test_base.py

import os
import shutil
import tempfile
import warnings
from functools import partial
from pathlib import Path
from pickle import dumps, loads

import numpy as np
import pytest

from sklearn.datasets import (
    clear_data_home,
    get_data_home,
    load_breast_cancer,
    load_diabetes,
    load_digits,
    load_files,
    load_iris,
    load_linnerud,
    load_sample_image,
    load_sample_images,
    load_wine,
)
from sklearn.datasets._base import (
    load_csv_data,
    load_gzip_compressed_csv_data,
)
from sklearn.datasets.tests.test_common import check_as_frame
from sklearn.preprocessing import scale
from sklearn.utils import Bunch
from sklearn.utils.fixes import _is_resource


class _DummyPath:
    """Minimal class that implements the os.PathLike interface."""

    def __init__(self, path):
        self.path = path

    def __fspath__(self):
        return self.path


def _remove_dir(path):
    if os.path.isdir(path):
        shutil.rmtree(path)


@pytest.fixture(scope="module")
def data_home(tmpdir_factory):
    tmp_file = str(tmpdir_factory.mktemp("scikit_learn_data_home_test"))
    yield tmp_file
    _remove_dir(tmp_file)


@pytest.fixture(scope="module")
def load_files_root(tmpdir_factory):
    tmp_file = str(tmpdir_factory.mktemp("scikit_learn_load_files_test"))
    yield tmp_file
    _remove_dir(tmp_file)


@pytest.fixture
def test_category_dir_1(load_files_root):
    test_category_dir1 = tempfile.mkdtemp(dir=load_files_root)
    sample_file = tempfile.NamedTemporaryFile(dir=test_category_dir1, delete=False)
    sample_file.write(b"Hello World!\n")
    sample_file.close()
    yield str(test_category_dir1)
    _remove_dir(test_category_dir1)


@pytest.fixture
def test_category_dir_2(load_files_root):
    test_category_dir2 = tempfile.mkdtemp(dir=load_files_root)
    yield str(test_category_dir2)
    _remove_dir(test_category_dir2)


@pytest.mark.parametrize("path_container", [None, Path, _DummyPath])
def test_data_home(path_container, data_home):
    # get_data_home will point to a pre-existing folder
    if path_container is not None:
        data_home = path_container(data_home)
    data_home = get_data_home(data_home=data_home)
    assert data_home == data_home
    assert os.path.exists(data_home)

    # clear_data_home will delete both the content and the folder it-self
    if path_container is not None:
        data_home = path_container(data_home)
    clear_data_home(data_home=data_home)
    assert not os.path.exists(data_home)

    # if the folder is missing it will be created again
    data_home = get_data_home(data_home=data_home)
    assert os.path.exists(data_home)


def test_default_empty_load_files(load_files_root):
    res = load_files(load_files_root)
    assert len(res.filenames) == 0
    assert len(res.target_names) == 0
    assert res.DESCR is None


def test_default_load_files(test_category_dir_1, test_category_dir_2, load_files_root):
    res = load_files(load_files_root)
    assert len(res.filenames) == 1
    assert len(res.target_names) == 2
    assert res.DESCR is None
    assert res.data == [b"Hello World!\n"]


def test_load_files_w_categories_desc_and_encoding(
    test_category_dir_1, test_category_dir_2, load_files_root
):
    category = os.path.abspath(test_category_dir_1).split(os.sep).pop()
    res = load_files(
        load_files_root, description="test", categories=[category], encoding="utf-8"
    )

    assert len(res.filenames) == 1
    assert len(res.target_names) == 1
    assert res.DESCR == "test"
    assert res.data == ["Hello World!\n"]


def test_load_files_wo_load_content(
    test_category_dir_1, test_category_dir_2, load_files_root
):
    res = load_files(load_files_root, load_content=False)
    assert len(res.filenames) == 1
    assert len(res.target_names) == 2
    assert res.DESCR is None
    assert res.get("data") is None


