def test_to_dict_returns_one_dictionary_when_cursor_has_one_result(self):
source = Source()
source.id = 1
source.name = 'foo_source'
source.save()
news_item = NewsItem()
news_item.id = 1
news_item.title = 'foo'
news_item.description = 'bar'
news_item.url = 'https://www.google.com'
news_item.added_at = '2018-11-23 01:00:00+00:00'
news_item.source = source
news_item.published = False
news_item.score = 1
news_item.save()
with connection.cursor() as cursor:
cursor.execute('SELECT * FROM news_item')
result = self.newsitem_metric.to_dict(cursor)
self.assertEquals(1, len(result))
self.assertEquals([{
'id': 1,
'title': 'foo',
'description': 'bar',
'url': 'https://www.google.com',
'added_at': datetime(2018, 11, 23, 1, 0),
'source_id': 1,
'published': False,
'score': 1
}], result)
def test_corpus_create_positive_creates_positive_corpora_and_enqueues_job_to_retrain_classifier_when_newsitems_in_query_set(
self):
news_item = NewsItem()
news_item.title = 'foo'
news_item.published = True
news_item.save()
query_set = [news_item]
self.newsitem.corpus_create_positive(None, None, query_set)
self.newsitem.enqueue_corpus_creation.assert_called_once()
corpora = Corpus.objects.all()
self.assertEquals(1, len(corpora))
self.assertEquals(True, corpora[0].positive)
def test_news_item_unpublish_unpublishes_newsitems_in_query_set(self):
news_items = NewsItem.objects.all()
self.assertEquals([], list(news_items))
news_item = NewsItem()
news_item.title = 'foo'
news_item.published = True
query_set = [news_item]
self.newsitem.news_item_unpublish(None, None, query_set)
news_items = NewsItem.objects.all()
self.assertEquals(1, len(news_items))
self.assertEquals(False, news_items[0].published)
def test_get_accuracy_does_not_include_unclassified_newsitems(self):
news_item = NewsItem()
news_item.score = None
news_item.added_at = '2018-11-24 01:00:00+00:00'
news_item.save()
news_item = NewsItem()
news_item.score = None
news_item.published = True
news_item.added_at = '2018-11-24 02:00:00+00:00'
news_item.save()
metrics = self.newsitem_metric.get_accuracy(self.date_range)
self.assertEquals([], metrics)
def test_news_item_publish_and_corpus_create_positive_publishes_newsitems_and_creates_positive_corpora_when_newsitems_in_query_set(
self):
news_item = NewsItem()
news_item.title = 'foo'
news_item.published = False
news_item.save()
query_set = [news_item]
self.newsitem.news_item_publish_and_corpus_create_positive(
None, None, query_set)
self.newsitem.enqueue_corpus_creation.assert_called_once()
corpora = Corpus.objects.all()
self.assertEquals(1, len(corpora))
self.assertEquals(True, corpora[0].positive)
news_items = NewsItem.objects.all()
self.assertEquals(1, len(news_items))
self.assertEquals(True, news_items[0].published)
def setUp(self):
# retain the original imported packages
classify.serializers_real = classify.serializers
# mock the tread target methods
self.thread_classify = mock.MagicMock()
self.thread_train = mock.MagicMock()
classify.threading.Thread = mock.MagicMock(
side_effect=[self.thread_classify, self.thread_train])
# mock pika.BlockingConnection
self.connection = mock.MagicMock()
classify.pika.BlockingConnection = mock.MagicMock(
return_value=self.connection)
# mock pika.BlockingConnection.channel
self.channel = mock.MagicMock()
self.connection.channel = mock.MagicMock(return_value=self.channel)
# mock ConnectionParameters
classify.pika.ConnectionParameters = mock.MagicMock()
# mock the ORM connection
self.db_connection = mock.MagicMock()
classify.connection = self.db_connection
# mock the classifier
classify.NaiveBayesClassifier = mock.MagicMock()
classify.settings.AUTO_PUBLISH = False
# a fake news item to be used as classification input
news_item = NewsItem()
news_item.title = 'foo'
self.serialized_news_item = serializers.serialize('json', [news_item])
news_item = NewsItem()
news_item.title = 'foo'
news_item.published = 'foo'
self.serialized_news_item_crap = serializers.serialize(
'json', [news_item])
# mock the logger
self.logger = mock.MagicMock()
classify.logging.getLogger = mock.MagicMock(return_value=self.logger)
self.command = classify.Command()
def test_news_returns_http_response_with_template_and_positive_newsitems_when_positive_newsitems_exist(self):
# create a positive news item
news_item = NewsItem()
news_item.title = 'foo'
news_item.score = 1
news_item.published = True
news_item.save()
# make request to news view
response = self.get_news()
# returns an instance of HttpResponse
self.assertTrue(type(response) is HttpResponse)
# request didn't fail
self.assertEquals(200, response.status_code)
# response contains news items
content = response.getvalue()
self.assertTrue('news-item' in str(content))
def test_about_returns_http_response_with_stats(self):
# create a news item
news_item = NewsItem()
news_item.title = 'foo'
news_item.score = 1
news_item.published = False
news_item.save()
# create an active source
source = Source()
source.name = 'foo'
source.url = 'http://www.foo.com'
source.homepage = 'http://www.foo.com'
source.save()
# create an inactive source
source = Source()
source.name = 'bar'
source.url = 'http://www.bar.com'
source.homepage = 'http://www.bar.com'
source.active = False
source.save()
# make request to news view
response = self.get_about()
# returns an instance of HttpResponse
self.assertTrue(type(response) is HttpResponse)
# request didn't fail
self.assertEquals(200, response.status_code)
# response does not contain any news items
content = response.getvalue()
self.assertTrue('Sources crawled</strong>: 1' in str(content))
self.assertTrue('News classified</strong>: 1' in str(content))
self.assertTrue('Corpora created</strong>: 0' in str(content))
self.assertTrue('Classification accuracy</strong>: 100%' in str(content))