Added Converstion Features.

Updated config.yaml, Snakefile, Features.Snakefile and documentation.

Snakefile

@@ -58,6 +58,9 @@ rule all:
         expand("data/processed/{pid}/light_{day_segment}.csv",
                             pid = config["PIDS"],
                             day_segment = config["LIGHT"]["DAY_SEGMENTS"]),
+        expand("data/processed/{pid}/conversation_{day_segment}.csv",
+                            pid = config["PIDS"],
+                            day_segment = config["CONVERSATION"]["DAY_SEGMENTS"]),
         expand("data/processed/{pid}/accelerometer_{day_segment}.csv",
                             pid = config["PIDS"],
                             day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"]),

config.yaml

@@ -1,5 +1,5 @@
 # Valid database table names
-SENSORS: [applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, plugin_ios_activity_recognition, screen]
+SENSORS: [applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, plugin_ios_activity_recognition, screen,plugin_studentlife_audio]
 
 FITBIT_TABLE: [fitbit_data]
 FITBIT_SENSORS: [heartrate, steps, sleep, calories]
@@ -138,6 +138,16 @@ WIFI:
   DAY_SEGMENTS: *day_segments
   FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
 
+CONVERSATION:
+  DAY_SEGMENTS: *day_segments
+  FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
+    "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy",
+    "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction",
+    "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
+    "unknownexpectedfraction"]
+  RECORDINGMINUTES: 1
+  PAUSEDMINUTES : 3
+
 PARAMS_FOR_ANALYSIS:
   GROUNDTRUTH_TABLE: participant_info
   SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"]

docs/features/extracted.rst

@@ -688,6 +688,78 @@ firstuseafter               minutes             Seconds until the first unlock e
 
 An ``unlock`` episode is considered as the time between an ``unlock`` event and a ``lock`` event. iOS recorded these episodes reliably (albeit some duplicated ``lock`` events within milliseconds from each other). However, in Android there are some events unrelated to the screen state because of multiple consecutive ``unlock``/``lock`` events, so we keep the closest pair. In our experiments these cases are less than 10% of the screen events collected. This happens because ``ACTION_SCREEN_OFF`` and ``ON`` are "sent when the device becomes non-interactive which may have nothing to do with the screen turning off". Additionally, in Android it is possible to measure the time spent on the ``lock`` screen before an ``unlock`` event as well as the total screen time (i.e. ``ON`` to ``OFF``) but we are only keeping ``unlock`` episodes (``unlock`` to ``OFF``) to be consistent with iOS. 
 
+.. _conversation-sensor-doc:
+
+Conversation
+""""""""
+
+See `Conversation Config Code`_
+
+**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night
+
+**Available Platforms:** Android and iOS
+
+**Snakefile entry to compute these features:**
+    
+     | ``expand("data/processed/{pid}/conversation_{day_segment}.csv",``
+     |                      ``pid = config["PIDS"],``
+     |                       ``day_segment = config["CONVERSATION"]["DAY_SEGMENTS"]),``
+    
+**Snakemake rule chain:**
+
+- Rule ``rules/preprocessing.snakefile/download_dataset``
+- Rule ``rules/preprocessing.snakefile/readable_datetime``
+- Rule ``rules/features.snakefile/conversation_features``
+
+.. _conversation-parameters:
+
+**Conversation Rule Parameters (conversation_features):**
+
+=========================    ===================
+Name	                     Description
+=========================    ===================
+day_segment                  The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
+recordingMinutes             The current default configuration is 1 min recording/3 min pause.
+features_deltas              Features to be computed, see table below
+pausedMinutes                The current default configuration is 1 min recording/3 min pause.
+=========================    ===================
+
+.. _conversation-available-features:
+
+**Available Conversation Features**
+
+=========================   =================   =============
+Name                        Units               Description
+=========================   =================   =============
+minutessilence              minutes             Total duration of all minutes silence.
+minutesnoise                minutes             Total duration of all minutes noise.
+minutesvoice                minutes             Total duration of all minutes voice.
+minutesunknown              minutes             Total duration of all minutes unknown.
+sumconversationduration     minutes             Total duration of all the conversation.
+maxconversationduration     minutes             Longest duration of all the conversation.
+minconversationduration     minutes             Shortest duration of all the conversation.
+avgconversationduration     minutes             Average duration of all the conversation.
+sdconversationduration      minutes             Standard Deviation duration of all the conversation.
+timefirstconversation       minutes             Starting time of first conversation of the Day/Epoch.
+timelastconversation        minutes             Starting time of last conversation of the Day/Epoch.
+sumenergy                   L2-norm             Total sum of all the energy.
+avgenergy                   L2-norm             Average of all the energy.
+sdenergy                    L2-norm             Standard Deviation of all the energy.
+minenergy                   L2-norm             Minimum of all the energy.
+maxenergy                   L2-norm             Maximum of all the energy.
+silencesensedfraction       minutes
+noisesensedfraction         minutes
+voicesensedfraction         minutes
+unknownsensedfraction       minutes
+silenceexpectedfraction     minutes
+noiseexpectedfraction       minutes
+voiceexpectedfraction       minutes
+unknownexpectedfraction     minutes
+=========================   =================   =============
+
+**Assumptions/Observations:** 
+
+
 .. ------------------------------- Begin Fitbit Section ----------------------------------- ..
 
