Uploaded Test files

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

@@ -0,0 +1,306 @@
+import os
+import shutil
+import tempfile
+import warnings
+import numpy
+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_return_X_y
+from sklearn.datasets.tests.test_common import check_as_frame
+from sklearn.datasets.tests.test_common import check_pandas_dependency_message
+
+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_digits():
+    digits = load_digits()
+    assert digits.data.shape == (1797, 64)
+    assert numpy.unique(digits.target).size == 10
+
+    # test return_X_y option
+    check_return_X_y(digits, partial(load_digits))
+
+
+def test_load_digits_n_class_lt_10():
+    digits = load_digits(n_class=9)
+    assert digits.data.shape == (1617, 64)
+    assert numpy.unique(digits.target).size == 9
+
+
+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.")
+
+
+def test_load_diabetes():
+    res = load_diabetes()
+    assert res.data.shape == (442, 10)
+    assert res.target.size, 442
+    assert len(res.feature_names) == 10
+    assert res.DESCR
+
+    # test return_X_y option
+    check_return_X_y(res, partial(load_diabetes))
+
+
+def test_load_linnerud():
+    res = load_linnerud()
+    assert res.data.shape == (20, 3)
+    assert res.target.shape == (20, 3)
+    assert len(res.target_names) == 3
+    assert res.DESCR
+    assert os.path.exists(res.data_filename)
+    assert os.path.exists(res.target_filename)
+
+    # test return_X_y option
+    check_return_X_y(res, partial(load_linnerud))
+
+
+def test_load_iris():
+    res = load_iris()
+    assert res.data.shape == (150, 4)
+    assert res.target.size == 150
+    assert res.target_names.size == 3
+    assert res.DESCR
+    assert os.path.exists(res.filename)
+
+    # test return_X_y option
+    check_return_X_y(res, partial(load_iris))
+
+
+def test_load_wine():
+    res = load_wine()
+    assert res.data.shape == (178, 13)
+    assert res.target.size == 178
+    assert res.target_names.size == 3
+    assert res.DESCR
+
+    # test return_X_y option
+    check_return_X_y(res, partial(load_wine))
+
+
+def test_load_breast_cancer():
+    res = load_breast_cancer()
+    assert res.data.shape == (569, 30)
+    assert res.target.size == 569
+    assert res.target_names.size == 2
+    assert res.DESCR
+    assert os.path.exists(res.filename)
+
+    # test return_X_y option
+    check_return_X_y(res, partial(load_breast_cancer))
+
+
+@pytest.mark.parametrize("loader_func, data_dtype, target_dtype", [
+    (load_breast_cancer, np.float64, np.int64),
+    (load_diabetes, np.float64, np.float64),
+    (load_digits, np.float64, np.int64),
+    (load_iris, np.float64, np.int64),
+    (load_linnerud, np.float64, np.float64),
+    (load_wine, np.float64, np.int64),
+])
+def test_toy_dataset_as_frame(loader_func, data_dtype, target_dtype):
+    default_result = loader_func()
+    check_as_frame(default_result, partial(loader_func),
+                   expected_data_dtype=data_dtype,
+                   expected_target_dtype=target_dtype)
+
+
+@pytest.mark.parametrize("loader_func", [
+    load_breast_cancer,
+    load_diabetes,
+    load_digits,
+    load_iris,
+    load_linnerud,
+    load_wine,
+])
+def test_toy_dataset_as_frame_no_pandas(loader_func):
+    check_pandas_dependency_message(loader_func)
+
+
+def test_load_boston():
+    res = load_boston()
+    assert res.data.shape == (506, 13)
+    assert res.target.size == 506
+    assert res.feature_names.size == 13
+    assert res.DESCR
+    assert os.path.exists(res.filename)
+
+    # test return_X_y option
+    check_return_X_y(res, partial(load_boston))
+
+
+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)