def main():
parser = ArgumentParser(description=__doc__)
parser.add_argument('res', help='Experiment result (res) file.')
parser.add_argument('--plot',
required=True,
nargs='*',
help='Where to save plot.')
args = parser.parse_args()
print('Loading previous results [{}]'.format(args.res))
input = pickle.load(open(args.res, 'rb'))
avstatsl = input['avstats']
print('Averaging results')
for i in range(1, len(avstatsl)):
for av in set(avstatsl[0].keys() + avstatsl[i].keys()):
avstatsl[0][av] += avstatsl[i][av]
for av in avstatsl[0].keys():
avstatsl[0][av] /= float(len(avstatsl))
avstats = avstatsl[0]
print('Plotting antivirus detection statistics')
plots.init_eurasip_style(figure_width=222.5, horizontal=False)
plots.plot_avstats(avstats, args.plot)
return 0
def main():
stats = ['OLD', 'Add', 'Del', 'Same', 'NEW']
metric_names = {
'Add': 'New features',
'Del': 'Obsolete features',
'Same': 'Unchanged features'
}
parser = ArgumentParser(description=__doc__)
parser.add_argument('--first',
nargs='+',
required=True,
help='Feature files of the first method.')
parser.add_argument('--second',
nargs='+',
required=True,
help='Feature files of the second method.')
parser.add_argument('--methods',
nargs='+',
required=True,
help='Names of all methods, in the same '
'order as result files.')
parser.add_argument('--metrics',
choices=metric_names.keys(),
nargs='+',
required=True,
help='Which metrics to compare on.')
parser.add_argument('--legend', default=False, help='Where to put legend.')
parser.add_argument('--plot',
required=True,
nargs='+',
help='Where to save plot.')
args = parser.parse_args()
dd = OrderedDict()
for feats, method in zip((args.first, args.second), args.methods):
data = []
for f1, f2 in zip(feats[:-1], feats[1:]):
l1, l2 = get_feats(f1), get_feats(f2)
tot_old = len(l1)
tot_new = len(l2)
s1, s2 = set(l1), set(l2)
add = len(s2.difference(s1))
rem = len(s1.difference(s2))
same = len(s1.intersection(s2))
data.append([tot_old, add, rem, same, tot_new])
dd[method] = dict(zip(stats, [numpy.array(d) for d in zip(*data)]))
for m, d in dd.iteritems():
print('{:#^79s}'.format(' {} '.format(m)))
print_table(d, stats)
print()
# Plot setup
plots.init_eurasip_style(figure_width=222.5,
figure_height=265.0,
horizontal=len(args.metrics) < 2)
datas = []
for metric in args.metrics:
datas.append([
dd[method][metric].astype(numpy.float32) / dd[method][stats[0]]
for method in args.methods
])
ylabels = [metric_names[msn] for msn in args.metrics]
xticklabels = map(str, range(2, len(args.first) + 1))
plots.sorted_multicomparison(datas=datas,
methods=args.methods,
legend=args.legend,
ylabels=ylabels,
xlabel='Retraining period',
xticklabels=xticklabels,
plotfs=args.plot,
autofmt_xdate=False)
return 0
def main():
stats = ['OLD', 'Add', 'Del', 'Same', 'NEW']
metric_names = {'Add': 'New features',
'Del': 'Obsolete features',
'Same': 'Unchanged features'}
parser = ArgumentParser(description=__doc__)
parser.add_argument('--first',
nargs='+',
required=True,
help='Feature files of the first method.')
parser.add_argument('--second',
nargs='+',
required=True,
help='Feature files of the second method.')
parser.add_argument('--methods',
nargs='+',
required=True,
help='Names of all methods, in the same '
'order as result files.')
parser.add_argument('--metrics',
choices=metric_names.keys(),
nargs='+',
required=True,
help='Which metrics to compare on.')
parser.add_argument('--legend',
default=False,
help='Where to put legend.')
parser.add_argument('--plot',
required=True,
nargs='+',
help='Where to save plot.')
