Migrate conversation to new file structure
JulioV
parent
8d87f6e497
commit
98ebf9bd13
18
Snakefile
18
Snakefile
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@ -135,15 +135,17 @@ if config["SLEEP"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"]))
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files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"]))
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if config["CONVERSATION"]["COMPUTE"]:
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pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"]))
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pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"]))
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pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"]))
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pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"]))
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for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]):
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table))
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files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"]))
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for provider in config["CONVERSATION"]["PROVIDERS"].keys():
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if config["CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]:
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for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]):
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table))
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files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CONVERSATION".lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CONVERSATION".lower()))
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for provider in config["LOCATIONS"]["PROVIDERS"].keys():
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if config["LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]:
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21
config.yaml
21
config.yaml
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@ -214,18 +214,21 @@ WIFI:
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SRC_LANGUAGE: "r"
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CONVERSATION:
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COMPUTE: False
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DB_TABLE:
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ANDROID: plugin_studentlife_audio_android
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IOS: plugin_studentlife_audio
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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","countconversation"]
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RECORDINGMINUTES: 1
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PAUSEDMINUTES : 3
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PROVIDERS:
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RAPIDS:
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COMPUTE: True
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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","countconversation"]
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RECORDING_MINUTES: 1
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PAUSED_MINUTES : 3
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SRC_FOLDER: "rapids" # inside src/features/conversation
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SRC_LANGUAGE: "python"
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### Visualizations ################################################################
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HEATMAP_FEATURES_CORRELATIONS:
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@ -197,18 +197,29 @@ rule light_python_features:
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script:
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"../src/features/light/light_entry.py"
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rule conversation_features:
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rule conversation_r_features:
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input:
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optional_conversation_input
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sensor_data = optional_conversation_input,
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day_segments_labels = "data/interim/day_segments_labels.csv"
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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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provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key],
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provider_key = "{provider_key}"
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output:
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"data/processed/{pid}/conversation_{day_segment}.csv"
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"data/interim/{pid}/conversation_features/conversation_r_{provider_key}.csv"
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script:
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"../src/features/conversation_features.py"
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"../src/features/conversation/conversation_entry.R"
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rule conversation_python_features:
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input:
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sensor_data = optional_conversation_input,
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day_segments_labels = "data/interim/day_segments_labels.csv"
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params:
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provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key],
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provider_key = "{provider_key}"
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output:
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"data/interim/{pid}/conversation_features/conversation_python_{provider_key}.csv"
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script:
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"../src/features/conversation/conversation_entry.py"
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rule accelerometer_features:
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input:
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@ -1,117 +0,0 @@
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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","countconversation"]
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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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conversation_features = pd.DataFrame(columns=["local_date"] + ["conversation_" + day_segment + "_" + x for x in features_to_compute])
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if not conversation_data.empty:
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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 not conversation_data.empty:
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conversation_features = pd.DataFrame()
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conversation_data = conversation_data.drop_duplicates(subset="local_time", keep="first")
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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()/60
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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()/60
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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()/60
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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()/60
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if "countconversation" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['double_convo_start'].nunique()
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conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60
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conversation_data = conversation_data.assign(conv_duration = conv_duration.values)
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conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_date"])['inference'].count()/60
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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()/60)/ conv_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()/60)/ conv_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()/60)/ conv_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()/60)/ conv_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()/60)/ 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()/60)/ 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()/60)/ 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()/60)/ 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_duration"].sum()
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if "avgconversationduration" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].mean()
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if "sdconversationduration" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].std()
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if "minconversationduration" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].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_duration"].max()
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if "timefirstconversation" in features_to_compute:
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timeFirstConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['local_time'].min()
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if len(list(timeFirstConversation.index)) > 0:
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for date in list(timeFirstConversation.index):
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conversation_features.loc[date,"conversation_" + day_segment + "_timefirstconversation"] = int(timeFirstConversation.loc[date].split(':')[0])*60 + int(timeFirstConversation.loc[date].split(':')[1])
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else:
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conversation_features["conversation_" + day_segment + "_timefirstconversation"] = 0
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if "timelastconversation" in features_to_compute:
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timeLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['local_time'].max()
