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moves gwr to data analysis provider framework

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This commit is contained in:
Andy Eschbacher
2017-01-04 11:52:44 -05:00
parent d404a66d99
commit e52dd64dad
2 changed files with 83 additions and 70 deletions
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9
src/py/crankshaft/crankshaft/analysis_data_provider.py
View File

@@ -65,3 +65,12 @@ class AnalysisDataProvider:
return data
except plpy.SPIError, err:
plpy.error('Analysis failed: %s' % err)
def get_gwr(self, params):
"""fetch data for gwr analysis"""
query = pu.gwr_query(params)
try:
query_result = plpy.execute(query)
return query_result
except plpy.SPIError, err:
plpy.error('Analysis failed: %s' % err)

144
src/py/crankshaft/crankshaft/regression/gwr_cs.py
View File

@@ -1,92 +1,96 @@
"""
Geographically weighted regression
"""
import numpy as np
from gwr.base.gwr import GWR
from gwr.base.sel_bw import Sel_BW
import plpy
import crankshaft.pysal_utils as pu
import json
from crankshaft.analysis_data_provider import AnalysisDataProvider
def gwr(subquery, dep_var, ind_vars, bw=None,
fixed=False, kernel='bisquare'):
"""
subquery: 'select * from demographics'
dep_var: 'pctbachelor'
ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack']
bw: value of bandwidth, if None then select optimal
fixed: False (kNN) or True ('distance')
kernel: 'bisquare' (default), or 'exponential', 'gaussian'
"""
class GWR:
def __init__(self, analysis_provider=None):
if analysis_provider:
self.analysis_provider = analysis_provider
else:
self.analysis_provider = AnalysisDataProvider()
# query_result = subquery
params = {'geom_col': 'the_geom',
'id_col': 'cartodb_id',
'subquery': subquery,
'dep_var': dep_var,
'ind_vars': ind_vars}
def gwr(self, subquery, dep_var, ind_vars,
bw=None, fixed=False, kernel='bisquare',
geom_col='the_geom', id_col='cartodb_id'):
"""
subquery: 'select * from demographics'
dep_var: 'pctbachelor'
ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack']
bw: value of bandwidth, if None then select optimal
fixed: False (kNN) or True ('distance')
kernel: 'bisquare' (default), or 'exponential', 'gaussian'
"""
try:
query = pu.gwr_query(params)
plpy.notice(query)
query_result = plpy.execute(query)
except plpy.SPIError, err:
plpy.notice(query)
plpy.error('Analysis failed: %s' % err)
params = {'geom_col': geom_col,
'id_col': id_col,
'subquery': subquery,
'dep_var': dep_var,
'ind_vars': ind_vars}
# unique ids and variable names list
rowid = np.array(query_result[0]['rowid'], dtype=np.int)
# retrieve data
query_result = self.analysis_data_provider.get_gwr(params)
# TODO: should x, y be centroids? point on surface?
# lat, long coordinates
x = np.array(query_result[0]['x'], dtype=float)
y = np.array(query_result[0]['y'], dtype=float)
coords = zip(x, y)
# unique ids and variable names list
rowid = np.array(query_result[0]['rowid'], dtype=np.int)
# extract dependent variable
Y = np.array(query_result[0]['dep_var'], dtype=float).reshape((-1, 1))
# TODO: should x, y be centroids? point on surface?
# lat, long coordinates
x = np.array(query_result[0]['x'], dtype=float)
y = np.array(query_result[0]['y'], dtype=float)
coords = zip(x, y)
n = Y.shape[0]
k = len(ind_vars)
X = np.zeros((n, k))
# extract dependent variable
Y = np.array(query_result[0]['dep_var'], dtype=float).reshape((-1, 1))
for attr in range(0, k):
attr_name = 'attr' + str(attr + 1)
X[:, attr] = np.array(
query_result[0][attr_name], dtype=float).flatten()
n = Y.shape[0]
k = len(ind_vars)
X = np.zeros((n, k))
# add intercept variable name
ind_vars.insert(0, 'intercept')
# extract query result
for attr in range(0, k):
attr_name = 'attr' + str(attr + 1)
X[:, attr] = np.array(
query_result[0][attr_name], dtype=float).flatten()
# calculate bandwidth if none is supplied
plpy.notice(str(bw))
if bw is None:
bw = Sel_BW(coords, Y, X,
fixed=fixed, kernel=kernel).search()
plpy.notice(str(bw))
model = GWR(coords, Y, X, bw,
fixed=fixed, kernel=kernel).fit()
# add intercept variable name
ind_vars.insert(0, 'intercept')
# TODO: iterate from 0, n-1 and fill objects like this, for a
# column called coeffs:
# {'pctrural': ..., 'pctpov': ..., ...}
# Follow the same structure for other outputs
# calculate bandwidth if none is supplied
plpy.notice(str(bw))
if bw is None:
bw = Sel_BW(coords, Y, X,
fixed=fixed, kernel=kernel).search()
plpy.notice(str(bw))
model = GWR(coords, Y, X, bw,
fixed=fixed, kernel=kernel).fit()
coefficients = []
stand_errs = []
t_vals = []
predicted = model.predy.flatten()
residuals = model.resid_response
r_squared = model.localR2.flatten()
bw = np.repeat(float(bw), n)
# containers for outputs
coeffs = []
stand_errs = []
t_vals = []
for idx in xrange(n):
coefficients.append(json.dumps({var: model.params[idx, k]
for k, var in enumerate(ind_vars)}))
stand_errs.append(json.dumps({var: model.bse[idx, k]
# extracted model information
predicted = model.predy.flatten()
residuals = model.resid_response
r_squared = model.localR2.flatten()
bw = np.repeat(float(bw), n)
# create lists of json objs for model outputs
for idx in xrange(n):
coeffs.append(json.dumps({var: model.params[idx, k]
for k, var in enumerate(ind_vars)}))
stand_errs.append(json.dumps({var: model.bse[idx, k]
for k, var in enumerate(ind_vars)}))
t_vals.append(json.dumps({var: model.tvalues[idx, k]
for k, var in enumerate(ind_vars)}))
t_vals.append(json.dumps({var: model.tvalues[idx, k]
for k, var in enumerate(ind_vars)}))
plpy.notice(str(zip(coefficients, stand_errs, t_vals,
predicted, residuals, r_squared, rowid, bw)))
return zip(coefficients, stand_errs, t_vals,
predicted, residuals, r_squared, rowid, bw)
return zip(coeffs, stand_errs, t_vals,
predicted, residuals, r_squared, rowid, bw)