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704 lines
24 KiB
704 lines
24 KiB
import warnings
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from copy import copy
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from unittest import SkipTest
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import numpy as np
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import pytest
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from scipy.stats import kstest
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import sklearn
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from sklearn.externals._packaging.version import parse as parse_version
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from sklearn.utils import _safe_indexing, resample, shuffle
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from sklearn.utils._array_api import (
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_convert_to_numpy,
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_get_namespace_device_dtype_ids,
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device,
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move_to,
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yield_namespace_device_dtype_combinations,
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)
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from sklearn.utils._indexing import (
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_determine_key_type,
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_get_column_indices,
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_safe_assign,
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)
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from sklearn.utils._mocking import MockDataFrame
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from sklearn.utils._testing import (
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_array_api_for_tests,
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_convert_container,
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assert_allclose,
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assert_allclose_dense_sparse,
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assert_array_equal,
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skip_if_array_api_compat_not_configured,
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)
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from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
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# toy array
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X_toy = np.arange(9).reshape((3, 3))
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def test_polars_indexing():
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"""Check _safe_indexing for polars as expected."""
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pl = pytest.importorskip("polars", minversion="0.18.2")
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df = pl.DataFrame(
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{"a": [1, 2, 3, 4], "b": [4, 5, 6, 8], "c": [1, 4, 1, 10]}, orient="row"
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)
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from polars.testing import assert_frame_equal
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str_keys = [["b"], ["a", "b"], ["b", "a", "c"], ["c"], ["a"]]
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for key in str_keys:
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out = _safe_indexing(df, key, axis=1)
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assert_frame_equal(df[key], out)
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bool_keys = [([True, False, True], ["a", "c"]), ([False, False, True], ["c"])]
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for bool_key, str_key in bool_keys:
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out = _safe_indexing(df, bool_key, axis=1)
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assert_frame_equal(df[:, str_key], out)
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int_keys = [([0, 1], ["a", "b"]), ([2], ["c"])]
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for int_key, str_key in int_keys:
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out = _safe_indexing(df, int_key, axis=1)
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assert_frame_equal(df[:, str_key], out)
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axis_0_keys = [[0, 1], [1, 3], [3, 2]]
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for key in axis_0_keys:
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out = _safe_indexing(df, key, axis=0)
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assert_frame_equal(df[key], out)
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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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@skip_if_array_api_compat_not_configured
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@pytest.mark.parametrize(
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"array_namespace, device_, dtype_name",
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yield_namespace_device_dtype_combinations(),
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ids=_get_namespace_device_dtype_ids,
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)
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def test_determine_key_type_array_api(array_namespace, device_, dtype_name):
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xp = _array_api_for_tests(array_namespace, device_)
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with sklearn.config_context(array_api_dispatch=True):
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int_array_key = xp.asarray([1, 2, 3], device=device_)
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assert _determine_key_type(int_array_key) == "int"
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bool_array_key = xp.asarray([True, False, True], device=device_)
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assert _determine_key_type(bool_array_key) == "bool"
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try:
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complex_array_key = xp.asarray([1 + 1j, 2 + 2j, 3 + 3j], device=device_)
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except TypeError:
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# Complex numbers are not supported by all Array API libraries.
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complex_array_key = None
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if complex_array_key is not None:
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with pytest.raises(ValueError, match="No valid specification of the"):
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_determine_key_type(complex_array_key)
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@skip_if_array_api_compat_not_configured
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@pytest.mark.parametrize(
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"array_namespace, device_, dtype_name",
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yield_namespace_device_dtype_combinations(),
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ids=_get_namespace_device_dtype_ids,
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)
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@pytest.mark.parametrize(
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"indexing_key",
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(
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0,
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-1,
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[1, 3],
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np.array([1, 3]),
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slice(1, 2),
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[True, False, True, True],
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np.asarray([False, False, False, False]),
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),
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)
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@pytest.mark.parametrize("axis", [0, 1])
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def test_safe_indexing_array_api_support(
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array_namespace, device_, dtype_name, indexing_key, axis
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):
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xp = _array_api_for_tests(array_namespace, device_)
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array_to_index_np = np.arange(16).reshape(4, 4)
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expected_result = _safe_indexing(array_to_index_np, indexing_key, axis=axis)
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array_to_index_xp = move_to(array_to_index_np, xp=xp, device=device_)
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with sklearn.config_context(array_api_dispatch=True):
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indexed_array_xp = _safe_indexing(array_to_index_xp, indexing_key, axis=axis)
