# We add the gyroscope data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values/
DataSet.add_numerical_dataset('gyroscope_phone.csv', 'timestamps',
['x', 'y', 'z'], 'avg', 'gyr_phone_')
DataSet.add_numerical_dataset('gyroscope_smartwatch.csv', 'timestamps',
['x', 'y', 'z'], 'avg', 'gyr_watch_')
# We add the heart rate (continuous numerical measurements) and aggregate by averaging again
DataSet.add_numerical_dataset('heart_rate_smartwatch.csv', 'timestamps',
['rate'], 'avg', 'hr_watch_')
# We add the labels provided by the users. These are categorical events that might overlap. We add them
# as binary attributes (i.e. add a one to the attribute representing the specific value for the label if it
# occurs within an interval).
DataSet.add_event_dataset('labels.csv', 'label_start', 'label_end',
'label', 'binary')
# We add the amount of light sensed by the phone (continuous numerical measurements) and aggregate by averaging again
DataSet.add_numerical_dataset('light_phone.csv', 'timestamps', ['lux'],
'avg', 'light_phone_')
# We add the magnetometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
DataSet.add_numerical_dataset('magnetometer_phone.csv', 'timestamps',
['x', 'y', 'z'], 'avg', 'mag_phone_')
DataSet.add_numerical_dataset('magnetometer_smartwatch.csv', 'timestamps',
['x', 'y', 'z'], 'avg', 'mag_watch_')
# We add the pressure sensed by the phone (continuous numerical measurements) and aggregate by averaging again
DataSet.add_numerical_dataset('pressure_phone.csv', 'timestamps',
['pressure'], 'avg', 'press_phone_')
def main():
# Set a granularity (the discrete step size of our time series data) and choose if all resulting datasets should
# be saved. A course-grained granularity of one instance per minute, and a fine-grained one with four instances
# per second are used.
GRANULARITIES = [60000, 250]
SAVE_VERSIONS = False
# We can call Path.mkdir(exist_ok=True) to make any required directories if they don't already exist.
[path.mkdir(exist_ok=True, parents=True) for path in [DATASET_PATH, RESULT_PATH]]
# Create object to visualize the data and save figures
DataViz = VisualizeDataset(module_path=__file__)
datasets = []
for milliseconds_per_instance in GRANULARITIES:
print(
f'Creating numerical datasets from files in {DATASET_PATH} using granularity {milliseconds_per_instance}.')
# Create an initial dataset object with the base directory for our data and a granularity and add selected
# measurements to it
data_engineer = CreateDataset(base_dir=DATASET_PATH, granularity=milliseconds_per_instance)
# Add the accelerometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
data_engineer.add_numerical_dataset(file='accelerometer_phone.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='acc_phone_')
data_engineer.add_numerical_dataset(file='accelerometer_smartwatch.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='acc_watch_')
# Add the gyroscope data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
data_engineer.add_numerical_dataset(file='gyroscope_phone.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='gyr_phone_')
data_engineer.add_numerical_dataset(file='gyroscope_smartwatch.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='gyr_watch_')
# Add the heart rate (continuous numerical measurements) and aggregate by averaging the values
data_engineer.add_numerical_dataset(file='heart_rate_smartwatch.csv', timestamp_col='timestamps',
value_cols=['rate'], aggregation='avg', prefix='hr_watch_')
