def _test_plot(self, evas):
"""Check if `stored_vars` is correctly populated.
Parameters
----------
evas : CAttackEvasionCleverhans
"""
if self.make_figures is False:
self.logger.debug("Skipping figures...")
return
fig = CFigure()
fig.sp.plot_path(evas.x_seq)
fig.sp.plot_fun(evas.objective_function,
plot_levels=False,
multipoint=True,
n_grid_points=50)
fig.sp.plot_decision_regions(self.clf,
plot_background=False,
n_grid_points=100)
fig.title("ATTACK: {}, y_target: {}".format(
evas._clvrh_attack_class.__name__, self.y_target))
name_file = '{}_evasion2D_target_{}.pdf'.format(
evas._clvrh_attack_class.__name__, self.y_target)
fig.savefig(fm.join(self.images_folder, name_file), file_format='pdf')
def _create_params_grad_plot(self, normalizer):
"""
Show the gradient of the classifier parameters w.r.t the poisoning
point
"""
self.logger.info("Create 2-dimensional plot of the poisoning "
"gradient")
self._test_init(normalizer)
pois_clf = self._clf_poisoning()[0]
if self.n_features == 2:
debug_pois_obj = _CAttackPoisoningLinTest(self.poisoning)
fig = CFigure(height=8, width=10)
n_rows = 2
n_cols = 2
fig.title(self.clf_idx)
fig.subplot(n_rows, n_cols, grid_slot=1)
fig.sp.title('w1 wrt xc')
self._plot_param_sub(fig, debug_pois_obj.w1,
debug_pois_obj.gradient_w1_xc, pois_clf)
fig.subplot(n_rows, n_cols, grid_slot=2)
fig.sp.title('w2 wrt xc')
self._plot_param_sub(fig, debug_pois_obj.w2,
debug_pois_obj.gradient_w2_xc, pois_clf)
fig.subplot(n_rows, n_cols, grid_slot=3)
fig.sp.title('b wrt xc')
self._plot_param_sub(fig, debug_pois_obj.b,
debug_pois_obj.gradient_b_xc, pois_clf)
fig.tight_layout()
exp_idx = "2d_grad_pois_"
exp_idx += self.clf_idx
if self.classifier.preprocess is not None:
exp_idx += "_norm"
fig.savefig(exp_idx + '.pdf', file_format='pdf')
from secml.array import CArray
from secml.figure import CFigure
fig = CFigure(fontsize=14)
fig.title('loglog base 4 on x')
t = CArray.arange(0.01, 20.0, 0.01)
fig.sp.loglog(t, 20 * (-t / 10.0).exp(), basex=2)
fig.sp.grid()
fig.show()
from secml.array import CArray
from secml.figure import CFigure
fig = CFigure(fontsize=14)
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * CArray.randn((10000, ))
num_bins = 50
# the histogram of the data
n, bins, patches = fig.sp.hist(x,
num_bins,
density=1,
facecolor='green',
alpha=0.5)
# add a 'best fit' line
y = bins.normpdf(mu, sigma)
fig.sp.plot(bins, y, 'r--')
fig.sp.xlabel('Smarts')
fig.sp.ylabel('Probability')
fig.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
# Tweak spacing to prevent clipping of ylabel
fig.subplots_adjust(left=0.15)
fig.sp.grid()
fig.show()
from secml.array import CArray
from secml.figure import CFigure
fig = CFigure(fontsize=16)
fig.title('Errorbars can go negative!')
fig.sp.xscale("symlog", nonposx='clip')
fig.sp.yscale("symlog", nonposy='clip')
x = CArray(10.0).pow(CArray.linspace(0.0, 2.0, 20))
y = x ** 2.0
fig.sp.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)
fig.sp.ylim(bottom=0.1)
fig.sp.grid()
fig.show()