Split accelerometer features in config file; drop count; add more stats features
Meng Li
parent
78a9d82a74
commit
a121c0fe7b
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@ -100,7 +100,11 @@ LIGHT:
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ACCELEROMETER:
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DAY_SEGMENTS: *day_segments
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude", "ratioexertionalactivityepisodes", "sumexertionalactivityepisodes", "longestexertionalactivityepisode", "longestnonexertionalactivityepisode", "countexertionalactivityepisodes", "countnonexertionalactivityepisodes"]
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FEATURES:
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MAGNITUDE: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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EXERTIONAL_ACTIVITY_EPISODE: ["sumexertionalactivityepisode", "maxexertionalactivityepisode", "minexertionalactivityepisode", "avgexertionalactivityepisode", "medianexertionalactivityepisode", "stdexertionalactivityepisode"]
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NONEXERTIONAL_ACTIVITY_EPISODE: ["sumnonexertionalactivityepisode", "maxnonexertionalactivityepisode", "minnonexertionalactivityepisode", "avgnonexertionalactivityepisode", "mediannonexertionalactivityepisode", "stdnonexertionalactivityepisode"]
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VALID_SENSED_MINUTES: True
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APPLICATIONS_FOREGROUND:
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DAY_SEGMENTS: *day_segments
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@ -173,7 +173,10 @@ rule accelerometer_features:
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"data/raw/{pid}/accelerometer_with_datetime.csv",
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params:
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day_segment = "{day_segment}",
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features = config["ACCELEROMETER"]["FEATURES"],
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magnitude = config["ACCELEROMETER"]["FEATURES"]["MAGNITUDE"],
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exertional_activity_episode = config["ACCELEROMETER"]["FEATURES"]["EXERTIONAL_ACTIVITY_EPISODE"],
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nonexertional_activity_episode = config["ACCELEROMETER"]["FEATURES"]["NONEXERTIONAL_ACTIVITY_EPISODE"],
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valid_sensed_minutes = config["ACCELEROMETER"]["FEATURES"]["VALID_SENSED_MINUTES"],
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output:
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"data/processed/{pid}/accelerometer_{day_segment}.csv"
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script:
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@ -1,27 +1,29 @@
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import pandas as pd
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import numpy as np
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def getActivityEpisodes(acc_minute, activity_type):
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col_name = ["nonexertional_episodes", "exertional_episodes"][activity_type]
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def getActivityEpisodes(acc_minute):
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# rebuild local date time for resampling
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acc_minute["local_datetime"] = pd.to_datetime(acc_minute["local_date"].dt.strftime("%Y-%m-%d") + \
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" " + acc_minute["local_hour"].apply(str) + ":" + acc_minute["local_minute"].apply(str) + ":00")
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# resample the data into 1 minute bins
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resampled_acc_minute = pd.DataFrame(acc_minute.resample("1T", on="local_datetime")["isexertionalactivity"].sum())
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if activity_type == 0:
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resampled_acc_minute["isexertionalactivity"] = resampled_acc_minute["isexertionalactivity"] * (-1) + 1
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# resample the data into 1 minute bins, set "isexertionalactivity" column to be NA if it is missing
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resampled_acc_minute = pd.DataFrame(acc_minute.resample("1T", on="local_datetime")["isexertionalactivity"].sum(min_count=1))
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# get the longest episode of exertional/non-exertional activity given as consecutive one minute periods
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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"]
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longest_activity_episodes = resampled_acc_minute.groupby(pd.Grouper(freq='D'))[["consecutive"]].max().rename(columns = {"consecutive": col_name})
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# group rows by consecutive values of "isexertionalactivity" column
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group = pd.DataFrame(resampled_acc_minute["isexertionalactivity"] != resampled_acc_minute["isexertionalactivity"].shift()).cumsum().rename(columns={"isexertionalactivity": "group_idx"})
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# get the count of exertional/non-exertional activity episodes
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resampled_acc_minute_shift = resampled_acc_minute.loc[resampled_acc_minute["consecutive"].shift() != resampled_acc_minute["consecutive"]]
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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)
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# combine resampled_acc_minute and group column
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resampled_acc_minute = pd.concat([resampled_acc_minute, group], axis=1)
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return longest_activity_episodes, count_activity_episodes
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# drop rows where "isexertionalactivity" column is missing and reset the index
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resampled_acc_minute.dropna(subset=["isexertionalactivity"], inplace=True)
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resampled_acc_minute.reset_index(inplace=True)
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resampled_acc_minute.loc[:, "local_date"] = resampled_acc_minute["local_datetime"].dt.date
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# duration column contains the number of minutes (rows) of exertional and nonexertional activity for each episode
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activity_episode = resampled_acc_minute.groupby(["isexertionalactivity", "group_idx", "local_date"]).count().rename(columns={"local_datetime": "duration"}).reset_index()
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return activity_episode
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def dropRowsWithCertainThreshold(data, threshold):
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data_grouped = data.groupby(["local_date", "local_hour", "local_minute"]).count()
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@ -31,12 +33,42 @@ def dropRowsWithCertainThreshold(data, threshold):
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data.drop(drop_dates, axis = 0, inplace = True)
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return data.reset_index()
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def statsFeatures(acc_data, day_segment, features_to_compute, features_type, acc_features):
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if features_type == "magnitude":
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col_name = features_type
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elif features_type == "exertionalactivityepisode" or features_type == "nonexertionalactivityepisode":
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col_name = "duration"
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else:
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raise ValueError("features_type can only be one of ['magnitude', 'exertionalactivityepisode', 'nonexertionalactivityepisode'].")
