Refactor fitbit step features
Meng Li
parent
925e986c1e
commit
0115fd14f6
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@ -193,7 +193,7 @@ rule fitbit_heartrate_features:
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rule fitbit_step_features:
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input:
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steps_data = "data/raw/{pid}/fitbit_steps_intraday_with_datetime.csv"
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step_data = "data/raw/{pid}/fitbit_steps_intraday_with_datetime.csv"
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params:
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day_segment = "{day_segment}",
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features_all_steps = config["STEP"]["FEATURES"]["ALL_STEPS"],
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@ -0,0 +1,96 @@
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import pandas as pd
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import numpy as np
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import datetime as dt
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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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 = ["countsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout", "sumdurationsedentarybout"]
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base_features_activebout = ["countactivebout", "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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resampled_data = step_data.set_index(step_data.local_date_time)
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resampled_data.index.names = ["datetime"]
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# Replace the first element of time_diff_minutes with its second element
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resampled_data["time_diff_minutes"] = resampled_data["local_date_time"].diff().fillna(resampled_data["local_date_time"].diff()[1]).dt.total_seconds().div(60).astype(int)
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# Sedentary Bout when you have less than 10 steps in a minute
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# Active Bout when you have greater or equal to 10 steps in a minute
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resampled_data["active_sedentary"] = np.where(resampled_data["steps"] < int(threshold_active_bout) * resampled_data["time_diff_minutes"],"sedentary","active")
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# Time Calculations of sedentary/active bouts:
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resampled_data["active_sedentary_groups"] = (resampled_data.active_sedentary != resampled_data.active_sedentary.shift()).cumsum().values
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# Get the total minutes for each episode
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minutes_per_episode = resampled_data.groupby(["local_date","active_sedentary","active_sedentary_groups"])["time_diff_minutes"].sum()
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# Get Stats for all episodes in terms of minutes
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stats_per_episode = minutes_per_episode.groupby(["local_date", "active_sedentary"]).agg([max, min, np.mean, np.std, np.sum])
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mux = pd.MultiIndex.from_product([stats_per_episode.index.levels[0], stats_per_episode.index.levels[1]], names=["local_date", "active_sedentary"])
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stats_per_episode = stats_per_episode.reindex(mux, fill_value=None).reset_index()
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stats_per_episode.set_index("local_date", inplace = True)
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# Descriptive Statistics Features:
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if "sumallsteps" in features_to_compute_allsteps:
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step_features["step_" + str(day_segment) + "_sumallsteps"] = resampled_data["steps"].resample("D").sum()
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if "maxallsteps" in features_to_compute_allsteps:
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step_features["step_" + str(day_segment) + "_maxallsteps"] = resampled_data["steps"].resample("D").max()
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if "minallsteps" in features_to_compute_allsteps:
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step_features["step_" + str(day_segment) + "_minallsteps"] = resampled_data["steps"].resample("D").min()
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if "avgallsteps" in features_to_compute_allsteps:
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step_features["step_" + str(day_segment) + "_avgallsteps"] = resampled_data["steps"].resample("D").mean()
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if "stdallsteps" in features_to_compute_allsteps:
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step_features["step_" + str(day_segment) + "_stdallsteps"] = resampled_data["steps"].resample("D").std()
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if "countsedentarybout" in features_to_compute_sedentarybout:
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step_features["step_" + str(day_segment) + "_countsedentarybout"] = resampled_data[resampled_data["active_sedentary"] == "sedentary"]["active_sedentary_groups"].resample("D").nunique()
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if "countactivebout" in features_to_compute_activebout:
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step_features["step_" + str(day_segment) + "_countactivebout"] = resampled_data[resampled_data["active_sedentary"] == "active"]["active_sedentary_groups"].resample("D").nunique()
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if "maxdurationsedentarybout" in features_to_compute_sedentarybout:
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step_features["step_" + str(day_segment) + "_maxdurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["max"]
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if "mindurationsedentarybout" in features_to_compute_sedentarybout:
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step_features["step_" + str(day_segment) + "_mindurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["min"]
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if "avgdurationsedentarybout" in features_to_compute_sedentarybout:
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step_features["step_" + str(day_segment) + "_avgdurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["mean"]
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if "stddurationsedentarybout" in features_to_compute_sedentarybout:
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step_features["step_" + str(day_segment) + "_stddurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["std"]
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if "sumdurationsedentarybout" in features_to_compute_sedentarybout:
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step_features["step_" + str(day_segment) + "_sumdurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["sum"]
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if "maxdurationactivebout" in features_to_compute_activebout:
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step_features["step_" + str(day_segment) + "_maxdurationactivebout"] = stats_per_episode[stats_per_episode["active_sedentary"]== "active"]["max"]
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if "mindurationactivebout" in features_to_compute_activebout:
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step_features["step_" + str(day_segment) + "_mindurationactivebout"] = stats_per_episode[stats_per_episode["active_sedentary"]== "active"]["min"]
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if "avgdurationactivebout" in features_to_compute_activebout:
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step_features["step_" + str(day_segment) + "_avgdurationactivebout"] = stats_per_episode[stats_per_episode["active_sedentary"]== "active"]["mean"]
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if "stddurationactivebout" in features_to_compute_activebout:
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step_features["step_" + str(day_segment) + "_stddurationactivebout"] = stats_per_episode[stats_per_episode["active_sedentary"]== "active"]["std"]
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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"] = resampled_data["steps"].resample("D").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.index.names = ["local_date"]
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step_features = step_features.reset_index()
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return step_features
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@ -1,106 +1,21 @@
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import pandas as pd
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import numpy as np
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import datetime as dt