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assert device(indexed_array_xp) == device(array_to_index_xp)
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assert indexed_array_xp.dtype == array_to_index_xp.dtype
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assert_allclose(_convert_to_numpy(indexed_array_xp, xp=xp), expected_result)
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@pytest.mark.parametrize(
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"array_type", ["list", "array", "sparse", "dataframe", "polars", "pyarrow"]
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)
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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(
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"array_type", ["list", "array", "series", "polars_series", "pyarrow_array"]
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)
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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(
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"array_type", ["array", "sparse", "dataframe", "polars", "pyarrow"]
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)
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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 in ("array", "sparse"):
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err_msg = (
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"Specifying the columns using strings is only supported for 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(
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"array_type", ["array", "sparse", "dataframe", "polars", "pyarrow"]
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)
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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)
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subset = _safe_indexing(array, indices, axis=axis)
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assert_allclose_dense_sparse(subset, _convert_container(expected_array, array_type))
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@pytest.mark.parametrize(
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"array_type", ["list", "array", "series", "polars_series", "pyarrow_array"]
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)
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@pytest.mark.parametrize("indices_type", ["list", "tuple", "array", "series"])
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def test_safe_indexing_1d_container_mask(array_type, indices_type):
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indices = [False] + [True] * 2 + [False] * 6
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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(
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"array_type", ["array", "sparse", "dataframe", "polars", "pyarrow"]
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)
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@pytest.mark.parametrize("indices_type", ["list", "tuple", "array", "series"])
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@pytest.mark.parametrize(
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"axis, expected_subset",
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[(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_mask(array_type, indices_type, axis, expected_subset):
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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 = [False, True, True]
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indices = _convert_container(indices, indices_type)
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subset = _safe_indexing(array, indices, axis=axis)
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assert_allclose_dense_sparse(
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subset, _convert_container(expected_subset, array_type)
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)
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@pytest.mark.parametrize(
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"array_type, expected_output_type",
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[
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("list", "list"),
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("array", "array"),
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("sparse", "sparse"),
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("dataframe", "series"),
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("polars", "polars_series"),
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("pyarrow", "pyarrow_array"),
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],
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)
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def test_safe_indexing_2d_scalar_axis_0(array_type, expected_output_type):
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array = _convert_container([[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type)
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indices = 2
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subset = _safe_indexing(array, indices, axis=0)
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expected_array = _convert_container([7, 8, 9], expected_output_type)
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assert_allclose_dense_sparse(subset, expected_array)
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@pytest.mark.parametrize(
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"array_type", ["list", "array", "series", "polars_series", "pyarrow_array"]
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)
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def test_safe_indexing_1d_scalar(array_type):
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array = _convert_container([1, 2, 3, 4, 5, 6, 7, 8, 9], array_type)
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indices = 2
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subset = _safe_indexing(array, indices, axis=0)
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assert subset == 3
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@pytest.mark.parametrize(
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"array_type, expected_output_type",
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[
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("array", "array"),
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("sparse", "sparse"),
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("dataframe", "series"),
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("polars", "polars_series"),
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("pyarrow", "pyarrow_array"),
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],
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)
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@pytest.mark.parametrize("indices", [2, "col_2"])
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def test_safe_indexing_2d_scalar_axis_1(array_type, expected_output_type, indices):
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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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if isinstance(indices, str) and array_type in ("array", "sparse"):
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err_msg = (
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"Specifying the columns using strings is only supported for dataframes"
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)
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with pytest.raises(ValueError, match=err_msg):
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_safe_indexing(array, indices, axis=1)
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else:
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subset = _safe_indexing(array, indices, axis=1)
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expected_output = [3, 6, 9]
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if expected_output_type == "sparse":
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# sparse matrix are keeping the 2D shape
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expected_output = [[3], [6], [9]]
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expected_array = _convert_container(expected_output, expected_output_type)
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assert_allclose_dense_sparse(subset, expected_array)
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@pytest.mark.parametrize("array_type", ["list", "array", "sparse"])
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def test_safe_indexing_None_axis_0(array_type):
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X = _convert_container([[1, 2, 3], [4, 5, 6], [7, 8, 9]], array_type)
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X_subset = _safe_indexing(X, None, axis=0)
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assert_allclose_dense_sparse(X_subset, X)
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def test_safe_indexing_pandas_no_matching_cols_error():
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pd = pytest.importorskip("pandas")
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err_msg = "No valid specification of the columns."
