def test_spatial_markov(self):
"""Test Spatial Markov."""
data = [ { 'id': d['id'],
'attr1': d['y1995'],
'attr2': d['y1996'],
'attr3': d['y1997'],
'attr4': d['y1998'],
'attr5': d['y1999'],
'attr6': d['y2000'],
'attr7': d['y2001'],
'attr8': d['y2002'],
'attr9': d['y2003'],
'attr10': d['y2004'],
'attr11': d['y2005'],
'attr12': d['y2006'],
'attr13': d['y2007'],
'attr14': d['y2008'],
'attr15': d['y2009'],
'neighbors': d['neighbors'] } for d in self.neighbors_data]
print(str(data[0]))
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
self.assertTrue(result != None)
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
print result[0]
expected = self.markov_data
for ([res_trend, res_up, res_down, res_vol, res_id],
[exp_trend, exp_up, exp_down, exp_vol, exp_id]
) in zip(result, expected):
self.assertAlmostEqual(res_trend, exp_trend)
def test_kmeans_nonspatial(self):
"""
test for k-means non-spatial
"""
# data from:
# http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn-cluster-kmeans
data_raw = [
OrderedDict([("arr_col1", [1, 1, 1, 4, 4, 4]),
("arr_col2", [2, 4, 0, 2, 4, 0]),
("rowid", [1, 2, 3, 4, 5, 6])])
]
random_seeds.set_random_seeds(1234)
kmeans = Kmeans(FakeDataProvider(data_raw))
clusters = kmeans.nonspatial('subquery', ['col1', 'col2'], 2)
cl1 = clusters[0][0]
cl2 = clusters[3][0]
for idx, val in enumerate(clusters):
if idx < 3:
self.assertEqual(val[0], cl1)
else:
self.assertEqual(val[0], cl2)
# raises exception for no data
with self.assertRaises(Exception):
kmeans = Kmeans(FakeDataProvider([]))
kmeans.nonspatial('subquery', ['col1', 'col2'], 2)
def test_moran(self):
"""Test Moran's I global"""
data = [{"id": d["id"], "attr1": d["value"], "neighbors": d["neighbors"]} for d in self.neighbors_data]
plpy._define_result("select", data)
random_seeds.set_random_seeds(1235)
result = cc.moran("table", "value", "knn", 5, 99, "the_geom", "cartodb_id")
print "result == None?", result == None
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)
def test_moran(self):
"""Test Moran's I global"""
data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1235)
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None?', result == None
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)
def test_moran_local_rate(self):
"""Test Moran's I rate"""
data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = cc.moran_local_rate('table', 'numerator', 'denominator', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
result = [(row[0], row[1]) for row in result]
expected = self.moran_data
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
self.assertAlmostEqual(res_val, exp_val)
def test_moran(self):
"""Test Moran's I global"""
data = [{"id": d["id"], "attr1": d["value"], "neighbors": d["neighbors"]} for d in self.neighbors_data]
random_seeds.set_random_seeds(1235)
moran = Moran(FakeDataProvider(data))
result = moran.global_stat("table", "value", "knn", 5, 99, "the_geom", "cartodb_id")
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)
def test_moran_local(self):
"""Test Moran's I local"""
data = [{"id": d["id"], "attr1": d["value"], "neighbors": d["neighbors"]} for d in self.neighbors_data]
plpy._define_result("select", data)
random_seeds.set_random_seeds(1234)
result = cc.moran_local("subquery", "value", "knn", 5, 99, "the_geom", "cartodb_id")
result = [(row[0], row[1]) for row in result]
expected = self.moran_data
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
self.assertAlmostEqual(res_val, exp_val)
self.assertEqual(res_quad, exp_quad)
def test_moran_local(self):
"""Test Moran's I local"""
data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1234)
result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
result = [(row[0], row[1]) for row in result]
expected = self.moran_data
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
self.assertAlmostEqual(res_val, exp_val)
self.assertEqual(res_quad, exp_quad)
def test_moran_local_rate(self):
"""Test Moran's I rate"""
data = [
{"id": d["id"], "attr1": d["value"], "attr2": 1, "neighbors": d["neighbors"]} for d in self.neighbors_data
]
random_seeds.set_random_seeds(1234)
moran = Moran(FakeDataProvider(data))
result = moran.local_rate_stat("subquery", "numerator", "denominator", "knn", 5, 99, "the_geom", "cartodb_id")
result = [(row[0], row[1]) for row in result]
zipped_values = zip(result, self.moran_data)
for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
self.assertAlmostEqual(res_val, exp_val)
def test_moran(self):
"""Test Moran's I global"""
data = [{
'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']
} for d in self.neighbors_data]
random_seeds.set_random_seeds(1235)
moran = Moran(FakeDataProvider(data))
result = moran.global_stat('table', 'value', 'knn', 5, 99, 'the_geom',
