Added Converstion Features.
Updated config.yaml, Snakefile, Features.Snakefile and documentation.pull/95/head
nikunjgoel95
parent
5627a73a67
commit
1409de2832
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@ -58,6 +58,9 @@ rule all:
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expand("data/processed/{pid}/light_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["LIGHT"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/conversation_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["CONVERSATION"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/accelerometer_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"]),
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12
config.yaml
12
config.yaml
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@ -1,5 +1,5 @@
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# Valid database table names
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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]
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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]
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FITBIT_TABLE: [fitbit_data]
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FITBIT_SENSORS: [heartrate, steps, sleep, calories]
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@ -138,6 +138,16 @@ WIFI:
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DAY_SEGMENTS: *day_segments
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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CONVERSATION:
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DAY_SEGMENTS: *day_segments
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FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
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"sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy",
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"avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction",
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"voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
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"unknownexpectedfraction"]
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RECORDINGMINUTES: 1
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PAUSEDMINUTES : 3
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PARAMS_FOR_ANALYSIS:
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GROUNDTRUTH_TABLE: participant_info
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SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"]
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@ -688,6 +688,78 @@ firstuseafter minutes Seconds until the first unlock e
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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.
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.. _conversation-sensor-doc:
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Conversation
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""""""""
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See `Conversation Config Code`_
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**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night
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**Available Platforms:** Android and iOS
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**Snakefile entry to compute these features:**
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| ``expand("data/processed/{pid}/conversation_{day_segment}.csv",``
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| ``pid = config["PIDS"],``
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| ``day_segment = config["CONVERSATION"]["DAY_SEGMENTS"]),``
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**Snakemake rule chain:**
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- Rule ``rules/preprocessing.snakefile/download_dataset``
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- Rule ``rules/preprocessing.snakefile/readable_datetime``
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- Rule ``rules/features.snakefile/conversation_features``
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.. _conversation-parameters:
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**Conversation Rule Parameters (conversation_features):**
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========================= ===================
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Name Description
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========================= ===================
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day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
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recordingMinutes The current default configuration is 1 min recording/3 min pause.
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features_deltas Features to be computed, see table below
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pausedMinutes The current default configuration is 1 min recording/3 min pause.
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========================= ===================
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.. _conversation-available-features:
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**Available Conversation Features**
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========================= ================= =============
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Name Units Description
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========================= ================= =============
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minutessilence minutes Total duration of all minutes silence.
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minutesnoise minutes Total duration of all minutes noise.
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minutesvoice minutes Total duration of all minutes voice.
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minutesunknown minutes Total duration of all minutes unknown.
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sumconversationduration minutes Total duration of all the conversation.
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maxconversationduration minutes Longest duration of all the conversation.
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minconversationduration minutes Shortest duration of all the conversation.
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avgconversationduration minutes Average duration of all the conversation.
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sdconversationduration minutes Standard Deviation duration of all the conversation.
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timefirstconversation minutes Starting time of first conversation of the Day/Epoch.
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timelastconversation minutes Starting time of last conversation of the Day/Epoch.
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sumenergy L2-norm Total sum of all the energy.
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avgenergy L2-norm Average of all the energy.
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sdenergy L2-norm Standard Deviation of all the energy.
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minenergy L2-norm Minimum of all the energy.
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maxenergy L2-norm Maximum of all the energy.
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silencesensedfraction minutes
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noisesensedfraction minutes
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voicesensedfraction minutes
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unknownsensedfraction minutes
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silenceexpectedfraction minutes
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noiseexpectedfraction minutes
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voiceexpectedfraction minutes
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unknownexpectedfraction minutes
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========================= ================= =============
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**Assumptions/Observations:**
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.. ------------------------------- Begin Fitbit Section ----------------------------------- ..
