# Copyright (c) 2019, MD2K Center of Excellence
# - Nasir Ali <nasir.ali08@gmail.com>
# All rights reserved.
#
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import numpy as np
import pandas as pd
from geopy.distance import great_circle
from pyspark.sql.functions import pandas_udf, PandasUDFType
from shapely.geometry.multipoint import MultiPoint
from sklearn.cluster import DBSCAN
from pyspark.sql.types import StructField, StructType, StringType, FloatType
EPSILON_CONSTANT = 1000/100.0
LATITUDE = 0
LONGITUDE = 1
ACCURACY = -1
GPS_ACCURACY_THRESHOLD = 41.0
KM_PER_RADIAN = 6371.0088
GEO_FENCE_DISTANCE = 2
MINIMUM_POINTS_IN_CLUSTER = 50
[docs]def get_centermost_point(cluster: object) -> object:
"""
:param cluster:
:return:
:rtype: object
"""
centroid = (
MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)
centermost_point = min(cluster, key=lambda point: great_circle(point,
centroid).m)
return tuple(centermost_point)
schema = StructType([
StructField("user", StringType()),
StructField("latitude", FloatType()),
StructField("longitude", FloatType())
])
[docs]@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def gps_clusters(data: object) -> object:
"""
Computes the clusters
:rtype: object
:param list data: list of interpolated gps data
:param float geo_fence_distance: Maximum distance between points in a
cluster
:param int min_points_in_cluster: Minimum number of points in a cluster
:return: list of cluster-centroids coordinates
"""
geo_fence_distance = GEO_FENCE_DISTANCE
min_points_in_cluster = MINIMUM_POINTS_IN_CLUSTER
data = data[data.accuracy < GPS_ACCURACY_THRESHOLD]
id = data.user.iloc[0]
dataframe = pd.DataFrame(
{'latitude': data.latitude, 'longitude': data.longitude})
coords = dataframe.as_matrix(columns=['latitude', 'longitude'])
epsilon = geo_fence_distance / (
EPSILON_CONSTANT * KM_PER_RADIAN)
db = DBSCAN(eps=epsilon, min_samples=min_points_in_cluster,
algorithm='ball_tree', metric='haversine').fit(
np.radians(coords))
cluster_labels = db.labels_
num_clusters = len(set(cluster_labels))
clusters = pd.Series(
[coords[cluster_labels == n] for n in range(-1, num_clusters)])
clusters = clusters.apply(lambda y: np.nan if len(y) == 0 else y)
clusters.dropna(how='any', inplace=True)
centermost_points = clusters.map(get_centermost_point)
centermost_points = np.array(centermost_points)
all_centroid = []
for cols in centermost_points:
cols = np.array(cols)
cols.flatten()
cs = ([id, cols[LATITUDE], cols[LONGITUDE]])
all_centroid.append(cs)
df = pd.DataFrame(all_centroid, columns=['user', 'latitude', 'longitude'])
return df