780 lines
26 KiB
Python
780 lines
26 KiB
Python
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from copy import copy
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from itertools import chain
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import warnings
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import string
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import timeit
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import pytest
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import numpy as np
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import scipy.sparse as sp
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from sklearn.utils._testing import (
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assert_array_equal,
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assert_allclose_dense_sparse,
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assert_no_warnings,
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_convert_container,
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)
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from sklearn.utils import check_random_state
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from sklearn.utils import _determine_key_type
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from sklearn.utils import deprecated
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from sklearn.utils import gen_batches
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from sklearn.utils import _get_column_indices
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from sklearn.utils import resample
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from sklearn.utils import safe_mask
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from sklearn.utils import column_or_1d
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from sklearn.utils import _safe_indexing
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from sklearn.utils import _safe_assign
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from sklearn.utils import shuffle
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from sklearn.utils import gen_even_slices
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from sklearn.utils import _message_with_time, _print_elapsed_time
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from sklearn.utils import get_chunk_n_rows
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from sklearn.utils import is_scalar_nan
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from sklearn.utils import _to_object_array
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from sklearn.utils import _approximate_mode
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from sklearn.utils._mocking import MockDataFrame
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from sklearn import config_context
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# toy array
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X_toy = np.arange(9).reshape((3, 3))
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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)
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rng_42 = np.random.RandomState(42)
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assert check_random_state(rng_42) is rng_42
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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)
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with pytest.raises(ValueError):
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check_random_state("some invalid seed")
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def test_gen_batches():
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# Make sure gen_batches errors on invalid batch_size
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assert_array_equal(list(gen_batches(4, 2)), [slice(0, 2, None), slice(2, 4, None)])
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msg_zero = "gen_batches got batch_size=0, must be positive"
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with pytest.raises(ValueError, match=msg_zero):
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next(gen_batches(4, 0))
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msg_float = "gen_batches got batch_size=0.5, must be an integer"
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with pytest.raises(TypeError, match=msg_float):
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next(gen_batches(4, 0.5))
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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
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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():
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return "spam"
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spam = ham()
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assert spam == "spam" # function must remain usable
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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()
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# ... 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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@deprecated("don't use this")
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class Ham:
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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()
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def test_resample():
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# Border case not worth mentioning in doctests
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assert resample() is None
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# Check that invalid arguments yield ValueError
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with pytest.raises(ValueError):
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resample([0], [0, 1])
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with pytest.raises(ValueError):
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resample([0, 1], [0, 1], replace=False, n_samples=3)
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# Issue:6581, n_samples can be more when replace is True (default).
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assert len(resample([1, 2], n_samples=5)) == 5
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def test_resample_stratified():
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# Make sure resample can stratify
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rng = np.random.RandomState(0)
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n_samples = 100
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p = 0.9
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X = rng.normal(size=(n_samples, 1))
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y = rng.binomial(1, p, size=n_samples)
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_, y_not_stratified = resample(X, y, n_samples=10, random_state=0, stratify=None)
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assert np.all(y_not_stratified == 1)
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_, y_stratified = resample(X, y, n_samples=10, random_state=0, stratify=y)
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assert not np.all(y_stratified == 1)
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assert np.sum(y_stratified) == 9 # all 1s, one 0
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def test_resample_stratified_replace():
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# Make sure stratified resampling supports the replace parameter
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rng = np.random.RandomState(0)
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n_samples = 100
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X = rng.normal(size=(n_samples, 1))
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y = rng.randint(0, 2, size=n_samples)
