352 lines
12 KiB
Python
352 lines
12 KiB
Python
import os
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import shutil
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import tempfile
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import warnings
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from pickle import loads
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from pickle import dumps
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from functools import partial
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import pytest
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import numpy as np
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from sklearn.datasets import get_data_home
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from sklearn.datasets import clear_data_home
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from sklearn.datasets import load_files
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from sklearn.datasets import load_sample_images
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from sklearn.datasets import load_sample_image
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from sklearn.datasets import load_digits
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from sklearn.datasets import load_diabetes
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from sklearn.datasets import load_linnerud
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from sklearn.datasets import load_iris
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from sklearn.datasets import load_breast_cancer
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from sklearn.datasets import load_wine
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from sklearn.datasets._base import (
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load_csv_data,
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load_gzip_compressed_csv_data,
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)
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from sklearn.preprocessing import scale
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from sklearn.utils import Bunch
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from sklearn.utils.fixes import _is_resource
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from sklearn.datasets.tests.test_common import check_as_frame
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def _remove_dir(path):
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if os.path.isdir(path):
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shutil.rmtree(path)
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@pytest.fixture(scope="module")
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def data_home(tmpdir_factory):
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tmp_file = str(tmpdir_factory.mktemp("scikit_learn_data_home_test"))
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yield tmp_file
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_remove_dir(tmp_file)
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@pytest.fixture(scope="module")
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def load_files_root(tmpdir_factory):
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tmp_file = str(tmpdir_factory.mktemp("scikit_learn_load_files_test"))
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yield tmp_file
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_remove_dir(tmp_file)
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@pytest.fixture
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def test_category_dir_1(load_files_root):
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test_category_dir1 = tempfile.mkdtemp(dir=load_files_root)
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sample_file = tempfile.NamedTemporaryFile(dir=test_category_dir1, delete=False)
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sample_file.write(b"Hello World!\n")
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sample_file.close()
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yield str(test_category_dir1)
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_remove_dir(test_category_dir1)
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@pytest.fixture
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def test_category_dir_2(load_files_root):
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test_category_dir2 = tempfile.mkdtemp(dir=load_files_root)
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yield str(test_category_dir2)
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_remove_dir(test_category_dir2)
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def test_data_home(data_home):
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# get_data_home will point to a pre-existing folder
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data_home = get_data_home(data_home=data_home)
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assert data_home == data_home
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assert os.path.exists(data_home)
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# clear_data_home will delete both the content and the folder it-self
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clear_data_home(data_home=data_home)
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assert not os.path.exists(data_home)
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# if the folder is missing it will be created again
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data_home = get_data_home(data_home=data_home)
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assert os.path.exists(data_home)
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def test_default_empty_load_files(load_files_root):
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res = load_files(load_files_root)
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assert len(res.filenames) == 0
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assert len(res.target_names) == 0
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assert res.DESCR is None
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def test_default_load_files(test_category_dir_1, test_category_dir_2, load_files_root):
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res = load_files(load_files_root)
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assert len(res.filenames) == 1
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assert len(res.target_names) == 2
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assert res.DESCR is None
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assert res.data == [b"Hello World!\n"]
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def test_load_files_w_categories_desc_and_encoding(
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test_category_dir_1, test_category_dir_2, load_files_root
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):
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category = os.path.abspath(test_category_dir_1).split("/").pop()
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res = load_files(
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load_files_root, description="test", categories=category, encoding="utf-8"
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)
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assert len(res.filenames) == 1
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assert len(res.target_names) == 1
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assert res.DESCR == "test"
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assert res.data == ["Hello World!\n"]
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def test_load_files_wo_load_content(
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test_category_dir_1, test_category_dir_2, load_files_root
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):
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res = load_files(load_files_root, load_content=False)
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assert len(res.filenames) == 1
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assert len(res.target_names) == 2
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assert res.DESCR is None
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assert res.get("data") is None
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@pytest.mark.parametrize("allowed_extensions", ([".txt"], [".txt", ".json"]))
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def test_load_files_allowed_extensions(tmp_path, allowed_extensions):
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"""Check the behaviour of `allowed_extension` in `load_files`."""
