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Cleaning the Doryab location features code to fix the bug of Emoty dataframe in ClusterAndLabel

pull/104/head
nikunjgoel95 2020-10-01 11:34:41 -04:00
parent d99775567d
commit 80ff3f4229
2 changed files with 30 additions and 69 deletions
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94
src/features/location_doryab/location_base.py
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@ -34,7 +34,9 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
location_data = location_data[(location_data['double_latitude']!=0.0) & (location_data['double_longitude']!=0.0)]
if location_data.empty:
dataEmptyFlag = 1
location_features = pd.DataFrame(columns=["local_date"] + ["location_" + day_segment + "_" + x for x in features_to_compute])
location_features = location_features.reset_index(drop=True)
return location_features
if "locationvariance" in features_to_compute:
location_features["location_" + day_segment + "_locationvariance"] = location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var()
@ -50,125 +52,87 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
preComputedDistanceandSpeed.loc[localDate,"avgspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].mean()
preComputedDistanceandSpeed.loc[localDate,"varspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].var()
if "totaldistance" in features_to_compute and dataEmptyFlag==0:
if "totaldistance" in features_to_compute:
for localDate in location_data['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_totaldistance"] = preComputedDistanceandSpeed.loc[localDate,"distance"]
else:
location_features["location_" + day_segment + "_totaldistance"] = 0
if "averagespeed" in features_to_compute and dataEmptyFlag==0:
if "averagespeed" in features_to_compute:
for localDate in location_data['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_averagespeed"] = preComputedDistanceandSpeed.loc[localDate,"avgspeed"]
else:
location_features["location_" + day_segment + "_averagespeed"] = 0
if "varspeed" in features_to_compute and dataEmptyFlag==0:
if "varspeed" in features_to_compute:
for localDate in location_data['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_varspeed"] = preComputedDistanceandSpeed.loc[localDate,"varspeed"]
else:
location_features["location_" + day_segment + "_varspeed"] = 0
if "circadianmovement" in features_to_compute and dataEmptyFlag==0:
if "circadianmovement" in features_to_compute:
for localDate in location_data['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_circadianmovement"] = circadian_movement(location_data[location_data['local_date']==localDate])
else:
location_features["location_" + day_segment + "_circadianmovement"] = 0
if dataEmptyFlag==0:
newLocationData = cluster_and_label(location_data, eps= distance_to_degrees(dbscan_eps), min_samples=dbscan_minsamples)
newLocationData = cluster_and_label(location_data, eps= distance_to_degrees(dbscan_eps), min_samples=dbscan_minsamples)
if "numberofsignificantplaces" in features_to_compute and dataEmptyFlag==0:
if "numberofsignificantplaces" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_numberofsignificantplaces"] = number_of_significant_places(newLocationData[newLocationData['local_date']==localDate])
else:
location_features["location_" + day_segment + "_numberofsignificantplaces"] = 0
if "numberlocationtransitions" in features_to_compute and dataEmptyFlag==0:
if "numberlocationtransitions" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_numberlocationtransitions"] = number_location_transitions(newLocationData[newLocationData['local_date']==localDate])
else:
location_features["location_" + day_segment + "_numberlocationtransitions"] = 0
if "radiusgyration" in features_to_compute and dataEmptyFlag==0:
if "radiusgyration" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
else:
location_features["location_" + day_segment + "_radiusgyration"] = 0
if "timeattop1location" in features_to_compute and dataEmptyFlag==0:
if "timeattop1location" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_timeattop1location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],1,sampling_frequency)
else:
location_features["location_" + day_segment + "_timeattop1location"] = 0
if "timeattop2location" in features_to_compute and dataEmptyFlag==0:
if "timeattop2location" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_timeattop2location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],2,sampling_frequency)
else:
location_features["location_" + day_segment + "_timeattop2location"] = 0
if "timeattop3location" in features_to_compute and dataEmptyFlag==0:
if "timeattop3location" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_timeattop3location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],3,sampling_frequency)
else:
location_features["location_" + day_segment + "_timeattop3location"] = 0
if "movingtostaticratio" in features_to_compute and dataEmptyFlag==0:
if "movingtostaticratio" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_movingtostaticratio"] = (newLocationData[newLocationData['local_date']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_date']==localDate].shape[0] * sampling_frequency)
else:
location_features["location_" + day_segment + "_movingtostaticratio"] = 0
if "outlierstimepercent" in features_to_compute and dataEmptyFlag==0:
if "outlierstimepercent" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
else:
location_features["location_" + day_segment + "_outlierstimepercent"] = 0
if dataEmptyFlag==0:
preComputedmaxminCluster = pd.DataFrame()
for localDate in newLocationData['local_date'].unique():
smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = smax
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = smin
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = sstd
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = smean
preComputedmaxminCluster = pd.DataFrame()
for localDate in newLocationData['local_date'].unique():
smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = smax
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = smin
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = sstd
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = smean
if "maxlengthstayatclusters" in features_to_compute and dataEmptyFlag==0:
if "maxlengthstayatclusters" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"]
else:
location_features["location_" + day_segment + "_maxlengthstayatclusters"] = 0
if "minlengthstayatclusters" in features_to_compute and dataEmptyFlag==0:
if "minlengthstayatclusters" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"]
else:
location_features["location_" + day_segment + "_minlengthstayatclusters"] = 0
if "stdlengthstayatclusters" in features_to_compute and dataEmptyFlag==0:
if "stdlengthstayatclusters" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"]
else:
location_features["location_" + day_segment + "_stdlengthstayatclusters"] = 0
if "meanlengthstayatclusters" in features_to_compute and dataEmptyFlag==0:
if "meanlengthstayatclusters" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"]
else:
location_features["location_" + day_segment + "_meanlengthstayatclusters"] = 0
if "locationentropy" in features_to_compute and dataEmptyFlag==0:
if "locationentropy" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_locationentropy"] = location_entropy(newLocationData[newLocationData['local_date']==localDate])
else:
location_features["location_" + day_segment + "_locationentropy"] = 0
if "normalizedlocationentropy" in features_to_compute and dataEmptyFlag==0:
if "normalizedlocationentropy" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_normalizedlocationentropy"] = location_entropy_normalized(newLocationData[newLocationData['local_date']==localDate])
else:
location_features["location_" + day_segment + "_normalizedlocationentropy"] = 0
location_features = location_features.reset_index()

5
src/features/location_doryab_features.py
View File

@ -17,10 +17,7 @@ if(minutes_data_used):
base_features = base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static,maximum_gap_allowed,sampling_frequency)
if base_features.empty:
location_features = base_features
else:
location_features = location_features.merge(base_features, on="local_date", how="outer")
location_features = location_features.merge(base_features, on="local_date", how="outer")
assert len(requested_features) + 1 == location_features.shape[1], "The number of features in the output dataframe (=" + str(location_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your location feature extraction functions"