Fix step bouts features

config.yaml

@@ -154,10 +154,10 @@ STEP:
       END: "07:00"
   FEATURES:
     ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"]
-    SEDENTARY_BOUT: ["countsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout", "sumdurationsedentarybout"]
+    SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
-    ACTIVE_BOUT: ["countactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"]
+    ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
   THRESHOLD_ACTIVE_BOUT: 10 # steps
-  INCLUDE_ZERO_STEP_ROWS: True
+  INCLUDE_ZERO_STEP_ROWS: False
 
 SLEEP:
   COMPUTE: False

src/features/fitbit_step/fitbit_step_base.py

@@ -1,6 +1,52 @@
 import pandas as pd
 import numpy as np
 
+def getBouts(step_data, time_interval):
+    # resample the data into time_interval minute bins, set "isactivebout" column to be NA if it is missing
+    resampled_step_minute = pd.DataFrame(step_data.resample(str(time_interval) + "T", on="local_date_time")["isactivebout"].sum(min_count=1))
+
+    # group rows by consecutive values of "isactivebout" column
+    group = pd.DataFrame(resampled_step_minute["isactivebout"] != resampled_step_minute["isactivebout"].shift()).cumsum().rename(columns={"isactivebout": "group_idx"})
+
+    # combine resampled_acc_minute and group column
+    resampled_step_minute = pd.concat([resampled_step_minute, group], axis=1)
+    
+    # drop rows where "isactivebout" column is missing and reset the index
+    resampled_step_minute.dropna(subset=["isactivebout"], inplace=True)
+    resampled_step_minute.reset_index(inplace=True)
+    resampled_step_minute.loc[:, "local_date"] = resampled_step_minute["local_date_time"].dt.date
+
+    # duration column contains the number of minutes (rows) of active and sedentary bout
+    bouts = resampled_step_minute.groupby(["isactivebout", "group_idx", "local_date"]).count().rename(columns={"local_date_time": "duration"}).reset_index()
+    bouts["duration"] = bouts["duration"] * time_interval
+
+    return bouts
+
+def statsFeatures(step_data, day_segment, features_to_compute, features_type, step_features):
+    if features_type == "allsteps":
+        col_name = "steps"
+    elif features_type == "durationsedentarybout" or features_type == "durationactivebout":
+        col_name = "duration"
+    else:
+        raise ValueError("features_type can only be one of ['allsteps', 'durationsedentarybout', 'durationactivebout'].")
+
+    if "count" + features_type.replace("duration", "episode") in features_to_compute:
+        step_features["step_" + day_segment + "_count" + features_type.replace("duration", "episode")] = step_data.groupby(["local_date"])[col_name].count()
+    if "sum" + features_type in features_to_compute:
+        step_features["step_" + day_segment + "_sum" + features_type] = step_data.groupby(["local_date"])[col_name].sum()
+    if "max" + features_type in features_to_compute:
+        step_features["step_" + day_segment + "_max" + features_type] = step_data.groupby(["local_date"])[col_name].max()
+    if "min" + features_type in features_to_compute:
+        step_features["step_" + day_segment + "_min" + features_type] = step_data.groupby(["local_date"])[col_name].min()
+    if "avg" + features_type in features_to_compute:
+        step_features["step_" + day_segment + "_avg" + features_type] = step_data.groupby(["local_date"])[col_name].mean()
+    if "median" + features_type in features_to_compute:
+        step_features["step_" + day_segment + "_median" + features_type] = step_data.groupby(["local_date"])[col_name].median()
+    if "std" + features_type in features_to_compute:
+        step_features["step_" + day_segment + "_std" + features_type] = step_data.groupby(["local_date"])[col_name].std()
+
+    return step_features
+
 def base_fitbit_step_features(step_data, day_segment, requested_features, threshold_active_bout, include_zero_step_rows):
     requested_features_allsteps = requested_features["features_all_steps"]
     requested_features_sedentarybout = requested_features["features_sedentary_bout"]
@@ -8,8 +54,8 @@ def base_fitbit_step_features(step_data, day_segment, requested_features, thresh
 
