def plot_stats()

in tools/stats.py [0:0]

• 71 lines of code
• 18 McCabe index (conditional complexity)

    def plot_stats(self, visdom_env=None, plot_file=None,
                   visdom_server=None, visdom_port=None):

        # use the cached visdom env if none supplied
        if visdom_env is None:
            visdom_env = self.visdom_env
        if visdom_server is None:
            visdom_server = self.visdom_server
        if visdom_port is None:
            visdom_port = self.visdom_port
        if plot_file is None:
            plot_file = self.plot_file

        stat_sets = list(self.stats.keys())

        print("printing charts to visdom env '%s' (%s:%d)" %
              (visdom_env, visdom_server, visdom_port))

        novisdom = False

        viz = get_visdom_connection(server=visdom_server, port=visdom_port)
        if not viz.check_connection():
            print("no visdom server! -> skipping visdom plots")
            novisdom = True

        lines = []

        # plot metrics
        if not novisdom:
            viz.close(env=visdom_env, win=None)

        for stat in self.log_vars:
            vals = []
            stat_sets_now = []
            for stat_set in stat_sets:
                val = self.stats[stat_set][stat].get_epoch_averages()
                if val is None:
                    continue
                else:
                    val = np.array(val)[:, None]
                    stat_sets_now.append(stat_set)
                vals.append(val)

            if len(vals) == 0:
                continue

            vals = np.concatenate(vals, axis=1)
            x = np.arange(vals.shape[0])

            lines.append((stat_sets_now, stat, x, vals,))

        if not novisdom:
            for idx, (tmodes, stat, x, vals) in enumerate(lines):
                title = "%s" % stat
                opts = dict(title=title, legend=list(tmodes))
                if vals.shape[1] == 1:
                    vals = vals[:, 0]
                viz.line(Y=vals, X=x, env=visdom_env, opts=opts)

        if plot_file:
            print("exporting stats to %s" % plot_file)
            ncol = 3
            nrow = int(np.ceil(float(len(lines))/ncol))
            matplotlib.rcParams.update({'font.size': 5})
            color = cycle(plt.cm.tab10(np.linspace(0, 1, 10)))
            fig = plt.figure(1)
            plt.clf()
            for idx, (tmodes, stat, x, vals) in enumerate(lines):
                c = next(color)
                plt.subplot(nrow, ncol, idx+1)
                for vali, vals_ in enumerate(vals.T):
                    c_ = c * (1. - float(vali) * 0.3)
                    plt.plot(x, vals_, c=c_, linewidth=1)
                plt.ylabel(stat)
                plt.xlabel("epoch")
                plt.gca().yaxis.label.set_color(c[0:3]*0.75)
                plt.legend(tmodes)
                gcolor = np.array(mcolors.to_rgba('lightgray'))
                plt.grid(b=True, which='major', color=gcolor,
                         linestyle='-', linewidth=0.4)
                plt.grid(b=True, which='minor', color=gcolor,
                         linestyle='--', linewidth=0.2)
                plt.minorticks_on()

            plt.tight_layout()
            plt.show()
            fig.savefig(plot_file)