Add accelerometer features of PANDA provider
Meng Li
parent
29dcd1f284
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ac9cf92732
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@ -167,6 +167,15 @@ ACCELEROMETER:
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SRC_FOLDER: "rapids" # inside src/features/accelerometer
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SRC_FOLDER: "rapids" # inside src/features/accelerometer
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SRC_LANGUAGE: "python"
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SRC_LANGUAGE: "python"
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PANDA:
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COMPUTE: False
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VALID_SENSED_MINUTES: False
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FEATURES:
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exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
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nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
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SRC_FOLDER: "panda" # inside src/features/accelerometer
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SRC_LANGUAGE: "python"
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APPLICATIONS_FOREGROUND:
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APPLICATIONS_FOREGROUND:
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DB_TABLE: applications_foreground
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DB_TABLE: applications_foreground
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PROVIDERS:
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PROVIDERS:
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@ -1,111 +0,0 @@
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import pandas as pd
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import numpy as np
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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, 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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# 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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# 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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# 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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drop_dates = data_grouped[data_grouped["timestamp"] == threshold].index
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data.set_index(["local_date", "local_hour", "local_minute"], inplace = True)
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if not drop_dates.empty:
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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 == "durationexertionalactivityepisode" or features_type == "durationnonexertionalactivityepisode":
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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', 'durationexertionalactivityepisode', 'durationnonexertionalactivityepisode'].")
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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_magnitude = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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base_features_names_exertionalactivityepisode = ["sumdurationexertionalactivityepisode", "maxdurationexertionalactivityepisode", "mindurationexertionalactivityepisode", "avgdurationexertionalactivityepisode", "mediandurationexertionalactivityepisode", "stddurationexertionalactivityepisode"]
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base_features_names_nonexertionalactivityepisode = ["sumdurationnonexertionalactivityepisode", "maxdurationnonexertionalactivityepisode", "mindurationnonexertionalactivityepisode", "avgdurationnonexertionalactivityepisode", "mediandurationnonexertionalactivityepisode", "stddurationnonexertionalactivityepisode"]
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# the subset of requested features this function can compute
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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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acc_features = pd.DataFrame(columns=["local_date"] + ["acc_" + day_segment + "_" + x for x in features_to_compute])
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if not acc_data.empty:
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if day_segment != "daily":
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acc_data = acc_data[acc_data["local_day_segment"] == day_segment]
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if not acc_data.empty:
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acc_features = pd.DataFrame()
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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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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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# drop rows where we only have one row per minute (no variance)
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acc_data = dropRowsWithCertainThreshold(acc_data, 1)
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if not acc_data.empty:
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# check if the participant performs exertional activity for each minute
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acc_minute = pd.DataFrame()
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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 valid_sensed_minutes:
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acc_features["acc_" + day_segment + "_validsensedminutes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].count()
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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, "durationexertionalactivityepisode", 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, "durationnonexertionalactivityepisode", 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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return acc_features
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@ -0,0 +1,89 @@
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import pandas as pd
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import numpy as np
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def dropRowsWithCertainThreshold(data, threshold):
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data_grouped = data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])
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data_cleaned = data_grouped.filter(lambda x: x["timestamp"].count() > threshold)
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return data_cleaned
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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"] + \
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" " + acc_minute["local_hour"].apply(str) + ":" + acc_minute["local_minute"].apply(str) + ":00")
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# compute time interval between consecutive rows in minutes
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acc_minute["rows_interval"] = round(acc_minute["local_datetime"].diff().dt.total_seconds() / 60, 0)
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# put consecutive rows into the same group if (1) the interval between two rows is 1 minute and (2) have the same values of "isexertionalactivity", "local_timezone", and "local_segment"
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acc_minute["group_idx"] = ((acc_minute[["isexertionalactivity", "local_timezone", "local_segment"]].shift() != acc_minute[["isexertionalactivity", "local_timezone", "local_segment"]]).any(axis=1) | (acc_minute["rows_interval"] != 1)).cumsum()
