Mercurial > hg > GlobalNeighbors

annotate tests/test_distance.py @ 22:e69cb496324e

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we have a data dump
author Jeff Hammel <k0scist@gmail.com>
date Sun, 25 Jun 2017 17:45:19 -0700
parents 27925261c137
children
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1 #!/usr/bin/env python
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2
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3 """
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4 test distance calculation
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5 """
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6
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7 import math
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8 import os
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9 import random
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10 import unittest
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11 from globalneighbors import distance
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12 from globalneighbors.constants import Rearth
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13 from globalneighbors.locations import locations
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14 from globalneighbors.read import read_cities
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15 from globalneighbors.read import read_city_list
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16 from globalneighbors.schema import primary_key
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17
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18 here = os.path.dirname(os.path.abspath(__file__))
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19 data = os.path.join(here, 'data')
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20 full_tsv_lines = 149092
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21
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22
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23 class DistanceTests(unittest.TestCase):
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24
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25 # created with
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26 # head -n 10 cities1000.txt > GlobalNeighbors/tests/data/sample.tsv
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27 test_tsv = os.path.join(data, 'sample.tsv')
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28 test_tsv_lines = 10
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29
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30 # full dataset: test with caution
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31 full_tsv = os.path.join(data, 'cities1000.txt')
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32 full_tsv_lines = 149092
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33
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34 # here's a smaller one
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35 moderate_tsv = os.path.join(data, '10000cities.tsv')
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36
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37 def test_haversine(self):
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38
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39 # a simple canned case
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40 # equator to pole
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41 lat1 = 0.
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42 lat2 = 90.
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43 lon2 = 70. # undefined, technically
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44 expected_distance = 0.5*math.pi
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45
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46 for lon1 in range(-135, 135, 15):
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47 radians = [distance.deg_to_rad(degrees)
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48 for degrees in (lat1, lon2, lat2, lon2)]
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49 error = (distance.haversine(*radians) == expected_distance)
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50 assert error < 1e-4
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51
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52 def test_distance(self):
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53 """test distance between two known cities"""
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54
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55 # Source:https://en.wikipedia.org/wiki/List_of_cities_by_latitude
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56 # http://www.distancefromto.net/distance-from-new-york-to-chicago-us
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57 chicago = (40.71278,
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58 -74.00594)
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59
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60 new_york = (41.85003,
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61 -87.65005)
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62 ref_distance = 1149.
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63
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64 args = [distance.deg_to_rad(i) for i in
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65 list(chicago) + list(new_york)]
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66 calculated = distance.haversine(*args, r=Rearth)
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67
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68 # Allow some error for circular projection approximation
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69 error = abs(calculated - ref_distance)/ref_distance
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70
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71 assert error < 0.01
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72
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73 def test_distances(self):
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74 """"ensure disances monotonically decay"""
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75
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76 # parse the data
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77 assert os.path.exists(self.test_tsv)
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78 cities = read_city_list(self.test_tsv)
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79 assert len(cities) == self.test_tsv_lines
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80 city_locations = locations(cities)
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81 assert len(city_locations) == self.test_tsv_lines
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82
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83 # calculate all the neighbors
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84 # WARNING: n*2 algorithm Too computationally intensive
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85 # for full data set
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86 for key, value in distance.calculate_distances(city_locations,
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87 r=Rearth):
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88 # for now, just make sure we can iterate over them
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89 pass
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90
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91 def test_neighbors(self):
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92
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93 # parse the data
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94 tsv = os.path.join(data, 'sample.tsv')
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95 assert os.path.exists(tsv)
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96 cities = read_city_list(tsv)
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97 city_locations = locations(cities)
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98
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99 # calculate the neighbors
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100 neighbors = distance.calculate_neighbors(city_locations,
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101 k=self.test_tsv_lines)
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102 assert len(neighbors) == self.test_tsv_lines
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103
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104 # ensure distance increases for each thing
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105 for src, value in neighbors.items():
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106 distances = [i[-1] for i in value]
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107 assert len(distances) == self.test_tsv_lines - 1
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108 for i in range(1, len(distances)):
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109 assert distances[i] >= distances[i-1]
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110
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111 def test_10000cities(self):
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112 """a moderate size test"""
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113
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114 assert os.path.exists(self.moderate_tsv)
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115 with open(self.moderate_tsv) as f:
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116 cities = locations(read_cities(f))
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117
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118 # test over different values of # of neighbors
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119 for k in (10, 100, 1000):
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120 neighbors = distance.calculate_neighbors(cities,
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121 k=k)
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122
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123 # ensure you have no more neighbors than you ask for
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124 assert max([len(value) for value in neighbors.values()]) <= k
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125
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126 # assert distances increase
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127 for key, value in neighbors.items():
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128 distances = [i[-1] for i in value]
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129 assert distances == sorted(distances)
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130 neighbors = [i[0] for i in value]
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131 assert key not in neighbors
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132 assert len(set(neighbors)) == len(neighbors)
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133
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134 def test_insert_distances(self):
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135 """test insert distances algorithm"""
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136
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137 values = [(i, random.random())
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138 for i in range(1500)]
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139 for k in (10, 100, 1000):
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140 _distances = []
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141 for i, value in values:
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142 distance.insert_distance(_distances,
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143 i,
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144 value,
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145 k)
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146 # since k is < 1500
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147 assert len(_distances) == k
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148 ordered = [value[-1] for value in _distances]
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149 assert sorted(ordered) == ordered
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150
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151
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152 if __name__ == '__main__':
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153 unittest.main()