68 lines
3.5 KiB
Python
68 lines
3.5 KiB
Python
import pandas as pd
|
|
import numpy as np
|
|
|
|
def statsFeatures(steps_data, features_to_compute, features_type, steps_features):
|
|
if features_type == "steps" or features_type == "sumsteps":
|
|
col_name = "steps"
|
|
elif features_type == "durationsedentarybout" or features_type == "durationactivebout":
|
|
col_name = "duration"
|
|
else:
|
|
raise ValueError("features_type can only be one of ['steps', 'sumsteps', 'durationsedentarybout', 'durationactivebout'].")
|
|
|
|
if "count" + features_type.replace("duration", "episode") in features_to_compute:
|
|
steps_features["count" + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count()
|
|
if "sum" + features_type in features_to_compute:
|
|
steps_features["sum" + features_type] = steps_data.groupby(["local_segment"])[col_name].sum()
|
|
if "max" + features_type in features_to_compute:
|
|
steps_features["max" + features_type] = steps_data.groupby(["local_segment"])[col_name].max()
|
|
if "min" + features_type in features_to_compute:
|
|
steps_features["min" + features_type] = steps_data.groupby(["local_segment"])[col_name].min()
|
|
if "avg" + features_type in features_to_compute:
|
|
steps_features["avg" + features_type] = steps_data.groupby(["local_segment"])[col_name].mean()
|
|
if "median" + features_type in features_to_compute:
|
|
steps_features["median" + features_type] = steps_data.groupby(["local_segment"])[col_name].median()
|
|
if "std" + features_type in features_to_compute:
|
|
steps_features["std" + features_type] = steps_data.groupby(["local_segment"])[col_name].std()
|
|
|
|
return steps_features
|
|
|
|
def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute):
|
|
steps_summary_features = pd.DataFrame()
|
|
|
|
# statistics features of daily steps count
|
|
steps_summary_features = statsFeatures(steps_summary_data, summary_features_to_compute, "sumsteps", steps_summary_features)
|
|
|
|
steps_summary_features.reset_index(inplace=True)
|
|
|
|
return steps_summary_features
|
|
|
|
|
|
|
|
def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
|
|
|
steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"])
|
|
requested_summary_features = provider["FEATURES"]
|
|
|
|
# name of the features this function can compute
|
|
base_summary_features = ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"]
|
|
# the subset of requested features this function can compute
|
|
summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features))
|
|
|
|
# extract features from summary data
|
|
steps_summary_features = pd.DataFrame(columns=["local_segment"] + summary_features_to_compute)
|
|
if not steps_summary_data.empty:
|
|
steps_summary_data = filter_data_by_segment(steps_summary_data, time_segment)
|
|
|
|
if not steps_summary_data.empty:
|
|
# only keep the segments start at 00:00:00 and end at 23:59:59
|
|
datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
|
|
datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
|
|
|
|
segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex)
|
|
steps_summary_data = steps_summary_data[steps_summary_data["local_segment"].str.match(segment_regex)]
|
|
|
|
if not steps_summary_data.empty:
|
|
steps_summary_features = extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute)
|
|
|
|
return steps_summary_features
|