 .. _fitbit-sleep-sensor-doc:

rules/features.snakefile

@@ -9,6 +9,15 @@ def optional_ar_input(wildcards):
         return ["data/raw/{pid}/plugin_ios_activity_recognition_with_datetime_unified.csv",
                 "data/processed/{pid}/plugin_ios_activity_recognition_deltas.csv"]
 
+def optional_conversation_input(wildcards):
+    with open("data/external/"+wildcards.pid, encoding="ISO-8859-1") as external_file:
+        external_file_content = external_file.readlines()
+    platform = external_file_content[1].strip()
+    if platform == "android":
+        return ["data/raw/{pid}/plugin_studentlife_audio_android_with_datetime.csv"]
+    else:
+        return ["data/raw/{pid}/plugin_studentlife_audio_with_datetime.csv"]
+
 def optional_location_input(wildcards):
     if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED":
         return rules.resample_fused_location.output
@@ -146,6 +155,19 @@ rule light_features:
     script:
         "../src/features/light_features.py"
 
+rule conversation_features:
+    input:
+        optional_conversation_input
+    params:
+        day_segment = "{day_segment}",
+        features = config["CONVERSATION"]["FEATURES"],
+        recordingMinutes = config["CONVERSATION"]["RECORDINGMINUTES"],
+        pausedMinutes = config["CONVERSATION"]["PAUSEDMINUTES"],
+    output:
+        "data/processed/{pid}/conversation_{day_segment}.csv"
+    script:
+        "../src/features/conversation_features.py"
+
 rule accelerometer_features:
     input:
         "data/raw/{pid}/accelerometer_with_datetime.csv",

src/features/conversation/conversation_base.py

@@ -0,0 +1,104 @@
+import pandas as pd
+
+def base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes):
+    # name of the features this function can compute
+    base_features_names = ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
+    "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy",
+    "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction",
+    "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
+    "unknownexpectedfraction"]
+
+    # the subset of requested features this function can compute
+    features_to_compute = list(set(requested_features) & set(base_features_names))
+
+
+    if conversation_data.empty:
+        conversation_features = pd.DataFrame(columns=["local_date"] + ["conversation_" + day_segment + "_" + x for x in features_to_compute])
+    else:
+        if day_segment != "daily":
+            conversation_data = conversation_data[conversation_data["local_day_segment"] == day_segment]
+
+        if conversation_data.empty:
+            conversation_features = pd.DataFrame(columns=["local_date"] + ["conversation_" + day_segment + "_" + x for x in features_to_compute])
+        else:
+            conversation_features = pd.DataFrame()
+
+            if "minutessilence" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()
+            
+            if "minutesnoise" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()
+
+            if "minutesvoice" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()
+
+            if "minutesunknown" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()
+
+            conversation_data['conv_Dur'] = conversation_data['double_convo_end'] - conversation_data['double_convo_start']
+            conversation_data['totalDuration'] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count() + conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count() + conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count() + conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()
+            
+            if "silencesensedfraction" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_silencesensedfraction"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration']
+
+            if "noisesensedfraction" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_noisesensedfraction"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration']
+
+            if "voicesensedfraction" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_voicesensedfraction"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration']
+
+            if "unknownsensedfraction" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_unknownsensedfraction"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration']    
+
+            if "silenceexpectedfraction" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_silenceexpectedfraction"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/ expectedMinutes
+
+            if "noiseexpectedfraction" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_noiseexpectedfraction"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/ expectedMinutes
+
+            if "voiceexpectedfraction" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_voiceexpectedfraction"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/ expectedMinutes
+
+            if "unknownexpectedfraction" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_unknownexpectedfraction"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/ expectedMinutes
+                
+            if "sumconversationduration" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_sumconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].sum()
+
+            if "avgconversationduration" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_avgconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].mean()
+
+            if "sdconversationduration" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_sdconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].std()
+
+            if "minconversationduration" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_minconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].min()
+
+            if "maxconversationduration" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_maxconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].max()
+
+            if "timefirstconversation" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_timefirstconversation"] = conversation_data[conversation_data["double_convo_start"]> 0].groupby(["local_date"])['double_convo_start'].min()
+
+            if "timelastconversation" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_timelastconversation"] = conversation_data.groupby(["local_date"])['double_convo_start'].max()
+            
+            if "sumenergy" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_sumenergy"] = conversation_data.groupby(["local_date"])['double_energy'].sum()
+
+            if "avgenergy" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_avgenergy"] = conversation_data.groupby(["local_date"])['double_energy'].mean()
+
+            if "sdenergy" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_sdenergy"] = conversation_data.groupby(["local_date"])['double_energy'].std()
+
+            if "minenergy" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_minenergy"] = conversation_data.groupby(["local_date"])['double_energy'].min()
+
+            if "maxenergy" in features_to_compute:
+                conversation_features["conversation_" + day_segment + "_maxenergy"] = conversation_data.groupby(["local_date"])['double_energy'].max()
+
+
+            conversation_features = conversation_features.reset_index()
+
+    return conversation_features

src/features/conversation_features.py

@@ -0,0 +1,15 @@
+import pandas as pd
+from conversation.conversation_base import base_conversation_features
+
+conversation_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
+day_segment = snakemake.params["day_segment"]
+requested_features = snakemake.params["features"]
+recordingMinutes = snakemake.params["recordingMinutes"]
+pausedMinutes = snakemake.params["pausedMinutes"]
+expectedMinutes =  1440 / (recordingMinutes + pausedMinutes)  
+conversation_features = pd.DataFrame(columns=["local_date"])
+
+conversation_features = conversation_features.merge(base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes), on="local_date", how="outer")
+assert len(requested_features) + 1 == conversation_features.shape[1], "The number of features in the output dataframe (=" + str(conversation_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your conversation feature extraction functions"
+
+conversation_features.to_csv(snakemake.output[0], index=False)