| 255 | self.k = k |
| 256 | |
| 257 | def fit(self, X, y): |
| 258 | self.label_binarizer = LabelBinarizer(sparse_output=True) |
| 259 | Y = self.label_binarizer.fit_transform(y) |
| 260 | Y = Y.tocsc() |
| 261 | self.classes = self.label_binarizer.classes_ |
| 262 | columns = (col.toarray().ravel() for col in Y.T) |
| 263 | |
| 264 | self.pipelines = Parallel(n_jobs=self.n_jobs)( |
| 265 | delayed(_fit_binary)( |
| 266 | self.estimator, self.use_bns, |
| 267 | self.use_text, self.use_frc, self.k, X, column) |
| 268 | for column in columns) |
| 269 | |
| 270 | def predict(self, X): |
| 271 | if(hasattr(self.pipelines[0], 'decision_function') and |