residual after each window.Raises
ImportError Ifquantile-forest is not installed. Deferred to here (rather
than construction) so this class stays constructible — and its
schema generable — without the [spci] extra.
QRFQuantileRegressor.predict_quantile
q-quantile of the conditional residual distribution.
Parameters
X : NDArray, shape (n_samples, n_features) q : float in (0, 1)Returns
NDArray, shape (n_samples,)Raises
RuntimeError If called before :meth:fit.
QRFQuantileRegressor.predict_quantiles
quantile-forest accepts a list of levels and answers them from one
pass over the leaf distributions, which is why this override exists:
SPCI’s beta search asks for ~42 levels per cell, and one call per level
measured 42x slower on a fitted forest.
Parameters
X : NDArray, shape (n_samples, n_features) qs : NDArray, shape (n_quantiles,)Returns
NDArray, shape (n_samples, n_quantiles)Raises
RuntimeError If called before :meth:fit.