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replace "metrics" with "features" of the accelerometer

pull/95/head
Mingze Cao 2020-04-02 16:40:40 -05:00
parent 6045df494d
commit 106f552442
1 changed files with 0 additions and 87 deletions
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src/features/accelerometer_metrics.py
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import pandas as pd
import numpy as np
def getActivityEpisodes(acc_minute, activity_type):
col_name = ["nonexertional_episodes", "exertional_episodes"][activity_type]
# rebuild local date time for resampling
acc_minute["local_datetime"] = pd.to_datetime(acc_minute["local_date"].dt.strftime("%Y-%m-%d") + \
" " + acc_minute["local_hour"].apply(str) + ":" + acc_minute["local_minute"].apply(str) + ":00")
# resample the data into 1 minute bins
resampled_acc_minute = pd.DataFrame(acc_minute.resample("1T", on="local_datetime")["isexertionalactivity"].sum())
if activity_type == 0:
resampled_acc_minute["isexertionalactivity"] = resampled_acc_minute["isexertionalactivity"] * (-1) + 1
# get the longest episode of exertional/non-exertional activity given as consecutive one minute periods
resampled_acc_minute['consecutive'] = resampled_acc_minute["isexertionalactivity"].groupby((resampled_acc_minute["isexertionalactivity"] != resampled_acc_minute["isexertionalactivity"].shift()).cumsum()).transform('size') * resampled_acc_minute["isexertionalactivity"]
longest_activity_episodes = resampled_acc_minute.groupby(pd.Grouper(freq='D'))[["consecutive"]].max().rename(columns = {"consecutive": col_name})
# get the count of exertional/non-exertional activity episodes
resampled_acc_minute_shift = resampled_acc_minute.loc[resampled_acc_minute["consecutive"].shift() != resampled_acc_minute["consecutive"]]
count_activity_episodes = resampled_acc_minute_shift.groupby(pd.Grouper(freq='D'))[["consecutive"]].apply(lambda x: np.count_nonzero(x)).to_frame(name = col_name)
return longest_activity_episodes, count_activity_episodes
def dropRowsWithCertainThreshold(data, threshold):
data_grouped = data.groupby(["local_date", "local_hour", "local_minute"]).count()
drop_dates = data_grouped[data_grouped["timestamp"] == threshold].index
data.set_index(["local_date", "local_hour", "local_minute"], inplace = True)
if not drop_dates.empty:
data.drop(drop_dates, axis = 0, inplace = True)
return data.reset_index()
acc_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
day_segment = snakemake.params["day_segment"]
metrics = snakemake.params["metrics"]
acc_features = pd.DataFrame(columns=["local_date"] + ["acc_" + day_segment + "_" + x for x in metrics])
if not acc_data.empty:
if day_segment != "daily":
acc_data = acc_data[acc_data["local_day_segment"] == day_segment]
if not acc_data.empty:
acc_features = pd.DataFrame()
# get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)
acc_data["magnitude"] = (acc_data["double_values_0"] ** 2 + acc_data["double_values_1"] ** 2 + acc_data["double_values_2"] ** 2).apply(np.sqrt)
if "maxmagnitude" in metrics:
acc_features["acc_" + day_segment + "_maxmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].max()
if "minmagnitude" in metrics:
acc_features["acc_" + day_segment + "_minmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].min()
if "avgmagnitude" in metrics:
acc_features["acc_" + day_segment + "_avgmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].mean()
if "medianmagnitude" in metrics:
acc_features["acc_" + day_segment + "_medianmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].median()
if "stdmagnitude" in metrics:
acc_features["acc_" + day_segment + "_stdmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].std()
# get extertional activity features
# reference: https://jamanetwork.com/journals/jamasurgery/fullarticle/2753807
# drop rows where we only have one row per minute (no variance)
acc_data = dropRowsWithCertainThreshold(acc_data, 1)
if not acc_data.empty:
# check if the participant performs exertional activity for each minute
acc_minute = pd.DataFrame()
acc_minute["isexertionalactivity"] = (acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_0"].var() + acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_1"].var() + acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_2"].var()).apply(lambda x: 1 if x > 0.15 * (9.807 ** 2) else 0)
acc_minute.reset_index(inplace=True)
if "ratioexertionalactivityepisodes" in metrics:
acc_features["acc_" + day_segment + "_ratioexertionalactivityepisodes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].sum()/acc_minute.groupby(["local_date"])["isexertionalactivity"].count()
if "sumexertionalactivityepisodes" in metrics:
acc_features["acc_" + day_segment + "_sumexertionalactivityepisodes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].sum()
longest_exertionalactivity_episodes, count_exertionalactivity_episodes = getActivityEpisodes(acc_minute, 1)
longest_nonexertionalactivity_episodes, count_nonexertionalactivity_episodes = getActivityEpisodes(acc_minute, 0)
if "longestexertionalactivityepisode" in metrics:
acc_features["acc_" + day_segment + "_longestexertionalactivityepisode"] = longest_exertionalactivity_episodes["exertional_episodes"]
if "longestnonexertionalactivityepisode" in metrics:
acc_features["acc_" + day_segment + "_longestnonexertionalactivityepisode"] = longest_nonexertionalactivity_episodes["nonexertional_episodes"]
if "countexertionalactivityepisodes" in metrics:
acc_features["acc_" + day_segment + "_countexertionalactivityepisodes"] = count_exertionalactivity_episodes["exertional_episodes"]
if "countnonexertionalactivityepisodes" in metrics:
acc_features["acc_" + day_segment + "_countnonexertionalactivityepisodes"] = count_nonexertionalactivity_episodes["nonexertional_episodes"]
acc_features = acc_features.reset_index()
acc_features.to_csv(snakemake.output[0], index=False)