def predict(self, url):
# TODO: url results can be cached
# Get named entities for requested url
test_article = Article(url, '', '', '')
# Return an error if no named entities were found
if len(test_article.named_entities) == 0:
raise NoNamedEntities()
# TODO: Add some caching for this
# Fetch articles with the same publish date
try:
articles = Article.load_articles_from_datasets(
test_article.published_at.strftime('%Y-%m-%d')
)
except SherlockMLDatasetsError:
raise NoArticlesFound()
# Return an error if no articles were found for the published date
if len(articles) == 0:
raise NoArticlesFound()
# List of named entities
named_entities_list = list(map(lambda x: ' '.join(x.named_entities), articles))
named_entities_list.append(' '.join(test_article.named_entities))
# TF-IDF matrix
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(named_entities_list)
# Fit KNN
nbrs = NearestNeighbors(n_neighbors=20)
nbrs.fit(tfidf_matrix)
# Predict
test_row = tfidf_matrix.getrow(len(named_entities_list) - 1)
distances, indices = nbrs.kneighbors(test_row)
# Format predictions
similar_articles = {
'left': None,
'center': None,
'right': None,
}
for idx, val in enumerate(indices.flatten()[1:]):
if val == len(articles):
continue
article = articles[val]
if article.url == test_article.url or len(article.named_entities) <= 1:
continue
distance = distances.flatten()[idx]
bias = article.bias
article_json = {
'distance': distance,
'title': article.title,
'url': article.url,
'description': article.description,
'source_id': article.source_id,
'named_entities': article.named_entities,
}
if bias == 1:
current_left = similar_articles['left']
if current_left is None or distance < current_left['distance']:
similar_articles['left'] = article_json
elif bias == 0:
current_center = similar_articles['center']
if current_center is None or distance < current_center['distance']:
similar_articles['center'] = article_json
elif bias == -1:
current_right = similar_articles['right']
if current_right is None or distance < current_right['distance']:
similar_articles['right'] = article_json
return similar_articles
