Interpreting sklearn haversine outputs to kilometers

user_2255198 0
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I can't figure out how to interpret the outputs of the haversine implementations in sklearn (version 20.2)

The documentation says,"Note that the haversine distance metric requires data in the form of [latitude, longitude] and both inputs and outputs are in units of radians.",so I should be able to convert to km multiplying by 6371 (great distance approx for radius).

A functioning distance calculation from two points would be as follows:

def distance(origin, destination):
    lat1, lon1 = origin
    lat2, lon2 = destination
    radius = 6371 # km

    dlat = math.radians(lat2-lat1)
    dlon = math.radians(lon2-lon1)
    a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \
        * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
    d = radius * c

    return d

distance([32.027240,-81.093190],[41.981876,-87.969982])
1263.103504537151

This is the correct distance.

Using the BallTree implementation:

from sklearn.neighbors import BallTree
test_points = [[32.027240,41.981876],[-81.093190,-87.969982]]
tree = BallTree(test_points,metric = 'haversine')
results = tree.query_radius(test_points,r = 10,return_distance  = True)

results[1]
array([array([0.        , 1.53274271]), array([1.53274271, 0.        ])],
      dtype=object)

Same for the distanceMetric implementation:

dist = DistanceMetric.get_metric('haversine')
dist.pairwise([[32.027240,41.981876],[-81.093190,-87.969982]])
array([[0.        , 1.53274271],
       [1.53274271, 0.        ]])

I also tried changing the order, in case it wasn't supposed to be input as [[lat1,lat2],[lon1,lon2]] and also didn't get results that I can interpret.

Does anyone know how I can get the distance in km from two coordinates using the sklearn implementations?

scikit-learn haversine python
2 Answers

So the issue is that sklearn requires everything to be in radians, but the latitude/longitude and radius I have were in degrees/meters respectively. Before using, I needed to do some conversions:

from sklearn.neighbors import BallTree
earth_radius = 6371000 # meters in earth
test_radius = 10 # meters

test_points = [[32.027240,41.981876],[-81.093190,-87.969982]]
test_points_rad = [[x[0] * np.pi / 180, x[1] * np.pi / 180] for x in test_points ]

tree = BallTree(test_points_rad, metric = 'haversine')
results = tree.query_radius(test_points, r=test_radius/earth_radius, return_distance  = True)
user_22551980

Just to clarify a previous answer by @flyingmeatball, a few things:

  1. Maybe due to changes in sklearn: you need to specify the coordinates row-wise
  2. A simple way to convert to radians is just to import radians module from maths
  3. The results that you get at the end needs to be multiplied by earth's radius again to get the answer in meters/kilometers.

Please see the code sample below...

from math import radians
earth_radius = 6371000 # meters in earth
test_radius = 1300000 # meters

test_points = [[32.027240,-81.093190],[41.981876,-87.969982]]
test_points_rad = np.array([[radians(x[0]), radians(x[1])] for x in test_points ])

tree = BallTree(test_points_rad, metric = 'haversine')
ind,results = tree.query_radius(test_points_rad, r=test_radius/earth_radius, 
return_distance  = True)
print(ind)
print(results * earth_radius/1000)
user_37828800
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