def __filter_output_data(self, classes_to_visualise):
"""
Filter the output or input data corresponding to the classes to visualise.
@param classes_to_visualise: A list containing names for the classes to visualise.
"""
class_names = get_all_class_names()
class_indices_to_visualise = []
for i in range(len(class_names)):
if class_names[i] in classes_to_visualise:
class_indices_to_visualise.append(i)
if not len(class_indices_to_visualise) == len(classes_to_visualise):
print 'Not all classes to visualise were correct.'
return
tmp_output_data = []
tmp_class_indices = []
for i in range(len(self.output_data)):
out = self.output_data[i]
idx = self.class_indices[i]
if idx in class_indices_to_visualise:
tmp_output_data.append(out)
tmp_class_indices.append(idx)
self.output_data = tmp_output_data
self.class_indices = tmp_class_indices
def confusion_matrix(self, no_of_neighbors):
evaluated_class_indices = self.k_nearest_neibors(no_of_neighbors)
font = {'family': 'normal',
'weight': 'bold',
'size': 16}
matplotlib.rc('font', **font)
lbls = get_all_class_names()
for i in range(len(lbls)):
if len(lbls[i]) > 12:
str = lbls[i][:10]
lbls[i] = str + "..."
cm = confusion_matrix(self.class_indices, evaluated_class_indices, sorted(set(self.class_indices)))
# Show confusion matrix
fig = plot.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm)
fig.colorbar(cax)
ax.set_xticklabels([''] + lbls)
plot.xticks(rotation=55)
ax.set_yticklabels([''] + lbls)
plot.yticks(rotation=55)
plot.ylabel('True label')
plot.xlabel('Predicted label')
plot.show()
def __filter_output_data(self, classes_to_visualise):
"""
Filter the output or input data corresponding to the classes to visualise.
@param classes_to_visualise: A list containing names for the classes to visualise.
"""
class_names = get_all_class_names()
class_indices_to_visualise = []
for i in range(len(class_names)):
if class_names[i] in classes_to_visualise:
class_indices_to_visualise.append(i)
if not len(class_indices_to_visualise) == len(classes_to_visualise):
print 'Not all classes to visualise were correct.'
return
tmp_output_data = []
tmp_class_indices = []
for i in range(len(self.output_data)):
out = self.output_data[i]
idx = self.class_indices[i]
if idx in class_indices_to_visualise:
tmp_output_data.append(out)
tmp_class_indices.append(idx)
self.output_data = tmp_output_data
self.class_indices = tmp_class_indices
