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