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sklearn PLS.fit_transform returns (X-scores, Y-scores); parameter constraints & type hints (6.1.0) (#49)

* IKPLS 6.1.0: sklearn PLS.fit_transform returns (X-scores, Y-scores)

ikpls.sklearn.PLS previously inherited TransformerMixin.fit_transform, which
returns only the X-scores of transform(X). Override it to fit (through fit, so a
subclass overriding fit still runs) and return transform(X, y) -- the
(x_scores, y_scores) tuple, matching sklearn.cross_decomposition.PLSRegression
and ikpls.numpy.PLS.fit_transform. This is a behavior change for callers that
relied on the previous X-scores-only return.

It also introduces two expected failures in scikit-learn's check_estimator suite
(check_transformer_general, check_transformer_data_not_an_array): those compare
fit_transform(X, y) against transform(X), and scikit-learn only special-cases the
tuple return for a hard-coded set of class names (CROSS_DECOMPOSITION), of which
"PLS" is not one. scikit-learn's own PLSRegression has identical tuple behavior
and passes only by virtue of its name, so these are not real conformance
defects; the conformance test marks them expected-fail.

Bumps the version to 6.1.0.

* Add type hints and _parameter_constraints to ikpls.sklearn.PLS

Annotate the public method signatures of the scikit-learn wrapper (fit,
fit_transform, transform, inverse_transform, predict, predict_all_components,
score, y_rotations_, __sklearn_tags__), matching the NumPy backend's
numpy.typing conventions. The constructor parameters are deliberately left
unannotated: the sklearn contract stores them verbatim and validates at fit, and
annotating them would make the typeguard-instrumented test suite reject
non-conforming values at construction instead of yielding the clean fit-time
error.

Replace the ad-hoc _validate_n_components with scikit-learn's
_parameter_constraints / _validate_params: n_components is Interval(Integral, 1,
None), algorithm is Options({1, 2}), the centering/scaling flags are bool... (continued)

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Jobs
ID Job ID Ran Files Coverage
1 windows-latest-python-3.12-numpy-only - 28934299777.1 13
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2 ubuntu-latest-python-3.11-numpy-only - 28934299777.2 13
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3 ubuntu-latest-python-3.12-jax - 28934299777.3 13
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4 ubuntu-latest-python-3.12-numpy-only - 28934299777.4 13
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5 ubuntu-latest-python-3.13-numpy-only - 28934299777.5 13
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6 windows-latest-python-3.14-jax - 28934299777.6 13
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7 windows-latest-python-3.11-numpy-only - 28934299777.7 13
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8 windows-latest-python-3.12-jax - 28934299777.8 13
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9 windows-latest-python-3.11-jax - 28934299777.9 13
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10 ubuntu-latest-python-3.11-jax - 28934299777.10 13
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11 ubuntu-latest-python-3.14-numpy-only - 28934299777.11 13
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12 windows-latest-python-3.10-numpy-only - 28934299777.12 13
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13 windows-latest-python-3.14-numpy-only - 28934299777.13 13
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14 windows-latest-python-3.13-numpy-only - 28934299777.14 13
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15 windows-latest-python-3.10-jax - 28934299777.15 13
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16 windows-latest-python-3.13-jax - 28934299777.16 13
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17 ubuntu-latest-python-3.10-numpy-only - 28934299777.17 13
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18 ubuntu-latest-python-3.13-jax - 28934299777.18 13
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19 ubuntu-latest-python-3.14-jax - 28934299777.19 13
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20 ubuntu-latest-python-3.10-jax - 28934299777.20 13
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Source Files on build 28934299777