import numpy as np
import pytest

from xlearn.feature_selection import VarianceThreshold
from xlearn.utils._testing import assert_array_equal
from xlearn.utils.fixes import BSR_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS

data = [[0, 1, 2, 3, 4], [0, 2, 2, 3, 5], [1, 1, 2, 4, 0]]

data2 = [[-0.13725701]] * 10


@pytest.mark.parametrize(
    "sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
)
def test_zero_variance(sparse_container):
    # Test VarianceThreshold with default setting, zero variance.
    X = data if sparse_container is None else sparse_container(data)
    sel = VarianceThreshold().fit(X)
    assert_array_equal([0, 1, 3, 4], sel.get_support(indices=True))


def test_zero_variance_value_error():
    # Test VarianceThreshold with default setting, zero variance, error cases.
    with pytest.raises(ValueError):
        VarianceThreshold().fit([[0, 1, 2, 3]])
    with pytest.raises(ValueError):
        VarianceThreshold().fit([[0, 1], [0, 1]])


@pytest.mark.parametrize("sparse_container", [None] + CSR_CONTAINERS)
def test_variance_threshold(sparse_container):
    # Test VarianceThreshold with custom variance.
    X = data if sparse_container is None else sparse_container(data)
    X = VarianceThreshold(threshold=0.4).fit_transform(X)
    assert (len(data), 1) == X.shape


@pytest.mark.skipif(
    np.var(data2) == 0,
    reason=(
        "This test is not valid for this platform, "
        "as it relies on numerical instabilities."
    ),
)
@pytest.mark.parametrize(
    "sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
)
def test_zero_variance_floating_point_error(sparse_container):
    # Test that VarianceThreshold(0.0).fit eliminates features that have
    # the same value in every sample, even when floating point errors
    # cause np.var not to be 0 for the feature.
    # See #13691
    X = data2 if sparse_container is None else sparse_container(data2)
    msg = "No feature in X meets the variance threshold 0.00000"
    with pytest.raises(ValueError, match=msg):
        VarianceThreshold().fit(X)


@pytest.mark.parametrize(
    "sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
)
def test_variance_nan(sparse_container):
    arr = np.array(data, dtype=np.float64)
    # add single NaN and feature should still be included
    arr[0, 0] = np.nan
    # make all values in feature NaN and feature should be rejected
    arr[:, 1] = np.nan

    X = arr if sparse_container is None else sparse_container(arr)
    sel = VarianceThreshold().fit(X)
    assert_array_equal([0, 3, 4], sel.get_support(indices=True))
