Python matplotlib.pylab 模块，gcf() 实例源码

def save_images(self, X, imgfile, density=False):
        ax = plt.axes()
        x = X[:, 0]
        y = X[:, 1]
        if density:
            xy = np.vstack([x,y])
            z = scipy.stats.gaussian_kde(xy)(xy)
            ax.scatter(x, y, c=z, marker='o', edgecolor='')
        else:
            ax.scatter(x, y, marker='o', c=range(x.shape[0]),
                        cmap=plt.cm.coolwarm)

        if self.collection is not None:
            self.collection.set_transform(ax.transData)
            ax.add_collection(self.collection)


        ax.text(x[0], y[0], str('start'), transform=ax.transAxes)
        ax.axis([-0.2, 1.2, -0.2, 1.2])
        fig = plt.gcf()

        plt.savefig(imgfile)
        plt.close()

def show_feature_importance(gbdt, feature_names=None):
    importance = gbdt.get_fscore(fmap='xgb.fmap')
    importance = sorted(importance.items(), key=operator.itemgetter(1))

    df = pd.DataFrame(importance, columns=['feature', 'fscore'])
    df['fscore'] = df['fscore'] / df['fscore'].sum()
    print "feature importance", df

    if feature_names is not None:
        used_features = df['feature']
        unused_features = [f for f in feature_names if f not in used_features]
        print "[IDF]Unused features:", str(unused_features)

    plt.figure()
    df.plot()
    df.plot(kind='barh', x='feature', y='fscore', legend=False, figsize=(6, 10))
    plt.title('XGBoost Feature Importance')
    plt.xlabel('relative importance')
    plt.gcf().savefig('feature_importance_xgb.png')

def main(args):
    e = Eligibility(length=args.length)
    if args.mode == "dexp":
        e.efunc_ = e.efunc_double_exp
    elif args.mode == "rect":
        e.efunc_ = e.efunc_rect
    elif args.mode == "ramp":
        e.efunc_ = e.efunc_ramp
    elif args.mode == "exp":
        e.efunc_ = e.efunc_exp
    e.gen_efunc_table()

    x = np.arange(args.length)
    print x
    et = e.efunc(x)
    # plot and test with array argument
    cmstr = "ko"
    pl.plot(x, et, cmstr, lw=1.)
    if args.mode == "rect":
        # negative time for readability without lines
        pl.plot(np.arange(-5, x[0]), np.zeros(5,), cmstr, lw=1.)
        # pl.plot([-10, -1, x[0]], [0, 0, et[0]], cmstr, lw=1.)
        pl.plot([x[-1], x[0] + args.length], [et[-1], 0.], cmstr, lw=1.)
        pl.plot(x + args.length, np.zeros((len(et))), cmstr, lw=1.)
        pl.ylim((-0.005, np.max(et) * 1.1))
    # pl.plot(x, et, "k-", lw=1.)
    # pl.yticks([])
    # line at zero
    # pl.axhline(0., c="black")
    pl.xlabel("t [steps]")
    pl.ylabel("Eligibility")
    if args.plotsave:    
        pl.gcf().set_size_inches((6, 2))
        pl.gcf().savefig("eligibility_window.pdf", dpi=300, bbox_inches="tight")
    pl.show()

    # check perf: loop, test with single integer arguments
    import time
    now = time.time()
    for i in range(100):
        for j in range(args.length):
            e.efunc(j)
    print "table took:", time.time() - now

    now = time.time()
    for i in range(100):
        for j in range(args.length):
            e.efunc_(j)
    print "feval took:", time.time() - now