/sklearn/utils/tests/test_utils.py
Python | 697 lines | 555 code | 117 blank | 25 comment | 51 complexity | 6ff64eacd57bf5a2b798e12d570f9d5c MD5 | raw file
Possible License(s): BSD-3-Clause
- from copy import copy
- from itertools import chain
- import warnings
- import string
- import timeit
- import pytest
- import numpy as np
- import scipy.sparse as sp
- from sklearn.utils._testing import (assert_array_equal,
- assert_allclose_dense_sparse,
- assert_warns_message,
- assert_no_warnings,
- _convert_container)
- from sklearn.utils import check_random_state
- from sklearn.utils import _determine_key_type
- from sklearn.utils import deprecated
- from sklearn.utils import gen_batches
- from sklearn.utils import _get_column_indices
- from sklearn.utils import resample
- from sklearn.utils import safe_mask
- from sklearn.utils import column_or_1d
- from sklearn.utils import _safe_indexing
- from sklearn.utils import shuffle
- from sklearn.utils import gen_even_slices
- from sklearn.utils import _message_with_time, _print_elapsed_time
- from sklearn.utils import get_chunk_n_rows
- from sklearn.utils import is_scalar_nan
- from sklearn.utils import _to_object_array
- from sklearn.utils._mocking import MockDataFrame
- from sklearn import config_context
- # toy array
- X_toy = np.arange(9).reshape((3, 3))
- def test_make_rng():
- # Check the check_random_state utility function behavior
- assert check_random_state(None) is np.random.mtrand._rand
- assert check_random_state(np.random) is np.random.mtrand._rand
- rng_42 = np.random.RandomState(42)
- assert check_random_state(42).randint(100) == rng_42.randint(100)
- rng_42 = np.random.RandomState(42)
- assert check_random_state(rng_42) is rng_42
- rng_42 = np.random.RandomState(42)
- assert check_random_state(43).randint(100) != rng_42.randint(100)
- with pytest.raises(ValueError):
- check_random_state("some invalid seed")
- def test_gen_batches():
- # Make sure gen_batches errors on invalid batch_size
- assert_array_equal(
- list(gen_batches(4, 2)),
- [slice(0, 2, None), slice(2, 4, None)]
- )
- msg_zero = "gen_batches got batch_size=0, must be positive"
- with pytest.raises(ValueError, match=msg_zero):
- next(gen_batches(4, 0))
- msg_float = "gen_batches got batch_size=0.5, must be an integer"
- with pytest.raises(TypeError, match=msg_float):
- next(gen_batches(4, 0.5))
- def test_deprecated():
- # Test whether the deprecated decorator issues appropriate warnings
- # Copied almost verbatim from https://docs.python.org/library/warnings.html
- # First a function...
- with warnings.catch_warnings(record=True) as w:
- warnings.simplefilter("always")
- @deprecated()
- def ham():
- return "spam"
- spam = ham()
- assert spam == "spam" # function must remain usable
- assert len(w) == 1
- assert issubclass(w[0].category, FutureWarning)
- assert "deprecated" in str(w[0].message).lower()
- # ... then a class.
- with warnings.catch_warnings(record=True) as w:
- warnings.simplefilter("always")
- @deprecated("don't use this")
- class Ham:
- SPAM = 1
- ham = Ham()
- assert hasattr(ham, "SPAM")
- assert len(w) == 1
- assert issubclass(w[0].category, FutureWarning)
- assert "deprecated" in str(w[0].message).lower()
- def test_resample():
- # Border case not worth mentioning in doctests
- assert resample() is None
- # Check that invalid arguments yield ValueError
- with pytest.raises(ValueError):
- resample([0], [0, 1])
- with pytest.raises(ValueError):
- resample([0, 1], [0, 1], replace=False, n_samples=3)
- with pytest.raises(ValueError):
- resample([0, 1], [0, 1], meaning_of_life=42)
- # Issue:6581, n_samples can be more when replace is True (default).
