# Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
#          Justin Vincent
#          Lars Buitinck
# License: BSD 3 clause

import math

import numpy as np
import pytest
import scipy.stats

from sklearn.utils._testing import assert_array_equal

from sklearn.utils.fixes import _joblib_parallel_args
from sklearn.utils.fixes import _object_dtype_isnan
from sklearn.utils.fixes import loguniform
from sklearn.utils.fixes import MaskedArray


@pytest.mark.parametrize('joblib_version', ('0.11', '0.12.0'))
def test_joblib_parallel_args(monkeypatch, joblib_version):
    import joblib
    monkeypatch.setattr(joblib, '__version__', joblib_version)

    if joblib_version == '0.12.0':
        # arguments are simply passed through
        assert _joblib_parallel_args(prefer='threads') == {'prefer': 'threads'}
        assert _joblib_parallel_args(prefer='processes', require=None) == {
                    'prefer': 'processes', 'require': None}
        assert _joblib_parallel_args(non_existing=1) == {'non_existing': 1}
    elif joblib_version == '0.11':
        # arguments are mapped to the corresponding backend
        assert _joblib_parallel_args(prefer='threads') == {
                    'backend': 'threading'}
        assert _joblib_parallel_args(prefer='processes') == {
                    'backend': 'multiprocessing'}
        with pytest.raises(ValueError):
            _joblib_parallel_args(prefer='invalid')
        assert _joblib_parallel_args(
                prefer='processes', require='sharedmem') == {
                    'backend': 'threading'}
        with pytest.raises(ValueError):
            _joblib_parallel_args(require='invalid')
        with pytest.raises(NotImplementedError):
            _joblib_parallel_args(verbose=True)
    else:
        raise ValueError


@pytest.mark.parametrize("dtype, val", ([object, 1],
                                        [object, "a"],
                                        [float, 1]))
def test_object_dtype_isnan(dtype, val):
    X = np.array([[val, np.nan],
                  [np.nan, val]], dtype=dtype)

    expected_mask = np.array([[False, True],
                              [True, False]])

    mask = _object_dtype_isnan(X)

    assert_array_equal(mask, expected_mask)


@pytest.mark.parametrize("low,high,base",
                         [(-1, 0, 10), (0, 2, np.exp(1)), (-1, 1, 2)])
def test_loguniform(low, high, base):
    rv = loguniform(base ** low, base ** high)
    assert isinstance(rv, scipy.stats._distn_infrastructure.rv_frozen)
    rvs = rv.rvs(size=2000, random_state=0)

    # Test the basics; right bounds, right size
    assert (base ** low <= rvs).all() and (rvs <= base ** high).all()
    assert len(rvs) == 2000

    # Test that it's actually (fairly) uniform
    log_rvs = np.array([math.log(x, base) for x in rvs])
    counts, _ = np.histogram(log_rvs)
    assert counts.mean() == 200
    assert np.abs(counts - counts.mean()).max() <= 40

    # Test that random_state works
    assert (
        loguniform(base ** low, base ** high).rvs(random_state=0)
        == loguniform(base ** low, base ** high).rvs(random_state=0)
    )


def test_masked_array_deprecated():  # TODO: remove in 0.25
    with pytest.warns(FutureWarning, match='is deprecated'):
        MaskedArray()