3b7a3e628b0dea98f0dcaad6d91b22c7c137d04d,bambi/models.py,Model,add,#Model#Any#Any#Any#Any#Any#Any#Any#,266

Before Change

                        lev_data = grpr_df.multiply(pred_df.iloc[:, ind],
                                                    axis=0)
                        // Skip terms with no columns left
                        if lev_data.shape[1] == 0:
                            continue

                        // Also rename intercepts and skip if already added.
                        // This can happen if user specifies something like
                        // random=["1|school", "student|school"].
                        if col == "Intercept":
                            if grpr in self.terms:
                                continue
                            label = "1|%s" % grpr

After Change

                    for col, i in pred_df.design_info.column_name_indexes.items():
                        pred_data = pred_df.iloc[:, i]
                        lev_data = grpr_df.multiply(pred_data, axis=0)

                        // Also rename intercepts and skip if already added.
                        // This can happen if user specifies something like
                        // random=["1|school", "student|school"].
                        if col == "Intercept":
                            if grpr in self.terms:
                                continue
                            label = "1|%s" % grpr
                        else:
                            label = col + "|" + grpr

                        prior = priors.pop(label, priors.get("random", None))

                        // Categorical or continuous is determined from data
                        ld = lev_data.values
                        if ((ld == 0) | (ld == 1)).all():
                            lev_data = lev_data.astype(int)
                            cat = True
                        else:
                            cat = False

                        pred_data = pred_data[:, None]  // Must be 2D later
                        term = RandomTerm(self, label, lev_data, pred_data,
                                          grpr_df.values, categorical=cat)
                        self.terms[label] = term

        self.built = False

    def _add_y(self, variable, prior=None, family="gaussian", link=None, *args,

Instances