def setUp(self):
# load raw data for analysis
self.neighbors_data = json.loads(
open(fixture_file('neighbors_getis.json')).read())
# load pre-computed/known values
self.getis_data = json.loads(open(fixture_file('getis.json')).read())
def setUp(self):
# load raw data for analysis
self.neighbors_data = json.loads(
open(fixture_file('neighbors_getis.json')).read())
# load pre-computed/known values
self.getis_data = json.loads(
open(fixture_file('getis.json')).read())
def setUp(self):
"""
fixture packed from canonical GWR georgia dataset using the
following query:
SELECT array_agg(x) As x,
array_agg(y) As y,
array_agg(pctbach) As dep_var,
array_agg(pctrural) As attr1,
array_agg(pctpov) As attr2,
array_agg(pctblack) As attr3,
array_agg(areakey) As rowid
FROM g_utm
WHERE pctbach is not NULL AND
pctrural IS NOT NULL AND
pctpov IS NOT NULL AND
pctblack IS NOT NULL
"""
import copy
# data packed from https://github.com/TaylorOshan/pysal/blob/1d6af33bda46b1d623f70912c56155064463383f/pysal/examples/georgia/GData_utm.csv
self.data = json.loads(
open(fixture_file('gwr_packed_data.json')).read())
# data packed from https://github.com/TaylorOshan/pysal/blob/a44c5541e2e0d10a99ff05edc1b7f81b70f5a82f/pysal/examples/georgia/georgia_BS_NN_listwise.csv
self.knowns = json.loads(
open(fixture_file('gwr_packed_knowns.json')).read())
# data for GWR prediction
self.data_predict = copy.deepcopy(self.data)
self.ids_of_unknowns = [13083, 13009, 13281, 13115, 13247, 13169]
self.idx_ids_of_unknowns = [
self.data_predict[0]['rowid'].index(idx)
for idx in self.ids_of_unknowns
]
for idx in self.idx_ids_of_unknowns:
self.data_predict[0]['dep_var'][idx] = None
self.predicted_knowns = {
13009: 10.879,
13083: 4.5259,
13115: 9.4022,
13169: 6.0793,
13247: 8.1608,
13281: 13.886
}
# params, with ind_vars in same ordering as query above
self.params = {
'subquery': 'select * from table',
'dep_var': 'pctbach',
'ind_vars': ['pctrural', 'pctpov', 'pctblack'],
'bw': 90.000,
'fixed': False,
'geom_col': 'the_geom',
'id_col': 'areakey'
}
def setUp(self):
plpy._reset()
self.params = {"id_col": "cartodb_id",
"attr1": "andy",
"attr2": "jay_z",
"table": "a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
self.moran_data = json.loads(open(fixture_file('moran.json')).read())
def setUp(self):
plpy._reset()
self.params = {"id_col": "cartodb_id",
"attr1": "andy",
"attr2": "jay_z",
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.params_markov = {"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
self.moran_data = json.loads(open(fixture_file('moran.json')).read())
def setUp(self):
self.params = {
"id_col": "cartodb_id",
"attr1": "andy",
"attr2": "jay_z",
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321,
}
self.params_markov = {
"id_col": "cartodb_id",
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321,
}
self.neighbors_data = json.loads(open(fixture_file("neighbors.json")).read())
self.moran_data = json.loads(open(fixture_file("moran.json")).read())
def setUp(self):
plpy._reset()
self.params = {
"id_col": "cartodb_id",
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321
}
self.neighbors_data = json.loads(
open(fixture_file('neighbors_markov.json')).read())
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
self.time_data = np.array(
[i * np.ones(10, dtype=float) for i in range(10)]).T
self.transition_matrix = np.array(
[[[0.96341463, 0.0304878, 0.00609756, 0., 0.],
[0.06040268, 0.83221477, 0.10738255, 0., 0.],
[0., 0.14, 0.74, 0.12, 0.],
[0., 0.03571429, 0.32142857, 0.57142857, 0.07142857],
[0., 0., 0., 0.16666667, 0.83333333]],
[[0.79831933, 0.16806723, 0.03361345, 0., 0.],
