F1_communities.py 文件源码

def __plot_scatter(prl, max_points=None, fileout="PR_scatter.png", title="Precision Recall Scatter Plot"):
        """

        :param prl: list of tuples (precision, recall)
        :param max_points: max number of tuples to plot
        :param fileout: output filename
        :param title: plot title
        """

        prs = [i[0] for i in prl]
        recs = [i[1] for i in prl]

        if max_points is not None:
            prs = prs[:max_points]
            recs = recs[:max_points]

        xy = np.vstack([prs, recs])
        z = gaussian_kde(xy)(xy)
        x = np.array(prs)
        y = np.array(recs)
        base = min(z)
        rg = max(z) - base
        z = np.array(z)
        idx = z.argsort()
        x, y, z = x[idx], y[idx], (z[idx] - base) / rg
        fig, ax = plt.subplots()
        sca = ax.scatter(x, y, c=z, s=50, edgecolor='', cmap=plt.cm.jet)
        fig.colorbar(sca)
        plt.ylabel("Recall", fontsize=20, labelpad=15)
        plt.xlabel("Precision", fontsize=20)
        plt.ylim([-0.01, 1.01])
        plt.xlim([-0.01, 1.01])
        plt.title(title)
        if matplotlib.get_backend().lower() in ['agg', 'macosx']:
            fig.set_tight_layout(True)
        else:
            fig.tight_layout()

        plt.savefig("%s" % fileout)