HAMMER/plot.py at master · Nikunj-Gupta/HAMMER · GitHub

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import os, glob, yaml, pprint, numpy as np, pandas as pd
from webbrowser import get
from collections import defaultdict
import matplotlib.pyplot as plt
from tensorboard.backend.event_processing import event_accumulator
# from pylab import rcParams, axes


SIZE_GUIDANCE = {
    'compressedHistograms': 500,
    'images': 4,
    'audio': 4,
    'scalars': 10000,
    'histograms': 1,
}

STORE_EVERYTHING_SIZE_GUIDANCE = {
    'compressedHistograms': 0,
    'images': 0,
    'audio': 0,
    'scalars': 0,
    'histograms': 0,
}
# params = {
#         'axes.labelsize': 28,
#         'axes.titlesize': 32,
#         'legend.fontsize': 14,
#         'xtick.labelsize': 'x-large',
#         'ytick.labelsize': 'x-large',
#         'text.usetex': True,
#         'figure.figsize': [14, 12]
#     }


SMOOTH = 3000
CUT = 52_000
def get_values(filename, scalar="Episodic_Reward"):
    ea = event_accumulator.EventAccumulator(filename, size_guidance=STORE_EVERYTHING_SIZE_GUIDANCE)
    ea.Reload()
    ea_scalar = ea.Scalars(tag=scalar)
    ea_scalar = pd.DataFrame(ea_scalar)
    return ea_scalar


def smooth(y, box_pts):
    box = np.ones(box_pts)/box_pts
    y_smooth = np.convolve(y, box, mode='same')
    return y_smooth


def preprocess(log_dir):

    params = {
        'axes.labelsize': 28,
        'axes.titlesize': 32,
        'legend.fontsize': 14,
        'xtick.labelsize': 'x-large',
        'ytick.labelsize': 'x-large',
        'text.usetex': True,
        'figure.figsize': [10, 8]
    }
    from pylab import plot, rcParams, legend, axes, grid

    rcParams.update(params)


    logs = glob.glob(os.path.join(log_dir, "*/logs/*"), recursive=True)

    # pprint.pprint(logs)
    l = []
    for event in logs:
        l.append(event.split('/')[2].split('--'))
    # pprint.pprint(l)
    merged = defaultdict(lambda: [])
    for i in l: merged['--'.join(i[:-1])].append('--'.join(i))
    # pprint.pprint(merged)
    # pprint.pprint(list(merged.keys()))


    count = -1
    colors = ['#006BB2', '#B22400']
    labels = ['HAMMER', 'IL']
    for exp in merged:
        if ('meslen_0' in exp) or ('dru_1--meslen_3' in exp):
            print("================================\n"+exp+"\n================================\n")
            vals = []
            count+=1
            for i in merged[exp]:
                # if ('randomseed_5327' in i):
                # if any(n in i for n in ['5327', '8651', '14712', '186',
                #                         '9538', '106', '90300', '4973', '310', '530', '606']):
                if any(n in i for n in ['5327', '8651', '14712', '186',
                                        '9538', '106']):
                    print(i)
                    logs = glob.glob(os.path.join('./save', i, "logs/*.npy"), recursive=True)[0]
                    ## save numpy arrays
                    # logs_dir = glob.glob(os.path.join('./save', i, "logs"), recursive=True)[0]
                    # with open(logs_dir+'/arr.npy', 'wb') as f:
                        # np.save(f, get_values(logs)['value'].to_numpy())

                    arr = np.load(logs)
                    print(arr.shape)
                    vals.append(smooth(
                        arr[:CUT],
                        box_pts=SMOOTH
                    )[2000:CUT-2000])
                    # break
            val_means = np.array(vals).mean(axis=0)
            val_stds = np.array(vals).std(axis=0)
            plt.plot(val_means, label=labels[count], color=colors[count])
            plt.fill_between(np.arange(1, len(val_means)+1),
                        val_means - val_stds,
                        val_means + val_stds,
                        alpha=0.2)
            # break

    rcParams.update(params)
    legend = plt.legend(loc="upper left")
    # legend = legend(loc='upper center', bbox_to_anchor=(0.5, 1.05), ncol=3, fancybox=True, shadow=True)
    frame = legend.get_frame()
    frame.set_facecolor('0.9')
    frame.set_edgecolor('0.9')

    plt.title('HAMMER on Multi-Agent Walker')
    plt.xlabel("Number of Episodes")
    plt.ylabel("Average Returns per Agent")
    plt.xlim((100, CUT-2000))
    plt.grid()
    plt.show()


if __name__ == '__main__':
    preprocess('./save')