# Add the labels provided by the users as binary attributes (i.e. add a one to the attribute representing the
# specific value for a label if it occurs within an interval). These are categorical events that might overlap.
data_engineer.add_event_dataset(file='labels.csv', start_timestamp_col='label_start',
end_timestamp_col='label_end',
value_col='label', aggregation='binary')
# Add the amount of light sensed by the phone (continuous numerical measurements) and aggregate by averaging
data_engineer.add_numerical_dataset(file='light_phone.csv', timestamp_col='timestamps', value_cols=['lux'],
aggregation='avg', prefix='light_phone_')
# Add the magnetometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
data_engineer.add_numerical_dataset(file='magnetometer_phone.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='mag_phone_')
data_engineer.add_numerical_dataset(file='magnetometer_smartwatch.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='mag_watch_')
# Add the pressure sensed by the phone (continuous numerical measurements) and aggregate by averaging again
data_engineer.add_numerical_dataset(file='pressure_phone.csv', timestamp_col='timestamps',
value_cols=['pressure'],
aggregation='avg', prefix='press_phone_')
# Get the resulting pandas data table
dataset = data_engineer.data_table
# Create boxplots
DataViz.plot_dataset_boxplot(dataset=dataset, cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z', 'acc_watch_x',
'acc_watch_y', 'acc_watch_z'])
# Plot all data
DataViz.plot_dataset(data_table=dataset,
columns=['acc_', 'gyr_', 'hr_watch_rate', 'light_phone_lux', 'mag_', 'press_phone_',
'label'],
match=['like', 'like', 'like', 'like', 'like', 'like', 'like', 'like'],
display=['line', 'line', 'line', 'line', 'line', 'line', 'points', 'points'])
# Print a summary of the dataset
util.print_statistics(dataset=dataset)
datasets.append(copy.deepcopy(dataset))
# Save the various versions of the created datasets with logical filenames if needed
if SAVE_VERSIONS:
dataset.to_csv(RESULT_PATH / f'chapter2_result_{milliseconds_per_instance}')
# Make a table like the one shown in the book, comparing the two datasets produced
util.print_latex_table_statistics_two_datasets(dataset1=datasets[0], dataset2=datasets[1])
# Finally, store the last dataset we generated (250 ms)
dataset.to_csv(RESULT_PATH / RESULT_FNAME)
# DataSetCS.add_numerical_dataset('accelerometer_phone.csv', 'timestamps', ['x','y','z'], 'avg', 'acc_phone_')
# DataSetCS.add_numerical_dataset('accelerometer_smartwatch.csv', 'timestamps', ['x','y','z'], 'avg', 'acc_watch_')
# We add the gyroscope data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values/
DataSetOwn.add_numerical_dataset('gyro_custom.csv', 'timestamps', ['x','y','z'], 'avg', 'gyr_phone_')
# DataSetCS.add_numerical_dataset('gyroscope_phone.csv', 'timestamps', ['x','y','z'], 'avg', 'gyr_phone_')
# DataSetCS.add_numerical_dataset('gyroscope_smartwatch.csv', 'timestamps', ['x','y','z'], 'avg', 'gyr_watch_')
# We add the heart rate (continuous numerical measurements) and aggregate by averaging again
# DataSetCS.add_numerical_dataset('heart_rate_smartwatch.csv', 'timestamps', ['rate'], 'avg', 'hr_watch_')
# We add the labels provided by the users. These are categorical events that might overlap. We add them
# as binary attributes (i.e. add a one to the attribute representing the specific value for the label if it
# occurs within an interval).
DataSetOwn.add_event_dataset('labels_custom.csv', 'label_start', 'label_end', 'label', 'binary')
# DataSetCS.add_event_dataset('labels.csv', 'label_start', 'label_end', 'label', 'binary')
# We add the amount of light sensed by the phone (continuous numerical measurements) and aggregate by averaging again
# amount of light sensed is missing from our dataset
# DataSetCS.add_numerical_dataset('light_phone.csv', 'timestamps', ['lux'], 'avg', 'light_phone_')
# We add the magnetometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
DataSetOwn.add_numerical_dataset('mag_custom.csv', 'timestamps', ['x','y','z'], 'avg', 'mag_phone_')
DataSetOwn.add_numerical_dataset('press_custom.csv', 'timestamps', ['pressure'], 'avg', 'press_phone_')
# DataSetCS.add_numerical_dataset('magnetometer_phone.csv', 'timestamps', ['x','y','z'], 'avg', 'mag_phone_')
# DataSetCS.add_numerical_dataset('magnetometer_smartwatch.csv', 'timestamps', ['x','y','z'], 'avg', 'mag_watch_')
# We add the pressure sensed by the phone (continuous numerical measurements) and aggregate by averaging again
DataSetOwn.add_numerical_dataset('pedom_custom.csv', 'timestamps', ['steps', 'distance'], 'avg', 'pedom_phone_')
# We add the accelerometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values/
DataSet.add_numerical_dataset('accelerometer-kx023.csv_out.csv',
'timestamp', ['x', 'y', 'z'], 'avg',
'acc_phone_')
print("first set")
# We add the gyroscope data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values/
DataSet.add_numerical_dataset('orientation.csv_out.csv', 'timestamp',
['x', 'y', 'z'], 'avg', 'gyr_phone_')
print("second set")
