def _suggest(self, text, project, params):
self.debug('Suggesting subjects for text "{}..." (len={})'.format(
text[:20], len(text)))
vectors = project.vectorizer.transform([text])
docsim = self._index[vectors[0]]
fullresult = VectorSuggestionResult(docsim, project.subjects)
return fullresult.filter(limit=int(self.params['limit']))
def _suggest(self, text, params):
self.debug('Suggesting subjects for text "{}..." (len={})'.format(
text[:20], len(text)))
tokens = self.project.analyzer.tokenize_words(text)
vectors = self.vectorizer.transform([" ".join(tokens)])
docsim = self._index[vectors[0]]
fullresult = VectorSuggestionResult(docsim, self.project.subjects)
return fullresult.filter(limit=int(params['limit']))
def _prediction_to_result(self, prediction, params):
vector = np.zeros(len(self.project.subjects), dtype=np.float32)
for score, subject_id in prediction:
vector[subject_id] = score
result = VectorSuggestionResult(vector)
return result.filter(self.project.subjects, limit=int(params['limit']))
