132 lines
10 KiB
Python
132 lines
10 KiB
Python
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","noisesumenergy",
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"noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy",
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"voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","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_date','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 "noisesumenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].sum()
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if "noiseavgenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].mean()
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if "noisesdenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].std()
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if "noiseminenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].min()
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if "noisemaxenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].max()
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if "voicesumenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].sum()
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if "voiceavgenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].mean()
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if "voicesdenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].std()
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if "voiceminenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].min()
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if "voicemaxenergy" in features_to_compute:
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conversation_features["conversation_" + day_segment + "_voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].max()
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conversation_features = conversation_features.reset_index()
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return conversation_features |