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if len(list(timeLastConversation.index)) > 0:
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for date in list(timeLastConversation.index):
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conversation_features.loc[date,"conversation_" + day_segment + "_timelastconversation"] = int(timeLastConversation.loc[date].split(':')[0])*60 + int(timeLastConversation.loc[date].split(':')[1])
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else:
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conversation_features["conversation_" + day_segment + "_timelastconversation"] = 0
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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 +1,13 @@
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source("renv/activate.R")
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source("src/features/utils/utils.R")
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library("dplyr")
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library("tidyr")
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sensor_data_file <- snakemake@input[["sensor_data"]]
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day_segments_file <- snakemake@input[["day_segments_labels"]]
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provider <- snakemake@params["provider"][["provider"]]
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provider_key <- snakemake@params["provider_key"]
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sensor_features <- fetch_provider_features(provider, provider_key, "conversation", sensor_data_file, day_segments_file)
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write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)
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@ -0,0 +1,18 @@
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import pandas as pd
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from importlib import import_module, util
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from pathlib import Path
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# import fetch_provider_features from src/features/utils/utils.py
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spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
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mod = util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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fetch_provider_features = getattr(mod, "fetch_provider_features")
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sensor_data_file = snakemake.input["sensor_data"][0]
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day_segments_file = snakemake.input["day_segments_labels"]
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provider = snakemake.params["provider"]
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provider_key = snakemake.params["provider_key"]
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sensor_features = fetch_provider_features(provider, provider_key, "conversation", sensor_data_file, day_segments_file)
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sensor_features.to_csv(snakemake.output[0], index=False)
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@ -0,0 +1,128 @@
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import pandas as pd
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import numpy as np
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# def rapids_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes):
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def rapids_features(conversation_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
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requested_features = provider["FEATURES"]
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recordingMinutes = provider["RECORDING_MINUTES"]
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pausedMinutes = provider["PAUSED_MINUTES"]
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expectedMinutes = 1440 / (recordingMinutes + pausedMinutes)
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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","countconversation"]
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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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conversation_features = pd.DataFrame(columns=["local_segment"] + ["conversation_rapids" + "_" + x for x in features_to_compute])
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if not conversation_data.empty:
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conversation_data = filter_data_by_segment(conversation_data, day_segment)
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if not conversation_data.empty:
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conversation_features = pd.DataFrame()
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conversation_data = conversation_data.drop_duplicates(subset=["local_date", "local_time"], keep="first")
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if "minutessilence" in features_to_compute:
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conversation_features["conversation_rapids" + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60
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if "minutesnoise" in features_to_compute:
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conversation_features["conversation_rapids" + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60
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if "minutesvoice" in features_to_compute:
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conversation_features["conversation_rapids" + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60
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if "minutesunknown" in features_to_compute:
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conversation_features["conversation_rapids" + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60
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if "countconversation" in features_to_compute:
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conversation_features["conversation_rapids" + "_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique()
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conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60
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conversation_data = conversation_data.assign(conv_duration = conv_duration.values)
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conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_segment"])['inference'].count()/60
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if "silencesensedfraction" in features_to_compute:
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conversation_features["conversation_rapids" + "_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
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if "noisesensedfraction" in features_to_compute:
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conversation_features["conversation_rapids" + "_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
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if "voicesensedfraction" in features_to_compute:
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conversation_features["conversation_rapids" + "_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
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if "unknownsensedfraction" in features_to_compute:
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conversation_features["conversation_rapids" + "_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration
|
||||
|
||||
if "silenceexpectedfraction" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
|
||||
|
||||
if "noiseexpectedfraction" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
|
||||
|
||||
if "voiceexpectedfraction" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
|
||||
|
||||
if "unknownexpectedfraction" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes
|
||||
|
||||
if "sumconversationduration" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum()
|
||||
|
||||
if "avgconversationduration" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean()
|
||||
|
||||
if "sdconversationduration" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std()
|
||||
|
||||
if "minconversationduration" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min()
|
||||
|
||||
if "maxconversationduration" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max()
|
||||
|
||||
if "timefirstconversation" in features_to_compute:
|
||||
timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].min()
|
||||
if len(list(timestampsLastConversation.index)) > 0:
|
||||
for date in list(timestampsLastConversation.index):
|
||||
lastimestamp = timestampsLastConversation.loc[date]
|
||||
lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0]
|
||||
conversation_features.loc[date,"conversation_rapids" + "_timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1])
|
||||
else:
|
||||
conversation_features["conversation_rapids" + "_timefirstconversation"] = np.nan
|
||||
|
||||
if "timelastconversation" in features_to_compute:
|
||||
timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].max()
|
||||
if len(list(timestampsLastConversation.index)) > 0:
|
||||
for date in list(timestampsLastConversation.index):
|
||||
lastimestamp = timestampsLastConversation.loc[date]
|
||||
lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0]
|
||||
conversation_features.loc[date,"conversation_rapids" + "_timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1])
|
||||
else:
|
||||
conversation_features["conversation_rapids" + "_timelastconversation"] = np.nan
|
||||
|
||||
if "sumenergy" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_sumenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].sum()
|
||||
|
||||
if "avgenergy" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_avgenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].mean()
|
||||
|
||||
if "sdenergy" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_sdenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].std()
|
||||
|
||||
if "minenergy" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_minenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].min()
|
||||
|
||||
if "maxenergy" in features_to_compute:
|
||||
conversation_features["conversation_rapids" + "_maxenergy"] = conversation_data.groupby(["local_segment"])["double_energy"].max()
|
||||
|
||||
|
||||
conversation_features = conversation_features.reset_index()
|
||||
|
||||
return conversation_features
|
||||
Loading…
Reference in New Issue