def test_get_newsitem_title_returns_title_when_news_item_is_not_none(self):
news_item = NewsItem()
news_item.title = 'foo'
corpus = Corpus()
corpus.news_item = news_item
admin = CorpusAdmin(Corpus, AdminSite())
self.assertEquals('foo', admin.get_news_item_title(corpus))
def run(self, queue_item, classification):
if classification == 'pos':
corpus = Corpus()
corpus.news_item = queue_item
corpus.positive = True
corpus.active = False
corpus.save()
def test_changelist_view_returns_metrics_when_corpora_exist_and_query_is_on_the_added_at_date(
self):
news_item = NewsItem()
news_item.title = 'foo'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = True
corpus.added_at = '2018-12-02 21:00:00+00:00'
corpus.save()
superuser = self.create_superuser('superuser')
request = self.mocked_authenticated_request(
'/admin/rss/corpusmetric/?added_at__month=12&added_at__year=2018',
superuser)
response = self.admin.changelist_view(request)
expected_metrics = [{'positive': True, 'total': 1}]
expected_metrics_total = {'total': 1}
self.assertEquals(expected_metrics,
response.context_data['corpus_metrics'])
self.assertEquals(expected_metrics_total,
response.context_data['corpus_metrics_total'])
class CorpusTestCase(TestCase):
corpus = None
def setUp(self):
news_item = NewsItem()
self.corpus = Corpus()
self.corpus.news_item = news_item
def test_get_classification_returns_pos_when_positive_is_true(self):
self.corpus.positive = True
self.assertEquals('pos', self.corpus.get_classification())
def test_get_classification_returns_neg_when_positive_is_false(self):
self.corpus.positive = False
self.assertEquals('neg', self.corpus.get_classification())
def test_str_returns_newsitem_title_and_classification(self):
self.corpus.positive = True
self.corpus.news_item.title = 'foo'
self.assertEquals('foo: POS', str(self.corpus))
def test_get_classifier_uses_unique_corpora(self):
# mock the shuffle list method
classify.shuffle = mock.MagicMock()
# regenerate the command class being tested as we mocked an extra module above
self.command = classify.Command()
# create dummy news items and corpora with same title and classification
for index in range(1, 3):
news_item = NewsItem()
news_item.title = 'foo'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = True
corpus.save()
# test that two corpora exist
corpora = Corpus.objects.all()
self.assertEquals(2, len(corpora))
# call the method being tested
classifier = self.command.get_classifier()
# shuffled the corpora with only one of the identical corpora
classify.shuffle.assert_called_once_with([('foo', 'pos')])
def test_get_classifier_returns_naive_bayes_classifier_with_corpora_when_corpora_exist(
self):
# mock the shuffle list method
classify.shuffle = mock.MagicMock()
# regenerate the command class being tested as we mocked an extra module above
self.command = classify.Command()
# create a dummy news item and corpus
news_item = NewsItem()
news_item.title = 'foo'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = True
corpus.save()
# call the method being tested
classifier = self.command.get_classifier()
# shuffled the corpora
classify.shuffle.assert_called_once_with([('foo', 'pos')])
# a classifier with corpora was created
classify.NaiveBayesClassifier.assert_called_once_with([('foo', 'pos')])
# the method returned a non-empty result
self.assertTrue(classifier != None)
def test_changelist_view_returns_metrics_when_accurate_and_inaccurate_newsitems_exist_and_finally_one_of_each_class(self):
news_item = NewsItem()
news_item.title = 'foo'
news_item.score = 1
news_item.added_at = '2018-12-03 21:00:00+00:00'
news_item.save()
corpus = Corpus()
corpus.positive = False
corpus.news_item = news_item
corpus.save()
news_item = NewsItem()
news_item.title = 'bar'
news_item.score = 1
news_item.added_at = '2018-12-03 22:00:00+00:00'
news_item.save()
superuser = self.create_superuser('superuser')
request = self.mocked_authenticated_request('/admin/rss/newsitemmetric/', superuser)
response = self.admin.changelist_view(request)
self.assertEquals(2, response.context_data['news_items_count'])
self.assertEquals(0, response.context_data['news_items_unclassified'])
