junos
/
rapids
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Added maximum_gap_allowed parameter

pull/95/head
nikunjgoel95 2020-07-21 18:56:43 -04:00
parent c86efb19d6
commit 0ada1292ed
4 changed files with 9 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
config.yaml
View File

@ -90,6 +90,7 @@ DORYAB_LOCATION:
DBSCAN_EPS: 10 # meters
DBSCAN_MINSAMPLES: 5
THRESHOLD_STATIC : 1 # km/h
MAXIMUM_GAP_ALLOWED: 300
BLUETOOTH:
COMPUTE: False

3
rules/features.snakefile
View File

@ -129,7 +129,8 @@ rule location_doryab_features:
day_segment = "{day_segment}",
dbscan_eps = config["DORYAB_LOCATION"]["DBSCAN_EPS"],
dbscan_minsamples = config["DORYAB_LOCATION"]["DBSCAN_MINSAMPLES"],
threshold_static = config["DORYAB_LOCATION"]["THRESHOLD_STATIC"]
threshold_static = config["DORYAB_LOCATION"]["THRESHOLD_STATIC"],
maximum_gap_allowed = config["DORYAB_LOCATION"]["MAXIMUM_GAP_ALLOWED"]
output:
"data/processed/{pid}/location_doryab_{day_segment}.csv"
script:

8
src/features/location_doryab/location_base.py
View File

@ -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):
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"]
@ -34,7 +34,7 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
preComputedDistanceandSpeed = pd.DataFrame()
for localDate in location_data['local_date'].unique():
distance, speeddf = get_all_travel_distances_meters_speed(location_data[location_data['local_date']==localDate],threshold_static)
distance, speeddf = get_all_travel_distances_meters_speed(location_data[location_data['local_date']==localDate],threshold_static,maximum_gap_allowed)
preComputedDistanceandSpeed.loc[localDate,"distance"] = distance.sum()
preComputedDistanceandSpeed.loc[localDate,"avgspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].mean()
preComputedDistanceandSpeed.loc[localDate,"varspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].var()
@ -133,7 +133,7 @@ def distance_to_degrees(d):
d = d / 60
return d
def get_all_travel_distances_meters_speed(locationData,threshold):
def get_all_travel_distances_meters_speed(locationData,threshold,maximum_gap_allowed):
lat_lon_temp = pd.DataFrame()
@ -146,7 +146,7 @@ def get_all_travel_distances_meters_speed(locationData,threshold):
lat_lon_temp['time_diff'] = lat_lon_temp['time_after'] - lat_lon_temp['time_before']
lat_lon_temp['timeInSeconds'] = lat_lon_temp['time_diff'].apply(lambda x: x.total_seconds())
lat_lon_temp = lat_lon_temp[lat_lon_temp['timeInSeconds'] <= 300]
lat_lon_temp = lat_lon_temp[lat_lon_temp['timeInSeconds'] <= maximum_gap_allowed]
if lat_lon_temp.empty:
return pd.Series(), pd.DataFrame({"speed": [], "speedTag": []})

3
src/features/location_doryab_features.py
View File

@ -8,8 +8,9 @@ location_features = pd.DataFrame(columns=["local_date"])
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"]
location_features = location_features.merge(base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static), on="local_date", how="outer")
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")
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"