- assert len(resample([1, 2], n_samples=5)) == 5
- def test_resample_stratified():
- # Make sure resample can stratify
- rng = np.random.RandomState(0)
- n_samples = 100
- p = .9
- X = rng.normal(size=(n_samples, 1))
- y = rng.binomial(1, p, size=n_samples)
- _, y_not_stratified = resample(X, y, n_samples=10, random_state=0,
- stratify=None)
- assert np.all(y_not_stratified == 1)
- _, y_stratified = resample(X, y, n_samples=10, random_state=0, stratify=y)
- assert not np.all(y_stratified == 1)
- assert np.sum(y_stratified) == 9 # all 1s, one 0
- def test_resample_stratified_replace():
- # Make sure stratified resampling supports the replace parameter
- rng = np.random.RandomState(0)
- n_samples = 100
- X = rng.normal(size=(n_samples, 1))
- y = rng.randint(0, 2, size=n_samples)
- X_replace, _ = resample(X, y, replace=True, n_samples=50,
- random_state=rng, stratify=y)
- X_no_replace, _ = resample(X, y, replace=False, n_samples=50,
- random_state=rng, stratify=y)
- assert np.unique(X_replace).shape[0] < 50
- assert np.unique(X_no_replace).shape[0] == 50
- # make sure n_samples can be greater than X.shape[0] if we sample with
- # replacement
- X_replace, _ = resample(X, y, replace=True, n_samples=1000,
- random_state=rng, stratify=y)
- assert X_replace.shape[0] == 1000
- assert np.unique(X_replace).shape[0] == 100
- def test_resample_stratify_2dy():
- # Make sure y can be 2d when stratifying
- rng = np.random.RandomState(0)
- n_samples = 100
- X = rng.normal(size=(n_samples, 1))
- y = rng.randint(0, 2, size=(n_samples, 2))
- X, y = resample(X, y, n_samples=50, random_state=rng, stratify=y)
- assert y.ndim == 2
- def test_resample_stratify_sparse_error():
- # resample must be ndarray
- rng = np.random.RandomState(0)
- n_samples = 100
- X = rng.normal(size=(n_samples, 2))
- y = rng.randint(0, 2, size=n_samples)
- stratify = sp.csr_matrix(y)
- with pytest.raises(TypeError, match='A sparse matrix was passed'):
- X, y = resample(X, y, n_samples=50, random_state=rng,
- stratify=stratify)
- def test_safe_mask():
- random_state = check_random_state(0)
- X = random_state.rand(5, 4)
- X_csr = sp.csr_matrix(X)
- mask = [False, False, True, True, True]
- mask = safe_mask(X, mask)
- assert X[mask].shape[0] == 3
- mask = safe_mask(X_csr, mask)
- assert X_csr[mask].shape[0] == 3
- def test_column_or_1d():
- EXAMPLES = [
- ("binary", ["spam", "egg", "spam"]),
- ("binary", [0, 1, 0, 1]),
- ("continuous", np.arange(10) / 20.),
- ("multiclass", [1, 2, 3]),
- ("multiclass", [0, 1, 2, 2, 0]),
- ("multiclass", [[1], [2], [3]]),
- ("multilabel-indicator", [[0, 1, 0], [0, 0, 1]]),
- ("multiclass-multioutput", [[1, 2, 3]]),
- ("multiclass-multioutput", [[1, 1], [2, 2], [3, 1]]),
- ("multiclass-multioutput", [[5, 1], [4, 2], [3, 1]]),
- ("multiclass-multioutput", [[1, 2, 3]]),
- ("continuous-multioutput", np.arange(30).reshape((-1, 3))),
- ]
- for y_type, y in EXAMPLES:
- if y_type in ["binary", 'multiclass', "continuous"]:
- assert_array_equal(column_or_1d(y), np.ravel(y))
- else:
- with pytest.raises(ValueError):
- column_or_1d(y)
- @pytest.mark.parametrize(
- "key, dtype",
- [(0, 'int'),
- ('0', 'str'),
- (True, 'bool'),
- (np.bool_(True), 'bool'),
- ([0, 1, 2], 'int'),
- (['0', '1', '2'], 'str'),
- ((0, 1, 2), 'int'),
- (('0', '1', '2'), 'str'),
- (slice(None, None), None),
- (slice(0, 2), 'int'),
- (np.array([0, 1, 2], dtype=np.int32), 'int'),
- (np.array([0, 1, 2], dtype=np.int64), 'int'),
- (np.array([0, 1, 2], dtype=np.uint8), 'int'),
- ([True, False], 'bool'),
- ((True, False), 'bool'),
- (np.array([True, False]), 'bool'),
- ('col_0', 'str'),
- (['col_0', 'col_1', 'col_2'], 'str'),
- (('col_0', 'col_1', 'col_2'), 'str'),
- (slice('begin', 'end'), 'str'),
