NetLearner/plot_mod_shared.py at master · littlepretty/NetLearner · GitHub

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from numpy import mean, std, arange, amax, sort
import pickle
from pprint import pprint
import matplotlib.pyplot as plt
import matplotlib
import sys


def autolabel(rects):
    for rect in rects:
        height = rect.get_height()
        ax.text(rect.get_x() + rect.get_width() / 2.0, height,
                '%.2f' % height, ha='center', va='bottom')


print('python %s num_runs U if_plot' % sys.argv[0])
num_runs = int(sys.argv[1])
U = int(sys.argv[2])
plot = str(sys.argv[3])

f = open('ModalityNets/result_runs%d_U%d.pkl' % (num_runs, U), 'rb')
data = pickle.load(f)
f.close()
modnet_unsw_train = data['modnet']['unsw']['train']
modnet_nsl_train = data['modnet']['nsl']['train']
modnet_unsw_test = data['modnet']['unsw']['test']
modnet_nsl_test = data['modnet']['nsl']['test']

unified_unsw_train = data['unified']['unsw']['train']
unified_nsl_train = data['unified']['nsl']['train']
unified_unsw_test = data['unified']['unsw']['test']
unified_nsl_test = data['unified']['nsl']['test']

f = open('SharedLayer/result_runs%d_U%d.pkl' % (num_runs, U), 'rb')
data = pickle.load(f)
f.close()
shared_unsw_train = data['unsw']['train']
shared_nsl_train = data['nsl']['train']
shared_unsw_test = sort(data['unsw']['test'])[::-1][:10]
shared_nsl_test = sort(data['nsl']['test'])[::-1][:10]

# print(modnet_unsw_test)
# print(modnet_nsl_test)
# print(unified_unsw_test)
# print(unified_nsl_test)
# print(shared_unsw_test)
# print(shared_nsl_test)

unsw_train_avgs = [mean(modnet_unsw_train), mean(unified_unsw_train),
                   mean(shared_unsw_train)]
nsl_train_avgs = [mean(modnet_nsl_train), mean(unified_nsl_train),
                  mean(shared_nsl_train)]
unsw_test_avgs = [mean(modnet_unsw_test), mean(unified_unsw_test),
                  mean(shared_unsw_test)]
nsl_test_avgs = [mean(modnet_nsl_test), mean(unified_nsl_test),
                 mean(shared_nsl_test)]

unsw_train_maxs = [amax(modnet_unsw_train), amax(unified_unsw_train),
                   amax(shared_unsw_train)]
nsl_train_maxs = [amax(modnet_nsl_train), amax(unified_nsl_train),
                  amax(shared_nsl_train)]
unsw_test_maxs = [amax(modnet_unsw_test), amax(unified_unsw_test),
                  amax(shared_unsw_test)]
nsl_test_maxs = [amax(modnet_nsl_test), amax(unified_nsl_test),
                 amax(shared_nsl_test)]

unsw_train_stds = [std(modnet_unsw_train), std(unified_unsw_train),
                   std(shared_unsw_train)]
nsl_train_stds = [std(modnet_nsl_train), std(unified_nsl_train),
                  std(shared_nsl_train)]
unsw_test_stds = [std(modnet_unsw_test), std(unified_unsw_test),
                  std(shared_unsw_test)]
nsl_test_stds = [std(modnet_nsl_test), std(unified_nsl_test),
                 std(shared_nsl_test)]

pprint(unsw_train_avgs)
pprint(nsl_train_avgs)
pprint(unsw_test_maxs)
pprint(unsw_test_avgs)
pprint(nsl_test_maxs)
pprint(nsl_test_avgs)

if plot == "bar":
    matplotlib.rc('font', size=18)
    ind = arange(len(unsw_train_avgs))
    width = 0.36
    tick_labels = ('modality', 'unified', 'shared')
    fig, ax = plt.subplots()
    rects1 = ax.bar(ind, [x * 100 for x in unsw_train_avgs], width, color='b',
                    yerr=unsw_train_stds)
    rects2 = ax.bar(ind + width, [x * 100 for x in unsw_test_avgs],
                    width, color='r', yerr=unsw_test_stds)

    ax.set_ylabel('Train/Test Accuracy(%) for UNSW-NB15 dataset')
    ax.set_xticks(ind + width / 2)
    ax.set_xticklabels(tick_labels)
    ax.legend((rects1[0], rects2[0]), ('Train', 'Test'))

    autolabel(rects1)
    autolabel(rects2)

    plt.ylim((80, 101))
    plt.grid()
    plt.show()

    ind = arange(len(nsl_train_avgs))
    width = 0.36
    fig, ax = plt.subplots()
    rects1 = ax.bar(ind, [x * 100 for x in nsl_train_avgs], width, color='b',
                    yerr=[x * 100 for x in nsl_train_stds])
    rects2 = ax.bar(ind + width, [x * 100 for x in nsl_test_avgs],
                    width, color='r', yerr=[x * 100 for x in nsl_test_stds])

    ax.set_ylabel('Train/Test Accuracy(%) for NSL-KDD dataset')
    ax.set_xticks(ind + width / 2)
    ax.set_xticklabels(tick_labels)
    ax.legend((rects1[0], rects2[0]), ('Train', 'Test'))

    autolabel(rects1)
    autolabel(rects2)

    plt.ylim((72, 102))
    plt.grid()
    plt.show()
elif plot == "box":
    matplotlib.rc('font', size=18)
    plt.figure()
    unsw_series = [modnet_unsw_test, unified_unsw_test, shared_unsw_test]
    nsl_series = [modnet_nsl_test, unified_nsl_test, shared_nsl_test]
    labels = ('modality', 'unified', 'shared')
    result = plt.boxplot(unsw_series, notch=False,
                         showmeans=True, labels=labels)
    unsw_means = [line.get_ydata()[0] for line in result['means']]
    plt.grid(linestyle=':')
    plt.ylabel('Test Accuracy for UNSW-NB15 dataset')
    plt.show()
    result = plt.boxplot(nsl_series, notch=False,
                         showmeans=True, labels=labels)
    nsl_means = [line.get_ydata()[0] for line in result['means']]
    plt.grid(linestyle=':')
    plt.ylabel('Test Accuracy for NSL-KDD dataset')
    plt.show()
    print(unsw_means, nsl_means)