696 lines
24 KiB
Python
696 lines
24 KiB
Python
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)
|
|
|
|
# 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),
|
|
(float(np.nan), True),
|
|
(np.float32(np.nan), True),
|
|
(np.float64(np.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
|