File size: 6,488 Bytes
19b102a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
import copy
import pytest
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
@pytest.mark.parametrize('model', [('kmeans_pca_topic_model'),
('base_topic_model'),
('custom_topic_model'),
('merged_topic_model'),
('reduced_topic_model')])
def test_update_topics(model, documents, request):
topic_model = copy.deepcopy(request.getfixturevalue(model))
old_ctfidf = topic_model.c_tf_idf_
old_topics = topic_model.topics_
topic_model.update_topics(documents, n_gram_range=(1, 3))
assert old_ctfidf.shape[1] < topic_model.c_tf_idf_.shape[1]
assert old_topics == topic_model.topics_
updated_topics = [topic if topic != 1 else 0 for topic in old_topics]
topic_model.update_topics(documents, topics=updated_topics, n_gram_range=(1, 3))
assert len(set(old_topics)) - 1 == len(set(topic_model.topics_))
old_topics = topic_model.topics_
updated_topics = [topic if topic != 2 else 0 for topic in old_topics]
topic_model.update_topics(documents, topics=updated_topics, n_gram_range=(1, 3))
assert len(set(old_topics)) - 1 == len(set(topic_model.topics_))
@pytest.mark.parametrize('model', [('kmeans_pca_topic_model'),
('base_topic_model'),
('custom_topic_model'),
('merged_topic_model'),
('reduced_topic_model'),
('online_topic_model')])
def test_extract_topics(model, documents, request):
topic_model = copy.deepcopy(request.getfixturevalue(model))
nr_topics = 5
documents = pd.DataFrame({"Document": documents,
"ID": range(len(documents)),
"Topic": np.random.randint(-1, nr_topics-1, len(documents))})
topic_model._update_topic_size(documents)
topic_model._extract_topics(documents)
freq = topic_model.get_topic_freq()
assert topic_model.c_tf_idf_.shape[0] == 5
assert topic_model.c_tf_idf_.shape[1] > 100
assert isinstance(freq, pd.DataFrame)
assert nr_topics == len(freq.Topic.unique())
assert freq.Count.sum() == len(documents)
assert len(freq.Topic.unique()) == len(freq)
@pytest.mark.parametrize('model', [('kmeans_pca_topic_model'),
('base_topic_model'),
('custom_topic_model'),
('merged_topic_model'),
('reduced_topic_model'),
('online_topic_model')])
def test_extract_topics_custom_cv(model, documents, request):
topic_model = copy.deepcopy(request.getfixturevalue(model))
nr_topics = 5
documents = pd.DataFrame({"Document": documents,
"ID": range(len(documents)),
"Topic": np.random.randint(-1, nr_topics-1, len(documents))})
cv = CountVectorizer(ngram_range=(1, 2))
topic_model.vectorizer_model = cv
topic_model._update_topic_size(documents)
topic_model._extract_topics(documents)
freq = topic_model.get_topic_freq()
assert topic_model.c_tf_idf_.shape[0] == 5
assert topic_model.c_tf_idf_.shape[1] > 100
assert isinstance(freq, pd.DataFrame)
assert nr_topics == len(freq.Topic.unique())
assert freq.Count.sum() == len(documents)
assert len(freq.Topic.unique()) == len(freq)
@pytest.mark.parametrize('model', [('kmeans_pca_topic_model'),
('base_topic_model'),
('custom_topic_model'),
('merged_topic_model'),
('reduced_topic_model'),
('online_topic_model')])
@pytest.mark.parametrize("reduced_topics", [2, 4, 10])
def test_topic_reduction(model, reduced_topics, documents, request):
topic_model = copy.deepcopy(request.getfixturevalue(model))
old_topics = copy.deepcopy(topic_model.topics_)
old_freq = topic_model.get_topic_freq()
topic_model.reduce_topics(documents, nr_topics=reduced_topics)
new_freq = topic_model.get_topic_freq()
if model != "online_topic_model":
assert old_freq.Count.sum() == new_freq.Count.sum()
assert len(old_freq.Topic.unique()) == len(old_freq)
assert len(new_freq.Topic.unique()) == len(new_freq)
assert len(topic_model.topics_) == len(old_topics)
assert topic_model.topics_ != old_topics
@pytest.mark.parametrize('model', [('kmeans_pca_topic_model'),
('base_topic_model'),
('custom_topic_model'),
('merged_topic_model'),
('reduced_topic_model'),
('online_topic_model')])
def test_topic_reduction_edge_cases(model, documents, request):
topic_model = copy.deepcopy(request.getfixturevalue(model))
topic_model.nr_topics = 100
nr_topics = 5
topics = np.random.randint(-1, nr_topics - 1, len(documents))
old_documents = pd.DataFrame({"Document": documents,
"ID": range(len(documents)),
"Topic": topics})
topic_model._update_topic_size(old_documents)
topic_model._extract_topics(old_documents)
old_freq = topic_model.get_topic_freq()
new_documents = topic_model._reduce_topics(old_documents)
new_freq = topic_model.get_topic_freq()
assert not set(old_documents.Topic).difference(set(new_documents.Topic))
pd.testing.assert_frame_equal(old_documents, new_documents)
pd.testing.assert_frame_equal(old_freq, new_freq)
@pytest.mark.parametrize('model', [('kmeans_pca_topic_model'),
('base_topic_model'),
('custom_topic_model'),
('merged_topic_model'),
('reduced_topic_model'),
('online_topic_model')])
def test_find_topics(model, request):
topic_model = copy.deepcopy(request.getfixturevalue(model))
similar_topics, similarity = topic_model.find_topics("car")
assert np.mean(similarity) > 0.1
assert len(similar_topics) > 0
|