Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

FIX: V1 correct doctrings examples #581

Merged
merged 1 commit into from
Dec 29, 2024
Merged

FIX: V1 correct doctrings examples #581

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions Makefile
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@ v1-type-check:

tests:
pytest -vs --doctest-modules mapie
pytest -vs --doctest-modules mapie_v1 --ignore=mapie_v1/integration_tests

integration-tests-v1:
@pip install git+https://github.com/scikit-learn-contrib/MAPIE@master --no-dependencies --target=./mapie_v1/integration_tests/$(mapie_v0_folder_name) >/dev/null 2>&1
Expand Down
125 changes: 95 additions & 30 deletions mapie_v1/regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@ class SplitConformalRegressor:
The conformity score method used to calculate the conformity error.
Valid options: see keys and values of the dictionnary
:py:const:`mapie_v1.conformity_scores.REGRESSION_CONFORMITY_SCORES_STRING_MAP`.
See :doc:`theoretical_description_conformity_scores`
See :ref:`theoretical_description_conformity_scores`

A custom score function inheriting from BaseRegressionScore may also
be provided.
Expand Down Expand Up @@ -74,11 +74,29 @@ class SplitConformalRegressor:

Examples
--------
>>> regressor = SplitConformalRegressor(estimator=LinearRegression(),
confidence_level=0.95)
>>> regressor.fit(X_train, y_train)
>>> regressor.conformalize(X_conf, y_conf)
>>> intervals = regressor.predict_set(X_test)
>>> from mapie_v1.regression import SplitConformalRegressor
>>> from sklearn.linear_model import Ridge
>>> from sklearn.datasets import make_regression

>>> X, y = make_regression(n_samples=500, n_features=2, noise=1.0)
>>> X_train, X_test_initial, y_train, y_test_initial = train_test_split(
... X,
... y,
... test_size=0.2,
... )
>>> X_conf, X_test, y_conf, y_test = train_test_split(
... X_test_initial,
... y_test_initial,
... test_size=0.5,
... )

>>> mapie_regressor = SplitConformalRegressor(
... estimator=Ridge(),
... confidence_level=0.95
... ).fit(X_train, y_train).conformalize(X_conf, y_conf)

>>> prediction_points = mapie_regressor.predict(X_test)
>>> prediction_intervals = mapie_regressor.predict_set(X_test)
"""

def __init__(
Expand Down Expand Up @@ -268,7 +286,7 @@ class CrossConformalRegressor:
The conformity score method used to calculate the conformity error.
Valid options: TODO : reference here the valid options, once the list
has been be created during the implementation.
See :doc:`theoretical_description_conformity_scores`
See :ref:`theoretical_description_conformity_scores`

A custom score function inheriting from BaseRegressionScore may also be
provided.
Expand Down Expand Up @@ -313,11 +331,29 @@ class CrossConformalRegressor:

Examples
--------
>>> regressor = CrossConformalRegressor(
... estimator=LinearRegression(), confidence_level=0.95, cv=10)
>>> regressor.fit(X, y)
>>> regressor.conformalize(X, y)
>>> intervals = regressor.predict_set(X_test)
>>> from mapie_v1.regression import CrossConformalRegressor
>>> from sklearn.linear_model import Ridge
>>> from sklearn.datasets import make_regression

>>> X_initial, y_initial = make_regression(
... n_samples=500,
... n_features=2,
... noise=1.0
... )
>>> X, X_test, y, y_test = train_test_split(
... X_initial,
... y_initial,
... test_size=0.2,
... )

>>> mapie_regressor = CrossConformalRegressor(
... estimator=Ridge(),
... confidence_level=0.95,
... cv=10
... ).fit(X, y).conformalize(X, y)

>>> prediction_points = mapie_regressor.predict(X_test)
>>> prediction_intervals = mapie_regressor.predict_set(X_test)
"""

_VALID_METHODS = ["base", "plus", "minmax"]
Expand Down Expand Up @@ -562,7 +598,7 @@ class JackknifeAfterBootstrapRegressor:
The conformity score method used to calculate the conformity error.
Valid options: TODO : reference here the valid options, once the list
has been be created during the implementation.
See :doc:`theoretical_description_conformity_scores`
See :ref:`theoretical_description_conformity_scores`

A custom score function inheriting from BaseRegressionScore may also
be provided.
Expand Down Expand Up @@ -606,14 +642,29 @@ class JackknifeAfterBootstrapRegressor:

Examples
--------
>>> regressor = JackknifeAfterBootstrapRegressor(
... estimator=LinearRegression(),
... confidence_level=0.9,
... resampling=8,
... aggregation_method="mean")
>>> regressor.fit(X_train, y_train)
>>> regressor.conformalize(X_conf, y_conf)
>>> intervals = regressor.predict_set(X_test)
>>> from mapie_v1.regression import JackknifeAfterBootstrapRegressor
>>> from sklearn.linear_model import Ridge
>>> from sklearn.datasets import make_regression

>>> X_initial, y_initial = make_regression(
... n_samples=500,
... n_features=2,
... noise=1.0
... )
>>> X, X_test, y, y_test = train_test_split(
... X_initial,
... y_initial,
... test_size=0.2,
... )

>>> mapie_regressor = JackknifeAfterBootstrapRegressor(
... estimator=Ridge(),
... confidence_level=0.95,
... resampling=25,
... ).fit(X, y).conformalize(X, y)

>>> prediction_points = mapie_regressor.predict(X_test)
>>> prediction_intervals = mapie_regressor.predict_set(X_test)
"""

_VALID_METHODS = ["plus", "minmax"]
Expand Down Expand Up @@ -907,11 +958,29 @@ class ConformalizedQuantileRegressor:

Examples
--------
>>> regressor = ConformalizedQuantileRegressor(
... estimator=QuantileRegressor(), confidence_level=0.95)
>>> regressor.fit(X_train, y_train)
>>> regressor.conformalize(X_conf, y_conf)
>>> intervals = regressor.predict_set(X_test)
>>> from mapie_v1.regression import ConformalizedQuantileRegressor
>>> from sklearn.linear_model import QuantileRegressor
>>> from sklearn.datasets import make_regression

>>> X, y = make_regression(n_samples=500, n_features=2, noise=1.0)
>>> X_train, X_test_initial, y_train, y_test_initial = train_test_split(
... X,
... y,
... test_size=0.2,
... )
>>> X_conf, X_test, y_conf, y_test = train_test_split(
... X_test_initial,
... y_test_initial,
... test_size=0.5,
... )

>>> mapie_regressor = ConformalizedQuantileRegressor(
... estimator=QuantileRegressor(),
... confidence_level=0.95,
... ).fit(X_train, y_train).conformalize(X_conf, y_conf)

>>> prediction_points = mapie_regressor.predict(X_test)
>>> prediction_intervals = mapie_regressor.predict_set(X_test)
"""

def __init__(
Expand Down Expand Up @@ -1098,7 +1167,3 @@ def predict(
estimator = self._mapie_quantile_regressor
predictions, _ = estimator.predict(X, **self.predict_params)
return predictions


class GibbsConformalRegressor:
pass # TODO
Loading