def visualize_cross_validation_bars_percentage(training_set, test_set, artifact_dataset, window_sizes,
name='figure_cross_validation_bars_difference'):
"""
:param artifact_dataset:
:param thresholds:
:param test_set:
:param window_sizes:
:param name:
:return:
"""
# Do cross validation
differences = {'max': [], 'avg': [], 'avg_max': []}
for window_size in window_sizes:
for i, threshold in enumerate(ExperimentorService.calibrate(training_set, window_size)):
original_windows = ExperimentorService.windows(test_set.clone(), window_size)
artifact_windows = ExperimentorService.windows(artifact_dataset.clone(), window_size)
current_difference = []
for idx, original_window in enumerate(original_windows):
reconstructed_window, rejected = ExperimentorService.pca_reconstruction(artifact_windows[idx],
window_size, threshold)
current_difference += ExperimentorService.difference(original_window, reconstructed_window)
if i == 0:
differences['max'] += [np.mean(current_difference)]
elif i == 1:
differences['avg'] += [np.mean(current_difference)]
else:
differences['avg_max'] += [np.mean(current_difference)]
print 'threshold: ' + differences.keys()[i] + ' - window size: ' + str(window_size) + ' - difference: ' + str(np.mean(current_difference))
fig, ax = plt.subplots()
indexs = np.arange(len(differences['max']))
width = 0.20
ax.bar(indexs, differences['max'], width, label='Max eigenvalue threshold', color='c', alpha=0.8)
ax.bar(indexs + width, differences['avg'], width, label='Average eigenvalue threshold', color='b', alpha=0.8)
ax.bar(indexs + width * 2, differences['avg_max'], width, label='Average of max eigenvalue threshold', color='m',
alpha=0.8)
ax.set_xticks(indexs + width * 1.5)
ax.set_xticklabels([str(window_size) for window_size in window_sizes])
plt.xticks(rotation=70)
ax.set_title('Difference cross validation')
ax.set_ylabel('Difference %')
ax.set_xlabel('Window size')
plt.legend(loc='upper right')
plt.savefig(name)
