10000 MAINT Parameters validation for sklearn.inspection.partial_dependence by yuanx749 · Pull Request #26209 · scikit-learn/scikit-learn · GitHub
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MAINT Parameters validation for sklearn.inspection.partial_dependence #26209

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Merged
merged 3 commits into from
Apr 26, 2023
Merged

MAINT Parameters validation for sklearn.inspection.partial_dependence #26209

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d94497d
MAINT Parameters validation for partial_dependence
yuanx749 Apr 18, 2023
908a7c5
Remove conflicting error messages
yuanx749 Apr 18, 2023
2b24af9
sparse matrix is valid
jeremiedbb Apr 26, 2023
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44 changes: 27 additions & 17 deletions sklearn/inspection/_partial_dependence.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,13 @@
from ..utils import _get_column_indices
from ..utils.validation import check_is_fitted
from ..utils import Bunch
from ..utils._param_validation import (
HasMethods,
Integral,
Interval,
StrOptions,
validate_params,
)
from ..tree import DecisionTreeRegressor
from ..ensemble import RandomForestRegressor
from ..exceptions import NotFittedError
Expand Down Expand Up @@ -223,6 +230,24 @@ def _partial_dependence_brute(est, grid, features, X, response_method):
return averaged_predictions, predictions


@validate_params(
{
"estimator": [
HasMethods(["fit", "predict"]),
HasMethods(["fit", "predict_proba"]),
HasMethods(["fit", "decision_function"]),
],
"X": ["array-like", "sparse matrix"],
"features": ["array-like", Integral, str],
"categorical_features": ["array-like", None],
"feature_names": ["array-like", None],
"response_method": [StrOptions({"auto", "predict_proba", "decision_function"})],
"percentiles": [tuple],
"grid_resolution": [Interval(Integral, 1, None, closed="left")],
"method": [StrOptions({"auto", "recursion", "brute"})],
"kind": [StrOptions({"average", "individual", "both"})],
}
)
def partial_dependence(
estimator,
X,
Expand Down Expand Up @@ -268,13 +293,13 @@ def partial_dependence(
:term:`predict_proba`, or :term:`decision_function`.
Multioutput-multiclass classifiers are not supported.

X : {array-like or dataframe} of shape (n_samples, n_features)
X : {array-like, sparse matrix or dataframe} of shape (n_samples, n_features)
``X`` is used to generate a grid of values for the target
``features`` (where the partial dependence will be evaluated), and
also to generate values for the complement features when the
`method` is 'brute'.

features : array-like of {int, str}
features : array-like of {int, str, bool} or int or str
The feature (e.g. `[0]`) or pair of interacting features
(e.g. `[(0, 1)]`) for which the partial dependency should be computed.

Expand Down Expand Up @@ -425,27 +450,12 @@ def partial_dependence(
if not (hasattr(X, "__array__") or sparse.issparse(X)):
X = check_array(X, force_all_finite="allow-nan", dtype=object)

accepted_responses = ("auto", "predict_proba", "decision_function")
if response_method not in accepted_responses:
raise ValueError(
"response_method {} is invalid. Accepted response_method names "
"are {}.".format(response_method, ", ".join(accepted_responses))
)

if is_regressor(estimator) and response_method != "auto":
raise ValueError(
"The response_method parameter is ignored for regressors and "
"must be 'auto'."
)

accepted_methods = ("brute", "recursion", "auto")
if method not in accepted_methods:
raise ValueError(
"method {} is invalid. Accepted method names are {}.".format(
method, ", ".join(accepted_methods)
)
)

if kind != "average":
if method == "recursion":
raise ValueError(
Expand Down
10 changes: 0 additions & 10 deletions sklearn/inspection/_plot/tests/test_plot_partial_dependence.py
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Expand Up @@ -611,16 +611,6 @@ def test_plot_partial_dependence_dataframe(pyplot, clf_diabetes, diabetes):
{"features": [1], "categorical_features": [1], "kind": "individual"},
"It is not possible to display individual effects",
),
(
dummy_classification_data,
{"features": [1], "kind": "foo"},
"Values provided to `kind` must be one of",
),
(
dummy_classification_data,
{"features": [0, 1], "kind": ["foo", "individual"]},
"Values provided to `kind` must be one of",
),
],
)
def test_plot_partial_dependence_error(pyplot, data, params, err_msg):
Expand Down
43 changes: 0 additions & 43 deletions sklearn/inspection/tests/test_partial_dependence.py
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Expand Up @@ -510,31 +510,6 @@ def fit(self, X, y):
{"features": [0], "response_method": "predict_proba", "method": "auto"},
"'recursion' method, the response_method must be 'decision_function'",
),
(
GradientBoostingClassifier(random_state=0),
{"features": [0], "response_method": "blahblah"},
"response_method blahblah is invalid. Accepted response_method",
),
(
NoPredictProbaNoDecisionFunction(),
{"features": [0], "response_method": "auto"},
"The estimator has no predict_proba and no decision_function method",
),
(
NoPredictProbaNoDecisionFunction(),
{"features": [0], "response_method": "predict_proba"},
"The estimator has no predict_proba method.",
),
(
NoPredictProbaNoDecisionFunction(),
{"features": [0], "response_method": "decision_function"},
"The estimator has no decision_function method.",
),
(
LinearRegression(),
{"features": [0], "method": "blahblah"},
"blahblah is invalid. Accepted method names are brute, recursion, auto",
),
(
LinearRegression(),
{"features": [0], "method": "recursion", "kind": "individual"},
Expand All @@ -560,24 +535,6 @@ def test_partial_dependence_error(estimator, params, err_msg):
partial_dependence(estimator, X, **params)


@pytest.mark.parametrize(
"with_dataframe, err_msg",
[
(True, "Only array-like or scalar are supported"),
(False, "Only array-like or scalar are supported"),
],
)
def test_partial_dependence_slice_error(with_dataframe, err_msg):
X, y = make_classification(random_state=0)
if with_dataframe:
pd = pytest.importorskip("pandas")
X = pd.DataFrame(X)
estimator = LogisticRegression().fit(X, y)

with pytest.raises(TypeError, match=err_msg):
partial_dependence(estimator, X, features=slice(0, 2, 1))


@pytest.mark.parametrize(
"estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
)
Expand Down
1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -165,6 +165,7 @@ def _check_function_param_validation(
"sklearn.feature_selection.mutual_info_classif",
"sklearn.feature_selection.mutual_info_regression",
"sklearn.feature_selection.r_regression",
"sklearn.inspection.partial_dependence",
"sklearn.inspection.permutation_importance",
"sklearn.linear_model.orthogonal_mp",
"sklearn.metrics.accuracy_score",
Expand Down
0