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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from fitbit_step.fitbit_step_base import base_fitbit_step_features
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step_data = pd.read_csv(snakemake.input["step_data"], parse_dates=["local_date_time"])
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day_segment = snakemake.params["day_segment"]
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all_steps = snakemake.params["features_all_steps"]
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sedentary_bout = snakemake.params["features_sedentary_bout"]
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active_bout = snakemake.params["features_active_bout"]
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threshold_active_bout = snakemake.params['threshold_active_bout']
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threshold_active_bout = snakemake.params["threshold_active_bout"]
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include_zero_step_rows = snakemake.params["include_zero_step_rows"]
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step_features = pd.DataFrame(columns=["local_date"])
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#Read csv into a pandas dataframe
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data = pd.read_csv(snakemake.input['steps_data'],parse_dates=['local_date_time'])
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columns = list("step_" + str(day_segment) + "_" + column for column in (all_steps + sedentary_bout + active_bout))
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requested_features = {}
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requested_features["features_all_steps"] = snakemake.params["features_all_steps"]
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requested_features["features_sedentary_bout"] = snakemake.params["features_sedentary_bout"]
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requested_features["features_active_bout"] = snakemake.params["features_active_bout"]
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if (day_segment != 'daily'):
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data = data.loc[data['local_day_segment'] == str(day_segment)]
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if data.empty:
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finalDataset = pd.DataFrame(columns = columns)
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else:
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finalDataset = pd.DataFrame()
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#Preprocessing:
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data.local_date_time = pd.to_datetime(data.local_date_time)
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resampledData = data.set_index(data.local_date_time)
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resampledData.index.names = ['datetime']
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resampledData['time_diff_minutes'] = resampledData['local_date_time'].diff().fillna(pd.Timedelta(seconds=0)).dt.total_seconds().div(60).astype(int)
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#Sedentary Bout when you have less than 10 steps in a minute
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#Active Bout when you have greater or equal to 10 steps in a minute
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resampledData['active_sedentary'] = np.where(resampledData['steps']<int(threshold_active_bout),'sedentary','active')
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#Time Calculations of sedentary/active bouts:
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resampledData['active_sedentary_groups'] = (resampledData.active_sedentary != resampledData.active_sedentary.shift()).cumsum().values
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#Get the total minutes for each episode
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minutesGroupedBy = resampledData.groupby(['local_date','active_sedentary','active_sedentary_groups'])['time_diff_minutes'].sum()
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#Get Stats for all episodes in terms of minutes
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statsMinutes = minutesGroupedBy.groupby(['local_date','active_sedentary']).agg([max,min,np.mean,np.std,np.sum])
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mux = pd.MultiIndex.from_product([statsMinutes.index.levels[0], statsMinutes.index.levels[1]],names=['local_date','active_sedentary'])
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statsMinutes = statsMinutes.reindex(mux, fill_value=None).reset_index()
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statsMinutes.set_index('local_date',inplace = True)
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#Descriptive Statistics Features:
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if("sumallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_sumallsteps"] = resampledData['steps'].resample('D').sum()
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if("maxallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_maxallsteps"] = resampledData['steps'].resample('D').max()
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if("minallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_minallsteps"] = resampledData['steps'].resample('D').min()
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if("avgallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_avgallsteps"] = resampledData['steps'].resample('D').mean()
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if("stdallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_stdallsteps"] = resampledData['steps'].resample('D').std()
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if("countsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_countsedentarybout"] = resampledData[resampledData["active_sedentary"] == "sedentary"]["active_sedentary_groups"].resample("D").nunique()
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if("countactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_countactivebout"] = resampledData[resampledData["active_sedentary"] == "active"]["active_sedentary_groups"].resample("D").nunique()
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if("maxdurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_maxdurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['max']
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if("mindurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_mindurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['min']
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if("avgdurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_avgdurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['mean']
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if("stddurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_stddurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['std']
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if("sumdurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_sumdurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['sum']
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if("maxdurationactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_maxdurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['max']
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if("mindurationactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_mindurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['min']
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if("avgdurationactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_avgdurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['mean']
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if("stddurationactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_stddurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['std']
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step_features = step_features.merge(base_fitbit_step_features(step_data, day_segment, requested_features, threshold_active_bout, include_zero_step_rows), on="local_date", how="outer")
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assert np.sum([len(x) for x in requested_features.values()]) + 1 == step_features.shape[1], "The number of features in the output dataframe (=" + str(step_features.shape[1]) + ") does not match the expected value (=" + str(np.sum([len(x) for x in requested_features.values()])) + " + 1). Verify your fitbit step feature extraction functions"
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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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finalDataset["sumallsteps_aux"] = resampledData["steps"].resample("D").sum()
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finalDataset = finalDataset.query("sumallsteps_aux != 0")
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del finalDataset["sumallsteps_aux"]
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finalDataset.index.names = ['local_date']
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finalDataset.to_csv(snakemake.output[0])
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step_features.to_csv(snakemake.output[0], index=False)
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