args = parser.parse_args()
dd = OrderedDict()
for feats, method in zip((args.first, args.second), args.methods):
data = []
for f1, f2 in zip(feats[:-1], feats[1:]):
l1, l2 = get_feats(f1), get_feats(f2)
tot_old = len(l1)
tot_new = len(l2)
s1, s2 = set(l1), set(l2)
add = len(s2.difference(s1))
rem = len(s1.difference(s2))
same = len(s1.intersection(s2))
data.append([tot_old, add, rem, same, tot_new])
dd[method] = dict(zip(stats, [numpy.array(d) for d in zip(*data)]))
for m, d in dd.iteritems():
print('{:#^79s}'.format(' {} '.format(m)))
print_table(d, stats)
print()
# Plot setup
plots.init_eurasip_style(figure_width=222.5,
figure_height=265.0,
horizontal=len(args.metrics) < 2)
datas = []
for metric in args.metrics:
datas.append([dd[method][metric].astype(numpy.float32) /
dd[method][stats[0]]
for method in args.methods])
ylabels = [metric_names[msn] for msn in args.metrics]
xticklabels = map(str, range(2, len(args.first) + 1))
plots.sorted_multicomparison(datas=datas,
methods=args.methods,
legend=args.legend,
ylabels=ylabels,
xlabel='Retraining period',
xticklabels=xticklabels,
plotfs=args.plot,
autofmt_xdate=False)
return 0
def main():
metric_names = {'neg_tr': 'Benign training',
'pos_tr': 'Malicious training',
'neg_te': 'Benign evaluation',
'pos_te': 'Malicious evaluation',
'acc': 'Accuracy',
'AUC': 'Area under ROC',
'TPR': 'True positive rate',
'FPR': 'False positive rate'}
parser = ArgumentParser(description=__doc__)
parser.add_argument('--res',
nargs='+',
required=True,
help='Result files of all methods.')
parser.add_argument('--methods',
nargs='+',
required=True,
help='Names of all methods, in the same '
'order as result files.')
parser.add_argument('--metrics',
choices=metric_names.keys(),
nargs='+',
required=True,
help='Which metrics to compare on.')
parser.add_argument('-l', '--log',
action='store_true',
help='Plot y-axis using log scale.')
parser.add_argument('--legend',
default=False,
help='Where to put legend.')
parser.add_argument('--plot',
required=True,
nargs='*',
help='Where to save plot(s).')
args = parser.parse_args()
assert len(args.res) == len(args.methods), ('There must be an equal '
'number of result and '
'TP files')
methods = {}
key_dates = []
stats = []
for res_f, method in zip(args.res, args.methods):
print('Loading results for method {} [{}]'.format(method, res_f))
input = pickle.load(open(res_f, 'rb'))
resl = input['res']
avstatsl = input['avstats']
key_dates = input['key_dates']
stats = input['stats']
print('Averaging results', end='\n\n')
means = numpy.mean(resl, axis=0)
means = means.reshape((len(means) / len(stats), len(stats)))
neg_tr, pos_tr, neg_te, pos_te, acc, AUC, TPR, FPR = zip(*means)
for i in range(1, len(avstatsl)):
for av in set(avstatsl[0].keys() + avstatsl[i].keys()):
avstatsl[0][av] += avstatsl[i][av]
for av in avstatsl[0].keys():
avstatsl[0][av] /= float(len(avstatsl))
avstats = avstatsl[0]
methods[method] = {'res': dict(zip(stats, zip(*means))),
'avstats': avstats}
print('Dates ranging from {} to {}'.format(key_dates[0], key_dates[-1]))
print('Total days: {}'.format((key_dates[-1] - key_dates[0]).days + 1))
# Plot setup
plots.init_eurasip_style(figure_width=222.5, horizontal=len(args.metrics) < 2)
ylabels = [metric_names[msn] for msn in args.metrics]
xticklabels = [d.strftime('%b %d') for d in key_dates]
years_range = sorted(set([d.strftime('%Y') for d in key_dates]))
if len(years_range) > 2:
years_range = [years_range[0], years_range[-1]]
xlabel = 'Date ({})'.format(' - '.join(years_range))
datas = []
for metric in args.metrics:
datas.append([methods[method]['res'][metric]
for method in args.methods])
plots.sorted_multicomparison(datas=datas,
methods=args.methods,
legend=args.legend,
ylabels=ylabels,
xlabel=xlabel,
xticklabels=xticklabels,
plotfs=args.plot,
autofmt_xdate=True)
return 0
def main():
parser = ArgumentParser(description=__doc__)
parser.add_argument('--train',
nargs='+',
required=True,
help='Training data file(s).')
