a09569767c6e477288e2df2b3507d37025a07c9a,bambi/models.py,Model,build,#Model#,82

Before Change

                arrs.append(t.data)
        X = np.concatenate(arrs + [self.y.data], axis=1)
        num_na = np.isnan(X).any(1).sum()
        if num_na:
            msg = "%d rows were found contain at least one missing value." % num_na
            if not self.dropna:
                msg += "Please make sure the dataset contains no missing " \
                       "values. Alternatively, if you want rows with missing " \
                       "values to be automatically deleted in a list-wise " \
                       "manner (not recommended), please set dropna=True at " \
                       "model initialization."
                raise ValueError(msg)
            msg += " Automatically removing %d rows from the dataset." % num_na
            warnings.warn(msg)

        // compute information used to set the default priors
        // X = fixed effects design matrix (excluding intercept/constant term)
        // r2_x = 1 - 1/VIF for each x, i.e., R2 for predicting each x from all
        // other x"s r2_y = R2 for predicting y from all x"s *other than* the
        // current x.
        // only compute these stats if there are multiple terms in the model
        terms = [t for t in self.fixed_terms.values() if t.name != "Intercept"]

        if len(self.fixed_terms) > 1:

After Change

                arrs.append(t.data)
        X = np.concatenate(arrs + [self.y.data], axis=1)
        na_index = np.isnan(X).any(1)
        if na_index.sum():
            msg = "%d rows were found contain at least one missing value." % na_index.sum()
            if not self.dropna:
                msg += "Please make sure the dataset contains no missing " \
                       "values. Alternatively, if you want rows with missing " \
                       "values to be automatically deleted in a list-wise " \
                       "manner (not recommended), please set dropna=True at " \
                       "model initialization."
                raise ValueError(msg)

            // warn and then remove missing values
            msg += " Automatically removing %d rows from the dataset." % na_index.sum()
            warnings.warn(msg)
            keeps = np.invert(na_index)
            for t in self.fixed_terms.values():
                t.data = t.data[keeps]
            self.y.data = self.y.data[keeps]

        // compute information used to set the default priors
        // X = fixed effects design matrix (excluding intercept/constant term)
        // r2_x = 1 - 1/VIF for each x, i.e., R2 for predicting each x from all
        // other x"s r2_y = R2 for predicting y from all x"s *other than* the
        // current x.
        // only compute these stats if there are multiple terms in the model
        terms = [t for t in self.fixed_terms.values() if t.name != "Intercept"]

        if len(self.fixed_terms) > 1:

Instances