Mercurial > hg > GlobalNeighbors
annotate tests/test_distance.py @ 18:87ae70245201
ubuntu wants .conf, redhat wants ini of course
author | Jeff Hammel <k0scist@gmail.com> |
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date | Sun, 25 Jun 2017 15:13:29 -0700 |
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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 |
3 | 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 |
3 | 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 |
3 | 34 # here's a smaller one |
35 moderate_tsv = os.path.join(data, '10000cities.tsv') | |
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 |
3 | 111 def test_10000cities(self): |
112 """a moderate size test""" | |
113 | |
114 assert os.path.exists(self.moderate_tsv) | |
115 with open(self.moderate_tsv) as f: | |
116 cities = locations(read_cities(f)) | |
117 | |
118 # test over different values of # of neighbors | |
119 for k in (10, 100, 1000): | |
120 neighbors = distance.calculate_neighbors(cities, | |
121 k=k) | |
122 | |
123 # ensure you have no more neighbors than you ask for | |
124 assert max([len(value) for value in neighbors.values()]) <= k | |
125 | |
126 # assert distances increase | |
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127 for key, value in neighbors.items(): |
3 | 128 distances = [i[-1] for i in value] |
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) |
3 | 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 |
1 | 151 |
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5dba84370182
initial commit; half-working prototype
Jeff Hammel <k0scist@gmail.com>
parents:
diff
changeset
|
152 if __name__ == '__main__': |
5dba84370182
initial commit; half-working prototype
Jeff Hammel <k0scist@gmail.com>
parents:
diff
changeset
|
153 unittest.main() |