parser.add_argument('--test',
nargs='+',
required=True,
help='Test data file(s).')
parser.add_argument('-l', '--log',
action='store_true',
help='X-axis log scale.')
parser.add_argument('--legend',
default=False,
help='Where to put legend.')
parser.add_argument('--data-plot',
required=True,
nargs='*',
help='Where to save data quantity plot.')
args = parser.parse_args()
print('\nEvaluating data in time periods')
res = []
key_dates = []
all_years = set()
for w, (f_tr, f_te) in enumerate(zip(args.train, args.test), start=1):
# Load test data
y_te = load_libsvm_labels(f_te)
pos_te, neg_te = (y_te > 0.5).sum(), (y_te < 0.5).sum()
# Load test dates
dates = numpy.array(load_dates(f_te))
week_s, week_e = dates.min(), dates.max()
key_dates.append(week_s)
print('Period {} [{} - {}]'.format(w, week_s, week_e))
all_years.add(str(week_s.year))
# Load training data
y_tr = load_libsvm_labels(f_tr)
pos_tr, neg_tr = (y_tr > 0.5).sum(), (y_tr < 0.5).sum()
print('Training: {} malicious, {} benign'.format(pos_tr, neg_tr))
print('Test: {} malicious, {} benign'.format(pos_te, neg_te),
end='\n\n')
res.append((pos_tr, neg_tr, pos_te, neg_te))
pos_tr, neg_tr, pos_te, neg_te = zip(*res)
print('Dates ranging from {} to {}'.format(key_dates[0], key_dates[-1]))
print('Total days: {}'.format((key_dates[-1] - key_dates[0]).days + 1))
print('Plotting training and test sizes')
bar_width = 0.35
spacing = 0.05 # spacing between a pair of training/test bars
xticks = numpy.arange(len(pos_tr)).astype(numpy.float32)
# Plot
plots.init_eurasip_style(figure_width=222.5, figure_height=170.0)
fig = pylab.figure()
ax = pylab.gca()
ax.bar(xticks - bar_width - spacing, neg_tr, width=bar_width,
color='#00691f', linewidth=0, label='Benign training')
ax.bar(xticks - bar_width - spacing, pos_tr, bottom=neg_tr,
width=bar_width, color='#a50007', linewidth=0,
label='Malicious training')
ax.bar(xticks + spacing, neg_te, width=bar_width, color='#67bc6b',
linewidth=0, label='Benign evaluation')
ax.bar(xticks + spacing, pos_te, bottom=neg_te, width=bar_width,
color='#ff767d', linewidth=0, label='Malicious evaluation')
# Set up x axis
ax.set_xticks(xticks)
ax.set_xticklabels([d.strftime('%b %d') for d in key_dates])
ax.set_xlim((-2.0 * spacing - bar_width,
len(pos_tr) - 1 + 2.0 * spacing + bar_width))
years_range = sorted(all_years)
if len(years_range) > 2:
years_range = [years_range[0], years_range[-1]]
ax.set_xlabel('Date ({})'.format(' - '.join(years_range)))
fig.autofmt_xdate()
# Set up y axis
pylab.ticklabel_format(axis='y', style='sci', scilimits=(0, 2),
useOffset=False)
ax.yaxis.grid() # vertical grid lines
ax.set_axisbelow(True) # grid lines are behind the rest
if args.log:
ax.set_yscale('log')
ax.set_ylabel('Samples')
# Set up legend
legend_loc = args.legend if args.legend else 'best'
if legend_loc != 'none':
pylab.legend(loc=legend_loc, fancybox=True, framealpha=0.5)
# Finalize plot setup
pylab.tight_layout(pad=0.5, h_pad=0.5, w_pad=0.5, rect=(0, 0, 1, 1))
for plot_file in args.data_plot:
pylab.savefig(plot_file)
return 0