def display(self, directory):
barplot = BarPlot(self.labels)
if not self.has_ground_truth:
self.display_label(barplot, 'all')
else:
self.display_label(barplot, MALICIOUS)
self.display_label(barplot, BENIGN)
barplot.export_to_json(path.join(directory, 'proba_barplot.json'))
def display(self, directory):
labels = ['0-10%', '10-20%', '20-30%', '30-40%', '40-50%',
'50-60%', '60-70%', '70-80%', '80-90%', '90-100%']
barplot = BarPlot(labels)
if not self.has_ground_truth:
dataset = PlotDataset(list(map(len, self.ranges)), 'num_instances')
dataset.set_color(get_label_color('all'))
barplot.add_dataset(dataset)
else:
self.display_label(barplot, MALICIOUS)
self.display_label(barplot, BENIGN)
barplot.export_to_json(path.join(directory, 'predictions_barplot.json'))
def display(self, directory):
labels = list(self.predictions.keys())
if self.multiclass:
xlabels = labels
else:
xlabels = [label_bool_to_str(l) for l in labels]
barplot = BarPlot(xlabels)
if not self.has_ground_truth:
self._display(barplot, labels)
else:
self._display(barplot, labels, error=False)
self._display(barplot, labels, error=True)
barplot.export_to_json(path.join(directory, 'pred_barplot.json'))
def to_barplot(self, directory):
head_coeff = self.coef_summary.head(n=NUM_COEFF_EXPORT)
coefficients = head_coeff['mean'].values
features_ids = list(head_coeff.index)
features_names = []
user_ids = []
for feature_id in features_ids:
query = self.exp.session.query(FeaturesAlchemy)
query = query.filter(FeaturesAlchemy.id == int(feature_id))
row = query.one()
features_names.append(row.name)
user_ids.append(row.user_id)
barplot = BarPlot(user_ids)
dataset = PlotDataset(coefficients, None)
score = self.classifier_conf.get_feature_importance()
if score == 'weight':
dataset.set_color(red)
else:
dataset.set_color(blue)
barplot.add_dataset(dataset)
if self.class_label is None:
out_filename = 'coeff_barplot.json'
else:
out_filename = 'coeff_barplot_%s.json' % self.class_label
return barplot.export_to_json(path.join(directory, out_filename),
tooltip_data=features_names)