projektAI/venv/Lib/site-packages/sklearn/datasets/tests/test_base.py

import os
import shutil
import tempfile
import warnings
from pickle import loads
from pickle import dumps
from functools import partial

import pytest

import numpy as np
from sklearn.datasets import get_data_home
from sklearn.datasets import clear_data_home
from sklearn.datasets import load_files
from sklearn.datasets import load_sample_images
from sklearn.datasets import load_sample_image
from sklearn.datasets import load_digits
from sklearn.datasets import load_diabetes
from sklearn.datasets import load_linnerud
from sklearn.datasets import load_iris
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import load_boston
from sklearn.datasets import load_wine
from sklearn.utils import Bunch
from sklearn.datasets.tests.test_common import check_as_frame

from sklearn.externals._pilutil import pillow_installed

from sklearn.utils import IS_PYPY


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)


def test_data_home(data_home):
    # get_data_home will point to a pre-existing folder
    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
    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):
    if IS_PYPY:
        pytest.xfail('[PyPy] fails due to string containing NUL characters')
    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):
    if IS_PYPY:
        pytest.xfail('[PyPy] fails due to string containing NUL characters')
    category = os.path.abspath(test_category_dir_1).split('/').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


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_missing_sample_image_error():
    if pillow_installed:
        with pytest.raises(AttributeError):
            load_sample_image('blop.jpg')
    else:
        warnings.warn("Could not load sample images, PIL is not available.")


@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, []),
     (load_boston, (506, 13), (506,), None, True, ["filename"])]
)
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 all([os.path.exists(bunch.get(f, False)) 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)