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REFACTOR: restucture the MapieQuantileRegressor Fit - Split the fit i…
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…nto prefit_estimators, fit_estimators and conformalize (#566)

* REFACTOR: restucture the MapieQuantileRegressor Fit - Split the fit into prefit_estimators, fit_estimators and conformalize

Co-authored-by: qroa <[email protected]>
Co-authored-by: Valentin Laurent <[email protected]>
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@jawadhussein462 @qroa @Valentin-Laurent
3 people authored Dec 16, 2024
1 parent e47171c commit c39946f
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Showing 2 changed files with 130 additions and 73 deletions.
2 changes: 1 addition & 1 deletion HISTORY.rst
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Expand Up @@ -13,7 +13,7 @@ History
* Fix issue 548 to correct labels generated in tutorial
* Fix issue 547 to fix wrong warning
* Fix issue 480 (correct display of mathematical equations in generated notebooks)
* Refactor MapieRegressor and EnsembleRegressor, deprecate EnsembleRegressor.fit
* Refactor MapieRegressor, EnsembleRegressor, and MapieQuantileRegressor, to prepare for the release of v1.0.0

0.9.1 (2024-09-13)
------------------
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201 changes: 129 additions & 72 deletions mapie/regression/quantile_regression.py
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@@ -1,7 +1,7 @@
from __future__ import annotations

import warnings
from typing import Iterable, List, Optional, Tuple, Union, cast
from typing import Iterable, Dict, List, Optional, Tuple, Union, cast

import numpy as np
from sklearn.base import RegressorMixin, clone
Expand Down Expand Up @@ -546,93 +546,150 @@ def fit(
MapieQuantileRegressor
The model itself.
"""
self.cv = self._check_cv(cast(str, self.cv))

# Initialization
self.estimators_: List[RegressorMixin] = []
if self.cv == "prefit":
estimator = cast(List, self.estimator)
alpha = self._check_alpha(self.alpha)
self._check_prefit_params(estimator)
X_calib, y_calib = indexable(X, y)
self.init_fit()

self.n_calib_samples = _num_samples(y_calib)
y_calib_preds = np.full(
shape=(3, self.n_calib_samples),
fill_value=np.nan
)
for i, est in enumerate(estimator):
self.estimators_.append(est)
y_calib_preds[i] = est.predict(X_calib).ravel()
self.single_estimator_ = self.estimators_[2]
if self.cv == "prefit":
X_calib, y_calib = self.prefit_estimators(X, y)
else:
# Checks
self._check_parameters()
checked_estimator = self._check_estimator(self.estimator)
alpha = self._check_alpha(self.alpha)
X, y = indexable(X, y)
random_state = check_random_state(random_state)
results = self._check_calib_set(
X,
y,
sample_weight,
X_calib,
y_calib,
calib_size,
random_state,
shuffle,
stratify,
X_calib, y_calib = self.fit_estimators(
X=X,
y=y,
sample_weight=sample_weight,
groups=groups,
X_calib=X_calib,
y_calib=y_calib,
calib_size=calib_size,
random_state=random_state,
shuffle=shuffle,
stratify=stratify,
**fit_params,
)
X_train, y_train, X_calib, y_calib, sample_weight_train = results
X_train, y_train = indexable(X_train, y_train)
X_calib, y_calib = indexable(X_calib, y_calib)
y_train, y_calib = _check_y(y_train), _check_y(y_calib)
self.n_calib_samples = _num_samples(y_calib)
check_alpha_and_n_samples(self.alpha, self.n_calib_samples)
sample_weight_train, X_train, y_train = check_null_weight(
sample_weight_train,

self.conformalize(X_calib, y_calib)

return self

def init_fit(self):

self.cv = self._check_cv(cast(str, self.cv))
self.alpha_np = self._check_alpha(self.alpha)
self.estimators_: List[RegressorMixin] = []

def prefit_estimators(
self,
X: ArrayLike,
y: ArrayLike
) -> Tuple[ArrayLike, ArrayLike]:

estimator = cast(List, self.estimator)
self._check_prefit_params(estimator)
self.estimators_ = list(estimator)
self.single_estimator_ = self.estimators_[2]

