8000 FIX validate parameter if `fit` in `KernelDensity` estimator (#21430) · scikit-learn/scikit-learn@aa675f9 · GitHub
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FIX validate parameter if fit in KernelDensity estimator (#21430)
Co-Authored-By: Lucy Jiménez lucy.jimenez.chem@gmail.com
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‎doc/whats_new/v1.1.rst

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@@ -95,6 +95,13 @@ Changelog
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message when running in a jupyter notebook that is not trusted. :pr:`21316`
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by `Thomas Fan`_.
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:mod:`sklearn.neighbors`
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........................
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- |Fix| :class:`neighbors.KernelDensity` now validates input parameters in `fit`
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instead of `__init__`. :pr:`21430` by :user:`Desislava Vasileva <DessyVV>` and
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:user:`Lucy Jimenez <LucyJimenez>`.
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Code and Documentation Contributors
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-----------------------------------
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‎sklearn/neighbors/_kde.py

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@@ -135,16 +135,6 @@ def __init__(
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self.leaf_size = leaf_size
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self.metric_params = metric_params
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# run the choose algorithm code so that exceptions will happen here
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# we're using clone() in the GenerativeBayes classifier,
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# so we can't do this kind of logic in __init__
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self._choose_algorithm(self.algorithm, self.metric)
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if bandwidth <= 0:
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raise ValueError("bandwidth must be positive")
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if kernel not in VALID_KERNELS:
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raise ValueError("invalid kernel: '{0}'".format(kernel))
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def _choose_algorithm(self, algorithm, metric):
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# given the algorithm string + metric string, choose the optimal
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# algorithm to compute the result.
@@ -188,7 +178,14 @@ def fit(self, X, y=None, sample_weight=None):
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self : object
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Returns the instance itself.
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"""
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algorithm = self._choose_algorithm(self.algorithm, self.metric)
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if self.bandwidth <= 0:
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raise ValueError("bandwidth must be positive")
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if self.kernel not in VALID_KERNELS:
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raise ValueError("invalid kernel: '{0}'".format(self.kernel))
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X = self._validate_data(X, order="C", dtype=DTYPE)
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if sample_weight is not None:

‎sklearn/neighbors/tests/test_kde.py

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@@ -107,11 +107,12 @@ def test_kde_algorithm_metric_choice(algorithm, metric):
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X = rng.randn(10, 2) # 2 features required for haversine dist.
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Y = rng.randn(10, 2)
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kde = KernelDensity(algorithm=algorithm, metric=metric)
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if algorithm == "kd_tree" and metric not in KDTree.valid_metrics:
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with pytest.raises(ValueError):
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KernelDensity(algorithm=algorithm, metric=metric)
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kde.fit(X)
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else:
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kde = KernelDensity(algorithm=algorithm, metric=metric)
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kde.fit(X)
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y_dens = kde.score_samples(Y)
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assert y_dens.shape == Y.shape[:1]
@@ -126,16 +127,17 @@ def test_kde_score(n_samples=100, n_features=3):
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def test_kde_badargs():
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X = np.random.random((200, 10))
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with pytest.raises(ValueError):
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KernelDensity(algorithm="blah")
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KernelDensity(algorithm="blah").fit(X)
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with pytest.raises(ValueError):
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KernelDensity(bandwidth=0)
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KernelDensity(bandwidth=0).fit(X)
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with pytest.raises(ValueError):
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KernelDensity(kernel="blah")
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KernelDensity(kernel="blah").fit(X)
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with pytest.raises(ValueError):
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KernelDensity(metric="blah")
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KernelDensity(metric="blah").fit(X)
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with pytest.raises(ValueError):
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KernelDensity(algorithm="kd_tree", metric="blah")
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KernelDensity(algorithm="kd_tree", metric="blah").fit(X)
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kde = KernelDensity()
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with pytest.raises(ValueError):
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kde.fit(np.random.random((200, 10)), sample_weight=np.random.random((200, 10)))

‎sklearn/tests/test_common.py

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@@ -412,7 +412,6 @@ def test_transformers_get_feature_names_out(transformer):
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"FeatureUnion",
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"GridSearchCV",
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"HalvingGridSearchCV",
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"KernelDensity",
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"KernelPCA",
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"LabelBinarizer",
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"NuSVC",

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