- (np.array(['col_0', 'col_1', 'col_2']), 'str'),
- (np.array(['col_0', 'col_1', 'col_2'], dtype=object), 'str')]
- )
- def test_determine_key_type(key, dtype):
- assert _determine_key_type(key) == dtype
- def test_determine_key_type_error():
- with pytest.raises(ValueError, match="No valid specification of the"):
- _determine_key_type(1.0)
- def test_determine_key_type_slice_error():
- with pytest.raises(TypeError, match="Only array-like or scalar are"):
- _determine_key_type(slice(0, 2, 1), accept_slice=False)
- @pytest.mark.parametrize(
- "array_type", ["list", "array", "sparse", "dataframe"]
- )
- @pytest.mark.parametrize(
- "indices_type", ["list", "tuple", "array", "series", "slice"]
- )
- def test_safe_indexing_2d_container_axis_0(array_type, indices_type):
- indices = [1, 2]
- if indices_type == 'slice' and isinstance(indices[1], int):
- indices[1] += 1
- array = _convert_container([[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type)
- indices = _convert_container(indices, indices_type)
- subset = _safe_indexing(array, indices, axis=0)
- assert_allclose_dense_sparse(
- subset, _convert_container([[4, 5, 6], [7, 8, 9]], array_type)
- )
- @pytest.mark.parametrize("array_type", ["list", "array", "series"])
- @pytest.mark.parametrize(
- "indices_type", ["list", "tuple", "array", "series", "slice"]
- )
- def test_safe_indexing_1d_container(array_type, indices_type):
- indices = [1, 2]
- if indices_type == 'slice' and isinstance(indices[1], int):
- indices[1] += 1
- array = _convert_container([1, 2, 3, 4, 5, 6, 7, 8, 9], array_type)
- indices = _convert_container(indices, indices_type)
- subset = _safe_indexing(array, indices, axis=0)
- assert_allclose_dense_sparse(
- subset, _convert_container([2, 3], array_type)
- )
- @pytest.mark.parametrize("array_type", ["array", "sparse", "dataframe"])
- @pytest.mark.parametrize(
- "indices_type", ["list", "tuple", "array", "series", "slice"]
- )
- @pytest.mark.parametrize("indices", [[1, 2], ["col_1", "col_2"]])
- def test_safe_indexing_2d_container_axis_1(array_type, indices_type, indices):
- # validation of the indices
- # we make a copy because indices is mutable and shared between tests
- indices_converted = copy(indices)
- if indices_type == 'slice' and isinstance(indices[1], int):
- indices_converted[1] += 1
- columns_name = ['col_0', 'col_1', 'col_2']
- array = _convert_container(
- [[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type, columns_name
- )
- indices_converted = _convert_container(indices_converted, indices_type)
- if isinstance(indices[0], str) and array_type != 'dataframe':
- err_msg = ("Specifying the columns using strings is only supported "
- "for pandas DataFrames")
- with pytest.raises(ValueError, match=err_msg):
- _safe_indexing(array, indices_converted, axis=1)
- else:
- subset = _safe_indexing(array, indices_converted, axis=1)
- assert_allclose_dense_sparse(
- subset, _convert_container([[2, 3], [5, 6], [8, 9]], array_type)
- )
- @pytest.mark.parametrize("array_read_only", [True, False])
- @pytest.mark.parametrize("indices_read_only", [True, False])
- @pytest.mark.parametrize("array_type", ["array", "sparse", "dataframe"])
- @pytest.mark.parametrize("indices_type", ["array", "series"])
- @pytest.mark.parametrize(
- "axis, expected_array",
- [(0, [[4, 5, 6], [7, 8, 9]]), (1, [[2, 3], [5, 6], [8, 9]])]
- )
- def test_safe_indexing_2d_read_only_axis_1(array_read_only, indices_read_only,
- array_type, indices_type, axis,
- expected_array):
- array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
- if array_read_only:
- array.setflags(write=False)
- array = _convert_container(array, array_type)
- indices = np.array([1, 2])
- if indices_read_only:
- indices.setflags(write=False)
