plot-qpth-perf.py

#!/usr/bin/env python3

import argparse

import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('bmh')
import numpy as np
import pandas as pd
import math

from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})

import os
import sys
import json
import glob

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--out', type=str, default='images/qpth-timing')
    args = parser.parse_args()

    # msesF = os.path.join(args.workDir, 'mses.csv')
    # msesDf = pd.read_csv(msesF, sep=',', names=['lam', 'mse'])

    # fig, ax = plt.subplots(1, 1, figsize=(5,3))
    # # fig.tight_layout()
    # plt.plot(msesDf['lam'].values, msesDf['mse'].values)
    # plt.xlabel("$\lambda$")
    # plt.ylabel("MSE")
    # ax.set_yscale('log')
    # for ext in ['pdf', 'png']:
    #     f = args.out + '.' + ext
    #     fig.savefig(f)
    #     if ext == 'pdf':
    #         os.system('pdfcrop "{}" "{}"'.format(f, f))
    #     print("Created {}".format(f))


    batchSzs = [1, 64, 128]
    gurobiMeans = [3.60602e-02, 2.33186e+00, 4.66950e+00]
    gurobiStds = [3.86304e-03, 2.29599e-02, 2.93522e-02]
    singleMeans = [7.75610e-02, 3.30584e+00, 6.64419e+00]
    singleStds = [2.73885e-02, 7.14157e-02, 6.35851e-02]
    batchedMeans = [6.98230e-02, 1.45570e-01, 1.83456e-01]
    batchedStds = [6.51156e-03, 2.80604e-03, 2.77888e-03]

    nSamples = len(gurobiMeans)
    indices = np.array(list(range(nSamples)))
    barWidth = 0.2

    cmap = plt.get_cmap("Set1")
    colors = cmap(np.linspace(0, 1, 9))
    alpha = 0.7

    # fig = plt.figure(figsize=(10, 4))
    # ax = fig.add_subplot(111)
    fig, ax = plt.subplots(1, 1, figsize=(5,3))
    plt.bar(indices, gurobiMeans, barWidth,
            yerr=gurobiStds, label='Gurobi',
            color=colors[0], ecolor='0.3', alpha=alpha)
    plt.bar(indices + barWidth, singleMeans, barWidth,
            yerr=singleStds, label='qpth (Single)',
            color=colors[1], ecolor='0.3', alpha=alpha)
    plt.bar(indices + 2 * barWidth, batchedMeans, barWidth,
            yerr=batchedStds, label='qpth (Batched)',
            color=colors[2], ecolor='0.3', alpha=alpha)

    # box = ax.get_position()
    # ax.set_position([box.x0, box.y0 + 0.05, box.width, box.height * 0.85])
    # plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.25), ncol=4,
    #             fancybox=True, shadow=True)
    plt.ylabel("Runtime (s)")
    plt.xlabel("Batch Size")

    ax.set_xticks(indices + 1.5 * barWidth)
    xticks = []
    for batchSz in batchSzs:
        xticks.append(batchSz)
    ax.set_xticklabels(xticks)
    ax.set_yscale('log')

    # locs, labels = plt.xticks()
    # plt.ylim(0, 1)

    for ext in ['pdf', 'png']:
        f = args.out + '.' + ext
        fig.savefig(f)
        if ext == 'pdf':
            os.system('pdfcrop "{}" "{}"'.format(f, f))
        print("Created {}".format(f))

if __name__ == '__main__':
    main()