104 lines
7.9 KiB
Python
104 lines
7.9 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","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 |