- indices = _convert_container(indices, indices_type)
- subset = _safe_indexing(array, indices, axis=axis)
- assert_allclose_dense_sparse(
- subset, _convert_container(expected_array, array_type)
- )
- @pytest.mark.parametrize("array_type", ["list", "array", "series"])
- @pytest.mark.parametrize("indices_type", ["list", "tuple", "array", "series"])
- def test_safe_indexing_1d_container_mask(array_type, indices_type):
- indices = [False] + [True] * 2 + [False] * 6
- array = _convert_container([1, 2, 3, 4, 5, 6, 7, 8, 9], array_type)
- indices = _convert_container(indices, indices_type)
- subset = _safe_indexing(array, indices, axis=0)
- assert_allclose_dense_sparse(
- subset, _convert_container([2, 3], array_type)
- )
- @pytest.mark.parametrize("array_type", ["array", "sparse", "dataframe"])
- @pytest.mark.parametrize("indices_type", ["list", "tuple", "array", "series"])
- @pytest.mark.parametrize(
- "axis, expected_subset",
- [(0, [[4, 5, 6], [7, 8, 9]]),
- (1, [[2, 3], [5, 6], [8, 9]])]
- )
- def test_safe_indexing_2d_mask(array_type, indices_type, axis,
- expected_subset):
- columns_name = ['col_0', 'col_1', 'col_2']
- array = _convert_container(
- [[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type, columns_name
- )
- indices = [False, True, True]
- indices = _convert_container(indices, indices_type)
- subset = _safe_indexing(array, indices, axis=axis)
- assert_allclose_dense_sparse(
- subset, _convert_container(expected_subset, array_type)
- )
- @pytest.mark.parametrize(
- "array_type, expected_output_type",
- [("list", "list"), ("array", "array"),
- ("sparse", "sparse"), ("dataframe", "series")]
- )
- def test_safe_indexing_2d_scalar_axis_0(array_type, expected_output_type):
- array = _convert_container([[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type)
- indices = 2
- subset = _safe_indexing(array, indices, axis=0)
- expected_array = _convert_container([7, 8, 9], expected_output_type)
- assert_allclose_dense_sparse(subset, expected_array)
- @pytest.mark.parametrize("array_type", ["list", "array", "series"])
- def test_safe_indexing_1d_scalar(array_type):
- array = _convert_container([1, 2, 3, 4, 5, 6, 7, 8, 9], array_type)
- indices = 2
- subset = _safe_indexing(array, indices, axis=0)
- assert subset == 3
- @pytest.mark.parametrize(
- "array_type, expected_output_type",
- [("array", "array"), ("sparse", "sparse"), ("dataframe", "series")]
- )
- @pytest.mark.parametrize("indices", [2, "col_2"])
- def test_safe_indexing_2d_scalar_axis_1(array_type, expected_output_type,
- indices):
- columns_name = ['col_0', 'col_1', 'col_2']
- array = _convert_container(
- [[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type, columns_name
- )
- if isinstance(indices, str) and array_type != 'dataframe':
- err_msg = ("Specifying the columns using strings is only supported "
- "for pandas DataFrames")
- with pytest.raises(ValueError, match=err_msg):
- _safe_indexing(array, indices, axis=1)
- else:
- subset = _safe_indexing(array, indices, axis=1)
- expected_output = [3, 6, 9]
- if expected_output_type == 'sparse':
- # sparse matrix are keeping the 2D shape
- expected_output = [[3], [6], [9]]
- expected_array = _convert_container(
- expected_output, expected_output_type
- )
- assert_allclose_dense_sparse(subset, expected_array)
- @pytest.mark.parametrize("array_type", ["list", "array", "sparse"])
- def test_safe_indexing_None_axis_0(array_type):
- X = _convert_container([[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type)
- X_subset = _safe_indexing(X, None, axis=0)
- assert_allclose_dense_sparse(X_subset, X)
- def test_safe_indexing_pandas_no_matching_cols_error():
- pd = pytest.importorskip('pandas')
- err_msg = "No valid specification of the columns."
