def visualize_mse_on_same(original_dataset, reconstructed_dataset_max,reconstructed_dataset_avg,reconstructed_dataset_avg_max, window_size, name='figure_mse'):
"""
:param window_size:
:param original_dataset:
:param reconstructed_dataset:
:param name:
:return:
"""
f = plt.figure(figsize=(10,4))
f.subplots_adjust(bottom=0.2)
ax = f.add_subplot(111)
original_windows = ExperimentorService.windows(original_dataset.clone(), window_size)
reconstructed_windows = ExperimentorService.windows(reconstructed_dataset_max.clone(), window_size)
mse_windows = []
for idx, original_window in enumerate(original_windows):
mse = ExperimentorService.mse(original_window, reconstructed_windows[idx])
mse_windows.append(mse)
ax.plot(mse_windows, label='Maximum Threshold', linestyle=':',color='b')
reconstructed_windows = ExperimentorService.windows(reconstructed_dataset_avg.clone(), window_size)
mse_windows = []
for idx, original_window in enumerate(original_windows):
mse = ExperimentorService.mse(original_window, reconstructed_windows[idx])
mse_windows.append(mse)
ax.plot(mse_windows, label='Average Threshold', linestyle=':',color='g')
reconstructed_windows = ExperimentorService.windows(reconstructed_dataset_avg_max.clone(), window_size)
mse_windows = []
for idx, original_window in enumerate(original_windows):
mse = ExperimentorService.mse(original_window, reconstructed_windows[idx])
mse_windows.append(mse)
ax.plot(mse_windows, label='Maximum average Threshold', linestyle=':', color='r')
ax.set_xlabel('Window #)')
ax.set_ylabel('Mean Squared Error')
legend = ax.legend(loc='upper right', prop={'size':6})
plt.savefig('Mean_Squared_Comparison')
def visualize_cross_validation_curves(training_set, test_set, artifact_dataset, window_sizes, name='figure_cross_validation_curves'):
"""
:param training_set:
:param artifact_dataset:
:param test_set:
:param window_sizes:
:param name:
:return:
"""
# Do cross validation
mse = {'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_mse = []
for idx, original_window in enumerate(original_windows):
reconstructed_window, rejected = ExperimentorService.pca_reconstruction(artifact_windows[idx],
window_size, threshold)
current_mse += ExperimentorService.mse(original_window, reconstructed_window)
if i == 0:
mse['max'] += [np.mean(current_mse)]
elif i == 1:
mse['avg'] += [np.mean(current_mse)]
else:
mse['avg_max'] += [np.mean(current_mse)]
fig, ax = plt.subplots()
ax.plot(mse['max'], label='Max eigenvalue threshold', color='c')
ax.plot(mse['avg'], label='Average eigenvalue threshold', color='b')
ax.plot(mse['avg_max'], label='Average of max eigenvalue threshold', color='m')
ax.set_xticks(range(len(window_sizes)))
ax.set_xticklabels([str(window_size) for window_size in window_sizes])
ax.set_title('mse cross validation')
ax.set_ylabel('Mean squared error')
ax.set_xlabel('Window size')
plt.legend(loc='upper right')
plt.savefig(name)
def visualize_mse(original_dataset, reconstructed_dataset, window_size, name='figure_mse'):
"""
:param window_size:
:param original_dataset:
:param reconstructed_dataset:
:param name:
:return:
"""
original_windows = ExperimentorService.windows(original_dataset.clone(), window_size)
reconstructed_windows = ExperimentorService.windows(reconstructed_dataset.clone(), window_size)
mse_windows = []
for idx, original_window in enumerate(original_windows):
mse = ExperimentorService.mse(original_window, reconstructed_windows[idx])
mse_windows.append(mse)
f = plt.figure()
ax = f.add_subplot(111)
ax.plot(mse_windows)
ax.set_xlabel('window #')
ax.set_ylabel('Mean Squared Error')
plt.savefig(name)
def visualize_cross_validation_bars(training_set, test_set, artifact_dataset, window_sizes, name='figure_cross_validation_bars'):
"""
:param training_set:
:param artifact_dataset:
:param test_set:
:param window_sizes:
:param name:
:return:
"""
# Do cross validation
mse = {'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_mse = []
for idx, original_window in enumerate(original_windows):
reconstructed_window, rejected = ExperimentorService.pca_reconstruction(artifact_windows[idx], window_size, threshold)
current_mse += ExperimentorService.mse(original_window, reconstructed_window)
if i == 0:
mse['max'] += [np.mean(current_mse)]
elif i == 1:
mse['avg'] += [np.mean(current_mse)]
else:
mse['avg_max'] += [np.mean(current_mse)]
best_index_max = mse['max'].index(min(mse['max']))
best_index_avg = mse['avg'].index(min(mse['avg']))
best_index_avg_max = mse['avg_max'].index(min(mse['avg_max']))
print 'Best window size for max threshold: ' + str(window_sizes[best_index_max])
print 'Best window size for avg threshold: ' + str(window_sizes[best_index_avg])
print 'Best window size for avg_max threshold: ' + str(window_sizes[best_index_avg_max])
fig, ax = plt.subplots()
indexs = np.arange(len(mse['max']))
width = 0.20
ax.bar(indexs, mse['max'], width, label='Max eigenvalue threshold', color='c', alpha=0.8)
ax.bar(indexs + width, mse['avg'], width, label='Average eigenvalue threshold', color='b', alpha=0.8)
ax.bar(indexs + width*2, mse['avg_max'], width, label='Average of max eigenvalue threshold', color='m', alpha=0.8)
ax.set_ylim([0,1500])
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('mse cross validation')
ax.set_ylabel('Mean squared error')
ax.set_xlabel('Window size')
plt.legend(loc='upper right')
plt.savefig(name)