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X = pd.DataFrame(X_toy)
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with pytest.raises(ValueError, match=err_msg):
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_safe_indexing(X, [1.0], axis=1)
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@pytest.mark.parametrize("axis", [None, 3])
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def test_safe_indexing_error_axis(axis):
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with pytest.raises(ValueError, match="'axis' should be either 0"):
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_safe_indexing(X_toy, [0, 1], axis=axis)
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|
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@pytest.mark.parametrize(
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"X_constructor", ["array", "series", "polars_series", "pyarrow_array"]
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)
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def test_safe_indexing_1d_array_error(X_constructor):
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# check that we are raising an error if the array-like passed is 1D and
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# we try to index on the 2nd dimension
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X = list(range(5))
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if X_constructor == "array":
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X_constructor = np.asarray(X)
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elif X_constructor == "series":
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pd = pytest.importorskip("pandas")
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X_constructor = pd.Series(X)
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elif X_constructor == "polars_series":
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pl = pytest.importorskip("polars")
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X_constructor = pl.Series(values=X)
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elif X_constructor == "pyarrow_array":
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pa = pytest.importorskip("pyarrow")
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X_constructor = pa.array(X)
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err_msg = "'X' should be a 2D NumPy array, 2D sparse matrix or dataframe"
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with pytest.raises(ValueError, match=err_msg):
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_safe_indexing(X_constructor, [0, 1], axis=1)
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def test_safe_indexing_container_axis_0_unsupported_type():
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indices = ["col_1", "col_2"]
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array = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
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err_msg = r"String indexing.*is not supported with 'axis=0'"
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with pytest.raises(ValueError, match=err_msg):
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_safe_indexing(array, indices, axis=0)
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def test_safe_indexing_pandas_no_settingwithcopy_warning():
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# Using safe_indexing with an array-like indexer gives a copy of the
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# DataFrame -> ensure it doesn't raise a warning if modified
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pd = pytest.importorskip("pandas")
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pd_version = parse_version(pd.__version__)
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pd_base_version = parse_version(pd_version.base_version)
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if pd_base_version >= parse_version("3"):
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raise SkipTest("SettingWithCopyWarning has been removed in pandas 3.0.0.dev")
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X = pd.DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]})
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subset = _safe_indexing(X, [0, 1], axis=0)
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if hasattr(pd.errors, "SettingWithCopyWarning"):
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SettingWithCopyWarning = pd.errors.SettingWithCopyWarning
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|
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("indices", [0, [0, 1], slice(0, 2), np.array([0, 1])])
|
|
def test_safe_indexing_list_axis_1_unsupported(indices):
|
|
"""Check that we raise a ValueError when axis=1 with input as list."""
|
|
X = [[1, 2], [4, 5], [7, 8]]
|
|
err_msg = "axis=1 is not supported for lists"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
_safe_indexing(X, indices, axis=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))
|
|
|
|
|
|
@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"),
|
|
(object(), "No valid specification of the columns"),
|
|
],
|
|
)
|
|
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_get_column_indices_interchange():
|
|
"""Check _get_column_indices for edge cases with the interchange"""
|
|
pl = pytest.importorskip("polars")
|
|
|
|
# Polars dataframes go down the interchange path.
|
|
df = pl.DataFrame([[1, 2, 3], [4, 5, 6]], schema=["a", "b", "c"])
|
|
|
|
key_results = [
|
|
(slice(1, None), [1, 2]),
|
|
(slice(None, 2), [0, 1]),
|
|
(slice(1, 2), [1]),
|
|
(["b", "c"], [1, 2]),
|
|
(slice("a", "b"), [0, 1]),
|
|
(slice("a", None), [0, 1, 2]),
|
|
(slice(None, "a"), [0]),
|
|
(["c", "a"], [2, 0]),
|
|
([], []),
|
|
]
|
|
for key, result in key_results:
|
|
assert _get_column_indices(df, key) == result
|
|
|
|
msg = "A given column is not a column of the dataframe"
|
|
with pytest.raises(ValueError, match=msg):
|
|
_get_column_indices(df, ["not_a_column"])
|
|
|
|
msg = "key.step must be 1 or None"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
_get_column_indices(df, slice("a", None, 2))
|
|
|
|
|
|
def test_resample():
|
|
# Border case not worth mentioning in doctests
|
|
assert resample() is None
|
|
|
|
# Check that invalid arguments yield ValueError
|
|
with pytest.raises(ValueError):
|
|
resample([0], [0, 1])
|
|
with pytest.raises(ValueError):
|
|
resample([0, 1], [0, 1], replace=False, n_samples=3)
|
|
|
|
# Issue:6581, n_samples can be more when replace is True (default).
|
|
assert len(resample([1, 2], n_samples=5)) == 5
|
|
|
|
|
|
def test_resample_weighted():
