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test_temporal.py
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test_temporal.py
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import numpy as np
from scipy.sparse import csr_matrix
from nose.tools import assert_equal
from numpy.testing import assert_array_almost_equal
from temporal import (strengths_over_periods,
doc_topic_strengths_over_periods)
def test_strengths_over_periods():
# words: topic, mining, computer, vision
# 2 docs, 4 words
period2matrix = {'p1': csr_matrix(np.asarray([[1, 1, 0, 0],
[2, 1, 1, 0]])),
'p2': csr_matrix(np.asarray([[1, 0, 1, 2],
[0, 0, 2, 1]]))}
# 2 topics, 4 words
topic_word_distribution = np.asarray([[0.6, 0.4, 0.000001, 0.000001],
[0.000001, 0.000001, 0.5, 0.5]])
period2strengh = strengths_over_periods(period2matrix,
topic_word_distribution,
n_top_words=2)
assert_equal(len(period2strengh), 2)
for p in ['p1', 'p2']:
assert_equal(period2strengh[p].shape, (2, ))
assert_equal(period2strengh['p1'][0], 1.3) # (0.6 + 0.4 + 1.2 + 0.4) / 2
assert_equal(period2strengh['p1'][1], 0.25) # (0.5 + 0) / 2
assert_equal(period2strengh['p2'][0], 0.3) # (0.6 + 0) / 2
assert_equal(period2strengh['p2'][1], 1.5) # (0.5 + 1.5 + 1.5 + 0.5) / 2
def test_doc_topic_strengths_over_periods():
# 2 topics, 2 periods(2+3 docs)
doc_topic_matrix = np.asarray([[0.1, 0.9],
[0.2, 0.8],
[0.8, 0.2],
[0.7, 0.3],
[0.3, 0.7]])
period2docs = {'p1': [0, 1],
'p2': [2, 3, 4]}
actual = doc_topic_strengths_over_periods(doc_topic_matrix, period2docs)
expected = {'p1': np.asarray([0.15, 0.85]),
'p2': np.asarray([0.6, 0.4])}
assert_equal(len(actual), 2)
assert_array_almost_equal(actual['p1'], expected['p1'])
assert_array_almost_equal(actual['p2'], expected['p2'])