Merge branch 'feature/doryab_location_empty_df_fix' into develop
Adding the branch to fix infer home locations and empty dataframe.pull/130/head
commit
7815c380a2
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@ -111,27 +111,30 @@ def haversine(lon1,lat1,lon2,lat2):
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origDf = pd.read_csv(snakemake.input[0])
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filteredDf = filterDatafromDf(origDf)
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dbscan_eps = snakemake.params["dbscan_eps"]
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dbscan_minsamples = snakemake.params["dbscan_minsamples"]
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threshold_static = snakemake.params["threshold_static"]
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clustering_algorithm = snakemake.params["clustering_algorithm"]
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if clustering_algorithm == "DBSCAN":
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hyperparameters = {'eps' : distance_to_degrees(dbscan_eps), 'min_samples': dbscan_minsamples}
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elif clustering_algorithm == "OPTICS":
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hyperparameters = {'max_eps': distance_to_degrees(dbscan_eps), 'min_samples': 2, 'metric':'euclidean', 'cluster_method' : 'dbscan'}
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if filteredDf.empty:
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filteredDf.to_csv(snakemake.output[0])
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else:
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dbscan_eps = snakemake.params["dbscan_eps"]
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dbscan_minsamples = snakemake.params["dbscan_minsamples"]
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threshold_static = snakemake.params["threshold_static"]
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clustering_algorithm = snakemake.params["clustering_algorithm"]
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if clustering_algorithm == "DBSCAN":
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hyperparameters = {'eps' : distance_to_degrees(dbscan_eps), 'min_samples': dbscan_minsamples}
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elif clustering_algorithm == "OPTICS":
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hyperparameters = {'max_eps': distance_to_degrees(dbscan_eps), 'min_samples': 2, 'metric':'euclidean', 'cluster_method' : 'dbscan'}
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else:
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raise ValueError("config[PHONE_LOCATIONS][HOME_INFERENCE][CLUSTERING ALGORITHM] only accepts DBSCAN or OPTICS but you provided ",clustering_algorithm)
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filteredDf = cluster_and_label(filteredDf,clustering_algorithm,threshold_static,**hyperparameters)
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filteredDf = cluster_and_label(filteredDf,clustering_algorithm,threshold_static,**hyperparameters)
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origDf['home_latitude'] = filteredDf[filteredDf['location_label']==1][['double_latitude','double_longitude']].mean()['double_latitude']
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origDf['home_longitude'] = filteredDf[filteredDf['location_label']==1][['double_latitude','double_longitude']].mean()['double_longitude']
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origDf['home_latitude'] = filteredDf[filteredDf['location_label']==1][['double_latitude','double_longitude']].mean()['double_latitude']
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origDf['home_longitude'] = filteredDf[filteredDf['location_label']==1][['double_latitude','double_longitude']].mean()['double_longitude']
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distanceFromHome = haversine(origDf.double_longitude,origDf.double_latitude,origDf.home_longitude,origDf.home_latitude)
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distanceFromHome = haversine(origDf.double_longitude,origDf.double_latitude,origDf.home_longitude,origDf.home_latitude)
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finalDf = origDf.drop(['home_latitude','home_longitude'], axis=1)
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finalDf.insert(len(finalDf.columns)-1,'distancefromhome',distanceFromHome)
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finalDf.to_csv(snakemake.output[0], index=False)
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finalDf = origDf.drop(['home_latitude','home_longitude'], axis=1)
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finalDf.insert(len(finalDf.columns)-1,'distancefromhome',distanceFromHome)
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finalDf.to_csv(snakemake.output[0], index=False)
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@ -188,12 +188,14 @@ def len_stay_timeattopn(locationData,maximum_gap_allowed,maximum_row_duration):
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calculationDf.loc[calculationDf.timeInSeconds >= maximum_gap_allowed,'timeInSeconds'] = maximum_row_duration
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timeArray = calculationDf.groupby('location_label')['timeInSeconds'].sum().reset_index()['timeInSeconds'].sort_values(ascending=False)/60
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if len(timeArray) > 2:
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if len(timeArray) == 3:
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return (timeArray[0],timeArray[1],timeArray[2],timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
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elif len(timeArray)==2:
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return (timeArray[0],timeArray[1],None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
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else:
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elif len(timeArray)==1:
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return (timeArray[0],None,None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
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else:
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return (None,None,None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
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def getMinutesData(locationData):
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