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def base_accelerometer_features(acc_data, day_segment, requested_features):
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if "sum" + features_type in features_to_compute:
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acc_features["acc_" + day_segment + "_sum" + features_type] = acc_data.groupby(["local_date"])[col_name].sum()
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if "max" + features_type in features_to_compute:
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acc_features["acc_" + day_segment + "_max" + features_type] = acc_data.groupby(["local_date"])[col_name].max()
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if "min" + features_type in features_to_compute:
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acc_features["acc_" + day_segment + "_min" + features_type] = acc_data.groupby(["local_date"])[col_name].min()
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if "avg" + features_type in features_to_compute:
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acc_features["acc_" + day_segment + "_avg" + features_type] = acc_data.groupby(["local_date"])[col_name].mean()
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if "median" + features_type in features_to_compute:
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acc_features["acc_" + day_segment + "_median" + features_type] = acc_data.groupby(["local_date"])[col_name].median()
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if "std" + features_type in features_to_compute:
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acc_features["acc_" + day_segment + "_std" + features_type] = acc_data.groupby(["local_date"])[col_name].std()
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return acc_features
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def base_accelerometer_features(acc_data, day_segment, requested_features, valid_sensed_minutes):
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# name of the features this function can compute
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base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude", "ratioexertionalactivityepisodes", "sumexertionalactivityepisodes", "longestexertionalactivityepisode", "longestnonexertionalactivityepisode", "countexertionalactivityepisodes", "countnonexertionalactivityepisodes"]
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base_features_names_magnitude = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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base_features_names_exertionalactivityepisode = ["sumexertionalactivityepisode", "maxexertionalactivityepisode", "minexertionalactivityepisode", "avgexertionalactivityepisode", "medianexertionalactivityepisode", "stdexertionalactivityepisode"]
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base_features_names_nonexertionalactivityepisode = ["sumnonexertionalactivityepisode", "maxnonexertionalactivityepisode", "minnonexertionalactivityepisode", "avgnonexertionalactivityepisode", "mediannonexertionalactivityepisode", "stdnonexertionalactivityepisode"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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features_to_compute_magnitude = list(set(requested_features["magnitude"]) & set(base_features_names_magnitude))
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features_to_compute_exertionalactivityepisode = list(set(requested_features["exertional_activity_episode"]) & set(base_features_names_exertionalactivityepisode))
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features_to_compute_nonexertionalactivityepisode = list(set(requested_features["nonexertional_activity_episode"]) & set(base_features_names_nonexertionalactivityepisode))
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features_to_compute = features_to_compute_magnitude + features_to_compute_exertionalactivityepisode + features_to_compute_nonexertionalactivityepisode + (["validsensedminutes"] if valid_sensed_minutes else [])
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if acc_data.empty:
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acc_features = pd.DataFrame(columns=["local_date"] + ["acc_" + day_segment + "_" + x for x in features_to_compute])
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@ -48,16 +80,8 @@ def base_accelerometer_features(acc_data, day_segment, requested_features):
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# get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)
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magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1)
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acc_data = acc_data.assign(magnitude = magnitude.values)
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if "maxmagnitude" in features_to_compute:
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acc_features["acc_" + day_segment + "_maxmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].max()
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if "minmagnitude" in features_to_compute:
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acc_features["acc_" + day_segment + "_minmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].min()
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if "avgmagnitude" in features_to_compute:
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acc_features["acc_" + day_segment + "_avgmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].mean()
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if "medianmagnitude" in features_to_compute:
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acc_features["acc_" + day_segment + "_medianmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].median()
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if "stdmagnitude" in features_to_compute:
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acc_features["acc_" + day_segment + "_stdmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].std()
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acc_features = statsFeatures(acc_data, day_segment, features_to_compute_magnitude, "magnitude", acc_features)
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# get extertional activity features
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# reference: https://jamanetwork.com/journals/jamasurgery/fullarticle/2753807
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@ -70,21 +94,17 @@ def base_accelerometer_features(acc_data, day_segment, requested_features):
