scikit-learn/sklearn/preprocessing/tests/test_function_transformer.py

import pytest
import numpy as np
from scipy import sparse

from sklearn.preprocessing import FunctionTransformer
from sklearn.utils.testing import (assert_equal, assert_array_equal,
                                   assert_allclose_dense_sparse)
from sklearn.utils.testing import assert_warns_message, assert_no_warnings
from sklearn.utils.testing import ignore_warnings


def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
    def _func(X, *args, **kwargs):
        args_store.append(X)
        args_store.extend(args)
        kwargs_store.update(kwargs)
        return func(X)

    return _func


def test_delegate_to_func():
    # (args|kwargs)_store will hold the positional and keyword arguments
    # passed to the function inside the FunctionTransformer.
    args_store = []
    kwargs_store = {}
    X = np.arange(10).reshape((5, 2))
    assert_array_equal(
        FunctionTransformer(_make_func(args_store, kwargs_store),
                            validate=False).transform(X),
        X, 'transform should have returned X unchanged',
    )

    # The function should only have received X.
    assert_equal(
        args_store,
        [X],
        'Incorrect positional arguments passed to func: {args}'.format(
            args=args_store,
        ),
    )
    assert_equal(
        kwargs_store,
        {},
        'Unexpected keyword arguments passed to func: {args}'.format(
            args=kwargs_store,
        ),
    )

    # reset the argument stores.
    args_store[:] = []  # python2 compatible inplace list clear.
    kwargs_store.clear()
    transformed = FunctionTransformer(
        _make_func(args_store, kwargs_store),
        validate=False).transform(X)

    assert_array_equal(transformed, X,
                       err_msg='transform should have returned X unchanged')

    # The function should have received X
    assert_equal(
        args_store,
        [X],
        'Incorrect positional arguments passed to func: {args}'.format(
            args=args_store,
        ),
    )
    assert_equal(
        kwargs_store,
        {},
        'Unexpected keyword arguments passed to func: {args}'.format(
            args=kwargs_store,
        ),
    )


@ignore_warnings(category=FutureWarning)
# ignore warning for validate=False 0.22
def test_np_log():
    X = np.arange(10).reshape((5, 2))

    # Test that the numpy.log example still works.
    assert_array_equal(
        FunctionTransformer(np.log1p).transform(X),
        np.log1p(X),
    )


@ignore_warnings(category=FutureWarning)
# ignore warning for validate=False 0.22
def test_kw_arg():
    X = np.linspace(0, 1, num=10).reshape((5, 2))

    F = FunctionTransformer(np.around, kw_args=dict(decimals=3))

    # Test that rounding is correct
    assert_array_equal(F.transform(X),
                       np.around(X, decimals=3))


@ignore_warnings(category=FutureWarning)
# ignore warning for validate=False 0.22
def test_kw_arg_update():
    X = np.linspace(0, 1, num=10).reshape((5, 2))

    F = FunctionTransformer(np.around, kw_args=dict(decimals=3))

    F.kw_args['decimals'] = 1

    # Test that rounding is correct
    assert_array_equal(F.transform(X), np.around(X, decimals=1))


@ignore_warnings(category=FutureWarning)
# ignore warning for validate=False 0.22
def test_kw_arg_reset():
    X = np.linspace(0, 1, num=10).reshape((5, 2))

    F = FunctionTransformer(np.around, kw_args=dict(decimals=3))

    F.kw_args = dict(decimals=1)

    # Test that rounding is correct
    assert_array_equal(F.transform(X), np.around(X, decimals=1))


@ignore_warnings(category=FutureWarning)
# ignore warning for validate=False 0.22
def test_inverse_transform():
    X = np.array([1, 4, 9, 16]).reshape((2, 2))

    # Test that inverse_transform works correctly
    F = FunctionTransformer(
        func=np.sqrt,
        inverse_func=np.around, inv_kw_args=dict(decimals=3),
    )
    assert_array_equal(
        F.inverse_transform(F.transform(X)),
        np.around(np.sqrt(X), decimals=3),
    )


@ignore_warnings(category=FutureWarning)
# ignore warning for validate=False 0.22
def test_check_inverse():
    X_dense = np.array([1, 4, 9, 16], dtype=np.float64).reshape((2, 2))

    X_list = [X_dense,
              sparse.csr_matrix(X_dense),
              sparse.csc_matrix(X_dense)]

    for X in X_list:
        if sparse.issparse(X):
            accept_sparse = True
        else:
            accept_sparse = False
        trans = FunctionTransformer(func=np.sqrt,
                                    inverse_func=np.around,
                                    accept_sparse=accept_sparse,
                                    check_inverse=True,
                                    validate=True)
        assert_warns_message(UserWarning,
                             "The provided functions are not strictly"
                             " inverse of each other. If you are sure you"
                             " want to proceed regardless, set"
                             " 'check_inverse=False'.",
                             trans.fit, X)

        trans = FunctionTransformer(func=np.expm1,
                                    inverse_func=np.log1p,
                                    accept_sparse=accept_sparse,
                                    check_inverse=True,
                                    validate=True)
        Xt = assert_no_warnings(trans.fit_transform, X)
        assert_allclose_dense_sparse(X, trans.inverse_transform(Xt))

    # check that we don't check inverse when one of the func or inverse is not
    # provided.
    trans = FunctionTransformer(func=np.expm1, inverse_func=None,
                                check_inverse=True, validate=True)
    assert_no_warnings(trans.fit, X_dense)
    trans = FunctionTransformer(func=None, inverse_func=np.expm1,
                                check_inverse=True, validate=True)
    assert_no_warnings(trans.fit, X_dense)


@pytest.mark.parametrize("validate, expected_warning",
                         [(None, FutureWarning),
                          (True, None),
                          (False, None)])
def test_function_transformer_future_warning(validate, expected_warning):
    # FIXME: to be removed in 0.22
    X = np.random.randn(100, 10)
    transformer = FunctionTransformer(validate=validate)
    with pytest.warns(expected_warning) as results:
        transformer.fit_transform(X)
    if expected_warning is None:
        assert len(results) == 0


def test_function_transformer_frame():
    pd = pytest.importorskip('pandas')
    X_df = pd.DataFrame(np.random.randn(100, 10))
    transformer = FunctionTransformer(validate=False)
    X_df_trans = transformer.fit_transform(X_df)
    assert hasattr(X_df_trans, 'loc')