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# get activity episodes: duration column contains the number of minutes (rows) of exertional and nonexertional activity for each episode
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grouped = acc_minute.groupby("group_idx")
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activity_episodes = grouped["local_segment"].agg(duration="count")
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activity_episodes[["local_segment", "isexertionalactivity"]] = grouped[["local_segment", "isexertionalactivity"]].first()
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return activity_episodes
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def statsFeatures(acc_data, features_to_compute, features_type, acc_features):
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if "sum" + features_type in features_to_compute:
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acc_features["acc_panda_sum" + features_type] = acc_data.groupby(["local_segment"])["duration"].sum()
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if "max" + features_type in features_to_compute:
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acc_features["acc_panda_max" + features_type] = acc_data.groupby(["local_segment"])["duration"].max()
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if "min" + features_type in features_to_compute:
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acc_features["acc_panda_min" + features_type] = acc_data.groupby(["local_segment"])["duration"].min()
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if "avg" + features_type in features_to_compute:
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acc_features["acc_panda_avg" + features_type] = acc_data.groupby(["local_segment"])["duration"].mean()
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if "median" + features_type in features_to_compute:
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acc_features["acc_panda_median" + features_type] = acc_data.groupby(["local_segment"])["duration"].median()
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if "std" + features_type in features_to_compute:
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acc_features["acc_panda_std" + features_type] = acc_data.groupby(["local_segment"])["duration"].std()
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return acc_features
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def panda_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
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acc_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_features = provider["FEATURES"]
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valid_sensed_minutes = provider["VALID_SENSED_MINUTES"]
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# name of the features this function can compute
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base_features_names_exertionalactivityepisode = ["sumdurationexertionalactivityepisode", "maxdurationexertionalactivityepisode", "mindurationexertionalactivityepisode", "avgdurationexertionalactivityepisode", "mediandurationexertionalactivityepisode", "stddurationexertionalactivityepisode"]
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base_features_names_nonexertionalactivityepisode = ["sumdurationnonexertionalactivityepisode", "maxdurationnonexertionalactivityepisode", "mindurationnonexertionalactivityepisode", "avgdurationnonexertionalactivityepisode", "mediandurationnonexertionalactivityepisode", "stddurationnonexertionalactivityepisode"]
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# the subset of requested features this function can compute
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features_to_compute_exertionalactivityepisode = list(set([x + "exertionalactivityepisode" for x in requested_features["exertional_activity_episode"]]) & set(base_features_names_exertionalactivityepisode))
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features_to_compute_nonexertionalactivityepisode = list(set([ x + "nonexertionalactivityepisode" for x in requested_features["nonexertional_activity_episode"]]) & set(base_features_names_nonexertionalactivityepisode))
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features_to_compute = features_to_compute_exertionalactivityepisode + features_to_compute_nonexertionalactivityepisode + (["validsensedminutes"] if valid_sensed_minutes else [])
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acc_features = pd.DataFrame(columns=["local_segment"] + ["acc_panda_" + x for x in features_to_compute])
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if not acc_data.empty:
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acc_data = filter_data_by_segment(acc_data, day_segment)
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if not acc_data.empty:
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acc_features = pd.DataFrame()
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# drop rows where we only have one row per minute (no variance)
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acc_data = dropRowsWithCertainThreshold(acc_data, 1)
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if not acc_data.empty:
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# check if the participant performs exertional activity for each minute
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acc_minute = pd.DataFrame()
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acc_minute["isexertionalactivity"] = (acc_data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])["double_values_0"].var() + acc_data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])["double_values_1"].var() + acc_data.groupby(["local_timezone", "local_segment", "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 valid_sensed_minutes:
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acc_features["acc_panda_validsensedminutes"] = acc_minute.groupby(["local_segment"])["isexertionalactivity"].count()
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activity_episodes = getActivityEpisodes(acc_minute)
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# compute exertional episodes features
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exertionalactivity_episodes = activity_episodes[activity_episodes["isexertionalactivity"] == 1]
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acc_features = statsFeatures(exertionalactivity_episodes, features_to_compute_exertionalactivityepisode, "durationexertionalactivityepisode", acc_features)
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# compute non-exertional episodes features
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nonexertionalactivity_episodes = activity_episodes[activity_episodes["isexertionalactivity"] == 0]
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acc_features = statsFeatures(nonexertionalactivity_episodes, features_to_compute_nonexertionalactivityepisode, "durationnonexertionalactivityepisode", 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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return acc_features
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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 = {}
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requested_features["magnitude"] = snakemake.params["magnitude"]
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requested_features["exertional_activity_episode"] = [feature + "exertionalactivityepisode" for feature in snakemake.params["exertional_activity_episode"]]
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requested_features["nonexertional_activity_episode"] = [feature + "nonexertionalactivityepisode" for feature in 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, valid_sensed_minutes), on="local_date", how="outer")
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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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