@pytest.mark.parametrize("allowed_extensions", ([".txt"], [".txt", ".json"]))
def test_load_files_allowed_extensions(tmp_path, allowed_extensions):
    """Check the behaviour of `allowed_extension` in `load_files`."""
    d = tmp_path / "sub"
    d.mkdir()
    files = ("file1.txt", "file2.json", "file3.json", "file4.md")
    paths = [d / f for f in files]
    for p in paths:
        p.write_bytes(b"hello")
    res = load_files(tmp_path, allowed_extensions=allowed_extensions)
    assert set([str(p) for p in paths if p.suffix in allowed_extensions]) == set(
        res.filenames
    )


@pytest.mark.parametrize(
    "filename, expected_n_samples, expected_n_features, expected_target_names",
    [
        ("wine_data.csv", 178, 13, ["class_0", "class_1", "class_2"]),
        ("iris.csv", 150, 4, ["setosa", "versicolor", "virginica"]),
        ("breast_cancer.csv", 569, 30, ["malignant", "benign"]),
    ],
)
def test_load_csv_data(
    filename, expected_n_samples, expected_n_features, expected_target_names
):
    actual_data, actual_target, actual_target_names = load_csv_data(filename)
    assert actual_data.shape[0] == expected_n_samples
    assert actual_data.shape[1] == expected_n_features
    assert actual_target.shape[0] == expected_n_samples
    np.testing.assert_array_equal(actual_target_names, expected_target_names)


def test_load_csv_data_with_descr():
    data_file_name = "iris.csv"
    descr_file_name = "iris.rst"

    res_without_descr = load_csv_data(data_file_name=data_file_name)
    res_with_descr = load_csv_data(
        data_file_name=data_file_name, descr_file_name=descr_file_name
    )
    assert len(res_with_descr) == 4
    assert len(res_without_descr) == 3

    np.testing.assert_array_equal(res_with_descr[0], res_without_descr[0])
    np.testing.assert_array_equal(res_with_descr[1], res_without_descr[1])
    np.testing.assert_array_equal(res_with_descr[2], res_without_descr[2])

    assert res_with_descr[-1].startswith(".. _iris_dataset:")


@pytest.mark.parametrize(
    "filename, kwargs, expected_shape",
    [
        ("diabetes_data_raw.csv.gz", {}, [442, 10]),
        ("diabetes_target.csv.gz", {}, [442]),
        ("digits.csv.gz", {"delimiter": ","}, [1797, 65]),
    ],
)
def test_load_gzip_compressed_csv_data(filename, kwargs, expected_shape):
    actual_data = load_gzip_compressed_csv_data(filename, **kwargs)
    assert actual_data.shape == tuple(expected_shape)


def test_load_gzip_compressed_csv_data_with_descr():
    data_file_name = "diabetes_target.csv.gz"
    descr_file_name = "diabetes.rst"

    expected_data = load_gzip_compressed_csv_data(data_file_name=data_file_name)
    actual_data, descr = load_gzip_compressed_csv_data(
        data_file_name=data_file_name,
        descr_file_name=descr_file_name,
    )

    np.testing.assert_array_equal(actual_data, expected_data)
    assert descr.startswith(".. _diabetes_dataset:")


def test_load_sample_images():
    try:
        res = load_sample_images()
        assert len(res.images) == 2
        assert len(res.filenames) == 2
        images = res.images

        # assert is china image
        assert np.all(images[0][0, 0, :] == np.array([174, 201, 231], dtype=np.uint8))
        # assert is flower image
        assert np.all(images[1][0, 0, :] == np.array([2, 19, 13], dtype=np.uint8))
        assert res.DESCR
    except ImportError:
        warnings.warn("Could not load sample images, PIL is not available.")


def test_load_sample_image():
    try:
        china = load_sample_image("china.jpg")
        assert china.dtype == "uint8"
        assert china.shape == (427, 640, 3)
    except ImportError:
        warnings.warn("Could not load sample images, PIL is not available.")


def test_load_diabetes_raw():
    """Test to check that we load a scaled version by default but that we can
    get an unscaled version when setting `scaled=False`."""
    diabetes_raw = load_diabetes(scaled=False)
    assert diabetes_raw.data.shape == (442, 10)
    assert diabetes_raw.target.size, 442
    assert len(diabetes_raw.feature_names) == 10
    assert diabetes_raw.DESCR

    diabetes_default = load_diabetes()

    np.testing.assert_allclose(
        scale(diabetes_raw.data) / (442**0.5), diabetes_default.data, atol=1e-04
    )


