117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
# Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
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# Justin Vincent
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# Lars Buitinck
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# License: BSD 3 clause
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import math
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import numpy as np
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import pytest
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import scipy.stats
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils.fixes import _joblib_parallel_args
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from sklearn.utils.fixes import _object_dtype_isnan
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from sklearn.utils.fixes import loguniform
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from sklearn.utils.fixes import linspace, parse_version, np_version
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@pytest.mark.parametrize("joblib_version", ("0.11", "0.12.0"))
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def test_joblib_parallel_args(monkeypatch, joblib_version):
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import joblib
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monkeypatch.setattr(joblib, "__version__", joblib_version)
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if joblib_version == "0.12.0":
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# arguments are simply passed through
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assert _joblib_parallel_args(prefer="threads") == {"prefer": "threads"}
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assert _joblib_parallel_args(prefer="processes", require=None) == {
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"prefer": "processes",
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"require": None,
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}
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assert _joblib_parallel_args(non_existing=1) == {"non_existing": 1}
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elif joblib_version == "0.11":
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# arguments are mapped to the corresponding backend
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assert _joblib_parallel_args(prefer="threads") == {"backend": "threading"}
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assert _joblib_parallel_args(prefer="processes") == {
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"backend": "multiprocessing"
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}
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with pytest.raises(ValueError):
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_joblib_parallel_args(prefer="invalid")
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assert _joblib_parallel_args(prefer="processes", require="sharedmem") == {
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"backend": "threading"
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}
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with pytest.raises(ValueError):
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_joblib_parallel_args(require="invalid")
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with pytest.raises(NotImplementedError):
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_joblib_parallel_args(verbose=True)
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else:
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raise ValueError
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@pytest.mark.parametrize("dtype, val", ([object, 1], [object, "a"], [float, 1]))
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def test_object_dtype_isnan(dtype, val):
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X = np.array([[val, np.nan], [np.nan, val]], dtype=dtype)
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expected_mask = np.array([[False, True], [True, False]])
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mask = _object_dtype_isnan(X)
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assert_array_equal(mask, expected_mask)
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@pytest.mark.parametrize("low,high,base", [(-1, 0, 10), (0, 2, np.exp(1)), (-1, 1, 2)])
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def test_loguniform(low, high, base):
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rv = loguniform(base ** low, base ** high)
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assert isinstance(rv, scipy.stats._distn_infrastructure.rv_frozen)
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rvs = rv.rvs(size=2000, random_state=0)
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# Test the basics; right bounds, right size
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assert (base ** low <= rvs).all() and (rvs <= base ** high).all()
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assert len(rvs) == 2000
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# Test that it's actually (fairly) uniform
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log_rvs = np.array([math.log(x, base) for x in rvs])
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counts, _ = np.histogram(log_rvs)
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assert counts.mean() == 200
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assert np.abs(counts - counts.mean()).max() <= 40
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# Test that random_state works
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assert loguniform(base ** low, base ** high).rvs(random_state=0) == loguniform(
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base ** low, base ** high
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).rvs(random_state=0)
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def test_linspace():
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"""Test that linespace works like np.linespace as of numpy version 1.16."""
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start, stop = 0, 10
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num = 6
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out = linspace(start=start, stop=stop, num=num, endpoint=True)
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assert_array_equal(out, np.array([0.0, 2, 4, 6, 8, 10]))
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start, stop = [0, 100], [10, 1100]
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num = 6
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out = linspace(start=start, stop=stop, num=num, endpoint=True)
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res = np.c_[[0.0, 2, 4, 6, 8, 10], [100, 300, 500, 700, 900, 1100]]
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assert_array_equal(out, res)
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out2 = linspace(start=start, stop=stop, num=num, endpoint=True, axis=1)
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assert_array_equal(out2, out.T)
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out, step = linspace(
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start=start,
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stop=stop,
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num=num,
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endpoint=True,
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retstep=True,
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)
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assert_array_equal(out, res)
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assert_array_equal(step, [2, 200])
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if np_version < parse_version("1.16"):
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with pytest.raises(ValueError):
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linspace(start=[0, 1], stop=10)
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else:
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linspace(start=[0, 1], stop=10)
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