def displayLabel(self, barplot, label):
label_bool = labels_tools.labelStringToBoolean(label)
ranges = [[x for x in l if x['ground_truth_label'] == label_bool]
for l in self.ranges]
dataset = PlotDataset(list(map(len, ranges)), label)
dataset.set_color(colors_tools.get_label_color(label))
barplot.add_dataset(dataset)
def getTopModelFeatures(exp_id, size, train_test, fold_id):
exp = updateCurrentExperiment(exp_id)
directory = exp.output_dir()
if fold_id != 'None' and fold_id != 'all':
directory = path.join(directory, fold_id)
directory = path.join(directory, train_test)
filename = path.join(directory, 'model_coefficients.csv')
with open(filename, 'r') as f:
coefficients_df = pd.read_csv(f,
header=0,
index_col=0,
nrows=int(size))
coefficients = list(coefficients_df['mean'])
features_ids = coefficients_df.index
tooltip_data = []
user_ids = []
for feature_id in features_ids:
query = session.query(FeaturesAlchemy)
query = query.filter(FeaturesAlchemy.id == int(feature_id))
row = query.one()
tooltip_data.append(row.name)
user_ids.append(row.user_id)
barplot = BarPlot(user_ids)
dataset = PlotDataset(coefficients, None)
score = exp.exp_conf.core_conf.classifier_conf.featureImportance()
if score == 'weight':
dataset.set_color(colors_tools.red)
barplot.add_dataset(dataset)
return jsonify(barplot.to_json(tooltip_data=tooltip_data))
def _gen_label_plot_dataset(self, instances, label):
if label != 'unlabeled':
instances = instances.getAnnotatedInstances(label=label)
else:
instances = instances.getUnlabeledInstances()
values = instances.features.getValuesFromIndex(self.feature_index)
dataset = PlotDataset(values, label)
dataset.set_color(colors_tools.get_label_color(label))
self.plot_datasets[label] = dataset
def _gen_binary_histogram(self):
self.barplot = BarPlot(['0', '1'])
for label, dataset in self.plot_datasets.items():
if len(dataset.values) > 0:
num_0 = sum(dataset.values == 0)
num_1 = sum(dataset.values == 1)
hist_dataset = PlotDataset([num_0, num_1], label)
hist_dataset.set_color(dataset.color)
self.barplot.add_dataset(hist_dataset)
def _gen_histogram(self):
# 10 equal-width bins computed on all the data
_, bin_edges = np.histogram(self.all_values, bins=10, density=False)
x_labels = ['%.2f - %.2f' % (bin_edges[e], bin_edges[e+1])
for e in range(len(bin_edges) - 1)]
self.barplot = BarPlot(x_labels)
for label, dataset in self.plot_datasets.items():
if len(dataset.values) > 0:
hist, _ = np.histogram(dataset.values, bins=bin_edges,
density=False)
hist_dataset = PlotDataset(hist, label)
hist_dataset.set_color(dataset.color)
self.barplot.add_dataset(hist_dataset)
def executionTimeDisplay(self):
lr = PlotDataset(None, 'Logistic Regression')
lr.set_linestyle('dotted')
nb = PlotDataset(None, 'Naive Bayes')
nb.set_linestyle('dashed')
v = [lr, nb]
v.extend(Strategy.executionTimeDisplay(self))
return v
def getFamiliesBarplot(annotations_id, iteration, label):
iteration = None if iteration == 'None' else int(iteration)
family_counts = annotations_db_tools.getFamiliesCounts(session,
annotations_id,
iter_max=iteration,
label=label)
df = pd.DataFrame({
'families': list(family_counts.keys()),
'counts': [family_counts[k] for k in list(family_counts.keys())]
})
matrix_tools.sort_data_frame(df, 'families', ascending=True, inplace=True)
barplot = BarPlot(list(df['families']))
dataset = PlotDataset(list(df['counts']), 'Num. Instances')
dataset.set_color(colors_tools.get_label_color(label))
barplot.add_dataset(dataset)
return jsonify(barplot.to_json())
def plotEvolutionMonitoring(self, evolution_file, monitoring_dir):
data = self.loadEvolutionMonitoring(evolution_file)
if self.labels_families == 'labels':
title = 'Labels Suggestions Accuracy'
elif self.labels_families == 'families':
title = 'Families Suggestions Accuracy'
plot = PlotDataset(data['true_suggestions'] / data['num_suggestions'],
title)
iterations = list(range(self.monitoring.iteration_number))
plt.clf()
max_value = 1
plt.plot(iterations,
plot.values,
label=plot.label,
color=plot.color,
linewidth=plot.linewidth,
marker=plot.marker)
plt.ylim(0, max_value)
plt.xlabel('Iteration')
plt.ylabel('Suggestions Accuracy')
lgd = plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=2,
mode='expand',
borderaxespad=0.,
fontsize='large')
filename = '_'.join(
[self.labels_families, self.kind, 'suggestions.png'])
filename = path.join(monitoring_dir, filename)
plt.savefig(filename, bbox_extra_artists=(lgd, ), bbox_inches='tight')
plt.clf()
self.data = data
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)), 'numInstances')
dataset.set_color(colors_tools.get_label_color('all'))
barplot.add_dataset(dataset)
else:
self.displayLabel(barplot, labels_tools.MALICIOUS)
self.displayLabel(barplot, labels_tools.BENIGN)
filename = path.join(directory, 'predictions_barplot.json')
with open(filename, 'w') as f:
barplot.export_json(f)
