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Added Sampling Frequency to Doryab Location Features.

pull/95/head
nikunjgoel95 2020-08-05 13:02:34 -04:00
parent 19b61a66aa
commit df7a52cd6c
4 changed files with 32 additions and 21 deletions
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1
config.yaml
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@ -92,6 +92,7 @@ DORYAB_LOCATION:
THRESHOLD_STATIC : 1 # km/h
MAXIMUM_GAP_ALLOWED: 300
MINUTES_DATA_USED: False
SAMPLING_FREQUENCY: 0
BLUETOOTH:
COMPUTE: False

3
rules/features.snakefile
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@ -141,7 +141,8 @@ rule location_doryab_features:
dbscan_minsamples = config["DORYAB_LOCATION"]["DBSCAN_MINSAMPLES"],
threshold_static = config["DORYAB_LOCATION"]["THRESHOLD_STATIC"],
maximum_gap_allowed = config["DORYAB_LOCATION"]["MAXIMUM_GAP_ALLOWED"],
minutes_data_used = config["DORYAB_LOCATION"]["MINUTES_DATA_USED"]
minutes_data_used = config["DORYAB_LOCATION"]["MINUTES_DATA_USED"],
sampling_frequency = config["DORYAB_LOCATION"]["SAMPLING_FREQUENCY"]
output:
"data/processed/{pid}/location_doryab_{day_segment}.csv"
script:

46
src/features/location_doryab/location_base.py
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@ -4,7 +4,7 @@ from astropy.timeseries import LombScargle
from sklearn.cluster import DBSCAN
from math import radians, cos, sin, asin, sqrt
def base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples, threshold_static, maximum_gap_allowed):
def base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples, threshold_static, maximum_gap_allowed,sampling_frequency):
# name of the features this function can compute
base_features_names = ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","minutesdataused"]
# the subset of requested features this function can compute
@ -22,6 +22,9 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
else:
location_features = pd.DataFrame()
if sampling_frequency == 0:
sampling_frequency = getSamplingFrequency(location_data)
if "minutesdataused" in features_to_compute:
for localDate in location_data["local_date"].unique():
location_features.loc[localDate,"location_" + day_segment + "_minutesdataused"] = getMinutesData(location_data[location_data["local_date"]==localDate])
@ -72,35 +75,35 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
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])
location_features.loc[localDate,"location_" + day_segment + "_radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
if "timeattop1location" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_timeattop1"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],1)
location_features.loc[localDate,"location_" + day_segment + "_timeattop1"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],1,sampling_frequency)
if "timeattop2location" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_timeattop2"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],2)
location_features.loc[localDate,"location_" + day_segment + "_timeattop2"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],2,sampling_frequency)
if "timeattop3location" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
location_features.loc[localDate,"location_" + day_segment + "_timeattop3"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],3)
location_features.loc[localDate,"location_" + day_segment + "_timeattop3"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],3,sampling_frequency)
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] / location_data[location_data['local_date']==localDate].shape[0])
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)
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])
location_features.loc[localDate,"location_" + day_segment + "_outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
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])
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.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = smax * sampling_frequency
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = smin * sampling_frequency
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = sstd * sampling_frequency
preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = smean * sampling_frequency
if "maxlengthstayatclusters" in features_to_compute:
for localDate in newLocationData['local_date'].unique():
@ -315,7 +318,7 @@ def number_location_transitions(locationData):
return df[df['boolCol'] == False].shape[0] - 1
def radius_of_gyration(locationData):
def radius_of_gyration(locationData,sampling_frequency):
if locationData is None or len(locationData) == 0:
return None
# Center is the centroid, not the home location
@ -333,14 +336,14 @@ def radius_of_gyration(locationData):
distance = haversine(lat_lon_dict) ** 2
time_in_cluster = locationData[locationData["location_label"]==labels].shape[0]
time_in_cluster = locationData[locationData["location_label"]==labels].shape[0]* sampling_frequency
rog = rog + (time_in_cluster * distance)
time_all_clusters = valid_clusters.shape[0]
time_all_clusters = valid_clusters.shape[0] * sampling_frequency
final_rog = (1/time_all_clusters) * rog
return np.sqrt(final_rog)
def time_at_topn_clusters_in_group(locationData,n): # relevant only for global location features since, top3_clusters = top3_clusters_in_group for local
def time_at_topn_clusters_in_group(locationData,n,sampling_frequency): # relevant only for global location features since, top3_clusters = top3_clusters_in_group for local
if locationData is None or len(locationData) == 0:
return None
@ -357,12 +360,12 @@ def time_at_topn_clusters_in_group(locationData,n): # relevant only for global
return topn_time
def outliers_time_percent(locationData):
def outliers_time_percent(locationData,sampling_frequency):
if locationData is None or len(locationData) == 0:
return None
clusters = locationData["location_label"]
numoutliers = clusters[(clusters == -1)].sum()
numtotal = len(clusters)
numoutliers = clusters[(clusters == -1)].sum() * sampling_frequency
numtotal = len(clusters) * sampling_frequency
return (float(-1*numoutliers) / numtotal)
@ -438,4 +441,9 @@ def location_entropy_normalized(locationData):
if num_clusters == 0 or len(locationData) == 0 or entropy is None:
return None
else:
return entropy / num_clusters
return entropy / num_clusters
def getSamplingFrequency(locationData):
return ((pd.to_datetime(locationData['local_time'], format="%H:%M:%S") - pd.to_datetime(locationData['local_time'].shift(periods=1), format="%H:%M:%S")).apply(lambda x: x.total_seconds())/60).median()

3
src/features/location_doryab_features.py
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@ -10,11 +10,12 @@ dbscan_minsamples = snakemake.params["dbscan_minsamples"]
threshold_static = snakemake.params["threshold_static"]
maximum_gap_allowed = snakemake.params["maximum_gap_allowed"]
minutes_data_used = snakemake.params["minutes_data_used"]
sampling_frequency = snakemake.params["sampling_frequency"]
if(minutes_data_used):
requested_features.append("minutesdataused")
base_features = base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static,maximum_gap_allowed)
base_features = base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static,maximum_gap_allowed,sampling_frequency)
location_features = location_features.merge(base_features, on="local_date", how="outer")