self.assertEquals(2, response.context_data['classification_initial']['positive'])
self.assertEquals(0, response.context_data['classification_initial']['negative'])
self.assertEquals(1, response.context_data['classification_supervised']['positive'])
self.assertEquals(1, response.context_data['classification_supervised']['negative'])
self.assertEquals(0, response.context_data['corpus_count']['positive'])
self.assertEquals(1, response.context_data['corpus_count']['negative'])
self.assertEquals([{'accuracy': 50, 'added_at': '2018-12-03'}], response.context_data['accuracy'])
def corpus_create_negative(model_admin, request, query_set):
for news_item in query_set:
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = False
corpus.save()
if query_set:
enqueue_corpus_creation()
def test_get_accuracy_total_returns_0_percent_when_only_not_accurate_newsitems_exist(
self):
news_item = NewsItem()
news_item.score = 1
news_item.added_at = '2018-11-24 01:00:00+00:00'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = False
corpus.save()
accuracy = self.newsitem_metric.get_accuracy_total(self.date_range)
self.assertEquals(0.0, accuracy)
def test_get_accuracy_returns_0_percent_when_only_not_accurate_newsitems_exist(
self):
news_item = NewsItem()
news_item.score = 1
news_item.added_at = '2018-11-24 01:00:00+00:00'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = False
corpus.save()
metrics = self.newsitem_metric.get_accuracy(self.date_range)
self.assertEquals(1, len(metrics))
self.assertEquals(0, metrics[0]['accuracy'])
self.assertEquals('2018-11-24', metrics[0]['added_at'])
def test_get_accuracy_total_uses_two_day_statistics_when_exist(self):
news_item = NewsItem()
news_item.score = 1
news_item.added_at = '2018-11-24 01:00:00+00:00'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = False
corpus.save()
news_item = NewsItem()
news_item.score = 1
news_item.added_at = '2018-11-25 01:00:00+00:00'
news_item.save()
accuracy = self.newsitem_metric.get_accuracy_total(self.date_range)
self.assertEquals(50.0, accuracy)
def test_get_classifier_uses_corpora_clean_of_stopwords(self):
# mock the shuffle list method
classify.shuffle = mock.MagicMock()
# regenerate the command class being tested as we mocked an extra module above
self.command = classify.Command()
# create dummy news item (having title that contains stopwords) and corpora
news_item = NewsItem()
news_item.title = 'when foo then bar'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = True
corpus.save()
# call the method being tested
classifier = self.command.get_classifier()
# shuffled the corpora with only one of the identical corpora
classify.shuffle.assert_called_once_with([('foo bar', 'pos')])
def test_get_accuracy_returns_two_day_statistics_when_newsitems_for_two_days_exist(
self):
news_item = NewsItem()
news_item.score = 1
news_item.added_at = '2018-11-24 01:00:00+00:00'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = False
corpus.save()
news_item = NewsItem()
news_item.score = 1
news_item.added_at = '2018-11-25 01:00:00+00:00'
news_item.save()
metrics = self.newsitem_metric.get_accuracy(self.date_range)
self.assertEquals(2, len(metrics))
self.assertEquals('2018-11-24', metrics[0]['added_at'])
self.assertEquals(0, metrics[0]['accuracy'])
self.assertEquals('2018-11-25', metrics[1]['added_at'])
self.assertEquals(100, metrics[1]['accuracy'])
def test_changelist_view_returns_negative_metrics_when_only_negative_corpora_exist(
self):
news_item = NewsItem()
news_item.title = 'foo'
news_item.save()
corpus = Corpus()
corpus.news_item = news_item
corpus.positive = False
corpus.save()
superuser = self.create_superuser('superuser')
request = self.mocked_authenticated_request('/admin/rss/corpusmetric/',
superuser)
response = self.admin.changelist_view(request)
expected_metrics = [{'positive': False, 'total': 1}]
expected_metrics_total = {'total': 1}
self.assertEquals(expected_metrics,
response.context_data['corpus_metrics'])
self.assertEquals(expected_metrics_total,
response.context_data['corpus_metrics_total'])
def setUp(self):
news_item = NewsItem()
self.corpus = Corpus()
self.corpus.news_item = news_item