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X_replace, _ = resample(
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X, y, replace=True, n_samples=50, random_state=rng, stratify=y
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)
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X_no_replace, _ = resample(
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X, y, replace=False, n_samples=50, random_state=rng, stratify=y
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)
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assert np.unique(X_replace).shape[0] < 50
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assert np.unique(X_no_replace).shape[0] == 50
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# make sure n_samples can be greater than X.shape[0] if we sample with
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# replacement
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X_replace, _ = resample(
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X, y, replace=True, n_samples=1000, random_state=rng, stratify=y
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)
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assert X_replace.shape[0] == 1000
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assert np.unique(X_replace).shape[0] == 100
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def test_resample_stratify_2dy():
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# Make sure y can be 2d when stratifying
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rng = np.random.RandomState(0)
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n_samples = 100
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X = rng.normal(size=(n_samples, 1))
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y = rng.randint(0, 2, size=(n_samples, 2))
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X, y = resample(X, y, n_samples=50, random_state=rng, stratify=y)
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assert y.ndim == 2
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def test_resample_stratify_sparse_error():
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# resample must be ndarray
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rng = np.random.RandomState(0)
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n_samples = 100
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X = rng.normal(size=(n_samples, 2))
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y = rng.randint(0, 2, size=n_samples)
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stratify = sp.csr_matrix(y)
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with pytest.raises(TypeError, match="A sparse matrix was passed"):
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X, y = resample(X, y, n_samples=50, random_state=rng, stratify=stratify)
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def test_safe_mask():
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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 = sp.csr_matrix(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
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def test_column_or_1d():
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EXAMPLES = [
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("binary", ["spam", "egg", "spam"]),
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("binary", [0, 1, 0, 1]),
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("continuous", np.arange(10) / 20.0),
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("multiclass", [1, 2, 3]),
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("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]]),
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("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]]),
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("continuous-multioutput", np.arange(30).reshape((-1, 3))),
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]
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for y_type, y in EXAMPLES:
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if y_type in ["binary", "multiclass", "continuous"]:
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assert_array_equal(column_or_1d(y), np.ravel(y))
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else:
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with pytest.raises(ValueError):
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column_or_1d(y)
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@pytest.mark.parametrize(
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"key, dtype",
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[
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(0, "int"),
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("0", "str"),
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(True, "bool"),
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(np.bool_(True), "bool"),
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([0, 1, 2], "int"),
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(["0", "1", "2"], "str"),
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((0, 1, 2), "int"),
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(("0", "1", "2"), "str"),
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(slice(None, None), None),
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(slice(0, 2), "int"),
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(np.array([0, 1, 2], dtype=np.int32), "int"),
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(np.array([0, 1, 2], dtype=np.int64), "int"),
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(np.array([0, 1, 2], dtype=np.uint8), "int"),
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([True, False], "bool"),
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((True, False), "bool"),
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(np.array([True, False]), "bool"),
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("col_0", "str"),
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(["col_0", "col_1", "col_2"], "str"),
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(("col_0", "col_1", "col_2"), "str"),
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(slice("begin", "end"), "str"),
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(np.array(["col_0", "col_1", "col_2"]), "str"),
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(np.array(["col_0", "col_1", "col_2"], dtype=object), "str"),
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],
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)
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def test_determine_key_type(key, dtype):
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assert _determine_key_type(key) == dtype
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def test_determine_key_type_error():
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with pytest.raises(ValueError, match="No valid specification of the"):
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_determine_key_type(1.0)
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def test_determine_key_type_slice_error():
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with pytest.raises(TypeError, match="Only array-like or scalar are"):
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_determine_key_type(slice(0, 2, 1), accept_slice=False)
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@pytest.mark.parametrize("array_type", ["list", "array", "sparse", "dataframe"])
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@pytest.mark.parametrize("indices_type", ["list", "tuple", "array", "series", "slice"])
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def test_safe_indexing_2d_container_axis_0(array_type, indices_type):
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indices = [1, 2]