     # name of the features this function can compute
     base_features_allsteps = ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"]
-    base_features_sedentarybout = ["countsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout", "sumdurationsedentarybout"]
+    base_features_sedentarybout = ["countepisodesedentarybout", "sumdurationsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout"]
-    base_features_activebout = ["countactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"]
+    base_features_activebout = ["countepisodeactivebout", "sumdurationactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"]
     # the subset of requested features this function can compute
     features_to_compute_allsteps = list(set(requested_features_allsteps) & set(base_features_allsteps))
     features_to_compute_sedentarybout = list(set(requested_features_sedentarybout) & set(base_features_sedentarybout))
@@ -25,70 +71,33 @@ def base_fitbit_step_features(step_data, day_segment, requested_features, thresh
         if not step_data.empty:
             step_features = pd.DataFrame()
 
-            resampled_data = step_data.set_index(step_data.local_date_time)
+            # statistics features of step count
-            resampled_data.index.names = ["datetime"]
+            step_features = statsFeatures(step_data, day_segment, features_to_compute_allsteps, "allsteps", step_features)
 
-            # Replace the first element of time_diff_minutes with its second element
+            # calculate time interval between two records in minutes
-            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)
+            time_interval = step_data["local_date_time"].diff().min().total_seconds() / 60
 
-            # Sedentary Bout when you have less than 10 steps in a minute
+            # sedentary bout: less than THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute
-            # Active Bout when you have greater or equal to 10 steps in a minute
+            # active bout: greater or equal to THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute
-            resampled_data["active_sedentary"] = np.where(resampled_data["steps"] < int(threshold_active_bout) * resampled_data["time_diff_minutes"],"sedentary","active")
+            isactivebout = np.where(step_data["steps"] < int(threshold_active_bout) * time_interval, 0, 1)
+            step_data = step_data.assign(isactivebout = isactivebout)
 
-            # Time Calculations of sedentary/active bouts:
+            bouts = getBouts(step_data, time_interval)
-            resampled_data["active_sedentary_groups"] = (resampled_data.active_sedentary != resampled_data.active_sedentary.shift()).cumsum().values
 
-            # Get the total minutes for each episode
+            # statistics features of sedentary bout
-            minutes_per_episode = resampled_data.groupby(["local_date","active_sedentary","active_sedentary_groups"])["time_diff_minutes"].sum()
+            sedentary_bout = bouts[bouts["isactivebout"] == 0]
-            
+            step_features = statsFeatures(sedentary_bout, day_segment, features_to_compute_sedentarybout, "durationsedentarybout", step_features)
-            # Get Stats for all episodes in terms of minutes
-            stats_per_episode = minutes_per_episode.groupby(["local_date", "active_sedentary"]).agg([max, min, np.mean, np.std, np.sum])
-            mux = pd.MultiIndex.from_product([stats_per_episode.index.levels[0], stats_per_episode.index.levels[1]], names=["local_date", "active_sedentary"])
-            stats_per_episode = stats_per_episode.reindex(mux, fill_value=None).reset_index()
-            stats_per_episode.set_index("local_date", inplace = True)
-            
-            # Descriptive Statistics Features:
-            if "sumallsteps" in features_to_compute_allsteps:
-                step_features["step_" + str(day_segment) + "_sumallsteps"] = resampled_data["steps"].resample("D").sum()
-            if "maxallsteps" in features_to_compute_allsteps:
-                step_features["step_" + str(day_segment) + "_maxallsteps"] = resampled_data["steps"].resample("D").max()
-            if "minallsteps" in features_to_compute_allsteps:
-                step_features["step_" + str(day_segment) + "_minallsteps"] = resampled_data["steps"].resample("D").min()
-            if "avgallsteps" in features_to_compute_allsteps:
-                step_features["step_" + str(day_segment) + "_avgallsteps"] = resampled_data["steps"].resample("D").mean()
-            if "stdallsteps" in features_to_compute_allsteps:
-                step_features["step_" + str(day_segment) + "_stdallsteps"] = resampled_data["steps"].resample("D").std()
 
