groups = GroupBy.get_data_groups(groups)
result = pd.DataFrame(groups)
result = result.transpose()
return result
if __name__ == '__main__':
import pprint
from glob import glob
from padar_parallel.grouper import MHealthGrouper
from padar_converter.mhealth import dataset
input_files = glob(
'D:/data/spades_lab/SPADES_[1-2]/MasterSynced/**/Actigraph*.sensor.csv',
recursive=True)
pprint.pprint(input_files)
grouper = MHealthGrouper(input_files)
groupby_obj = GroupBy(input_files) \
.split(grouper.pid_group(),
grouper.sid_group(),
group_types=['PID', 'SID'],
ingroup_sortkey_func=lambda x: dataset.get_file_timestamp(x['data']))
groupby_obj.apply(count_total_rows) \
.post_join(join_func=sum_rows) \
.final_join(join_func=as_dataframe)
groupby_obj.visualize_workflow(filename='test_apply.pdf')
result = groupby_obj.compute(scheduler='processes').get_result()
print(result)
def prepare_feature_set(input_folder,
*,
output_folder=None,
debug=False,
sampling_rate=80,
resample_sr=80,
scheduler='processes',
profiling=True,
force=True):
"""Compute feature set for "Location Matters" paper by Tang et al.
Process the given raw dataset (stored in mhealth format) and generate feature set file in csv format along with a profiling report and feature computation pipeline diagram.
:param input_folder: Folder path of input raw dataset
:param output_folder: Use auto path if None
:param debug: Use this flag to output results to 'debug_run' folder
:param sampling_rate: The sampling rate of the raw accelerometer data in Hz
:param resample_sr: The new sampling rate we desire to resample the raw data to.
:param scheduler: 'processes': Use multi-core processing;
'threads': Use python threads (not-in-parallel);
'sync': Use a single thread in sequential order
:param profiling: Use profiling or not.
"""
if output_folder is None:
output_folder = utils.generate_run_folder(input_folder, debug=debug)
if not os.path.exists(output_folder):
os.makedirs(output_folder)
feature_filepath = os.path.join(output_folder, 'muss.feature.csv')
if not force and os.path.exists(feature_filepath):
logging.info('Feature set file exists, skip regenerating it...')
return feature_filepath
sensor_files = glob(os.path.join(input_folder, '*', 'MasterSynced', '**',
'Actigraph*sensor.csv'),
recursive=True)
groupby = GroupBy(sensor_files,
**MhealthWindowing.make_metas(sensor_files))
grouper = MHealthGrouper(sensor_files)
groups = [
grouper.pid_group(),
grouper.sid_group(),
grouper.auto_init_placement_group()
]
groupby.split(*groups,
group_types=['PID', 'SID', 'SENSOR_PLACEMENT'],
ingroup_sortkey_func=sort_by_file_timestamp,
descending=False)
groupby.apply(load_data, old_sr=sampling_rate, new_sr=resample_sr)
if resample_sr != sampling_rate:
sr = resample_sr
else:
sr = sampling_rate
groupby.apply(compute_features, interval=12.8, step=12.8, sr=sr)
groupby.final_join(delayed(join_as_dataframe))
result = groupby.compute(scheduler=scheduler,
profiling=profiling).get_result()
# rename placements
result = result.reset_index()
result.loc[:,
'SENSOR_PLACEMENT'] = result.loc[:, 'SENSOR_PLACEMENT'].apply(
dataset.get_placement_abbr)
if not os.path.exists(output_folder):
os.makedirs(output_folder)
profiling_filepath = os.path.join(output_folder,
'feature_computation_profiling.html')
workflow_filepath = os.path.join(output_folder,
'feature_computation_workflow.pdf')
result.to_csv(feature_filepath, float_format='%.9f', index=False)
if profiling:
groupby.show_profiling(file_path=profiling_filepath)
try:
groupby.visualize_workflow(filename=workflow_filepath)
except Exception as e:
print(e)
print('skip generating workflow pdf')
return feature_filepath