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Commit f61dd6c

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jeremiedbbjeremiedbb
authored
MAINT Clean-up utils.__init__: move tests into corresponding test files (scikit-learn#28842)
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‎sklearn/utils/tests/test_mask.py

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+19Lines changed: 19 additions & 0 deletions
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import pytest
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from sklearn.utils._mask import safe_mask
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from sklearn.utils.fixes import CSR_CONTAINERS
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from sklearn.utils.validation import check_random_state
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_safe_mask(csr_container):
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random_state = check_random_state(0)
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X = random_state.rand(5, 4)
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X_csr = csr_container(X)
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mask = [False, False, True, True, True]
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mask = safe_mask(X, mask)
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assert X[mask].shape[0] == 3
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mask = safe_mask(X_csr, mask)
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assert X_csr[mask].shape[0] == 3

‎sklearn/utils/tests/test_missing.py

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+27Lines changed: 27 additions & 0 deletions
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import numpy as np
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import pytest
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from sklearn.utils._missing import is_scalar_nan
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@pytest.mark.parametrize(
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"value, result",
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[
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(float("nan"), True),
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(np.nan, True),
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(float(np.nan), True),
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(np.float32(np.nan), True),
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(np.float64(np.nan), True),
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(0, False),
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(0.0, False),
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(None, False),
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("", False),
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("nan", False),
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([np.nan], False),
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(9867966753463435747313673, False), # Python int that overflows with C type
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],
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)
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def test_is_scalar_nan(value, result):
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assert is_scalar_nan(value) is result
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# make sure that we are returning a Python bool
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assert isinstance(is_scalar_nan(value), bool)

‎sklearn/utils/tests/test_utils.py

Copy file name to clipboardExpand all lines: sklearn/utils/tests/test_utils.py
+1-147Lines changed: 1 addition & 147 deletions
Original file line numberDiff line numberDiff line change
@@ -1,153 +1,7 @@
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import warnings
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31
import joblib
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import numpy as np
52
import pytest
63

7-
from sklearn.utils import (
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check_random_state,
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column_or_1d,
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deprecated,
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parallel_backend,
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register_parallel_backend,
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safe_mask,
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tosequence,
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)
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from sklearn.utils._missing import is_scalar_nan
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils.fixes import CSR_CONTAINERS
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from sklearn.utils.validation import _is_polars_df
20-
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def test_make_rng():
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# Check the check_random_state utility function behavior
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assert check_random_state(None) is np.random.mtrand._rand
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assert check_random_state(np.random) is np.random.mtrand._rand
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rng_42 = np.random.RandomState(42)
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assert check_random_state(42).randint(100) == rng_42.randint(100)
29-
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rng_42 = np.random.RandomState(42)
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assert check_random_state(rng_42) is rng_42
32-
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rng_42 = np.random.RandomState(42)
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assert check_random_state(43).randint(100) != rng_42.randint(100)
35-
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with pytest.raises(ValueError):
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check_random_state("some invalid seed")
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39-
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def test_deprecated():
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# Test whether the deprecated decorator issues appropriate warnings
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# Copied almost verbatim from https://docs.python.org/library/warnings.html
43-
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# First a function...
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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@deprecated()
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def ham():
50-
return "spam"
51-
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spam = ham()
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assert spam == "spam" # function must remain usable
55-
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assert len(w) == 1
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assert issubclass(w[0].category, FutureWarning)
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assert "deprecated" in str(w[0].message).lower()
59-
60-
# ... then a class.
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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64-
@deprecated("don't use this")
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class Ham:
66-
SPAM = 1
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ham = Ham()
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assert hasattr(ham, "SPAM")
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assert len(w) == 1
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assert issubclass(w[0].category, FutureWarning)
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assert "deprecated" in str(w[0].message).lower()
75-
76-
77-
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
78-
def test_safe_mask(csr_container):
79-
random_state = check_random_state(0)
80-
X = random_state.rand(5, 4)
81-
X_csr = csr_container(X)
82-
mask = [False, False, True, True, True]
83-
84-
mask = safe_mask(X, mask)
85-
assert X[mask].shape[0] == 3
86-
87-
mask = safe_mask(X_csr, mask)
88-
assert X_csr[mask].shape[0] == 3
89-
90-
91-
def test_column_or_1d():
92-
EXAMPLES = [
93-
("binary", ["spam", "egg", "spam"]),
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("binary", [0, 1, 0, 1]),
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("continuous", np.arange(10) / 20.0),
96-
("multiclass", [1, 2, 3]),
97-
("multiclass", [0, 1, 2, 2, 0]),
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("multiclass", [[1], [2], [3]]),
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("multilabel-indicator", [[0, 1, 0], [0, 0, 1]]),
100-
("multiclass-multioutput", [[1, 2, 3]]),
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("multiclass-multioutput", [[1, 1], [2, 2], [3, 1]]),
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("multiclass-multioutput", [[5, 1], [4, 2], [3, 1]]),
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("multiclass-multioutput", [[1, 2, 3]]),
104-
("continuous-multioutput", np.arange(30).reshape((-1, 3))),
105-
]
106-
107-
for y_type, y in EXAMPLES:
108-
if y_type in ["binary", "multiclass", "continuous"]:
109-
assert_array_equal(column_or_1d(y), np.ravel(y))
110-
else:
111-
with pytest.raises(ValueError):
112-
column_or_1d(y)
113-
114-
115-
@pytest.mark.parametrize(
116-
"value, result",
117-
[
118-
(float("nan"), True),
119-
(np.nan, True),
120-
(float(np.nan), True),
121-
(np.float32(np.nan), True),
122-
(np.float64(np.nan), True),
123-
(0, False),
124-
(0.0, False),
125-
(None, False),
126-
("", False),
127-
("nan", False),
128-
([np.nan], False),
129-
(9867966753463435747313673, False), # Python int that overflows with C type
130-
],
131-
)
132-
def test_is_scalar_nan(value, result):
133-
assert is_scalar_nan(value) is result
134-
# make sure that we are returning a Python bool
135-
assert isinstance(is_scalar_nan(value), bool)
136-
137-
138-
def dummy_func():
139-
pass
140-
141-
142-
def test__is_polars_df():
143-
"""Check that _is_polars_df return False for non-dataframe objects."""
144-
145-
class LooksLikePolars:
146-
def __init__(self):
147-
self.columns = ["a", "b"]
148-
self.schema = ["a", "b"]
149-
150-
assert not _is_polars_df(LooksLikePolars())
4+
from sklearn.utils import parallel_backend, register_parallel_backend, tosequence
1515