@pytest.mark.parametrize(
    "loader_func, data_shape, target_shape, n_target, has_descr, filenames",
    [
        (load_breast_cancer, (569, 30), (569,), 2, True, ["filename"]),
        (load_wine, (178, 13), (178,), 3, True, []),
        (load_iris, (150, 4), (150,), 3, True, ["filename"]),
        (
            load_linnerud,
            (20, 3),
            (20, 3),
            3,
            True,
            ["data_filename", "target_filename"],
        ),
        (load_diabetes, (442, 10), (442,), None, True, []),
        (load_digits, (1797, 64), (1797,), 10, True, []),
        (partial(load_digits, n_class=9), (1617, 64), (1617,), 10, True, []),
    ],
)
def test_loader(loader_func, data_shape, target_shape, n_target, has_descr, filenames):
    bunch = loader_func()

    assert isinstance(bunch, Bunch)
    assert bunch.data.shape == data_shape
    assert bunch.target.shape == target_shape
    if hasattr(bunch, "feature_names"):
        assert len(bunch.feature_names) == data_shape[1]
    if n_target is not None:
        assert len(bunch.target_names) == n_target
    if has_descr:
        assert bunch.DESCR
    if filenames:
        assert "data_module" in bunch
        assert all(
            [
                f in bunch and _is_resource(bunch["data_module"], bunch[f])
                for f in filenames
            ]
        )


@pytest.mark.parametrize(
    "loader_func, data_dtype, target_dtype",
    [
        (load_breast_cancer, np.float64, int),
        (load_diabetes, np.float64, np.float64),
        (load_digits, np.float64, int),
        (load_iris, np.float64, int),
        (load_linnerud, np.float64, np.float64),
        (load_wine, np.float64, int),
    ],
)
def test_toy_dataset_frame_dtype(loader_func, data_dtype, target_dtype):
    default_result = loader_func()
    check_as_frame(
        default_result,
        loader_func,
        expected_data_dtype=data_dtype,
        expected_target_dtype=target_dtype,
    )


def test_loads_dumps_bunch():
    bunch = Bunch(x="x")
    bunch_from_pkl = loads(dumps(bunch))
    bunch_from_pkl.x = "y"
    assert bunch_from_pkl["x"] == bunch_from_pkl.x


def test_bunch_pickle_generated_with_0_16_and_read_with_0_17():
    bunch = Bunch(key="original")
    # This reproduces a problem when Bunch pickles have been created
    # with scikit-learn 0.16 and are read with 0.17. Basically there
    # is a surprising behaviour because reading bunch.key uses
    # bunch.__dict__ (which is non empty for 0.16 Bunch objects)
    # whereas assigning into bunch.key uses bunch.__setattr__. See
    # https://github.com/scikit-learn/scikit-learn/issues/6196 for
    # more details
    bunch.__dict__["key"] = "set from __dict__"
    bunch_from_pkl = loads(dumps(bunch))
    # After loading from pickle the __dict__ should have been ignored
    assert bunch_from_pkl.key == "original"
    assert bunch_from_pkl["key"] == "original"
    # Making sure that changing the attr does change the value
    # associated with __getitem__ as well
    bunch_from_pkl.key = "changed"
    assert bunch_from_pkl.key == "changed"
    assert bunch_from_pkl["key"] == "changed"


def test_bunch_dir():
    # check that dir (important for autocomplete) shows attributes
    data = load_iris()
    assert "data" in dir(data)


def test_load_boston_error():
    """Check that we raise the ethical warning when trying to import `load_boston`."""
    msg = "The Boston housing prices dataset has an ethical problem"
    with pytest.raises(ImportError, match=msg):
        from sklearn.datasets import load_boston  # noqa

    # other non-existing function should raise the usual import error
    msg = "cannot import name 'non_existing_function' from 'sklearn.datasets'"
    with pytest.raises(ImportError, match=msg):
        from sklearn.datasets import non_existing_function  # noqa