'cartodb_id')
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)
def test_getis_ord(self):
"""Test Getis-Ord's G*"""
data = [{'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
random_seeds.set_random_seeds(1234)
getis = Getis(FakeDataProvider(data))
result = getis.getis_ord('subquery', 'value',
'queen', None, 999, 'the_geom',
'cartodb_id')
result = [(row[0], row[1]) for row in result]
expected = np.array(self.getis_data)[:, 0:2]
for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
self.assertAlmostEqual(res_z, exp_z, delta=1e-2)
def test_local_stat(self):
"""Test Moran's I local"""
data = [
OrderedDict([("id", d["id"]), ("attr1", d["value"]), ("neighbors", d["neighbors"])])
for d in self.neighbors_data
]
moran = Moran(FakeDataProvider(data))
random_seeds.set_random_seeds(1234)
result = moran.local_stat("subquery", "value", "knn", 5, 99, "the_geom", "cartodb_id")
result = [(row[0], row[1]) for row in result]
zipped_values = zip(result, self.moran_data)
for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
self.assertAlmostEqual(res_val, exp_val)
self.assertEqual(res_quad, exp_quad)
def test_getis_ord(self):
"""Test Getis-Ord's G*"""
data = [{
'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']
} for d in self.neighbors_data]
random_seeds.set_random_seeds(1234)
getis = Getis(FakeDataProvider(data))
result = getis.getis_ord('subquery', 'value', 'queen', None, 999,
'the_geom', 'cartodb_id')
result = [(row[0], row[1]) for row in result]
expected = np.array(self.getis_data)[:, 0:2]
for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
self.assertAlmostEqual(res_z, exp_z, delta=1e-2)
def test_kmeans(self):
"""
"""
data = [{'xs': d['xs'],
'ys': d['ys'],
'ids': d['ids']} for d in self.cluster_data]
random_seeds.set_random_seeds(1234)
kmeans = Kmeans(FakeDataProvider(data))
clusters = kmeans.spatial('subquery', 2)
labels = [a[1] for a in clusters]
c1 = [a for a in clusters if a[1] == 0]
c2 = [a for a in clusters if a[1] == 1]
self.assertEqual(len(np.unique(labels)), 2)
self.assertEqual(len(c1), 20)
self.assertEqual(len(c2), 20)
def test_local_stat(self):
"""Test Moran's I local"""
data = [
OrderedDict([('id', d['id']), ('attr1', d['value']),
('neighbors', d['neighbors'])])
for d in self.neighbors_data
]
moran = Moran(FakeDataProvider(data))
random_seeds.set_random_seeds(1234)
result = moran.local_stat('subquery', 'value', 'knn', 5, 99,
'the_geom', 'cartodb_id')
result = [(row[0], row[6]) for row in result]
zipped_values = zip(result, self.moran_data)
for ([res_quad, res_val], [exp_val, exp_quad]) in zipped_values:
self.assertAlmostEqual(res_val, exp_val)
self.assertEqual(res_quad, exp_quad)
def test_kmeans(self):
"""
"""
data = [{
'xs': d['xs'],
'ys': d['ys'],
'ids': d['ids']
} for d in self.cluster_data]
random_seeds.set_random_seeds(1234)
kmeans = Kmeans(FakeDataProvider(data))
clusters = kmeans.spatial('subquery', 2)
labels = [a[1] for a in clusters]
c1 = [a for a in clusters if a[1] == 0]
c2 = [a for a in clusters if a[1] == 1]
self.assertEqual(len(np.unique(labels)), 2)
self.assertEqual(len(c1), 20)
self.assertEqual(len(c2), 20)
def test_moran_local_rate(self):
"""Test Moran's I rate"""
data = [{
'id': d['id'],
'attr1': d['value'],
'attr2': 1,
'neighbors': d['neighbors']
} for d in self.neighbors_data]
random_seeds.set_random_seeds(1234)
moran = Moran(FakeDataProvider(data))
result = moran.local_rate_stat('subquery', 'numerator', 'denominator',
'knn', 5, 99, 'the_geom', 'cartodb_id')
result = [(row[0], row[6]) for row in result]
zipped_values = zip(result, self.moran_data)
for ([res_quad, res_val], [exp_val, exp_quad]) in zipped_values:
self.assertAlmostEqual(res_val, exp_val)
def test_spatial_markov(self):
"""Test Spatial Markov."""
data = [{'id': d['id'],
'attr1': d['y1995'],
'attr2': d['y1996'],
'attr3': d['y1997'],
'attr4': d['y1998'],
'attr5': d['y1999'],
'attr6': d['y2000'],
'attr7': d['y2001'],
'attr8': d['y2002'],
'attr9': d['y2003'],
'attr10': d['y2004'],
'attr11': d['y2005'],
'attr12': d['y2006'],
'attr13': d['y2007'],
'attr14': d['y2008'],
'attr15': d['y2009'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
# print(str(data[0]))
markov = Markov(FakeDataProvider(data))
random_seeds.set_random_seeds(1234)
result = markov.spatial_trend('subquery',
['y1995', 'y1996', 'y1997', 'y1998',
'y1999', 'y2000', 'y2001', 'y2002',
'y2003', 'y2004', 'y2005', 'y2006',
'y2007', 'y2008', 'y2009'],
5, 'knn', 5, 0, 'the_geom',
'cartodb_id')
self.assertTrue(result is not None)
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
print(result[0])
expected = self.markov_data
for ([res_trend, res_up, res_down, res_vol, res_id],
[exp_trend, exp_up, exp_down, exp_vol, exp_id]
) in zip(result, expected):
self.assertAlmostEqual(res_trend, exp_trend)
