File size: 5,340 Bytes
7885a28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
"""Test the 20news downloader, if the data is available,
or if specifically requested via environment variable
(e.g. for CI jobs)."""
from functools import partial
from unittest.mock import patch
import numpy as np
import pytest
import scipy.sparse as sp
from sklearn.datasets.tests.test_common import (
check_as_frame,
check_pandas_dependency_message,
check_return_X_y,
)
from sklearn.preprocessing import normalize
from sklearn.utils._testing import assert_allclose_dense_sparse
def test_20news(fetch_20newsgroups_fxt):
data = fetch_20newsgroups_fxt(subset="all", shuffle=False)
assert data.DESCR.startswith(".. _20newsgroups_dataset:")
# 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.issparse(bunch.data) and bunch.data.format == "csr"
assert bunch.data.shape == (11314, 130107)
assert bunch.target.shape[0] == 11314
assert bunch.data.dtype == np.float64
assert bunch.DESCR.startswith(".. _20newsgroups_dataset:")
# test subset = test
bunch = fetch_20newsgroups_vectorized_fxt(subset="test")
assert sp.issparse(bunch.data) and bunch.data.format == "csr"
assert bunch.data.shape == (7532, 130107)
assert bunch.target.shape[0] == 7532
assert bunch.data.dtype == np.float64
assert bunch.DESCR.startswith(".. _20newsgroups_dataset:")
# 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.issparse(bunch.data) and bunch.data.format == "csr"
assert bunch.data.shape == (11314 + 7532, 130107)
assert bunch.target.shape[0] == 11314 + 7532
assert bunch.data.dtype == np.float64
assert bunch.DESCR.startswith(".. _20newsgroups_dataset:")
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)
def test_20news_as_frame(fetch_20newsgroups_vectorized_fxt):
pd = pytest.importorskip("pandas")
bunch = fetch_20newsgroups_vectorized_fxt(as_frame=True)
check_as_frame(bunch, fetch_20newsgroups_vectorized_fxt)
frame = bunch.frame
assert frame.shape == (11314, 130108)
assert all([isinstance(col, pd.SparseDtype) for col in bunch.data.dtypes])
# Check a small subset of features
for expected_feature in [
"beginner",
"beginners",
"beginning",
"beginnings",
"begins",
"begley",
"begone",
]:
assert expected_feature in frame.keys()
assert "category_class" in frame.keys()
assert bunch.target.name == "category_class"
def test_as_frame_no_pandas(fetch_20newsgroups_vectorized_fxt, hide_available_pandas):
check_pandas_dependency_message(fetch_20newsgroups_vectorized_fxt)
def test_outdated_pickle(fetch_20newsgroups_vectorized_fxt):
with patch("os.path.exists") as mock_is_exist:
with patch("joblib.load") as mock_load:
# mock that the dataset was cached
mock_is_exist.return_value = True
# mock that we have an outdated pickle with only X and y returned
mock_load.return_value = ("X", "y")
err_msg = "The cached dataset located in"
with pytest.raises(ValueError, match=err_msg):
fetch_20newsgroups_vectorized_fxt(as_frame=True)
|