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if indices_type == "slice" and isinstance(indices[1], int):
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indices[1] += 1
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array = _convert_container([[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type)
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indices = _convert_container(indices, indices_type)
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subset = _safe_indexing(array, indices, axis=0)
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assert_allclose_dense_sparse(
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subset, _convert_container([[4, 5, 6], [7, 8, 9]], array_type)
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)
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@pytest.mark.parametrize("array_type", ["list", "array", "series"])
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@pytest.mark.parametrize("indices_type", ["list", "tuple", "array", "series", "slice"])
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def test_safe_indexing_1d_container(array_type, indices_type):
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indices = [1, 2]
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if indices_type == "slice" and isinstance(indices[1], int):
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indices[1] += 1
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array = _convert_container([1, 2, 3, 4, 5, 6, 7, 8, 9], array_type)
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indices = _convert_container(indices, indices_type)
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subset = _safe_indexing(array, indices, axis=0)
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assert_allclose_dense_sparse(subset, _convert_container([2, 3], array_type))
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@pytest.mark.parametrize("array_type", ["array", "sparse", "dataframe"])
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@pytest.mark.parametrize("indices_type", ["list", "tuple", "array", "series", "slice"])
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@pytest.mark.parametrize("indices", [[1, 2], ["col_1", "col_2"]])
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def test_safe_indexing_2d_container_axis_1(array_type, indices_type, indices):
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# validation of the indices
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# we make a copy because indices is mutable and shared between tests
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indices_converted = copy(indices)
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if indices_type == "slice" and isinstance(indices[1], int):
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indices_converted[1] += 1
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columns_name = ["col_0", "col_1", "col_2"]
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array = _convert_container(
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[[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type, columns_name
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)
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indices_converted = _convert_container(indices_converted, indices_type)
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if isinstance(indices[0], str) and array_type != "dataframe":
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err_msg = (
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"Specifying the columns using strings is only supported "
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"for pandas DataFrames"
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)
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with pytest.raises(ValueError, match=err_msg):
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_safe_indexing(array, indices_converted, axis=1)
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else:
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subset = _safe_indexing(array, indices_converted, axis=1)
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assert_allclose_dense_sparse(
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subset, _convert_container([[2, 3], [5, 6], [8, 9]], array_type)
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)
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@pytest.mark.parametrize("array_read_only", [True, False])
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@pytest.mark.parametrize("indices_read_only", [True, False])
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@pytest.mark.parametrize("array_type", ["array", "sparse", "dataframe"])
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@pytest.mark.parametrize("indices_type", ["array", "series"])
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@pytest.mark.parametrize(
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"axis, expected_array", [(0, [[4, 5, 6], [7, 8, 9]]), (1, [[2, 3], [5, 6], [8, 9]])]
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)
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def test_safe_indexing_2d_read_only_axis_1(
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array_read_only, indices_read_only, array_type, indices_type, axis, expected_array
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):
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array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
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if array_read_only:
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array.setflags(write=False)
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array = _convert_container(array, array_type)
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indices = np.array([1, 2])
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if indices_read_only:
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indices.setflags(write=False)
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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)
|
||
|
|
|
||
|
|
|
||
|
|
def test_safe_indexing_pandas_no_settingwithcopy_warning():
|
||
|
|
# Using safe_indexing with an array-like indexer gives a copy of the
|
||
|
|
# DataFrame -> ensure it doesn't raise a warning if modified
|
||
|
|
pd = pytest.importorskip("pandas")
|
||
|
|
|
||
|
|
X = pd.DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]})
|
||
|
|
subset = _safe_indexing(X, [0, 1], axis=0)
|
||
|
|
if hasattr(pd.errors, "SettingWithCopyWarning"):
|
||
|
|
SettingWithCopyWarning = pd.errors.SettingWithCopyWarning
|
||
|
|
else:
|
||
|
|
# backward compatibility for pandas < 1.5
|
||
|
|
SettingWithCopyWarning = pd.core.common.SettingWithCopyWarning
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("error", SettingWithCopyWarning)
|
||
|
|
subset.iloc[0, 0] = 10
|
||
|
|
# The original dataframe is unaffected by the assignment on the subset:
|
||
|
|
assert X.iloc[0, 0] == 1
|
||
|
|
|
||
|
|
|
||
|
|
@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"),
|
||
|
|
[
|
||
|
|
(1024, None, 1, 1024),
|
||
|
|
(1024, None, 0.99999999, 1023),
|
||
|
|
(1023, None, 1, 1025),
|
||
|
|
(1025, None, 1, 1023),
|
||
|
|
(1024, None, 2, 2048),
|
||
|
|
(1024, 7, 1, 7),
|
||
|
|
(1024 * 1024, None, 1, 1),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_get_chunk_n_rows(row_bytes, max_n_rows, working_memory, expected):
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("error", UserWarning)
|
||
|
|
actual = 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):
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("error", UserWarning)
|
||
|
|
actual = get_chunk_n_rows(row_bytes=row_bytes, max_n_rows=max_n_rows)
|
||
|
|
assert actual == expected
|
||
|
|
assert type(actual) is type(expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_get_chunk_n_rows_warns():
|
||
|
|
"""Check that warning is raised when working_memory is too low."""
