metrics.py 文件源码

def _process_metric(self, ax, metric):
        if not metric.data.size:
            ax.tick_params(colors=(0, 0, 0, 0))
            ax.set_axis_bgcolor(cm.get_cmap('viridis')(0))
            divider = make_axes_locatable(ax)
            divider.append_axes('right', size='7%', pad=0.1).axis('off')
            return
        domain = self._domain(metric)
        categorical = self._is_categorical(metric.data)
        if metric.data.shape[1] == 1 and not categorical:
            self._plot_scalar(ax, domain, metric.data[:, 0])
        elif metric.data.shape[1] == 1:
            indices = metric.data[:, 0].astype(int)
            min_, max_ = indices.min(), indices.max()
            count = np.eye(max_ - min_ + 1)[indices - min_]
            self._plot_distribution(ax, domain, count)
        elif metric.data.shape[1] > 1:
            self._plot_counts(ax, domain, metric.data)