- X = pd.DataFrame(X_toy)
- with pytest.raises(ValueError, match=err_msg):
- _safe_indexing(X, [1.0], axis=1)
- @pytest.mark.parametrize("axis", [None, 3])
- def test_safe_indexing_error_axis(axis):
- with pytest.raises(ValueError, match="'axis' should be either 0"):
- _safe_indexing(X_toy, [0, 1], axis=axis)
- @pytest.mark.parametrize("X_constructor", ['array', 'series'])
- def test_safe_indexing_1d_array_error(X_constructor):
- # check that we are raising an error if the array-like passed is 1D and
- # we try to index on the 2nd dimension
- X = list(range(5))
- if X_constructor == 'array':
- X_constructor = np.asarray(X)
- elif X_constructor == 'series':
- pd = pytest.importorskip("pandas")
- X_constructor = pd.Series(X)
- err_msg = "'X' should be a 2D NumPy array, 2D sparse matrix or pandas"
- with pytest.raises(ValueError, match=err_msg):
- _safe_indexing(X_constructor, [0, 1], axis=1)
- def test_safe_indexing_container_axis_0_unsupported_type():
- indices = ["col_1", "col_2"]
- array = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
- err_msg = "String indexing is not supported with 'axis=0'"
- with pytest.raises(ValueError, match=err_msg):
- _safe_indexing(array, indices, axis=0)
- @pytest.mark.parametrize(
- "key, err_msg",
- [(10, r"all features must be in \[0, 2\]"),
- ('whatever', 'A given column is not a column of the dataframe')]
- )
- def test_get_column_indices_error(key, err_msg):
- pd = pytest.importorskip("pandas")
- X_df = pd.DataFrame(X_toy, columns=['col_0', 'col_1', 'col_2'])
- with pytest.raises(ValueError, match=err_msg):
- _get_column_indices(X_df, key)
- @pytest.mark.parametrize(
- "key",
- [['col1'], ['col2'], ['col1', 'col2'], ['col1', 'col3'], ['col2', 'col3']]
- )
- def test_get_column_indices_pandas_nonunique_columns_error(key):
- pd = pytest.importorskip('pandas')
- toy = np.zeros((1, 5), dtype=int)
- columns = ['col1', 'col1', 'col2', 'col3', 'col2']
- X = pd.DataFrame(toy, columns=columns)
- err_msg = "Selected columns, {}, are not unique in dataframe".format(key)
- with pytest.raises(ValueError) as exc_info:
- _get_column_indices(X, key)
- assert str(exc_info.value) == err_msg
- def test_shuffle_on_ndim_equals_three():
- def to_tuple(A): # to make the inner arrays hashable
- return tuple(tuple(tuple(C) for C in B) for B in A)
- A = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) # A.shape = (2,2,2)
- S = set(to_tuple(A))
- shuffle(A) # shouldn't raise a ValueError for dim = 3
- assert set(to_tuple(A)) == S
- def test_shuffle_dont_convert_to_array():
- # Check that shuffle does not try to convert to numpy arrays with float
- # dtypes can let any indexable datastructure pass-through.
- a = ['a', 'b', 'c']
- b = np.array(['a', 'b', 'c'], dtype=object)
- c = [1, 2, 3]
- d = MockDataFrame(np.array([['a', 0],
- ['b', 1],
- ['c', 2]],
- dtype=object))
- e = sp.csc_matrix(np.arange(6).reshape(3, 2))
- a_s, b_s, c_s, d_s, e_s = shuffle(a, b, c, d, e, random_state=0)
- assert a_s == ['c', 'b', 'a']
- assert type(a_s) == list
- assert_array_equal(b_s, ['c', 'b', 'a'])
- assert b_s.dtype == object
- assert c_s == [3, 2, 1]
- assert type(c_s) == list
- assert_array_equal(d_s, np.array([['c', 2],
- ['b', 1],
- ['a', 0]],
- dtype=object))
- assert type(d_s) == MockDataFrame
- assert_array_equal(e_s.toarray(), np.array([[4, 5],
- [2, 3],
- [0, 1]]))
- def test_gen_even_slices():
- # check that gen_even_slices contains all samples
- some_range = range(10)
- joined_range = list(chain(*[some_range[slice] for slice in
- gen_even_slices(10, 3)]))
- assert_array_equal(some_range, joined_range)
- # check that passing negative n_chunks raises an error
- slices = gen_even_slices(10, -1)
- with pytest.raises(ValueError, match="gen_even_slices got n_packs=-1,"
