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Merge branch 'master' of https://github.com/carissalow/rapids 

Refactoring tests
pull/95/head
kaguillera 2020-06-23 18:29:32 -04:00
parent 7bcf674793 a47eb6823b
commit 211aec1234
1 changed files with 30 additions and 28 deletions
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src/features/conversation/conversation_base.py
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@ -24,87 +24,89 @@ def base_conversation_features(conversation_data, day_segment, requested_feature
conversation_features = pd.DataFrame() conversation_features = pd.DataFrame()
if "minutessilence" in features_to_compute: 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: 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: 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: 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'] conv_duration = 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() 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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() 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: if "local_hour" in timeFirstConversation.columns:
conversation_features["conversation_" + day_segment + "_timefirstconversation"] = timeFirstConversation["local_hour"]*60 + timeFirstConversation["local_minute"] conversation_features["conversation_" + day_segment + "_timefirstconversation"] = timeFirstConversation["local_hour"]*60 + timeFirstConversation["local_minute"]
else: else:
conversation_features["conversation_" + day_segment + "_timefirstconversation"] = 0 conversation_features["conversation_" + day_segment + "_timefirstconversation"] = 0
if "timelastconversation" in features_to_compute: 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() 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: if "local_hour" in timeLastConversation:
conversation_features["conversation_" + day_segment + "_timelastconversation"] = timeLastConversation["local_hour"]*60 + timeLastConversation["local_minute"] conversation_features["conversation_" + day_segment + "_timelastconversation"] = timeLastConversation["local_hour"]*60 + timeLastConversation["local_minute"]
else: else:
conversation_features["conversation_" + day_segment + "_timelastconversation"] = 0 conversation_features["conversation_" + day_segment + "_timelastconversation"] = 0
if "sumenergy" in features_to_compute: 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: 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: 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: 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: 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() conversation_features = conversation_features.reset_index()