|
|
# Check that sampling with replacement with integer weights yields the
|
|
# samples from the same distribution as sampling uniformly with
|
|
# repeated data points.
|
|
data = np.array([-1, 0, 1])
|
|
sample_weight = np.asarray([0, 100, 1])
|
|
|
|
mean_repeated = []
|
|
mean_reweighted = []
|
|
|
|
for seed in range(100):
|
|
mean_repeated.append(
|
|
resample(
|
|
data.repeat(sample_weight),
|
|
replace=True,
|
|
random_state=seed,
|
|
n_samples=data.shape[0],
|
|
).mean()
|
|
)
|
|
mean_reweighted.append(
|
|
resample(
|
|
data,
|
|
sample_weight=sample_weight,
|
|
replace=True,
|
|
random_state=seed,
|
|
n_samples=data.shape[0],
|
|
).mean()
|
|
)
|
|
|
|
mean_repeated = np.asarray(mean_repeated)
|
|
mean_reweighted = np.asarray(mean_reweighted)
|
|
|
|
test_result = kstest(mean_repeated, mean_reweighted)
|
|
# Should never be negative because -1 has a 0 weight.
|
|
assert np.all(mean_reweighted >= 0)
|
|
# The null-hypothesis (the computed means are identically distributed)
|
|
# cannot be rejected.
|
|
assert test_result.pvalue > 0.05
|
|
|
|
|
|
def test_resample_stratified():
|
|
# Make sure resample can stratify
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 100
|
|
p = 0.9
|
|
X = rng.normal(size=(n_samples, 1))
|
|
y = rng.binomial(1, p, size=n_samples)
|
|
|
|
_, y_not_stratified = resample(X, y, n_samples=10, random_state=0, stratify=None)
|
|
assert np.all(y_not_stratified == 1)
|
|
|
|
_, y_stratified = resample(X, y, n_samples=10, random_state=0, stratify=y)
|
|
assert not np.all(y_stratified == 1)
|
|
assert np.sum(y_stratified) == 9 # all 1s, one 0
|
|
|
|
|
|
def test_resample_stratified_replace():
|
|
# Make sure stratified resampling supports the replace parameter
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 100
|
|
X = rng.normal(size=(n_samples, 1))
|
|
y = rng.randint(0, 2, size=n_samples)
|
|
|
|
X_replace, _ = resample(
|
|
X, y, replace=True, n_samples=50, random_state=rng, stratify=y
|
|
)
|
|
X_no_replace, _ = resample(
|
|
X, y, replace=False, n_samples=50, random_state=rng, stratify=y
|
|
)
|
|
assert np.unique(X_replace).shape[0] < 50
|
|
assert np.unique(X_no_replace).shape[0] == 50
|
|
|
|
# make sure n_samples can be greater than X.shape[0] if we sample with
|
|
# replacement
|
|
X_replace, _ = resample(
|
|
X, y, replace=True, n_samples=1000, random_state=rng, stratify=y
|
|
)
|
|
assert X_replace.shape[0] == 1000
|
|
assert np.unique(X_replace).shape[0] == 100
|
|
|
|
|
|
def test_resample_stratify_2dy():
|
|
# Make sure y can be 2d when stratifying
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 100
|
|
X = rng.normal(size=(n_samples, 1))
|
|
y = rng.randint(0, 2, size=(n_samples, 2))
|
|
X, y = resample(X, y, n_samples=50, random_state=rng, stratify=y)
|
|
assert y.ndim == 2
|
|
|
|
|
|
def test_notimplementederror():
|
|
with pytest.raises(
|
|
NotImplementedError,
|
|
match="Resampling with sample_weight is only implemented for replace=True.",
|
|
):
|
|
resample([0, 1], [0, 1], sample_weight=[1, 1], replace=False)
|
|
|
|
with pytest.raises(
|
|
NotImplementedError,
|
|
match="Resampling with sample_weight is only implemented for stratify=None",
|
|
):
|
|
resample([0, 1], [0, 1], sample_weight=[1, 1], stratify=[0, 1])
|
|
|
|
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
|
def test_resample_stratify_sparse_error(csr_container):
|
|
# resample must be ndarray
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 100
|
|
X = rng.normal(size=(n_samples, 2))
|
|
y = rng.randint(0, 2, size=n_samples)
|
|
stratify = csr_container(y.reshape(-1, 1))
|
|
with pytest.raises(TypeError, match="Sparse data was passed"):
|
|
X, y = resample(X, y, n_samples=50, random_state=rng, stratify=stratify)
|
|
|
|
|
|
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
|
|
|
|
|
|
@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
|
|
def test_shuffle_dont_convert_to_array(csc_container):
|
|
# 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 = csc_container(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]]))
|