Fix warning of conversation features; replace ' with "
Meng Li
parent
36017d5dca
commit
a47eb6823b
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@ -24,87 +24,89 @@ def base_conversation_features(conversation_data, day_segment, requested_feature
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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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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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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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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_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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conv_duration = conversation_data["double_convo_end"] - conversation_data["double_convo_start"]
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conversation_data = conversation_data.assign(conv_duration = conv_duration.values)
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conversation_data["total_duration"] = 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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conversation_features["conversation_" + day_segment + "_silencesensedfraction"] = conversation_data[conversation_data["inference"]==0].groupby(["local_date"])["inference"].count()/ conversation_data["total_duration"]
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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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conversation_features["conversation_" + day_segment + "_noisesensedfraction"] = conversation_data[conversation_data["inference"]==1].groupby(["local_date"])["inference"].count()/ conversation_data["total_duration"]
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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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conversation_features["conversation_" + day_segment + "_voicesensedfraction"] = conversation_data[conversation_data["inference"]==2].groupby(["local_date"])["inference"].count()/ conversation_data["total_duration"]
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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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conversation_features["conversation_" + day_segment + "_unknownsensedfraction"] = conversation_data[conversation_data["inference"]==3].groupby(["local_date"])["inference"].count()/ conversation_data["total_duration"]
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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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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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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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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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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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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.groupby(["local_date"])['conv_Dur'].mean()
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conversation_features["conversation_" + day_segment + "_avgconversationduration"] = conversation_data.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.groupby(["local_date"])['conv_Dur'].std()
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conversation_features["conversation_" + day_segment + "_sdconversationduration"] = conversation_data.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.groupby(["local_date"])['conv_Dur'].min()
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conversation_features["conversation_" + day_segment + "_minconversationduration"] = conversation_data.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_Dur'].max()
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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"])[['double_convo_start','local_hour','local_minute']].min()
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if 'local_hour' in timeFirstConversation.columns:
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timeFirstConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])[["double_convo_start","local_hour","local_minute"]].min()
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if "local_hour" in timeFirstConversation.columns:
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conversation_features["conversation_" + day_segment + "_timefirstconversation"] = timeFirstConversation["local_hour"]*60 + timeFirstConversation["local_minute"]
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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"])[['double_convo_start','local_hour','local_minute']].max()
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if 'local_hour' in timeLastConversation:
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timeLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])[["double_convo_start","local_hour","local_minute"]].max()
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if "local_hour" in timeLastConversation:
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conversation_features["conversation_" + day_segment + "_timelastconversation"] = timeLastConversation["local_hour"]*60 + timeLastConversation["local_minute"]
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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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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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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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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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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_" + 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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