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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)
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acc_minute.reset_index(inplace=True)
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if "ratioexertionalactivityepisodes" in features_to_compute:
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acc_features["acc_" + day_segment + "_ratioexertionalactivityepisodes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].sum()/acc_minute.groupby(["local_date"])["isexertionalactivity"].count()
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if "sumexertionalactivityepisodes" in features_to_compute:
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acc_features["acc_" + day_segment + "_sumexertionalactivityepisodes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].sum()
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if valid_sensed_minutes:
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acc_features["acc_" + day_segment + "_validsensedminutes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].count()
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longest_exertionalactivity_episodes, count_exertionalactivity_episodes = getActivityEpisodes(acc_minute, 1)
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longest_nonexertionalactivity_episodes, count_nonexertionalactivity_episodes = getActivityEpisodes(acc_minute, 0)
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if "longestexertionalactivityepisode" in features_to_compute:
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acc_features["acc_" + day_segment + "_longestexertionalactivityepisode"] = longest_exertionalactivity_episodes["exertional_episodes"]
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if "longestnonexertionalactivityepisode" in features_to_compute:
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acc_features["acc_" + day_segment + "_longestnonexertionalactivityepisode"] = longest_nonexertionalactivity_episodes["nonexertional_episodes"]
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if "countexertionalactivityepisodes" in features_to_compute:
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acc_features["acc_" + day_segment + "_countexertionalactivityepisodes"] = count_exertionalactivity_episodes["exertional_episodes"]
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if "countnonexertionalactivityepisodes" in features_to_compute:
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acc_features["acc_" + day_segment + "_countnonexertionalactivityepisodes"] = count_nonexertionalactivity_episodes["nonexertional_episodes"]
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activity_episode = getActivityEpisodes(acc_minute)
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exertionalactivity_episodes = activity_episode[activity_episode["isexertionalactivity"] == 1]
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acc_features = statsFeatures(exertionalactivity_episodes, day_segment, features_to_compute_exertionalactivityepisode, "exertionalactivityepisode", acc_features)
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nonexertionalactivity_episodes = activity_episode[activity_episode["isexertionalactivity"] == 0]
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acc_features = statsFeatures(nonexertionalactivity_episodes, day_segment, features_to_compute_nonexertionalactivityepisode, "nonexertionalactivityepisode", acc_features)
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acc_features[[colname for colname in acc_features.columns if "std" not in colname]] = acc_features[[colname for colname in acc_features.columns if "std" not in colname]].fillna(0)
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acc_features = acc_features.reset_index()
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@ -1,14 +1,22 @@
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import numpy as np
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import pandas as pd
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from accelerometer.accelerometer_base import base_accelerometer_features
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acc_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
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day_segment = snakemake.params["day_segment"]
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requested_features = snakemake.params["features"]
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requested_features = {}
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requested_features["magnitude"] = snakemake.params["magnitude"]
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requested_features["exertional_activity_episode"] = snakemake.params["exertional_activity_episode"]
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requested_features["nonexertional_activity_episode"] = snakemake.params["nonexertional_activity_episode"]
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valid_sensed_minutes = snakemake.params["valid_sensed_minutes"]
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acc_features = pd.DataFrame(columns=["local_date"])
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acc_features = acc_features.merge(base_accelerometer_features(acc_data, day_segment, requested_features), on="local_date", how="outer")
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acc_features = acc_features.merge(base_accelerometer_features(acc_data, day_segment, requested_features, valid_sensed_minutes), on="local_date", how="outer")
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assert len(requested_features) + 1 == acc_features.shape[1], "The number of features in the output dataframe (=" + str(acc_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your accelerometer feature extraction functions"
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assert np.sum([len(x) for x in requested_features.values()]) + (1 if valid_sensed_minutes else 0) + 1 == acc_features.shape[1], "The number of features in the output dataframe (=" + str(acc_features.shape[1]) + ") does not match the expected value (=" + str(np.sum([len(x) for x in requested_features.values()]) + (1 if valid_sensed_minutes else 0)) + " + 1). Verify your accelerometer feature extraction functions"
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acc_features.to_csv(snakemake.output[0], index=False)
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