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refactor

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This commit is contained in:
Andy Eschbacher 2017-01-06 10:37:12 -05:00
parent e2cf12aaba
commit cdb81ea896
2 changed files with 26 additions and 11 deletions
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1
src/py/crankshaft/crankshaft/analysis_data_provider.py
View File

@ -74,6 +74,7 @@ class AnalysisDataProvider:
return query_result
except plpy.SPIError, err:
plpy.error('Analysis failed: %s' % err)
def get_gwr_predict(self, params):
"""fetch data for gwr predict"""
query = pu.gwr_predict_query(params)

36
src/py/crankshaft/crankshaft/regression/gwr_cs.py
View File

@ -2,10 +2,11 @@
Geographically weighted regression
"""
import numpy as np
from gwr.base.gwr import GWR as pysal_GWR
from gwr.base.gwr import GWR as PySAL_GWR
from gwr.base.sel_bw import Sel_BW
import json
from crankshaft.analysis_data_provider import AnalysisDataProvider
import plpy
class GWR:
@ -33,9 +34,14 @@ class GWR:
'dep_var': dep_var,
'ind_vars': ind_vars}
# retrieve data
# get data from data provider
query_result = self.data_provider.get_gwr(params)
# exit if data to analyze is empty
if len(query_result) == 0:
plpy.error('No data passed to analysis or independent variables '
'are all null-valued')
# unique ids and variable names list
rowid = np.array(query_result[0]['rowid'], dtype=np.int)
@ -64,7 +70,7 @@ class GWR:
if bw is None:
bw = Sel_BW(coords, Y, X,
fixed=fixed, kernel=kernel).search()
model = pysal_GWR(coords, Y, X, bw,
model = PySAL_GWR(coords, Y, X, bw,
fixed=fixed, kernel=kernel).fit()
# containers for outputs
@ -108,26 +114,32 @@ class GWR:
'dep_var': dep_var,
'ind_vars': ind_vars}
# get data from data provider
query_result = self.data_provider.get_gwr_predict(params)
# exit if data to analyze is empty
if len(query_result) == 0:
plpy.error('No data passed to analysis or independent variables '
'are all null-valued')
# unique ids and variable names list
rowid = np.array(query_result[0]['rowid'], dtype=np.int)
x = np.array(query_result[0]['x'])
y = np.array(query_result[0]['y'])
coords = np.array(zip(x, y))
x = np.array(query_result[0]['x'], dtype=float)
y = np.array(query_result[0]['y'], dtype=float)
coords = np.array(zip(x, y), dtype=float)
# extract dependent variable
Y = np.array(query_result[0]['dep_var']).reshape((-1, 1))
n = Y.shape[0]
k = len(ind_vars)
X = np.zeros((n, k))
X = np.empty((n, k), dtype=float)
for attr in range(0, k):
attr_name = 'attr' + str(attr + 1)
X[:, attr] = np.array(
query_result[0][attr_name]).flatten()
query_result[0][attr_name], dtype=float).flatten()
# add intercept variable name
ind_vars.insert(0, 'intercept')
@ -137,10 +149,11 @@ class GWR:
train = np.where(Y != np.array(None))[0]
test = np.where(Y == np.array(None))[0]
# report error if there is no data to predict
if len(test) < 1:
plpy.error('No rows flagged for prediction: verify that rows '
'denoting prediction locations have a dependent '
'variable value of null')
'variable value of `null`')
# split dependent variable (only need training which is non-Null's)
Y_train = Y[train].reshape((-1, 1))
@ -160,8 +173,9 @@ class GWR:
fixed=fixed, kernel=kernel).search()
# estimate model and predict at new locations
model = GWR(coords_train, Y_train, X_train, bw,
fixed=fixed, kernel=kernel).predict(coords_test, X_test)
model = PySAL_GWR(coords_train, Y_train, X_train, bw,
fixed=fixed,
kernel=kernel).predict(coords_test, X_test)
coefficients = []
stand_errs = []