|
||
|
|
row_bytes = 1024 * 1024 + 1
|
||
|
|
max_n_rows = None
|
||
|
|
working_memory = 1
|
||
|
|
expected = 1
|
||
|
|
|
||
|
|
warn_msg = (
|
||
|
|
"Could not adhere to working_memory config. Currently 1MiB, 2MiB required."
|
||
|
|
)
|
||
|
|
with pytest.warns(UserWarning, match=warn_msg):
|
||
|
|
actual = 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):
|
||
|
|
with pytest.warns(UserWarning, match=warn_msg):
|
||
|
|
actual = 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 + "\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),
|
||
|
|
(0.0, False),
|
||
|
|
(None, False),
|
||
|
|
("", False),
|
||
|
|
("nan", False),
|
||
|
|
([np.nan], False),
|
||
|
|
(9867966753463435747313673, False), # Python int that overflows with C type
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_is_scalar_nan(value, result):
|
||
|
|
assert is_scalar_nan(value) is result
|
||
|
|
# make sure that we are returning a Python bool
|
||
|
|
assert isinstance(is_scalar_nan(value), bool)
|
||
|
|
|
||
|
|
|
||
|
|
def test_approximate_mode():
|
||
|
|
"""Make sure sklearn.utils._approximate_mode returns valid
|
||
|
|
results for cases where "class_counts * n_draws" is enough
|
||
|
|
to overflow 32-bit signed integer.
|
||
|
|
|
||
|
|
Non-regression test for:
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/20774
|
||
|
|
"""
|
||
|
|
X = np.array([99000, 1000], dtype=np.int32)
|
||
|
|
ret = _approximate_mode(class_counts=X, n_draws=25000, rng=0)
|
||
|
|
|
||
|
|
# Draws 25% of the total population, so in this case a fair draw means:
|
||
|
|
# 25% * 99.000 = 24.750
|
||
|
|
# 25% * 1.000 = 250
|
||
|
|
assert_array_equal(ret, [24750, 250])
|
||
|
|
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("array_type", ["array", "sparse", "dataframe"])
|
||
|
|
def test_safe_assign(array_type):
|
||
|
|
"""Check that `_safe_assign` works as expected."""
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X_array = rng.randn(10, 5)
|
||
|
|
|
||
|
|
row_indexer = [1, 2]
|
||
|
|
values = rng.randn(len(row_indexer), X_array.shape[1])
|
||
|
|
X = _convert_container(X_array, array_type)
|
||
|
|
_safe_assign(X, values, row_indexer=row_indexer)
|
||
|
|
|
||
|
|
assigned_portion = _safe_indexing(X, row_indexer, axis=0)
|
||
|
|
assert_allclose_dense_sparse(
|
||
|
|
assigned_portion, _convert_container(values, array_type)
|
||
|
|
)
|
||
|
|
|
||
|
|
column_indexer = [1, 2]
|
||
|
|
values = rng.randn(X_array.shape[0], len(column_indexer))
|
||
|
|
X = _convert_container(X_array, array_type)
|
||
|
|
_safe_assign(X, values, column_indexer=column_indexer)
|
||
|
|
|
||
|
|
assigned_portion = _safe_indexing(X, column_indexer, axis=1)
|
||
|
|
assert_allclose_dense_sparse(
|
||
|
|
assigned_portion, _convert_container(values, array_type)
|
||
|
|
)
|
||
|
|
|
||
|
|
row_indexer, column_indexer = None, None
|
||
|
|
values = rng.randn(*X.shape)
|
||
|
|
X = _convert_container(X_array, array_type)
|
||
|
|
_safe_assign(X, values, column_indexer=column_indexer)
|
||
|
|
|
||
|
|
assert_allclose_dense_sparse(X, _convert_container(values, array_type))
|