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Added minutes of data in a day/epoch.

pull/95/head
nikunjgoel95 2020-07-23 14:23:32 -04:00
parent f42753d28a
commit faad8f5a8f
4 changed files with 22 additions and 5 deletions
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1
config.yaml
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@ -91,6 +91,7 @@ DORYAB_LOCATION:
DBSCAN_MINSAMPLES: 5
THRESHOLD_STATIC : 1 # km/h
MAXIMUM_GAP_ALLOWED: 300
MINUTES_DATA_USED: True
BLUETOOTH:
COMPUTE: False

3
rules/features.snakefile
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@ -130,7 +130,8 @@ rule location_doryab_features:
dbscan_eps = config["DORYAB_LOCATION"]["DBSCAN_EPS"],
dbscan_minsamples = config["DORYAB_LOCATION"]["DBSCAN_MINSAMPLES"],
threshold_static = config["DORYAB_LOCATION"]["THRESHOLD_STATIC"],
maximum_gap_allowed = config["DORYAB_LOCATION"]["MAXIMUM_GAP_ALLOWED"]
maximum_gap_allowed = config["DORYAB_LOCATION"]["MAXIMUM_GAP_ALLOWED"],
minutes_data_used = config["DORYAB_LOCATION"]["MINUTES_DATA_USED"]
output:
"data/processed/{pid}/location_doryab_{day_segment}.csv"
script:

15
src/features/location_doryab/location_base.py
View File

@ -6,8 +6,7 @@ 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):
# 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"]
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
features_to_compute = list(set(requested_features) & set(base_features_names))
@ -23,6 +22,12 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
else:
location_features = pd.DataFrame()
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])
location_features.index.name = 'local_date'
location_data = location_data[(location_data['double_latitude']!=0.0) & (location_data['double_longitude']!=0.0)]
if "locationvariance" in features_to_compute:
@ -120,12 +125,16 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
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])
location_features = location_features.reset_index()
return location_features
def getMinutesData(locationData):
return locationData[['local_hour','local_minute']].drop_duplicates(inplace = False).shape[0]
def distance_to_degrees(d):
#Just an approximation, but speeds up clustering by a huge amount and doesnt introduce much error
#over small distances

8
src/features/location_doryab_features.py
View File

@ -9,8 +9,14 @@ dbscan_eps = snakemake.params["dbscan_eps"]
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"]
location_features = location_features.merge(base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static,maximum_gap_allowed), on="local_date", how="outer")
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)
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"