85aabb5014e22659ac722280607a1f4b44e1fb32,tslearn/shapelets.py,ShapeletModel,fit,#ShapeletModel#Any#Any#,380

Before Change

        else:
            y_ = y
            self.categorical_y_ = True
            self.classes_ = numpy.unique(y)
            assert y_.shape[1] != 2, ("Binary classification case, " +
                                      "monodimensional y should be passed.")

        if y_.ndim == 1 or y_.shape[1] == 1:

After Change

        self.label_to_ind_ = None
        self.d_ = d

        self.classes_ = [int(lab) for lab in set(y)]
        n_labels = len(self.classes_)
        self.label_to_ind_ = {int(lab): ind
                              for ind, lab in enumerate(self.classes_)}
        y_ind = numpy.array(
            [self.label_to_ind_[lab] for lab in y]
        )
        y_ = to_categorical(y_ind)
        if n_labels == 2:
            y_ = y_[:, 1:]  // Keep only indicator of max index class

        if self.n_shapelets_per_size is None:
            sizes = grabocka_params_to_shapelet_size_dict(n_ts, sz, n_labels,
                                                          self.shapelet_length,
                                                          self.total_lengths)

Instances