10BC0 Merge branch 'master' into pls_float_preserve · scikit-learn/scikit-learn@508d95e · GitHub
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Merge branch 'master' into pls_float_preserve
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‎doc/whats_new/v0.20.rst

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@@ -91,9 +91,12 @@ Preprocessing
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- Added :class:`MICEImputer`, which is a strategy for imputing missing
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values by modeling each feature with missing values as a function of
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other features in a round-robin fashion. :issue:`8478` by
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other features in a round-robin fashion. :issue:`8478` by
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:user:`Sergey Feldman <sergeyf>`.
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- Updated :class:`preprocessing.MinMaxScaler` to pass through NaN values. :issue:`10404`
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by :user:`Lucija Gregov <LucijaGregov>`.
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Model evaluation
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- Added the :func:`metrics.balanced_accuracy_score` metric and a corresponding
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``inverse_transform`` on unseen labels. :issue:`9816` by :user:`Charlie Newey
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<newey01c>`.
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Model evaluation and meta-estimators
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- Add improved error message in :func:`model_selection.cross_val_score` when
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multiple metrics are passed in ``scoring`` keyword.
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:issue:`11006` by :user:`Ming Li <minggli>`.
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Datasets
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- Fixed a bug in :func:`dataset.load_boston` which had a wrong data point.

‎sklearn/metrics/scorer.py

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# License: Simplified BSD
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from abc import ABCMeta, abstractmethod
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from collections import Iterable
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import warnings
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import numpy as np
@@ -300,6 +301,10 @@ def check_scoring(estimator, scoring=None, allow_none=False):
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"If no scoring is specified, the estimator passed should "
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"have a 'score' method. The estimator %r does not."
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% estimator)
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elif isinstance(scoring, Iterable):
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raise ValueError("For evaluating multiple scores, use "
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"sklearn.model_selection.cross_validate instead. "
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"{0} was passed.".format(scoring))
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else:
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raise ValueError("scoring value should either be a callable, string or"
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" None. %r was passed" % scoring)

‎sklearn/model_selection/tests/test_search.py

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@@ -1217,10 +1217,10 @@ def test_fit_grid_point():
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assert_equal(n_test_samples, test.size)
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# Should raise an error upon multimetric scorer
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assert_raise_message(ValueError, "scoring value should either be a "
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"callable, string or None.", fit_grid_point, X, y,
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svc, params, train, test, {'score': scorer},
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verbose=True)
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assert_raise_message(ValueError, "For evaluating multiple scores, use "
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"sklearn.model_selection.cross_validate instead.",
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fit_grid_point, X, y, svc, params, train, test,
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{'score': scorer}, verbose=True)
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def test_pickle():

‎sklearn/preprocessing/data.py

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@@ -276,6 +276,9 @@ class MinMaxScaler(BaseEstimator, TransformerMixin):
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Notes
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-----
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NaNs are treated as missing values: disregarded in fit, and maintained in
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transform.
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For a comparison of the different scalers, transformers, and normalizers,
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see :ref:`examples/preprocessing/plot_all_scaling.py
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<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
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"You may consider to use MaxAbsScaler instead.")
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X = check_array(X, copy=self.copy, warn_on_dtype=True,
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estimator=self, dtype=FLOAT_DTYPES)
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estimator=self, dtype=FLOAT_DTYPES,
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force_all_finite="allow-nan")
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data_min = np.min(X, axis=0)
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data_max = np.max(X, axis=0)
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data_min = np.nanmin(X, axis=0)
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data_max = np.nanmax(X, axis=0)
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# First pass
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if not hasattr(self, 'n_samples_seen_'):
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"""
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check_is_fitted(self, 'scale_')
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X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)
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X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES,
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force_all_finite="allow-nan")
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X *= self.scale_
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X += self.min_
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"""
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check_is_fitted(self, 'scale_')
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X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)
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X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES,
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force_all_finite="allow-nan")
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X -= self.min_
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X /= self.scale_

‎sklearn/preprocessing/tests/test_common.py

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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import QuantileTransformer
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_allclose
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@pytest.mark.parametrize(
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"est",
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[QuantileTransformer(n_quantiles=10, random_state=42)]
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[MinMaxScaler(),
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QuantileTransformer(n_quantiles=10, random_state=42)]
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)
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def test_missing_value_handling(est):
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# check that the preprocessing method let pass nan

‎sklearn/utils/estimator_checks.py

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'RANSACRegressor', 'RadiusNeighborsRegressor',
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'RandomForestRegressor', 'Ridge', 'RidgeCV']
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ALLOW_NAN = ['QuantileTransformer', 'Imputer', 'SimpleImputer', 'MICEImputer']
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ALLOW_NAN = ['Imputer', 'SimpleImputer', 'MICEImputer',
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'MinMaxScaler', 'QuantileTransformer']
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SKIP_DTYPE_CONVERSION_CHECK = [
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'AdditiveChi2Sampler', 'BernoulliRBM', 'Binarizer', 'Birch',
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'CCA', 'DictionaryLearning', 'FactorAnalysis', 'FastICA',

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