X_calib, y_calib = indexable(X, y)
return X_calib, y_calib

def fit_estimators(
self,
X: ArrayLike,
y: ArrayLike,
sample_weight: Optional[ArrayLike] = None,
groups: Optional[ArrayLike] = None,
X_calib: Optional[ArrayLike] = None,
y_calib: Optional[ArrayLike] = None,
calib_size: Optional[float] = 0.3,
random_state: Optional[Union[int, np.random.RandomState]] = None,
shuffle: Optional[bool] = True,
stratify: Optional[ArrayLike] = None,
**fit_params,
) -> Tuple[ArrayLike, ArrayLike]:

self._check_parameters()
checked_estimator = self._check_estimator(self.estimator)
random_state = check_random_state(random_state)
X, y = indexable(X, y)

results = self._check_calib_set(
X,
y,
sample_weight,
X_calib,
y_calib,
calib_size,
random_state,
shuffle,
stratify,
)

X_train, y_train, X_calib, y_calib, sample_weight_train = results
X_train, y_train = indexable(X_train, y_train)
X_calib, y_calib = indexable(X_calib, y_calib)
y_train, y_calib = _check_y(y_train), _check_y(y_calib)
self.n_calib_samples = _num_samples(y_calib)
check_alpha_and_n_samples(self.alpha, self.n_calib_samples)
sample_weight_train, X_train, y_train = check_null_weight(
sample_weight_train,
X_train,
y_train
)
y_train = cast(NDArray, y_train)

if isinstance(checked_estimator, Pipeline):
estimator = checked_estimator[-1]
else:
estimator = checked_estimator
name_estimator = estimator.__class__.__name__
alpha_name = self.quantile_estimator_params[
name_estimator
]["alpha_name"]
for i, alpha_ in enumerate(self.alpha_np):
cloned_estimator_ = clone(checked_estimator)
params = {alpha_name: alpha_}
if isinstance(checked_estimator, Pipeline):
cloned_estimator_[-1].set_params(**params)
else:
cloned_estimator_.set_params(**params)
self.estimators_.append(fit_estimator(
cloned_estimator_,
X_train,
y_train
y_train,
sample_weight_train,
**fit_params,
)
)
y_train = cast(NDArray, y_train)
self.single_estimator_ = self.estimators_[2]

y_calib_preds = np.full(
X_calib = cast(ArrayLike, X_calib)
y_calib = cast(ArrayLike, y_calib)

return X_calib, y_calib

def conformalize(
self,
X_conf: ArrayLike,
y_conf: ArrayLike,
sample_weight: Optional[ArrayLike] = None,
predict_params: Dict = {},
):

self.n_calib_samples = _num_samples(y_conf)

y_calib_preds = np.full(
shape=(3, self.n_calib_samples),
fill_value=np.nan
)

if isinstance(checked_estimator, Pipeline):
estimator = checked_estimator[-1]
else:
estimator = checked_estimator
name_estimator = estimator.__class__.__name__
alpha_name = self.quantile_estimator_params[
name_estimator
]["alpha_name"]
for i, alpha_ in enumerate(alpha):
cloned_estimator_ = clone(checked_estimator)
params = {alpha_name: alpha_}
if isinstance(checked_estimator, Pipeline):
cloned_estimator_[-1].set_params(**params)
else:
cloned_estimator_.set_params(**params)
self.estimators_.append(fit_estimator(
cloned_estimator_,
X_train,
y_train,
sample_weight_train,
**fit_params,
)
)
y_calib_preds[i] = self.estimators_[-1].predict(X_calib)
self.single_estimator_ = self.estimators_[2]
for i, est in enumerate(self.estimators_):
y_calib_preds[i] = est.predict(X_conf, **predict_params).ravel()

self.conformity_scores_ = np.full(
shape=(3, self.n_calib_samples),
fill_value=np.nan
)
self.conformity_scores_[0] = y_calib_preds[0] - y_calib
self.conformity_scores_[1] = y_calib - y_calib_preds[1]

self.conformity_scores_[0] = y_calib_preds[0] - y_conf
self.conformity_scores_[1] = y_conf - y_calib_preds[1]
self.conformity_scores_[2] = np.max(
[
self.conformity_scores_[0],
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