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Merge branch 'feature/doryab_location_empty_df_fix' into develop 

Adding the branch to fix infer home locations and empty dataframe.
pull/130/head
nikunjgoel95 2021-03-19 11:17:43 -04:00
parent 294d84277d cfc5039918
commit 7815c380a2
2 changed files with 24 additions and 19 deletions
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37
src/data/infer_home_location.py
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@ -111,27 +111,30 @@ def haversine(lon1,lat1,lon2,lat2):
origDf = pd.read_csv(snakemake.input[0]) origDf = pd.read_csv(snakemake.input[0])
filteredDf = filterDatafromDf(origDf) filteredDf = filterDatafromDf(origDf)
dbscan_eps = snakemake.params["dbscan_eps"] if filteredDf.empty:
dbscan_minsamples = snakemake.params["dbscan_minsamples"] filteredDf.to_csv(snakemake.output[0])
threshold_static = snakemake.params["threshold_static"]
clustering_algorithm = snakemake.params["clustering_algorithm"]
if clustering_algorithm == "DBSCAN":
hyperparameters = {'eps' : distance_to_degrees(dbscan_eps), 'min_samples': dbscan_minsamples}
elif clustering_algorithm == "OPTICS":
hyperparameters = {'max_eps': distance_to_degrees(dbscan_eps), 'min_samples': 2, 'metric':'euclidean', 'cluster_method' : 'dbscan'}
else: else:
raise ValueError("config[PHONE_LOCATIONS][HOME_INFERENCE][CLUSTERING ALGORITHM] only accepts DBSCAN or OPTICS but you provided ",clustering_algorithm) dbscan_eps = snakemake.params["dbscan_eps"]
dbscan_minsamples = snakemake.params["dbscan_minsamples"]
threshold_static = snakemake.params["threshold_static"]
clustering_algorithm = snakemake.params["clustering_algorithm"]
filteredDf = cluster_and_label(filteredDf,clustering_algorithm,threshold_static,**hyperparameters) if clustering_algorithm == "DBSCAN":
hyperparameters = {'eps' : distance_to_degrees(dbscan_eps), 'min_samples': dbscan_minsamples}
elif clustering_algorithm == "OPTICS":
hyperparameters = {'max_eps': distance_to_degrees(dbscan_eps), 'min_samples': 2, 'metric':'euclidean', 'cluster_method' : 'dbscan'}
else:
raise ValueError("config[PHONE_LOCATIONS][HOME_INFERENCE][CLUSTERING ALGORITHM] only accepts DBSCAN or OPTICS but you provided ",clustering_algorithm)
origDf['home_latitude'] = filteredDf[filteredDf['location_label']==1][['double_latitude','double_longitude']].mean()['double_latitude'] filteredDf = cluster_and_label(filteredDf,clustering_algorithm,threshold_static,**hyperparameters)
origDf['home_longitude'] = filteredDf[filteredDf['location_label']==1][['double_latitude','double_longitude']].mean()['double_longitude']
distanceFromHome = haversine(origDf.double_longitude,origDf.double_latitude,origDf.home_longitude,origDf.home_latitude) origDf['home_latitude'] = filteredDf[filteredDf['location_label']==1][['double_latitude','double_longitude']].mean()['double_latitude']
origDf['home_longitude'] = filteredDf[filteredDf['location_label']==1][['double_latitude','double_longitude']].mean()['double_longitude']
finalDf = origDf.drop(['home_latitude','home_longitude'], axis=1) distanceFromHome = haversine(origDf.double_longitude,origDf.double_latitude,origDf.home_longitude,origDf.home_latitude)
finalDf.insert(len(finalDf.columns)-1,'distancefromhome',distanceFromHome)
finalDf.to_csv(snakemake.output[0], index=False) finalDf = origDf.drop(['home_latitude','home_longitude'], axis=1)
finalDf.insert(len(finalDf.columns)-1,'distancefromhome',distanceFromHome)
finalDf.to_csv(snakemake.output[0], index=False)

6
src/features/phone_locations/doryab/main.py
View File

@ -188,12 +188,14 @@ def len_stay_timeattopn(locationData,maximum_gap_allowed,maximum_row_duration):
calculationDf.loc[calculationDf.timeInSeconds >= maximum_gap_allowed,'timeInSeconds'] = maximum_row_duration calculationDf.loc[calculationDf.timeInSeconds >= maximum_gap_allowed,'timeInSeconds'] = maximum_row_duration
timeArray = calculationDf.groupby('location_label')['timeInSeconds'].sum().reset_index()['timeInSeconds'].sort_values(ascending=False)/60 timeArray = calculationDf.groupby('location_label')['timeInSeconds'].sum().reset_index()['timeInSeconds'].sort_values(ascending=False)/60
if len(timeArray) > 2: if len(timeArray) == 3:
return (timeArray[0],timeArray[1],timeArray[2],timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean()) return (timeArray[0],timeArray[1],timeArray[2],timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
elif len(timeArray)==2: elif len(timeArray)==2:
return (timeArray[0],timeArray[1],None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean()) return (timeArray[0],timeArray[1],None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
else: elif len(timeArray)==1:
return (timeArray[0],None,None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean()) return (timeArray[0],None,None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
else:
return (None,None,None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
def getMinutesData(locationData): def getMinutesData(locationData):