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import warnings
import pytest
import numpy as np
from scipy import sparse
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.base import clone
from sklearn.preprocessing import maxabs_scale
from sklearn.preprocessing import minmax_scale
from sklearn.preprocessing import scale
from sklearn.preprocessing import power_transform
from sklearn.preprocessing import quantile_transform
from sklearn.preprocessing import robust_scale
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import RobustScaler
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_allclose
iris = load_iris()
def _get_valid_samples_by_column(X, col):
"""Get non NaN samples in column of X"""
return X[:, [col]][~np.isnan(X[:, col])]
@pytest.mark.parametrize(
"est, func, support_sparse, strictly_positive",
[(MaxAbsScaler(), maxabs_scale, True, False),
(MinMaxScaler(), minmax_scale, False, False),
(StandardScaler(), scale, False, False),
(StandardScaler(with_mean=False), scale, True, False),
(PowerTransformer('yeo-johnson'), power_transform, False, False),
(PowerTransformer('box-cox'), power_transform, False, True),
(QuantileTransformer(n_quantiles=10), quantile_transform, True, False),
(RobustScaler(), robust_scale, False, False),
(RobustScaler(with_centering=False), robust_scale, True, False)]
)
def test_missing_value_handling(est, func, support_sparse, strictly_positive):
# check that the preprocessing method let pass nan
rng = np.random.RandomState(42)
X = iris.data.copy()
n_missing = 50
X[rng.randint(X.shape[0], size=n_missing),
rng.randint(X.shape[1], size=n_missing)] = np.nan
if strictly_positive:
X += np.nanmin(X) + 0.1
X_train, X_test = train_test_split(X, random_state=1)
# sanity check
assert not np.all(np.isnan(X_train), axis=0).any()
assert np.any(np.isnan(X_train), axis=0).all()
assert np.any(np.isnan(X_test), axis=0).all()
X_test[:, 0] = np.nan # make sure this boundary case is tested
with pytest.warns(None) as records:
Xt = est.fit(X_train).transform(X_test)
# ensure no warnings are raised
assert len(records) == 0
# missing values should still be missing, and only them
assert_array_equal(np.isnan(Xt), np.isnan(X_test))
# check that the function leads to the same results as the class
with pytest.warns(None) as records:
Xt_class = est.transform(X_train)
assert len(records) == 0
Xt_func = func(X_train, **est.get_params())
assert_array_equal(np.isnan(Xt_func), np.isnan(Xt_class))
assert_allclose(Xt_func[~np.isnan(Xt_func)], Xt_class[~np.isnan(Xt_class)])
# check that the inverse transform keep NaN
Xt_inv = est.inverse_transform(Xt)
assert_array_equal(np.isnan(Xt_inv), np.isnan(X_test))
# FIXME: we can introduce equal_nan=True in recent version of numpy.
# For the moment which just check that non-NaN values are almost equal.
assert_allclose(Xt_inv[~np.isnan(Xt_inv)], X_test[~np.isnan(X_test)])
for i in range(X.shape[1]):
# train only on non-NaN
est.fit(_get_valid_samples_by_column(X_train, i))
# check transforming with NaN works even when training without NaN
with pytest.warns(None) as records:
Xt_col = est.transform(X_test[:, [i]])
assert len(records) == 0
assert_allclose(Xt_col, Xt[:, [i]])
# check non-NaN is handled as before - the 1st column is all nan
if not np.isnan(X_test[:, i]).all():
Xt_col_nonan = est.transform(
_get_valid_samples_by_column(X_test, i))
assert_array_equal(Xt_col_nonan,
Xt_col[~np.isnan(Xt_col.squeeze())])
if support_sparse:
est_dense = clone(est)
est_sparse = clone(est)
with pytest.warns(None) as records:
Xt_dense = est_dense.fit(X_train).transform(X_test)
Xt_inv_dense = est_dense.inverse_transform(Xt_dense)
assert len(records) == 0
for sparse_constructor in (sparse.csr_matrix, sparse.csc_matrix,
sparse.bsr_matrix, sparse.coo_matrix,
sparse.dia_matrix, sparse.dok_matrix,
sparse.lil_matrix):
# check that the dense and sparse inputs lead to the same results
# precompute the matrix to avoid catching side warnings
X_train_sp = sparse_constructor(X_train)
X_test_sp = sparse_constructor(X_test)
with pytest.warns(None) as records:
warnings.simplefilter('ignore', PendingDeprecationWarning)
Xt_sp = est_sparse.fit(X_train_sp).transform(X_test_sp)
assert len(records) == 0
assert_allclose(Xt_sp.A, Xt_dense)
with pytest.warns(None) as records:
warnings.simplefilter('ignore', PendingDeprecationWarning)
Xt_inv_sp = est_sparse.inverse_transform(Xt_sp)
assert len(records) == 0
assert_allclose(Xt_inv_sp.A, Xt_inv_dense)
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