# We add the labels provided by the users. These are categorical events that might overlap. We add them
# as binary attributes (i.e. add a one to the attribute representing the specific value for the label if it
# occurs within an interval).
DataSet.add_event_dataset('status.csv', 'timestampBeg', 'timestampEnd',
'label', 'binary')
# We add the amount of light sensed by the phone (continuous numerical measurements) and aggregate by averaging again
DataSet.add_numerical_dataset('light-bh1745.csv_out.csv', 'timestamp',
['lux'], 'avg', 'light_phone_')
print("third set")
# We add the magnetometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
DataSet.add_numerical_dataset('mag-akm09911.csv_out.csv', 'timestamp',
['x', 'y', 'z'], 'avg', 'mag_phone_')
print("fourth set")
# We add the pressure sensed by the phone (continuous numerical measurements) and aggregate by averaging again
# Get the resulting pandas data table
dataset = DataSet.data_table
#dataset.add_numerical_dataset('accelerometer_smartwatch.csv', 'timestamps', ['x','y','z'], 'avg', 'acc_watch_')
# We add the gyroscope data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
dataset.add_numerical_dataset('heart_rate.csv', 'time', ['heartrate'],
'avg', '')
dataset.add_numerical_dataset('steps.csv', 'time', ['steps'], 'avg', '')
#dataset.add_numerical_dataset('labels.csv', 'time', ['label'], 'avg', '')
# We add the heart rate (continuous numerical measurements) and aggregate by averaging again
#dataset.add_numerical_dataset('heart_rate_smartwatch.csv', 'timestamps', ['rate'], 'avg', 'hr_watch_')
# We add the labels provided by the users. These are categorical events that might overlap. We add them
# as binary attributes (i.e. add a one to the attribute representing the specific value for the label if it
# occurs within an interval).
dataset.add_event_dataset('labels.csv', 'time', 'label', 'sum')
# We add the amount of light sensed by the phone (continuous numerical measurements) and aggregate by averaging
#dataset.add_numerical_dataset('light_phone.csv', 'timestamps', ['lux'], 'avg', 'light_phone_')
# We add the magnetometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
#dataset.add_numerical_dataset('magnetometer_phone.csv', 'timestamps', ['x','y','z'], 'avg', 'mag_phone_')
#dataset.add_numerical_dataset('magnetometer_smartwatch.csv', 'timestamps', ['x','y','z'], 'avg', 'mag_watch_')
# We add the pressure sensed by the phone (continuous numerical measurements) and aggregate by averaging again
#dataset.add_numerical_dataset('pressure_phone.csv', 'timestamps', ['pressure'], 'avg', 'press_phone_')
# Get the resulting pandas data table
dataset = dataset.data_table
#dataset = dataset[dataset['heartrate'].notnull()]
]
datasets = []
for milliseconds_per_instance in GRANULARITIES:
print(
f'Creating numerical datasets from files in {DATASET_PATH} using granularity {milliseconds_per_instance}.'
)
# Create an initial dataset object with the base directory for our data and a granularity
dataset = CreateDataset(DATASET_PATH, milliseconds_per_instance)
# Add the selected measurements to it.
dataset.add_numerical_dataset('accelerometer.csv', 'timestamp',
['x', 'y', 'z'], 'avg', 'acc_phone_', True)
dataset.add_event_dataset('labels.csv', 'label_start', 'label_end',
'label', 'binary', True)
dataset.add_numerical_dataset('gyroscope.csv', 'timestamp',
['x', 'y', 'z'], 'avg', 'gyr_phone_', True)
dataset.add_numerical_dataset('barometer.csv', 'timestamp', ['x'], 'avg',
'bar_phone_', True)
dataset.add_numerical_dataset('linear_accelerometer.csv', 'timestamp',
['x', 'y', 'z'], 'avg', 'lin_acc_phone_',
True)
# dataset.add_numerical_dataset('location.csv', 'timestamp', ['latitude','longitude','height', 'velocity', 'direction', 'horizontal_accuracy', 'vertical_accuracy'], 'avg', 'loc_phone_', True)
dataset.add_numerical_dataset('magnetometer.csv', 'timestamp',
['x', 'y', 'z'], 'avg', 'mag_phone_', True)
dataset.add_numerical_dataset('proximity.csv', 'timestamp', ['distance'],
'avg', 'prox_phone_', True)
# Get the resulting pandas data table
dataset = dataset.data_table