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d = tmp_path / "sub"
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d.mkdir()
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files = ("file1.txt", "file2.json", "file3.json", "file4.md")
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paths = [d / f for f in files]
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for p in paths:
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p.write_bytes(b"hello")
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res = load_files(tmp_path, allowed_extensions=allowed_extensions)
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assert set([str(p) for p in paths if p.suffix in allowed_extensions]) == set(
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res.filenames
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)
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@pytest.mark.parametrize(
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"filename, expected_n_samples, expected_n_features, expected_target_names",
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[
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("wine_data.csv", 178, 13, ["class_0", "class_1", "class_2"]),
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("iris.csv", 150, 4, ["setosa", "versicolor", "virginica"]),
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("breast_cancer.csv", 569, 30, ["malignant", "benign"]),
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],
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)
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def test_load_csv_data(
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filename, expected_n_samples, expected_n_features, expected_target_names
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):
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actual_data, actual_target, actual_target_names = load_csv_data(filename)
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assert actual_data.shape[0] == expected_n_samples
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assert actual_data.shape[1] == expected_n_features
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assert actual_target.shape[0] == expected_n_samples
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np.testing.assert_array_equal(actual_target_names, expected_target_names)
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def test_load_csv_data_with_descr():
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data_file_name = "iris.csv"
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descr_file_name = "iris.rst"
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res_without_descr = load_csv_data(data_file_name=data_file_name)
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res_with_descr = load_csv_data(
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data_file_name=data_file_name, descr_file_name=descr_file_name
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)
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assert len(res_with_descr) == 4
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assert len(res_without_descr) == 3
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np.testing.assert_array_equal(res_with_descr[0], res_without_descr[0])
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np.testing.assert_array_equal(res_with_descr[1], res_without_descr[1])
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np.testing.assert_array_equal(res_with_descr[2], res_without_descr[2])
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assert res_with_descr[-1].startswith(".. _iris_dataset:")
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@pytest.mark.parametrize(
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"filename, kwargs, expected_shape",
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[
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("diabetes_data_raw.csv.gz", {}, [442, 10]),
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("diabetes_target.csv.gz", {}, [442]),
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("digits.csv.gz", {"delimiter": ","}, [1797, 65]),
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],
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)
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def test_load_gzip_compressed_csv_data(filename, kwargs, expected_shape):
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actual_data = load_gzip_compressed_csv_data(filename, **kwargs)
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assert actual_data.shape == tuple(expected_shape)
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def test_load_gzip_compressed_csv_data_with_descr():
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data_file_name = "diabetes_target.csv.gz"
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descr_file_name = "diabetes.rst"
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expected_data = load_gzip_compressed_csv_data(data_file_name=data_file_name)
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actual_data, descr = load_gzip_compressed_csv_data(
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data_file_name=data_file_name,
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descr_file_name=descr_file_name,
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)
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np.testing.assert_array_equal(actual_data, expected_data)
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assert descr.startswith(".. _diabetes_dataset:")
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def test_load_sample_images():
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try:
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res = load_sample_images()
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assert len(res.images) == 2
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assert len(res.filenames) == 2
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images = res.images
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# assert is china image
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assert np.all(images[0][0, 0, :] == np.array([174, 201, 231], dtype=np.uint8))
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# assert is flower image
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assert np.all(images[1][0, 0, :] == np.array([2, 19, 13], dtype=np.uint8))
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assert res.DESCR
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except ImportError:
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warnings.warn("Could not load sample images, PIL is not available.")
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def test_load_sample_image():
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try:
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china = load_sample_image("china.jpg")
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assert china.dtype == "uint8"
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assert china.shape == (427, 640, 3)
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except ImportError:
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warnings.warn("Could not load sample images, PIL is not available.")
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def test_load_missing_sample_image_error():
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pytest.importorskip("PIL")
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with pytest.raises(AttributeError):
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load_sample_image("blop.jpg")
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def test_load_diabetes_raw():
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"""Test to check that we load a scaled version by default but that we can
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get an unscaled version when setting `scaled=False`."""