- " must be >=1"):
- next(slices)
- @pytest.mark.parametrize(
- ('row_bytes', 'max_n_rows', 'working_memory', 'expected', 'warning'),
- [(1024, None, 1, 1024, None),
- (1024, None, 0.99999999, 1023, None),
- (1023, None, 1, 1025, None),
- (1025, None, 1, 1023, None),
- (1024, None, 2, 2048, None),
- (1024, 7, 1, 7, None),
- (1024 * 1024, None, 1, 1, None),
- (1024 * 1024 + 1, None, 1, 1,
- 'Could not adhere to working_memory config. '
- 'Currently 1MiB, 2MiB required.'),
- ])
- def test_get_chunk_n_rows(row_bytes, max_n_rows, working_memory,
- expected, warning):
- if warning is not None:
- def check_warning(*args, **kw):
- return assert_warns_message(UserWarning, warning, *args, **kw)
- else:
- check_warning = assert_no_warnings
- actual = check_warning(get_chunk_n_rows,
- row_bytes=row_bytes,
- max_n_rows=max_n_rows,
- working_memory=working_memory)
- assert actual == expected
- assert type(actual) is type(expected)
- with config_context(working_memory=working_memory):
- actual = check_warning(get_chunk_n_rows,
- row_bytes=row_bytes,
- max_n_rows=max_n_rows)
- assert actual == expected
- assert type(actual) is type(expected)
- @pytest.mark.parametrize(
- ['source', 'message', 'is_long'],
- [
- ('ABC', string.ascii_lowercase, False),
- ('ABCDEF', string.ascii_lowercase, False),
- ('ABC', string.ascii_lowercase * 3, True),
- ('ABC' * 10, string.ascii_lowercase, True),
- ('ABC', string.ascii_lowercase + u'\u1048', False),
- ])
- @pytest.mark.parametrize(
- ['time', 'time_str'],
- [
- (0.2, ' 0.2s'),
- (20, ' 20.0s'),
- (2000, '33.3min'),
- (20000, '333.3min'),
- ])
- def test_message_with_time(source, message, is_long, time, time_str):
- out = _message_with_time(source, message, time)
- if is_long:
- assert len(out) > 70
- else:
- assert len(out) == 70
- assert out.startswith('[' + source + '] ')
- out = out[len(source) + 3:]
- assert out.endswith(time_str)
- out = out[:-len(time_str)]
- assert out.endswith(', total=')
- out = out[:-len(', total=')]
- assert out.endswith(message)
- out = out[:-len(message)]
- assert out.endswith(' ')
- out = out[:-1]
- if is_long:
- assert not out
- else:
- assert list(set(out)) == ['.']
- @pytest.mark.parametrize(
- ['message', 'expected'],
- [
- ('hello', _message_with_time('ABC', 'hello', 0.1) + '\n'),
- ('', _message_with_time('ABC', '', 0.1) + '\n'),
- (None, ''),
- ])
- def test_print_elapsed_time(message, expected, capsys, monkeypatch):
- monkeypatch.setattr(timeit, 'default_timer', lambda: 0)
- with _print_elapsed_time('ABC', message):
- monkeypatch.setattr(timeit, 'default_timer', lambda: 0.1)
- assert capsys.readouterr().out == expected
- @pytest.mark.parametrize("value, result", [(float("nan"), True),
- (np.nan, True),
- (np.float("nan"), True),
- (np.float32("nan"), True),
- (np.float64("nan"), True),
- (0, False),
- (0., False),
- (None, False),
- ("", False),
- ("nan", False),
- ([np.nan], False)])
- def test_is_scalar_nan(value, result):
- assert is_scalar_nan(value) is result
- def dummy_func():
- pass
- def test_deprecation_joblib_api(tmpdir):
- # Only parallel_backend and register_parallel_backend are not deprecated in
- # sklearn.utils
- from sklearn.utils import parallel_backend, register_parallel_backend
- assert_no_warnings(parallel_backend, 'loky', None)
- assert_no_warnings(register_parallel_backend, 'failing', None)
- from sklearn.utils._joblib import joblib
- del joblib.parallel.BACKENDS['failing']
- @pytest.mark.parametrize(
- "sequence",
- [[np.array(1), np.array(2)], [[1, 2], [3, 4]]]
- )
- def test_to_object_array(sequence):
- out = _to_object_array(sequence)
- assert isinstance(out, np.ndarray)
- assert out.dtype.kind == 'O'
- assert out.ndim == 1