[0.0754717, 0.88207547, 0.04245283, 0., 0.],
[0.00537634, 0.06989247, 0.8655914, 0.05913978, 0.],
[0., 0., 0.06372549, 0.90196078, 0.03431373],
[0., 0., 0., 0.19444444, 0.80555556]],
[[0.84693878, 0.15306122, 0., 0., 0.],
[0.08133971, 0.78947368, 0.1291866, 0., 0.],
[0.00518135, 0.0984456, 0.79274611, 0.0984456, 0.00518135],
[0., 0., 0.09411765, 0.87058824, 0.03529412],
[0., 0., 0., 0.10204082, 0.89795918]],
[[0.8852459, 0.09836066, 0., 0.01639344, 0.],
[0.03875969, 0.81395349, 0.13953488, 0., 0.00775194],
[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
[0., 0.02339181, 0.12865497, 0.75438596, 0.09356725],
[0., 0., 0., 0.09661836, 0.90338164]],
[[0.33333333, 0.66666667, 0., 0., 0.],
[0.0483871, 0.77419355, 0.16129032, 0.01612903, 0.],
[0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.],
[0., 0.01036269, 0.06217617, 0.89637306, 0.03108808],
[0., 0., 0., 0.02352941, 0.97647059]]])
def setUp(self):
self.params = {"id_col": "cartodb_id",
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
"subquery": "SELECT * FROM a_list",
"geom_col": "the_geom",
"num_ngbrs": 321}
self.neighbors_data = json.loads(
open(fixture_file('neighbors_markov.json')).read())
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
self.time_data = np.array([i * np.ones(10, dtype=float)
for i in range(10)]).T
self.transition_matrix = np.array([
[[0.96341463, 0.0304878, 0.00609756, 0., 0.],
[0.06040268, 0.83221477, 0.10738255, 0., 0.],
[0., 0.14, 0.74, 0.12, 0.],
[0., 0.03571429, 0.32142857, 0.57142857, 0.07142857],
[0., 0., 0., 0.16666667, 0.83333333]],
[[0.79831933, 0.16806723, 0.03361345, 0., 0.],
[0.0754717, 0.88207547, 0.04245283, 0., 0.],
[0.00537634, 0.06989247, 0.8655914, 0.05913978, 0.],
[0., 0., 0.06372549, 0.90196078, 0.03431373],
[0., 0., 0., 0.19444444, 0.80555556]],
[[0.84693878, 0.15306122, 0., 0., 0.],
[0.08133971, 0.78947368, 0.1291866, 0., 0.],
[0.00518135, 0.0984456, 0.79274611, 0.0984456, 0.00518135],
[0., 0., 0.09411765, 0.87058824, 0.03529412],
[0., 0., 0., 0.10204082, 0.89795918]],
[[0.8852459, 0.09836066, 0., 0.01639344, 0.],
[0.03875969, 0.81395349, 0.13953488, 0., 0.00775194],
[0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
[0., 0.02339181, 0.12865497, 0.75438596, 0.09356725],
[0., 0., 0., 0.09661836, 0.90338164]],
[[0.33333333, 0.66666667, 0., 0., 0.],
[0.0483871, 0.77419355, 0.16129032, 0.01612903, 0.],
[0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.],
[0., 0.01036269, 0.06217617, 0.89637306, 0.03108808],
[0., 0., 0., 0.02352941, 0.97647059]]]
)
def setUp(self):
self.params = {
"query": 'SELECT * FROM segmentation_data',
"variable": 'price',
"feature_columns": ['m1', 'm2', 'm3', 'm4', 'm5', 'm6'],
"target_query": 'SELECT * FROM segmentation_result',
"id_col": 'cartodb_id',
"model_params": {
'n_estimators': 1200,
'max_depth': 3,
'subsample': 0.5,
'learning_rate': 0.01,
'min_samples_leaf': 1
}
}
self.model_data = json.loads(
open(fixture_file('model_data.json')).read())
self.data = json.loads(open(fixture_file('data.json')).read())
self.predict_data = json.loads(
open(fixture_file('predict_data.json')).read())
self.result_seg = json.loads(
open(fixture_file('segmentation_result.json')).read())
self.true_result = json.loads(
open(fixture_file('true_result.json')).read())
def setUp(self):
self.cluster_data = json.loads(
open(fixture_file('kmeans.json')).read())
self.params = {"subquery": "select * from table", "no_clusters": "10"}
def setUp(self):
plpy._reset()
self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
self.params = {"subquery": "select * from table",
"no_clusters": "10"
}