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diabetes_raw = load_diabetes(scaled=False)
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assert diabetes_raw.data.shape == (442, 10)
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assert diabetes_raw.target.size, 442
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assert len(diabetes_raw.feature_names) == 10
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assert diabetes_raw.DESCR
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diabetes_default = load_diabetes()
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np.testing.assert_allclose(
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scale(diabetes_raw.data) / (442**0.5), diabetes_default.data, atol=1e-04
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)
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@pytest.mark.parametrize(
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"loader_func, data_shape, target_shape, n_target, has_descr, filenames",
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[
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(load_breast_cancer, (569, 30), (569,), 2, True, ["filename"]),
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(load_wine, (178, 13), (178,), 3, True, []),
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(load_iris, (150, 4), (150,), 3, True, ["filename"]),
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(
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load_linnerud,
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(20, 3),
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(20, 3),
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3,
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True,
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["data_filename", "target_filename"],
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),
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(load_diabetes, (442, 10), (442,), None, True, []),
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(load_digits, (1797, 64), (1797,), 10, True, []),
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(partial(load_digits, n_class=9), (1617, 64), (1617,), 10, True, []),
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],
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)
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def test_loader(loader_func, data_shape, target_shape, n_target, has_descr, filenames):
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bunch = loader_func()
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assert isinstance(bunch, Bunch)
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assert bunch.data.shape == data_shape
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assert bunch.target.shape == target_shape
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if hasattr(bunch, "feature_names"):
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assert len(bunch.feature_names) == data_shape[1]
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if n_target is not None:
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assert len(bunch.target_names) == n_target
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if has_descr:
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assert bunch.DESCR
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if filenames:
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assert "data_module" in bunch
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assert all(
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[
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f in bunch and _is_resource(bunch["data_module"], bunch[f])
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for f in filenames
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]
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)
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@pytest.mark.parametrize(
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"loader_func, data_dtype, target_dtype",
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[
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(load_breast_cancer, np.float64, int),
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(load_diabetes, np.float64, np.float64),
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(load_digits, np.float64, int),
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(load_iris, np.float64, int),
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(load_linnerud, np.float64, np.float64),
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(load_wine, np.float64, int),
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],
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)
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def test_toy_dataset_frame_dtype(loader_func, data_dtype, target_dtype):
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default_result = loader_func()
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check_as_frame(
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default_result,
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loader_func,
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expected_data_dtype=data_dtype,
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expected_target_dtype=target_dtype,
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)
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def test_loads_dumps_bunch():
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bunch = Bunch(x="x")
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bunch_from_pkl = loads(dumps(bunch))
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bunch_from_pkl.x = "y"
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assert bunch_from_pkl["x"] == bunch_from_pkl.x
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def test_bunch_pickle_generated_with_0_16_and_read_with_0_17():
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bunch = Bunch(key="original")
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# This reproduces a problem when Bunch pickles have been created
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# with scikit-learn 0.16 and are read with 0.17. Basically there
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# is a surprising behaviour because reading bunch.key uses
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# bunch.__dict__ (which is non empty for 0.16 Bunch objects)
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# whereas assigning into bunch.key uses bunch.__setattr__. See
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# https://github.com/scikit-learn/scikit-learn/issues/6196 for
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# more details
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bunch.__dict__["key"] = "set from __dict__"
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bunch_from_pkl = loads(dumps(bunch))
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# After loading from pickle the __dict__ should have been ignored
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assert bunch_from_pkl.key == "original"
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assert bunch_from_pkl["key"] == "original"
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# Making sure that changing the attr does change the value
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# associated with __getitem__ as well
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bunch_from_pkl.key = "changed"
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assert bunch_from_pkl.key == "changed"
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assert bunch_from_pkl["key"] == "changed"
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def test_bunch_dir():
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# check that dir (important for autocomplete) shows attributes
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data = load_iris()
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assert "data" in dir(data)
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def test_load_boston_error():
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"""Check that we raise the ethical warning when trying to import `load_boston`."""
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msg = "The Boston housing prices dataset has an ethical problem"
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with pytest.raises(ImportError, match=msg):
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from sklearn.datasets import load_boston # noqa
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# other non-existing function should raise the usual import error
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msg = "cannot import name 'non_existing_function' from 'sklearn.datasets'"
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with pytest.raises(ImportError, match=msg):
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from sklearn.datasets import non_existing_function # noqa
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