(self, X, Y)
| 33 | self.clf.fit(X_train, Y) |
| 34 | |
| 35 | def evaluate(self, X, Y): |
| 36 | top_k_list = [len(l) for l in Y] |
| 37 | Y_ = self.predict(X, top_k_list) |
| 38 | Y = self.binarizer.transform(Y) |
| 39 | averages = ["micro", "macro", "samples", "weighted"] |
| 40 | results = {} |
| 41 | for average in averages: |
| 42 | results[average] = f1_score(Y, Y_, average=average) |
| 43 | results['acc'] = accuracy_score(Y, Y_) |
| 44 | print('-------------------') |
| 45 | print(results) |
| 46 | return results |
| 47 | |
| 48 | def predict(self, X, top_k_list): |
| 49 | X_ = numpy.asarray([self.embeddings[x] for x in X]) |
no test coverage detected