8000 Allow `n_knots=None` if knots are explicitly specified in `SplineTran… · scikit-learn/scikit-learn@7781256 · GitHub
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Allow n_knots=None if knots are explicitly specified in SplineTransformer (#20191)
 Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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-9
lines changed

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lines changed

‎sklearn/preprocessing/_polynomial.py

Lines changed: 9 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -350,7 +350,8 @@ class SplineTransformer(TransformerMixin, BaseEstimator):
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----------
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n_knots : int, default=5
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Number of knots of the splines if `knots` equals one of
353-
{'uniform', 'quantile'}. Must be larger or equal 2.
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{'uniform', 'quantile'}. Must be larger or equal 2. Ignored if `knots`
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is array-like.
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degree : int, default=3
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The polynomial degree of the spline basis. Must be a non-negative
@@ -546,15 +547,17 @@ def fit(self, X, y=None):
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):
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raise ValueError("degree must be a non-negative integer.")
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549-
if not (
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isinstance(self.n_knots, numbers.Integral) and self.n_knots >= 2
551-
):
552-
raise ValueError("n_knots must be a positive integer >= 2.")
553-
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if isinstance(self.knots, str) and self.knots in [
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"uniform",
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"quantile",
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]:
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if not (
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isinstance(self.n_knots, numbers.Integral)
556+
and self.n_knots >= 2
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):
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raise ValueError("n_knots must be a positive integer >= 2, "
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f"got: {self.n_knots}")
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base_knots = self._get_base_knot_positions(
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X, n_knots=self.n_knots, knots=self.knots
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)

‎sklearn/preprocessing/tests/test_polynomial.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -96,9 +96,9 @@ def test_spline_transformer_manual_knot_input():
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"""
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X = np.arange(20).reshape(10, 2)
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knots = [[0.5, 1], [1.5, 2], [5, 10]]
99-
st1 = SplineTransformer(degree=3, knots=knots).fit(X)
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st1 = SplineTransformer(degree=3, knots=knots, n_knots=None).fit(X)
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knots = np.asarray(knots)
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st2 = SplineTransformer(degree=3, knots=knots).fit(X)
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st2 = SplineTransformer(degree=3, knots=knots, n_knots=None).fit(X)
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for i in range(X.shape[1]):
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assert_allclose(st1.bsplines_[i].t, st2.bsplines_[i].t)
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@@ -216,7 +216,7 @@ def test_spline_transformer_linear_regression(bias, intercept):
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("uniform", 12, 8),
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(
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[[-1.0, 0.0], [0, 1.0], [0.1, 2.0], [0.2, 3.0], [0.3, 4.0], [1, 5.0]],
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100, # this gets ignored.
219+
None,
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3
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)
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])

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