-            if "countsedentarybout" in features_to_compute_sedentarybout:
+            # statistics features of active bout
-                step_features["step_" + str(day_segment) + "_countsedentarybout"] = resampled_data[resampled_data["active_sedentary"] == "sedentary"]["active_sedentary_groups"].resample("D").nunique()
+            active_bout = bouts[bouts["isactivebout"] == 1]
-            if "countactivebout" in features_to_compute_activebout:
+            step_features = statsFeatures(active_bout, day_segment, features_to_compute_activebout, "durationactivebout", step_features)
-                step_features["step_" + str(day_segment) + "_countactivebout"] = resampled_data[resampled_data["active_sedentary"] == "active"]["active_sedentary_groups"].resample("D").nunique()
+
-            if "maxdurationsedentarybout" in features_to_compute_sedentarybout:
+            # exclude data when the total step count is ZERO during the whole epoch
-                step_features["step_" + str(day_segment) + "_maxdurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["max"]
-            if "mindurationsedentarybout" in features_to_compute_sedentarybout:
-                step_features["step_" + str(day_segment) + "_mindurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["min"]
-            if "avgdurationsedentarybout" in features_to_compute_sedentarybout:
-                step_features["step_" + str(day_segment) + "_avgdurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["mean"]
-            if "stddurationsedentarybout" in features_to_compute_sedentarybout:
-                step_features["step_" + str(day_segment) + "_stddurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["std"]
-            if "sumdurationsedentarybout" in features_to_compute_sedentarybout:
-                step_features["step_" + str(day_segment) + "_sumdurationsedentarybout"] = stats_per_episode[stats_per_episode["active_sedentary"]=="sedentary"]["sum"]
-            if "maxdurationactivebout" in features_to_compute_activebout:
-                step_features["step_" + str(day_segment) + "_maxdurationactivebout"] = stats_per_episode[stats_per_episode["active_sedentary"]== "active"]["max"]
-            if "mindurationactivebout" in features_to_compute_activebout:
-                step_features["step_" + str(day_segment) + "_mindurationactivebout"] = stats_per_episode[stats_per_episode["active_sedentary"]== "active"]["min"]
-            if "avgdurationactivebout" in features_to_compute_activebout:
-                step_features["step_" + str(day_segment) + "_avgdurationactivebout"] = stats_per_episode[stats_per_episode["active_sedentary"]== "active"]["mean"]
-            if "stddurationactivebout" in features_to_compute_activebout:
-                step_features["step_" + str(day_segment) + "_stddurationactivebout"] = stats_per_episode[stats_per_episode["active_sedentary"]== "active"]["std"]
-            
-            #Exclude data when the total step count is ZERO during the whole epoch
             if not include_zero_step_rows:
-                step_features["sumallsteps_aux"] = resampled_data["steps"].resample("D").sum()
+                step_features["sumallsteps_aux"] = step_data.groupby(["local_date"])["steps"].sum()
                 step_features = step_features.query("sumallsteps_aux != 0")
                 del step_features["sumallsteps_aux"]
 
-            step_features.index.names = ["local_date"]
             step_features = step_features.reset_index()
 
     return step_features

src/features/fitbit_step_features.py

@@ -42,8 +42,8 @@ exclude_sleep_fixed_end = snakemake.params["exclude_sleep_fixed_end"]
 step_features = pd.DataFrame(columns=["local_date"])
 requested_features = {}
 requested_features["features_all_steps"] = snakemake.params["features_all_steps"]
-requested_features["features_sedentary_bout"] = snakemake.params["features_sedentary_bout"]
+requested_features["features_sedentary_bout"] = [feature + "sedentarybout" for feature in snakemake.params["features_sedentary_bout"]]
-requested_features["features_active_bout"] = snakemake.params["features_active_bout"]
+requested_features["features_active_bout"] = [feature + "activebout" for feature in snakemake.params["features_active_bout"]]
 
 if exclude_sleep == True:
     if exclude_sleep_type == "FIXED":