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.. _fitbit-sleep-sensor-doc:
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@ -9,6 +9,15 @@ def optional_ar_input(wildcards):
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return ["data/raw/{pid}/plugin_ios_activity_recognition_with_datetime_unified.csv",
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"data/processed/{pid}/plugin_ios_activity_recognition_deltas.csv"]
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def optional_conversation_input(wildcards):
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with open("data/external/"+wildcards.pid, encoding="ISO-8859-1") as external_file:
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external_file_content = external_file.readlines()
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platform = external_file_content[1].strip()
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if platform == "android":
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return ["data/raw/{pid}/plugin_studentlife_audio_android_with_datetime.csv"]
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else:
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return ["data/raw/{pid}/plugin_studentlife_audio_with_datetime.csv"]
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def optional_location_input(wildcards):
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if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED":
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return rules.resample_fused_location.output
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@ -146,6 +155,19 @@ rule light_features:
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script:
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"../src/features/light_features.py"
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rule conversation_features:
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input:
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optional_conversation_input
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params:
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day_segment = "{day_segment}",
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features = config["CONVERSATION"]["FEATURES"],
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recordingMinutes = config["CONVERSATION"]["RECORDINGMINUTES"],
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pausedMinutes = config["CONVERSATION"]["PAUSEDMINUTES"],
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output:
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"data/processed/{pid}/conversation_{day_segment}.csv"
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script:
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"../src/features/conversation_features.py"
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rule accelerometer_features:
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input:
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"data/raw/{pid}/accelerometer_with_datetime.csv",
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@ -0,0 +1,104 @@
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import pandas as pd
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def base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes):
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# name of the features this function can compute
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base_features_names = ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
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"sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy",
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"avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction",
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"voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
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"unknownexpectedfraction"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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if conversation_data.empty:
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conversation_features = pd.DataFrame(columns=["local_date"] + ["conversation_" + day_segment + "_" + x for x in features_to_compute])
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else:
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if day_segment != "daily":
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conversation_data = conversation_data[conversation_data["local_day_segment"] == day_segment]
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if conversation_data.empty:
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conversation_features = pd.DataFrame(columns=["local_date"] + ["conversation_" + day_segment + "_" + x for x in features_to_compute])
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else:
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conversation_features = pd.DataFrame()
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if "minutessilence" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()
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if "minutesnoise" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()
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if "minutesvoice" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()
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if "minutesunknown" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()
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conversation_data['conv_Dur'] = conversation_data['double_convo_end'] - conversation_data['double_convo_start']
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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()
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if "silencesensedfraction" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_silencesensedfraction"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration']
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if "noisesensedfraction" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_noisesensedfraction"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration']
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if "voicesensedfraction" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_voicesensedfraction"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration']
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if "unknownsensedfraction" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_unknownsensedfraction"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration']
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if "silenceexpectedfraction" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_silenceexpectedfraction"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/ expectedMinutes
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if "noiseexpectedfraction" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_noiseexpectedfraction"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/ expectedMinutes
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if "voiceexpectedfraction" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_voiceexpectedfraction"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/ expectedMinutes
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if "unknownexpectedfraction" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_unknownexpectedfraction"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/ expectedMinutes
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if "sumconversationduration" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_sumconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].sum()
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if "avgconversationduration" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_avgconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].mean()
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if "sdconversationduration" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_sdconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].std()
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if "minconversationduration" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_minconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].min()
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if "maxconversationduration" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_maxconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].max()
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if "timefirstconversation" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_timefirstconversation"] = conversation_data[conversation_data["double_convo_start"]> 0].groupby(["local_date"])['double_convo_start'].min()
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if "timelastconversation" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_timelastconversation"] = conversation_data.groupby(["local_date"])['double_convo_start'].max()
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if "sumenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_sumenergy"] = conversation_data.groupby(["local_date"])['double_energy'].sum()
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if "avgenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_avgenergy"] = conversation_data.groupby(["local_date"])['double_energy'].mean()
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if "sdenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_sdenergy"] = conversation_data.groupby(["local_date"])['double_energy'].std()
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if "minenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_minenergy"] = conversation_data.groupby(["local_date"])['double_energy'].min()
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if "maxenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_maxenergy"] = conversation_data.groupby(["local_date"])['double_energy'].max()
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conversation_features = conversation_features.reset_index()
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return conversation_features
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@ -0,0 +1,15 @@
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import pandas as pd
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from conversation.conversation_base import base_conversation_features
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conversation_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 = snakemake.params["features"]
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recordingMinutes = snakemake.params["recordingMinutes"]
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pausedMinutes = snakemake.params["pausedMinutes"]
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expectedMinutes = 1440 / (recordingMinutes + pausedMinutes)
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conversation_features = pd.DataFrame(columns=["local_date"])
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conversation_features = conversation_features.merge(base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes), on="local_date", how="outer")
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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"
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conversation_features.to_csv(snakemake.output[0], index=False)
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