def test_max_sensitivity_specificity(self):
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
training_set, test_set = ExperimentorService.split_dataset(dataset, ratio=0.1)
artifact_size = 20
window_size = 20
threshold_max, threshold_avg, threshold_avg_max = ExperimentorService.calibrate(training_set, window_size)
artifact_test_set, artifact_list = ExperimentorService.artifactify(test_set, artifact_size, randomly_add_artifacts=True)
reconstructed_test_set_max, rejections_max = ExperimentorService.pca_reconstruction(artifact_test_set, window_size, threshold_max)
reconstructed_test_set_avg, rejections_avg = ExperimentorService.pca_reconstruction(artifact_test_set, window_size, threshold_avg)
reconstructed_test_set_avg_max, rejections_max_avg = ExperimentorService.pca_reconstruction(artifact_test_set, window_size, threshold_avg_max)
sensitivity_max, specificity_max = ExperimentorService.sensitivity_specificity(rejections_max, artifact_list)
sensitivity_avg, specificity_avg = ExperimentorService.sensitivity_specificity(rejections_avg, artifact_list)
sensitivity_avg_max, specificity_avg_max = ExperimentorService.sensitivity_specificity(rejections_max_avg, artifact_list)
print '--- MAX THRESHOLD ---'
print 'Sensitivity: ', sensitivity_max
print 'Specificity: ', specificity_max
print '--- AVG THRESHOLD ---'
print 'Sensitivity: ', sensitivity_avg
print 'Specificity: ', specificity_avg
print '--- AVG_MAX THRESHOLD ---'
print 'Sensitivity: ', sensitivity_avg_max
print 'Specificity: ', specificity_avg_max
def test_visualize_mse_bars_difference(self):
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
training_set, test_set = ExperimentorService.split_dataset(dataset, ratio=0.2)
artifact_size = 60
artifact_dataset, _ = ExperimentorService.artifactify(test_set, artifact_size, True)
window_sizes = range(5, 151, 5)
Visualizer.visualize_cross_validation_bars_percentage(training_set, test_set, artifact_dataset, window_sizes, name="figure_cross_validation_bars_difference_no_artifacts")
def test_visualize_mse_bars(self):
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
training_set, test_set = ExperimentorService.split_dataset(dataset, ratio=0.2)
artifact_size = 20
artifact_dataset, _ = ExperimentorService.artifactify(test_set, artifact_size, randomly_add_artifacts=True)
window_sizes = range(10, 301, 5)
Visualizer.visualize_cross_validation_bars(training_set, test_set, artifact_dataset, window_sizes, name="figure_cross_validation_bars2")
def test_visualize_mse_bars_no_artifacts(self):
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
training_set, test_set = ExperimentorService.split_dataset(dataset, ratio=0.2)
window_size = 40
threshold_max, threshold_avg, threshold_avg_max = ExperimentorService.calibrate(training_set, window_size)
thresholds = [threshold_max, threshold_avg, threshold_avg_max]
window_sizes = [10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]
Visualizer.visualize_cross_validation_bars(test_set, test_set, thresholds, window_sizes, name="figure_cross_validation_bars_no_artifacts")
def test_knn_threshold(self):
# filename = '../../data/subject1_csv/eeg_200605191428_epochs/small.csv'
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
dataset_slice = DataSet(dataset[0:40])
artificer = Artificer(dataset_slice, add_artifacts=True)
threshold = artificer.knn_threshold()
artificer.pca_reconstruction(threshold)
artificer.visualize()
def test_visualize_mse_curves(self):
"""
:return:
"""
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
training_set, test_set = ExperimentorService.split_dataset(dataset, ratio=0.2)
artifact_size = 60
artifact_dataset, _ = ExperimentorService.artifactify(test_set, artifact_size, randomly_add_artifacts=True)
window_sizes = range(5, 401, 1)
Visualizer.visualize_cross_validation_curves(training_set, test_set, artifact_dataset, window_sizes, "figure_cross_validation_curves")
def test_pca(self):
# filename = '../../data/subject1_csv/eeg_200605191428_epochs/small.csv'
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
threshold = 0
for idx in range(len(dataset) // 40):
current_dataset = DataSet(dataset[idx * 40:(idx + 1) * 40])
if idx < 10:
artificer = Artificer(current_dataset, add_artifacts=False)
max_eigenvalue, rejected = artificer.pca_reconstruction()
threshold = max(threshold, max_eigenvalue)
else:
artificer = Artificer(current_dataset, add_artifacts=True)
artificer.pca_reconstruction(threshold)
artificer.visualize()
break
def test_for_report(self):
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
training_set, test_set = ExperimentorService.split_dataset(dataset, ratio=0.2)
artifact_size = 20
window_size = 20
threshold_max, threshold_avg, threshold_avg_max = ExperimentorService.calibrate(training_set, window_size)
print 'max: ' + str(threshold_max)
print 'avg: ' + str(threshold_avg)
print 'avg_max: ' + str(threshold_avg_max)
artifact_dataset, _ = ExperimentorService.artifactify(test_set, artifact_size, True)
reconstructed_dataset_max, rejections = ExperimentorService.pca_reconstruction(artifact_dataset, window_size, threshold_max)
Visualizer.visualize_timeLine(dataset, test_set, artifact_dataset, reconstructed_dataset_max)
def test_pca_speed(self):
filename = '../../data/subject1_csv/eeg_200605191428_epochs/eeg_200605191428_epochs.csv'
dataset = DataReader.read_data(filename, ',')
print 'Length of dataset: ' + str(len(dataset))
print 'Number of windows: ' + str(len(dataset) // 40)
print 'Calculating thresholds...'
