104 lines
6.4 KiB
Python
104 lines
6.4 KiB
Python
import pandas as pd
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import numpy as np
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def getBouts(step_data, time_interval):
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# resample the data into time_interval minute bins, set "isactivebout" column to be NA if it is missing
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resampled_step_minute = pd.DataFrame(step_data.resample(str(time_interval) + "T", on="local_date_time")["isactivebout"].sum(min_count=1))
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# group rows by consecutive values of "isactivebout" column
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group = pd.DataFrame(resampled_step_minute["isactivebout"] != resampled_step_minute["isactivebout"].shift()).cumsum().rename(columns={"isactivebout": "group_idx"})
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# combine resampled_acc_minute and group column
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resampled_step_minute = pd.concat([resampled_step_minute, group], axis=1)
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# drop rows where "isactivebout" column is missing and reset the index
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resampled_step_minute.dropna(subset=["isactivebout"], inplace=True)
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resampled_step_minute.reset_index(inplace=True)
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resampled_step_minute.loc[:, "local_date"] = resampled_step_minute["local_date_time"].dt.date
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# duration column contains the number of minutes (rows) of active and sedentary bout
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bouts = resampled_step_minute.groupby(["isactivebout", "group_idx", "local_date"]).count().rename(columns={"local_date_time": "duration"}).reset_index()
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bouts["duration"] = bouts["duration"] * time_interval
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return bouts
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def statsFeatures(step_data, day_segment, features_to_compute, features_type, step_features):
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if features_type == "allsteps":
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col_name = "steps"
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elif features_type == "durationsedentarybout" or features_type == "durationactivebout":
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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 ['allsteps', 'durationsedentarybout', 'durationactivebout'].")
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if "count" + features_type.replace("duration", "episode") in features_to_compute:
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step_features["step_" + day_segment + "_count" + features_type.replace("duration", "episode")] = step_data.groupby(["local_date"])[col_name].count()
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if "sum" + features_type in features_to_compute:
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step_features["step_" + day_segment + "_sum" + features_type] = step_data.groupby(["local_date"])[col_name].sum()
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if "max" + features_type in features_to_compute:
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step_features["step_" + day_segment + "_max" + features_type] = step_data.groupby(["local_date"])[col_name].max()
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if "min" + features_type in features_to_compute:
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step_features["step_" + day_segment + "_min" + features_type] = step_data.groupby(["local_date"])[col_name].min()
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if "avg" + features_type in features_to_compute:
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step_features["step_" + day_segment + "_avg" + features_type] = step_data.groupby(["local_date"])[col_name].mean()
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if "median" + features_type in features_to_compute:
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step_features["step_" + day_segment + "_median" + features_type] = step_data.groupby(["local_date"])[col_name].median()
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if "std" + features_type in features_to_compute:
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step_features["step_" + day_segment + "_std" + features_type] = step_data.groupby(["local_date"])[col_name].std()
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return step_features
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def base_fitbit_step_features(step_data, day_segment, requested_features, threshold_active_bout, include_zero_step_rows):
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requested_features_allsteps = requested_features["features_all_steps"]
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requested_features_sedentarybout = requested_features["features_sedentary_bout"]
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requested_features_activebout = requested_features["features_active_bout"]
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# name of the features this function can compute
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base_features_allsteps = ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"]
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base_features_sedentarybout = ["countepisodesedentarybout", "sumdurationsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout"]
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base_features_activebout = ["countepisodeactivebout", "sumdurationactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"]
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# the subset of requested features this function can compute
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features_to_compute_allsteps = list(set(requested_features_allsteps) & set(base_features_allsteps))
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features_to_compute_sedentarybout = list(set(requested_features_sedentarybout) & set(base_features_sedentarybout))
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features_to_compute_activebout = list(set(requested_features_activebout) & set(base_features_activebout))
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features_to_compute = features_to_compute_allsteps + features_to_compute_sedentarybout + features_to_compute_activebout
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step_features = pd.DataFrame(columns=["local_date"] + ["step_" + day_segment + "_" + x for x in features_to_compute])
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if not step_data.empty:
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if day_segment != "daily":
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step_data =step_data[step_data["local_day_segment"] == day_segment]
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if not step_data.empty:
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step_features = pd.DataFrame()
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# statistics features of step count
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step_features = statsFeatures(step_data, day_segment, features_to_compute_allsteps, "allsteps", step_features)
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# calculate time interval between two records in minutes
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time_interval = step_data["local_date_time"].diff().min().total_seconds() / 60
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# sedentary bout: less than THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute
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# active bout: greater or equal to THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute
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isactivebout = np.where(step_data["steps"] < int(threshold_active_bout) * time_interval, 0, 1)
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step_data = step_data.assign(isactivebout = isactivebout)
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bouts = getBouts(step_data, time_interval)
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# statistics features of sedentary bout
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sedentary_bout = bouts[bouts["isactivebout"] == 0]
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step_features = statsFeatures(sedentary_bout, day_segment, features_to_compute_sedentarybout, "durationsedentarybout", step_features)
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# statistics features of active bout
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active_bout = bouts[bouts["isactivebout"] == 1]
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step_features = statsFeatures(active_bout, day_segment, features_to_compute_activebout, "durationactivebout", step_features)
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# exclude data when the total step count is ZERO during the whole epoch
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if not include_zero_step_rows:
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step_features["sumallsteps_aux"] = step_data.groupby(["local_date"])["steps"].sum()
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step_features = step_features.query("sumallsteps_aux != 0")
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del step_features["sumallsteps_aux"]
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step_features = step_features.reset_index()
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return step_features
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