some new features
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ilgazca
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import warnings
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import numpy as np
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import pytest
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from sklearn.base import clone
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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 (
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MaxAbsScaler,
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MinMaxScaler,
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PowerTransformer,
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QuantileTransformer,
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RobustScaler,
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StandardScaler,
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maxabs_scale,
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minmax_scale,
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power_transform,
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quantile_transform,
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robust_scale,
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scale,
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)
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from sklearn.utils._testing import assert_allclose, assert_array_equal
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from sklearn.utils.fixes import (
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BSR_CONTAINERS,
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COO_CONTAINERS,
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CSC_CONTAINERS,
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CSR_CONTAINERS,
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DIA_CONTAINERS,
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DOK_CONTAINERS,
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LIL_CONTAINERS,
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)
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iris = load_iris()
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def _get_valid_samples_by_column(X, col):
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"""Get non NaN samples in column of X"""
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return X[:, [col]][~np.isnan(X[:, col])]
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@pytest.mark.parametrize(
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"est, func, support_sparse, strictly_positive, omit_kwargs",
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[
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(MaxAbsScaler(), maxabs_scale, True, False, []),
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(MinMaxScaler(), minmax_scale, False, False, ["clip"]),
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(StandardScaler(), scale, False, False, []),
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(StandardScaler(with_mean=False), scale, True, False, []),
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(PowerTransformer("yeo-johnson"), power_transform, False, False, []),
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(PowerTransformer("box-cox"), power_transform, False, True, []),
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(QuantileTransformer(n_quantiles=10), quantile_transform, True, False, []),
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(RobustScaler(), robust_scale, False, False, []),
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(RobustScaler(with_centering=False), robust_scale, True, False, []),
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],
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)
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def test_missing_value_handling(
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est, func, support_sparse, strictly_positive, omit_kwargs
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):
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# check that the preprocessing method let pass nan
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rng = np.random.RandomState(42)
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X = iris.data.copy()
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n_missing = 50
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X[
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rng.randint(X.shape[0], size=n_missing), rng.randint(X.shape[1], size=n_missing)
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] = np.nan
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if strictly_positive:
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X += np.nanmin(X) + 0.1
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X_train, X_test = train_test_split(X, random_state=1)
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# sanity check
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assert not np.all(np.isnan(X_train), axis=0).any()
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assert np.any(np.isnan(X_train), axis=0).all()
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assert np.any(np.isnan(X_test), axis=0).all()
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X_test[:, 0] = np.nan # make sure this boundary case is tested
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with warnings.catch_warnings():
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warnings.simplefilter("error", RuntimeWarning)
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Xt = est.fit(X_train).transform(X_test)
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# ensure no warnings are raised
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# missing values should still be missing, and only them
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assert_array_equal(np.isnan(Xt), np.isnan(X_test))
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# check that the function leads to the same results as the class
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with warnings.catch_warnings():
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warnings.simplefilter("error", RuntimeWarning)
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Xt_class = est.transform(X_train)
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kwargs = est.get_params()
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# remove the parameters which should be omitted because they
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# are not defined in the counterpart function of the preprocessing class
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for kwarg in omit_kwargs:
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_ = kwargs.pop(kwarg)
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Xt_func = func(X_train, **kwargs)
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assert_array_equal(np.isnan(Xt_func), np.isnan(Xt_class))
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assert_allclose(Xt_func[~np.isnan(Xt_func)], Xt_class[~np.isnan(Xt_class)])
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# check that the inverse transform keep NaN
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Xt_inv = est.inverse_transform(Xt)
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assert_array_equal(np.isnan(Xt_inv), np.isnan(X_test))
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# FIXME: we can introduce equal_nan=True in recent version of numpy.
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# For the moment which just check that non-NaN values are almost equal.
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assert_allclose(Xt_inv[~np.isnan(Xt_inv)], X_test[~np.isnan(X_test)])
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for i in range(X.shape[1]):
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# train only on non-NaN
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est.fit(_get_valid_samples_by_column(X_train, i))
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# check transforming with NaN works even when training without NaN
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with warnings.catch_warnings():
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warnings.simplefilter("error", RuntimeWarning)
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Xt_col = est.transform(X_test[:, [i]])
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assert_allclose(Xt_col, Xt[:, [i]])
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# check non-NaN is handled as before - the 1st column is all nan
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if not np.isnan(X_test[:, i]).all():
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Xt_col_nonan = est.transform(_get_valid_samples_by_column(X_test, i))
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assert_array_equal(Xt_col_nonan, Xt_col[~np.isnan(Xt_col.squeeze())])
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if support_sparse:
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est_dense = clone(est)
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est_sparse = clone(est)
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with warnings.catch_warnings():
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warnings.simplefilter("error", RuntimeWarning)
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Xt_dense = est_dense.fit(X_train).transform(X_test)
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Xt_inv_dense = est_dense.inverse_transform(Xt_dense)
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for sparse_container in (
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BSR_CONTAINERS
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+ COO_CONTAINERS
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+ CSC_CONTAINERS
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+ CSR_CONTAINERS
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+ DIA_CONTAINERS
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+ DOK_CONTAINERS
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+ LIL_CONTAINERS
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):
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# check that the dense and sparse inputs lead to the same results
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# precompute the matrix to avoid catching side warnings
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X_train_sp = sparse_container(X_train)
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X_test_sp = sparse_container(X_test)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", PendingDeprecationWarning)
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warnings.simplefilter("error", RuntimeWarning)
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Xt_sp = est_sparse.fit(X_train_sp).transform(X_test_sp)
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assert_allclose(Xt_sp.toarray(), Xt_dense)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", PendingDeprecationWarning)
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warnings.simplefilter("error", RuntimeWarning)
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Xt_inv_sp = est_sparse.inverse_transform(Xt_sp)
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assert_allclose(Xt_inv_sp.toarray(), Xt_inv_dense)
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@pytest.mark.parametrize(
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"est, func",
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[
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(MaxAbsScaler(), maxabs_scale),
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(MinMaxScaler(), minmax_scale),
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(StandardScaler(), scale),
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(StandardScaler(with_mean=False), scale),
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(PowerTransformer("yeo-johnson"), power_transform),
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(
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PowerTransformer("box-cox"),
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power_transform,
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),
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(QuantileTransformer(n_quantiles=3), quantile_transform),
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(RobustScaler(), robust_scale),
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(RobustScaler(with_centering=False), robust_scale),
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],
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)
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def test_missing_value_pandas_na_support(est, func):
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# Test pandas IntegerArray with pd.NA
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pd = pytest.importorskip("pandas")
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X = np.array(
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[
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[1, 2, 3, np.nan, np.nan, 4, 5, 1],
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[np.nan, np.nan, 8, 4, 6, np.nan, np.nan, 8],
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[1, 2, 3, 4, 5, 6, 7, 8],
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]
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).T
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# Creates dataframe with IntegerArrays with pd.NA
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X_df = pd.DataFrame(X, dtype="Int16", columns=["a", "b", "c"])
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X_df["c"] = X_df["c"].astype("int")
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X_trans = est.fit_transform(X)
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X_df_trans = est.fit_transform(X_df)
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assert_allclose(X_trans, X_df_trans)
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