max_threshold = 0
avg_threshold = 0
for idx in range(25):
current_dataset = DataSet(dataset[idx * 40:(idx + 1) * 40])
artificer = Artificer(current_dataset, add_artifacts=False)
avg_eigenvalue, max_eigenvalue, rejected = artificer.pca_reconstruction()
max_threshold = max(max_threshold, max_eigenvalue)
avg_threshold = max(avg_threshold, avg_eigenvalue)
print 'Largest threshold: ' + str(max_threshold)
print 'Average threshold: ' + str(avg_threshold)
print '\nTiming PCA reconstructor...'
times = []
for idx in range(len(dataset) // 40):
current_dataset = DataSet(dataset[idx * 40:(idx + 1) * 40])
start_time_constructor = time.time()
artificer = Artificer(current_dataset, add_artifacts=False)
midway_time = time.time() - start_time_constructor
artificer.put_artifacts()
start_time_reconstructor = time.time()
artificer.pca_reconstruction(max_threshold)
times.append(time.time() - start_time_reconstructor + midway_time)
print 'Time per PCA reconstruction: ' + str(reduce(lambda x, y: x + y, times) / len(times)) + ' sec'
def test_compare_mse(self):
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
training_set, test_set = ExperimentorService.split_dataset(dataset, ratio=0.2)
artifact_size = 20
window_size = 40
threshold_max, threshold_avg, threshold_avg_max = ExperimentorService.calibrate(training_set, window_size)
print threshold_max
print threshold_avg
print threshold_avg_max
artifact_dataset, _ = ExperimentorService.artifactify(test_set, artifact_size, True)
reconstructed_dataset_avg, rejections = ExperimentorService.pca_reconstruction(artifact_dataset, window_size, threshold_avg)
reconstructed_dataset_max, rejections = ExperimentorService.pca_reconstruction(artifact_dataset, window_size, threshold_max)
reconstructed_dataset_avg_max, rejections = ExperimentorService.pca_reconstruction(artifact_dataset, window_size, threshold_avg_max)
Visualizer.visualize_mse_on_same(test_set, reconstructed_dataset_max, reconstructed_dataset_avg, reconstructed_dataset_avg_max, window_size)
def test_speed(self):
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset = DataReader.read_data(filename, ',')
training_set, test_set = ExperimentorService.split_dataset(dataset, ratio=0.2)
artifact_size = 20
window_size = 20
threshold_max, threshold_avg, threshold_avg_max = ExperimentorService.calibrate(training_set, window_size)
artifact_dataset, _ = ExperimentorService.artifactify(test_set, artifact_size, randomly_add_artifacts=False)
start_time_max = time.time()
reconstructed_dataset_max, _ = ExperimentorService.pca_reconstruction(artifact_dataset, window_size, threshold_max)
end_time_max = time.time() - start_time_max
# reconstructed_dataset_avg, _ = ExperimentorService.pca_reconstruction(artifact_dataset, window_size, threshold_avg)
# reconstructed_dataset_avg_max, _ = ExperimentorService.pca_reconstr
print 'We were able to reconstruct the entire test set in ' + str(end_time_max) + ' seconds.'
print 'There are ' + str(len(ExperimentorService.windows(test_set, window_size))) + ' windows in the test set.'
print 'On average, we can reconstruct a window in ' + str(end_time_max / len(ExperimentorService.windows(test_set, window_size))) + ' seconds.'
print 'We can do pca projection at a rate of ' + str(1/(end_time_max / len(ExperimentorService.windows(test_set, window_size)))) + 'Hz'
def test_equals(self):
filename = '../../data/emotiv/EEG_Data_filtered.csv'
dataset1 = DataReader.read_data(filename, ',')
dataset2 = DataReader.read_data(filename, ',')
print dataset1 == dataset2
