def test_fitting():
"""
Verify that the square error diminishes with fitting
"""
num_sentences = 5000
seed = 10
corpus = Corpus()
corpus.fit(generate_training_corpus(num_sentences, vocabulary_size=50, seed=seed))
# Check that the performance is poor without fitting
glove_model = Glove(no_components=100, learning_rate=0.05)
glove_model.fit(corpus.matrix, epochs=0, no_threads=2)
log_cooc_mat = corpus.matrix.copy()
log_cooc_mat.data = np.log(log_cooc_mat.data)
log_cooc_mat = np.asarray(log_cooc_mat.todense())
repr_matrix = _reproduce_input_matrix(glove_model)
assert ((repr_matrix - log_cooc_mat) ** 2).sum() > 30000.0
# Check that it is good with fitting
glove_model = Glove(no_components=100, learning_rate=0.05)
glove_model.fit(corpus.matrix, epochs=500, no_threads=2)
repr_matrix = _reproduce_input_matrix(glove_model)
assert ((repr_matrix - log_cooc_mat) ** 2).sum() < 1500.0
def test_large_corpus_construction():
num_sentences = 5000
seed = 10
corpus = Corpus()
corpus.fit(generate_training_corpus(num_sentences, seed=seed))
matrix = corpus.matrix.tocsr().tocoo()
check_matrix = build_coocurrence_matrix(
generate_training_corpus(num_sentences, seed=seed))
assert (matrix.row == check_matrix.row).all()
assert (matrix.col == check_matrix.col).all()
assert np.allclose(matrix.data, check_matrix.data)
assert (matrix.data > 0).all()
def test_large_corpus_construction_wo_weighting_symmetric():
num_sentences = 5000
seed = 10
corpus = Corpus()
corpus.fit(generate_training_corpus(num_sentences, seed=seed),
distance_weighting=False,
symmetric=True)
matrix = corpus.matrix.tocsr().tocoo()
check_matrix = build_coocurrence_matrix_wo_weighting_symmetric(
generate_training_corpus(num_sentences, seed=seed))
assert (matrix.row == check_matrix.row).all()
assert (matrix.col == check_matrix.col).all()
assert np.allclose(matrix.data, check_matrix.data)
assert (matrix.data > 0).all()
def test_fitting():
"""
Verify that the square error diminishes with fitting
"""
num_sentences = 5000
seed = 10
corpus = Corpus()
corpus.fit(generate_training_corpus(num_sentences,
vocabulary_size=50,
seed=seed))
# Check that the performance is poor without fitting
glove_model = Glove(no_components=100, learning_rate=0.05)
glove_model.fit(corpus.matrix,
epochs=0,
no_threads=2)
log_cooc_mat = corpus.matrix.copy()
log_cooc_mat.data = np.log(log_cooc_mat.data)
log_cooc_mat = np.asarray(log_cooc_mat.todense())
repr_matrix = _reproduce_input_matrix(glove_model)
assert ((repr_matrix - log_cooc_mat) ** 2).sum() > 30000.0
# Check that it is good with fitting
glove_model = Glove(no_components=100, learning_rate=0.05)
glove_model.fit(corpus.matrix,
epochs=500,
no_threads=2)
repr_matrix = _reproduce_input_matrix(glove_model)
assert ((repr_matrix - log_cooc_mat) ** 2).sum() < 1500.0
