Uploaded Test files

venv/Lib/site-packages/sklearn/datasets/tests/test_20news.py

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+"""Test the 20news downloader, if the data is available,
+or if specifically requested via environment variable
+(e.g. for travis cron job)."""
+from functools import partial
+
+import numpy as np
+import scipy.sparse as sp
+
+from sklearn.utils._testing import assert_allclose_dense_sparse
+from sklearn.datasets.tests.test_common import check_return_X_y
+from sklearn.preprocessing import normalize
+
+
+def test_20news(fetch_20newsgroups_fxt):
+    data = fetch_20newsgroups_fxt(subset='all', shuffle=False)
+
+    # Extract a reduced dataset
+    data2cats = fetch_20newsgroups_fxt(
+        subset='all', categories=data.target_names[-1:-3:-1], shuffle=False)
+    # Check that the ordering of the target_names is the same
+    # as the ordering in the full dataset
+    assert data2cats.target_names == data.target_names[-2:]
+    # Assert that we have only 0 and 1 as labels
+    assert np.unique(data2cats.target).tolist() == [0, 1]
+
+    # Check that the number of filenames is consistent with data/target
+    assert len(data2cats.filenames) == len(data2cats.target)
+    assert len(data2cats.filenames) == len(data2cats.data)
+
+    # Check that the first entry of the reduced dataset corresponds to
+    # the first entry of the corresponding category in the full dataset
+    entry1 = data2cats.data[0]
+    category = data2cats.target_names[data2cats.target[0]]
+    label = data.target_names.index(category)
+    entry2 = data.data[np.where(data.target == label)[0][0]]
+    assert entry1 == entry2
+
+    # check that return_X_y option
+    X, y = fetch_20newsgroups_fxt(subset='all', shuffle=False, return_X_y=True)
+    assert len(X) == len(data.data)
+    assert y.shape == data.target.shape
+
+
+def test_20news_length_consistency(fetch_20newsgroups_fxt):
+    """Checks the length consistencies within the bunch
+
+    This is a non-regression test for a bug present in 0.16.1.
+    """
+    # Extract the full dataset
+    data = fetch_20newsgroups_fxt(subset='all')
+    assert len(data['data']) == len(data.data)
+    assert len(data['target']) == len(data.target)
+    assert len(data['filenames']) == len(data.filenames)
+
+
+def test_20news_vectorized(fetch_20newsgroups_vectorized_fxt):
+    # test subset = train
+    bunch = fetch_20newsgroups_vectorized_fxt(subset="train")
+    assert sp.isspmatrix_csr(bunch.data)
+    assert bunch.data.shape == (11314, 130107)
+    assert bunch.target.shape[0] == 11314
+    assert bunch.data.dtype == np.float64
+
+    # test subset = test
+    bunch = fetch_20newsgroups_vectorized_fxt(subset="test")
+    assert sp.isspmatrix_csr(bunch.data)
+    assert bunch.data.shape == (7532, 130107)
+    assert bunch.target.shape[0] == 7532
+    assert bunch.data.dtype == np.float64
+
+    # test return_X_y option
+    fetch_func = partial(fetch_20newsgroups_vectorized_fxt, subset='test')
+    check_return_X_y(bunch, fetch_func)
+
+    # test subset = all
+    bunch = fetch_20newsgroups_vectorized_fxt(subset='all')
+    assert sp.isspmatrix_csr(bunch.data)
+    assert bunch.data.shape == (11314 + 7532, 130107)
+    assert bunch.target.shape[0] == 11314 + 7532
+    assert bunch.data.dtype == np.float64
+
+
+def test_20news_normalization(fetch_20newsgroups_vectorized_fxt):
+    X = fetch_20newsgroups_vectorized_fxt(normalize=False)
+    X_ = fetch_20newsgroups_vectorized_fxt(normalize=True)
+    X_norm = X_['data'][:100]
+    X = X['data'][:100]
+
+    assert_allclose_dense_sparse(X_norm, normalize(X))
+    assert np.allclose(np.linalg.norm(X_norm.todense(), axis=1), 1)