69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
|
"""Test the rcv1 loader, if the data is available,
|
||
|
|
or if specifically requested via environment variable
|
||
|
|
(e.g. for CI jobs)."""
|
||
|
|
|
||
|
|
import scipy.sparse as sp
|
||
|
|
import numpy as np
|
||
|
|
from functools import partial
|
||
|
|
from sklearn.datasets.tests.test_common import check_return_X_y
|
||
|
|
from sklearn.utils._testing import assert_almost_equal
|
||
|
|
from sklearn.utils._testing import assert_array_equal
|
||
|
|
|
||
|
|
|
||
|
|
def test_fetch_rcv1(fetch_rcv1_fxt):
|
||
|
|
data1 = fetch_rcv1_fxt(shuffle=False)
|
||
|
|
X1, Y1 = data1.data, data1.target
|
||
|
|
cat_list, s1 = data1.target_names.tolist(), data1.sample_id
|
||
|
|
|
||
|
|
# test sparsity
|
||
|
|
assert sp.issparse(X1)
|
||
|
|
assert sp.issparse(Y1)
|
||
|
|
assert 60915113 == X1.data.size
|
||
|
|
assert 2606875 == Y1.data.size
|
||
|
|
|
||
|
|
# test shapes
|
||
|
|
assert (804414, 47236) == X1.shape
|
||
|
|
assert (804414, 103) == Y1.shape
|
||
|
|
assert (804414,) == s1.shape
|
||
|
|
assert 103 == len(cat_list)
|
||
|
|
|
||
|
|
# test descr
|
||
|
|
assert data1.DESCR.startswith(".. _rcv1_dataset:")
|
||
|
|
|
||
|
|
# test ordering of categories
|
||
|
|
first_categories = ["C11", "C12", "C13", "C14", "C15", "C151"]
|
||
|
|
assert_array_equal(first_categories, cat_list[:6])
|
||
|
|
|
||
|
|
# test number of sample for some categories
|
||
|
|
some_categories = ("GMIL", "E143", "CCAT")
|
||
|
|
number_non_zero_in_cat = (5, 1206, 381327)
|
||
|
|
for num, cat in zip(number_non_zero_in_cat, some_categories):
|
||
|
|
j = cat_list.index(cat)
|
||
|
|
assert num == Y1[:, j].data.size
|
||
|
|
|
||
|
|
# test shuffling and subset
|
||
|
|
data2 = fetch_rcv1_fxt(shuffle=True, subset="train", random_state=77)
|
||
|
|
X2, Y2 = data2.data, data2.target
|
||
|
|
s2 = data2.sample_id
|
||
|
|
|
||
|
|
# test return_X_y option
|
||
|
|
fetch_func = partial(fetch_rcv1_fxt, shuffle=False, subset="train")
|
||
|
|
check_return_X_y(data2, fetch_func)
|
||
|
|
|
||
|
|
# The first 23149 samples are the training samples
|
||
|
|
assert_array_equal(np.sort(s1[:23149]), np.sort(s2))
|
||
|
|
|
||
|
|
# test some precise values
|
||
|
|
some_sample_ids = (2286, 3274, 14042)
|
||
|
|
for sample_id in some_sample_ids:
|
||
|
|
idx1 = s1.tolist().index(sample_id)
|
||
|
|
idx2 = s2.tolist().index(sample_id)
|
||
|
|
|
||
|
|
feature_values_1 = X1[idx1, :].toarray()
|
||
|
|
feature_values_2 = X2[idx2, :].toarray()
|
||
|
|
assert_almost_equal(feature_values_1, feature_values_2)
|
||
|
|
|
||
|
|
target_values_1 = Y1[idx1, :].toarray()
|
||
|
|
target_values_2 = Y2[idx2, :].toarray()
|
||
|
|
assert_almost_equal(target_values_1, target_values_2)
|