From a47eb6823b7b3b48daeaf6043370c5738282bb06 Mon Sep 17 00:00:00 2001 From: Meng Li <34143965+Meng6@users.noreply.github.com> Date: Tue, 23 Jun 2020 14:12:38 -0400 Subject: [PATCH] Fix warning of conversation features; replace ' with " --- .../conversation/conversation_base.py | 58 ++++++++++--------- 1 file changed, 30 insertions(+), 28 deletions(-) diff --git a/src/features/conversation/conversation_base.py b/src/features/conversation/conversation_base.py index 5e99b25d..b06907a8 100644 --- a/src/features/conversation/conversation_base.py +++ b/src/features/conversation/conversation_base.py @@ -24,87 +24,89 @@ def base_conversation_features(conversation_data, day_segment, requested_feature conversation_features = pd.DataFrame() if "minutessilence" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count() + conversation_features["conversation_" + day_segment + "_minutessilence"] = conversation_data[conversation_data["inference"]==0].groupby(["local_date"])["inference"].count() if "minutesnoise" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count() + conversation_features["conversation_" + day_segment + "_minutesnoise"] = conversation_data[conversation_data["inference"]==1].groupby(["local_date"])["inference"].count() if "minutesvoice" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count() + conversation_features["conversation_" + day_segment + "_minutesvoice"] = conversation_data[conversation_data["inference"]==2].groupby(["local_date"])["inference"].count() if "minutesunknown" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count() + conversation_features["conversation_" + day_segment + "_minutesunknown"] = conversation_data[conversation_data["inference"]==3].groupby(["local_date"])["inference"].count() - conversation_data['conv_Dur'] = conversation_data['double_convo_end'] - conversation_data['double_convo_start'] - 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() + conv_duration = conversation_data["double_convo_end"] - conversation_data["double_convo_start"] + conversation_data = conversation_data.assign(conv_duration = conv_duration.values) + + 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() if "silencesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_silencesensedfraction"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration'] + conversation_features["conversation_" + day_segment + "_silencesensedfraction"] = conversation_data[conversation_data["inference"]==0].groupby(["local_date"])["inference"].count()/ conversation_data["total_duration"] if "noisesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noisesensedfraction"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration'] + conversation_features["conversation_" + day_segment + "_noisesensedfraction"] = conversation_data[conversation_data["inference"]==1].groupby(["local_date"])["inference"].count()/ conversation_data["total_duration"] if "voicesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voicesensedfraction"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration'] + conversation_features["conversation_" + day_segment + "_voicesensedfraction"] = conversation_data[conversation_data["inference"]==2].groupby(["local_date"])["inference"].count()/ conversation_data["total_duration"] if "unknownsensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_unknownsensedfraction"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/ conversation_data['totalDuration'] + conversation_features["conversation_" + day_segment + "_unknownsensedfraction"] = conversation_data[conversation_data["inference"]==3].groupby(["local_date"])["inference"].count()/ conversation_data["total_duration"] if "silenceexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_silenceexpectedfraction"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/ expectedMinutes + conversation_features["conversation_" + day_segment + "_silenceexpectedfraction"] = conversation_data[conversation_data["inference"]==0].groupby(["local_date"])["inference"].count()/ expectedMinutes if "noiseexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noiseexpectedfraction"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/ expectedMinutes + conversation_features["conversation_" + day_segment + "_noiseexpectedfraction"] = conversation_data[conversation_data["inference"]==1].groupby(["local_date"])["inference"].count()/ expectedMinutes if "voiceexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voiceexpectedfraction"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/ expectedMinutes + conversation_features["conversation_" + day_segment + "_voiceexpectedfraction"] = conversation_data[conversation_data["inference"]==2].groupby(["local_date"])["inference"].count()/ expectedMinutes if "unknownexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_unknownexpectedfraction"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/ expectedMinutes + conversation_features["conversation_" + day_segment + "_unknownexpectedfraction"] = conversation_data[conversation_data["inference"]==3].groupby(["local_date"])["inference"].count()/ expectedMinutes if "sumconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sumconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].sum() + conversation_features["conversation_" + day_segment + "_sumconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].sum() if "avgconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_avgconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].mean() + conversation_features["conversation_" + day_segment + "_avgconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].mean() if "sdconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sdconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].std() + conversation_features["conversation_" + day_segment + "_sdconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].std() if "minconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].min() + conversation_features["conversation_" + day_segment + "_minconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].min() if "maxconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_maxconversationduration"] = conversation_data.groupby(["local_date"])['conv_Dur'].max() + conversation_features["conversation_" + day_segment + "_maxconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].max() if "timefirstconversation" in features_to_compute: - timeFirstConversation = conversation_data[conversation_data["double_convo_start"]> 0].groupby(["local_date"])[['double_convo_start','local_hour','local_minute']].min() - if 'local_hour' in timeFirstConversation.columns: + timeFirstConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])[["double_convo_start","local_hour","local_minute"]].min() + if "local_hour" in timeFirstConversation.columns: conversation_features["conversation_" + day_segment + "_timefirstconversation"] = timeFirstConversation["local_hour"]*60 + timeFirstConversation["local_minute"] else: conversation_features["conversation_" + day_segment + "_timefirstconversation"] = 0 if "timelastconversation" in features_to_compute: - timeLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])[['double_convo_start','local_hour','local_minute']].max() - if 'local_hour' in timeLastConversation: + timeLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])[["double_convo_start","local_hour","local_minute"]].max() + if "local_hour" in timeLastConversation: conversation_features["conversation_" + day_segment + "_timelastconversation"] = timeLastConversation["local_hour"]*60 + timeLastConversation["local_minute"] else: conversation_features["conversation_" + day_segment + "_timelastconversation"] = 0 if "sumenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sumenergy"] = conversation_data.groupby(["local_date"])['double_energy'].sum() + conversation_features["conversation_" + day_segment + "_sumenergy"] = conversation_data.groupby(["local_date"])["double_energy"].sum() if "avgenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_avgenergy"] = conversation_data.groupby(["local_date"])['double_energy'].mean() + conversation_features["conversation_" + day_segment + "_avgenergy"] = conversation_data.groupby(["local_date"])["double_energy"].mean() if "sdenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sdenergy"] = conversation_data.groupby(["local_date"])['double_energy'].std() + conversation_features["conversation_" + day_segment + "_sdenergy"] = conversation_data.groupby(["local_date"])["double_energy"].std() if "minenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minenergy"] = conversation_data.groupby(["local_date"])['double_energy'].min() + conversation_features["conversation_" + day_segment + "_minenergy"] = conversation_data.groupby(["local_date"])["double_energy"].min() if "maxenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_maxenergy"] = conversation_data.groupby(["local_date"])['double_energy'].max() + conversation_features["conversation_" + day_segment + "_maxenergy"] = conversation_data.groupby(["local_date"])["double_energy"].max() conversation_features = conversation_features.reset_index()