From 929db5515cb9c62d2f2e073959054d60382057d4 Mon Sep 17 00:00:00 2001 From: nikunjgoel95 Date: Wed, 24 Jun 2020 17:02:47 -0400 Subject: [PATCH] Adding countConversation and Fixing duplicate bugs in data. Co-authored-by: JulioV --- .../conversation/conversation_base.py | 37 +++++++++++-------- 1 file changed, 21 insertions(+), 16 deletions(-) diff --git a/src/features/conversation/conversation_base.py b/src/features/conversation/conversation_base.py index b06907a8..7d69e179 100644 --- a/src/features/conversation/conversation_base.py +++ b/src/features/conversation/conversation_base.py @@ -6,7 +6,7 @@ def base_conversation_features(conversation_data, day_segment, requested_feature "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction"] + "unknownexpectedfraction","countconversation"] # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) @@ -23,46 +23,51 @@ def base_conversation_features(conversation_data, day_segment, requested_feature else: conversation_features = pd.DataFrame() + conversation_data = conversation_data.drop_duplicates(subset = 'local_time', keep= first) + 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()/60 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()/60 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()/60 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()/60 - conv_duration = conversation_data["double_convo_end"] - conversation_data["double_convo_start"] + if "countconversation" in features_to_compute: + conversation_features["conversation_" + day_segment + "_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['double_convo_start'].nunique() + + conv_duration = (conversation_data['double_convo_end'] - conversation_data['double_convo_start'])/60 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() + + conversation_data['totalDuration'] = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_date"])['inference'].count()/60 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["total_duration"] + conversation_features["conversation_" + day_segment + "_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/60)/ conversation_data['totalDuration'] 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["total_duration"] + conversation_features["conversation_" + day_segment + "_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/60)/ conversation_data['totalDuration'] 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["total_duration"] + conversation_features["conversation_" + day_segment + "_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/60)/ conversation_data['totalDuration'] 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["total_duration"] + conversation_features["conversation_" + day_segment + "_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/60)/ conversation_data['totalDuration'] 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()/60)/ 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()/60)/ 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()/60)/ 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()/60)/ expectedMinutes if "sumconversationduration" in features_to_compute: conversation_features["conversation_" + day_segment + "_sumconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].sum()