def getTopWeightedFeatures(exp_id, inst_exp_id, instance_id, size, fold_id):
if fold_id == 'all':
return None
instance_id = int(instance_id)
exp = updateCurrentExperiment(exp_id)
inst_exp = updateCurrentExperiment(inst_exp_id)
# get the features
features_from_exp = FeaturesFromExp(inst_exp)
features_names, features_values = features_from_exp.get_instance(
instance_id)
features_values = [float(value) for value in features_values]
# get the pipeline with scaler and logistic model
experiment_dir = exp.output_dir()
if fold_id != 'None':
experiment_dir = path.join(experiment_dir, fold_id)
pipeline = joblib.load(path.join(experiment_dir, 'model', 'model.out'))
# scale the features
scaled_values = pipeline.named_steps['scaler'].transform(
np.reshape(features_values, (1, -1)))
weighted_values = np.multiply(scaled_values,
pipeline.named_steps['model'].coef_)
features = list(
map(lambda name, value, w_value: (name, value, w_value),
features_names, features_values, weighted_values[0]))
features.sort(key=lambda tup: abs(tup[2]))
features = features[:-int(size) - 1:-1]
features_names = [x[0] for x in features]
features_values = [x[1] for x in features]
features_weighted_values = [x[2] for x in features]
labels = [str(name) for name in features_names]
tooltips = [
'%s (%.2f)' % (name, features_values[i])
for i, name in enumerate(features_names)
]
barplot = BarPlot(labels)
dataset = PlotDataset(features_weighted_values, None)
dataset.set_color(colors_tools.red)
barplot.add_dataset(dataset)
return jsonify(barplot.to_json(tooltip_data=tooltips))
def plotEvolutionMonitoring(self, monitoring_dir):
iterations = list(range(1, self.monitoring.iteration_number + 1))
plt.clf()
# Labels
data = self.labels_accuracy.high_confidence_counts.data
values = data['true_suggestions'] / data['num_suggestions']
plot = PlotDataset(values, 'Labels Suggestions')
max_value = 1
plt.plot(iterations,
plot.values,
label=plot.label,
color=plot.color,
linewidth=plot.linewidth,
marker=plot.marker)
# Families
data = self.families_accuracy.high_confidence_counts.data
values = data['true_suggestions'] / data['num_suggestions']
plot = PlotDataset(values, 'Families Suggestions')
max_value = 1
plt.plot(iterations,
plot.values,
label=plot.label,
color='purple',
linewidth=plot.linewidth,
marker=plot.marker)
# Plot
plt.ylim(0, max_value)
plt.xlabel('Iteration')
plt.ylabel('Suggestions Accuracy')
lgd = plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=2,
mode='expand',
borderaxespad=0.,
fontsize='large')
filename = path.join(
monitoring_dir, 'labels_families_high_confidence_suggestions.png')
plt.savefig(filename, bbox_extra_artists=(lgd, ), bbox_inches='tight')
plt.clf()
def getClusterStats(experiment_id):
experiment = updateCurrentExperiment(experiment_id)
clustering = ClusteringExp.from_json(experiment.output_dir())
num_clusters = clustering.num_clusters
num_instances_v = []
labels = []
for c in range(num_clusters):
instances_in_cluster = clustering.clusters[c].instances_ids
num_instances = len(instances_in_cluster)
# the empty clusters are not displayed
# if num_instances > 0:
num_instances_v.append(num_instances)
#labels.append('c_' + str(c))
labels.append(clustering.clusters[c].label)
barplot = BarPlot(labels)
dataset = PlotDataset(num_instances_v, 'Num. Instances')
barplot.add_dataset(dataset)
return jsonify(barplot.to_json())
def plotPerfEvolution(self, estimators, output_filename, data,
monitoring_dir):
iterations = list(range(1, self.monitoring.iteration_number + 1))
plt.clf()
for estimator in estimators:
plot = PlotDataset(data[estimator], estimator)
plt.plot(iterations,
plot.values,
label=plot.label,
color=plot.color,
linewidth=plot.linewidth,
marker=plot.marker)
plt.ylim(0, 1)
plt.xlabel('Iteration')
plt.ylabel('Performance')
lgd = plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=3,
mode='expand',
borderaxespad=0.,
fontsize='large')
filename = self.outputFilename(monitoring_dir, output_filename, 'png')
plt.savefig(filename, bbox_extra_artists=(lgd, ), bbox_inches='tight')
plt.clf()
def executionTimeDisplay(self):
clustering = PlotDataset(None, 'Analysis')
v = [clustering]
v.extend(Strategy.executionTimeDisplay(self))
return v
def executionTimeDisplay(self):
uncertain = PlotDataset(None, 'Uncertain Queries')
malicious = PlotDataset(None, 'Malicious Queries')
malicious.set_linestyle('dotted')
malicious.set_color(colors_tools.get_label_color(labels_tools.MALICIOUS))
benign = PlotDataset(None, 'Benign Queries')
benign.set_linestyle('dashed')
benign.set_color(colors_tools.get_label_color(labels_tools.BENIGN))
return [malicious, uncertain, benign]
def executionTimeDisplay(self):
binary_model = PlotDataset(None, 'Binary model')
v = [binary_model]
v.extend(Strategy.executionTimeDisplay(self))
return v
def executionTimeDisplay(self):
generate_queries = PlotDataset(None, 'Queries generation')
generate_queries.set_color('purple')
return [generate_queries]
