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_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]
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_kmeans(self):
data = self.cluster_data
plpy._define_result('select' ,data)
clusters = cc.kmeans('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(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_kmeans(self):
data = self.cluster_data
plpy._define_result('select', data)
clusters = cc.kmeans('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_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_create_and_predict_segment(self):
n_samples = 1000
random_state_train = np.random.RandomState(13)
random_state_test = np.random.RandomState(134)
training_data = self.generate_random_data(n_samples,
random_state_train)
test_data, test_y = self.generate_random_data(n_samples,
random_state_test,
row_type=True)
ids = [{'cartodb_ids': range(len(test_data))}]
rows = [{'x1': 0, 'x2': 0, 'x3': 0, 'y': 0, 'cartodb_id': 0}]
plpy._define_result(
'select \* from \(select \* from training\) a limit 1', rows)
plpy._define_result('.*from \(select \* from training\) as a',
training_data)
plpy._define_result(
'select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',
ids)
plpy._define_result('.*select \* from test.*', test_data)
model_parameters = {
'n_estimators': 1200,
'max_depth': 3,
'subsample': 0.5,
'learning_rate': 0.01,
'min_samples_leaf': 1
}
result = segmentation.create_and_predict_segment(
'select * from training', 'target', 'select * from test',
model_parameters)
prediction = [r[1] for r in result]
accuracy = np.sqrt(
np.mean(np.square(np.array(prediction) - np.array(test_y))))
self.assertEqual(len(result), len(test_data))
self.assertTrue(result[0][2] < 0.01)
self.assertTrue(accuracy < 0.5 * np.mean(test_y))
def test_create_and_predict_segment(self):
n_samples = 1000
random_state_train = np.random.RandomState(13)
random_state_test = np.random.RandomState(134)
training_data = self.generate_random_data(n_samples, random_state_train)
test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
ids = [{'cartodb_ids': range(len(test_data))}]
rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
plpy._define_result('select \* from \(select \* from training\) a limit 1',rows)
plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
plpy._define_result('.*select \* from test.*' ,test_data)
model_parameters = {'n_estimators': 1200,
'max_depth': 3,
'subsample' : 0.5,
'learning_rate': 0.01,
'min_samples_leaf': 1}
result = segmentation.create_and_predict_segment(
'select * from training',
'target',
'select * from test',
model_parameters)
prediction = [r[1] for r in result]
accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
self.assertEqual(len(result),len(test_data))
self.assertTrue( result[0][2] < 0.01)
self.assertTrue( accuracy < 0.5*np.mean(test_y) )