1526

1537
# TODO(1.7): remove

‎sklearn/utils/tests/test_validation.py

Copy file name to clipboardExpand all lines: sklearn/utils/tests/test_validation.py
+55Lines changed: 55 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -80,11 +80,31 @@
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check_is_fitted,
8181
check_memory,
8282
check_non_negative,
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check_random_state,
8384
check_scalar,
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column_or_1d,
8486
has_fit_parameter,
8587
)
8688

8789

90+
def test_make_rng():
91+
# Check the check_random_state utility function behavior
92+
assert check_random_state(None) is np.random.mtrand._rand
93+
assert check_random_state(np.random) is np.random.mtrand._rand
94+
95+
rng_42 = np.random.RandomState(42)
96+
assert check_random_state(42).randint(100) == rng_42.randint(100)
97+
98+
rng_42 = np.random.RandomState(42)
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assert check_random_state(rng_42) is rng_42
100+
101+
rng_42 = np.random.RandomState(42)
102+
assert check_random_state(43).randint(100) != rng_42.randint(100)
103+
104+
with pytest.raises(ValueError):
105+
check_random_state("some invalid seed")
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107+
88108
def test_as_float_array():
89109
# Test function for as_float_array
90110
X = np.ones((3, 10), dtype=np.int32)
@@ -2061,3 +2081,38 @@ def test_to_object_array(sequence):
20612081
assert isinstance(out, np.ndarray)
20622082
assert out.dtype.kind == "O"
20632083
assert out.ndim == 1
2084+
2085+
2086+
def test_column_or_1d():
2087+
EXAMPLES = [
2088+
("binary", ["spam", "egg", "spam"]),
2089+
("binary", [0, 1, 0, 1]),
2090+
("continuous", np.arange(10) / 20.0),
2091+
("multiclass", [1, 2, 3]),
2092+
("multiclass", [0, 1, 2, 2, 0]),
2093+
("multiclass", [[1], [2], [3]]),
2094+
("multilabel-indicator", [[0, 1, 0], [0, 0, 1]]),
2095+
("multiclass-multioutput", [[1, 2, 3]]),
2096+
("multiclass-multioutput", [[1, 1], [2, 2], [3, 1]]),
2097+
("multiclass-multioutput", [[5, 1], [4, 2], [3, 1]]),
2098+
("multiclass-multioutput", [[1, 2, 3]]),
2099+
("continuous-multioutput", np.arange(30).reshape((-1, 3))),
2100+
]
2101+
2102+
for y_type, y in EXAMPLES:
2103+
if y_type in ["binary", "multiclass", "continuous"]:
2104+
assert_array_equal(column_or_1d(y), np.ravel(y))
2105+
else:
2106+
with pytest.raises(ValueError):
2107+
column_or_1d(y)
2108+
2109+
2110+
def test__is_polars_df():
2111+
"""Check that _is_polars_df return False for non-dataframe objects."""
2112+
2113+
class LooksLikePolars:
2114+
def __init__(self):
2115+
self.columns = ["a", "b"]
2116+
self.schema = ["a", "b"]
2117+
2118+
assert not _is_polars_df(LooksLikePolars())

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