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Merged
TaylorOshan merged 7 commits into from
Jul 3, 2018
Merged

Adding summary output #18

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e73a2ac
fork
Ziqi-Li Jun 21, 2018
a9dd093
gwr sum done
Ziqi-Li Jun 21, 2018
9439373
summary for mgwr
Ziqi-Li Jun 21, 2018
22e7d8b
summary
Ziqi-Li Jun 21, 2018
9ec1612
rm pyc
Ziqi-Li Jun 21, 2018
6b2f1b4
aicc for global
Ziqi-Li Jun 22, 2018
3eac546
add adj t and alpha for mgwr
Ziqi-Li Jun 25, 2018
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27 changes: 23 additions & 4 deletions mgwr/gwr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
from spglm.utils import cache_readonly
from .diagnostics import get_AIC, get_AICc, get_BIC, corr
from .kernels import *
from .summary import *

fk = {'gaussian': fix_gauss, 'bisquare': fix_bisquare, 'exponential': fix_exp}
ak = {'gaussian': adapt_gauss, 'bisquare': adapt_bisquare, 'exponential': adapt_exp}
Expand Down Expand Up @@ -1153,6 +1154,16 @@ def predictions(self):
predictions = np.sum(P*self.params, axis=1).reshape((-1,1))
return predictions

def summary(self):
"""
Print out GWR summary
"""
summary = summaryModel(self) + summaryGLM(self) + summaryGWR(self)
print(summary)
return



class GWRResultsLite(object):
"""
Lightweight GWR that computes the minimum diagnostics needed for bandwidth
Expand Down Expand Up @@ -1780,15 +1791,15 @@ def spatial_variability(self, selector, n_iters=1000, seed=None):
the number of Monte Carlo iterations to include for
the tests of spatial variability.

seed : int
seed : int
optional parameter to select a custom seed to ensure
stochastic results are replicable. Default is none
which automatically sets the seed to 5536

Returns
-------
Returns
-------

p values : list
p values : list
a list of psuedo p-values that correspond to the model
parameter surfaces. Allows us to assess the
probability of obtaining the observed spatial
Expand Down Expand Up @@ -1824,3 +1835,11 @@ def spatial_variability(self, selector, n_iters=1000, seed=None):

p_vals = (np.sum(np.array(SDs) > init_sd, axis=0) / float(n_iters))
return p_vals

def summary(self):
"""
Print out MGWR summary
"""
summary = summaryModel(self) + summaryGLM(self) + summaryMGWR(self)
print(summary)
return
150 changes: 150 additions & 0 deletions mgwr/summary.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,150 @@
import numpy as np
from spglm.family import Gaussian, Binomial, Poisson
from spglm.glm import GLM
from .diagnostics import get_AICc

def summaryModel(self):
summary = '=' * 75 + '\n'
summary += "%-54s %20s\n" % ('Model type', self.family.__class__.__name__)
summary += "%-60s %14d\n" % ('Number of observations:', self.n)
summary += "%-60s %14d\n\n" % ('Number of covariates:', self.k)
return summary

def summaryGLM(self):

XNames = ["X"+str(i) for i in range(self.k)]
glm_rslt = GLM(self.model.y,self.model.X,constant=False,family=self.family).fit()

summary = "%s\n" %('Global Regression Results')
summary += '-' * 75 + '\n'

if isinstance(self.family, Gaussian):
summary += "%-62s %12.3f\n" % ('Residual sum of squares:', glm_rslt.deviance)
summary += "%-62s %12.3f\n" % ('Log-likelihood:', glm_rslt.llf)
summary += "%-62s %12.3f\n" % ('AIC:', glm_rslt.aic)
summary += "%-62s %12.3f\n" % ('AICc:', get_AICc(glm_rslt))
summary += "%-62s %12.3f\n" % ('BIC:', glm_rslt.bic)
summary += "%-62s %12.3f\n" % ('R2:', glm_rslt.D2)
summary += "%-62s %12.3f\n\n" % ('Adj. R2:', glm_rslt.adj_D2)
else:
summary += "%-62s %12.3f\n" % ('Deviance:', glm_rslt.deviance)
summary += "%-62s %12.3f\n" % ('Log-likelihood:', glm_rslt.llf)
summary += "%-62s %12.3f\n" % ('AIC:', glm_rslt.aic)
summary += "%-62s %12.3f\n" % ('AICc:', get_AICc(glm_rslt))
summary += "%-62s %12.3f\n" % ('BIC:', glm_rslt.bic)
summary += "%-62s %12.3f\n" % ('Percent deviance explained:', glm_rslt.D2)
summary += "%-62s %12.3f\n\n" % ('Adj. percent deviance explained:', glm_rslt.adj_D2)

summary += "%-31s %10s %10s %10s %10s\n" % ('Variable', 'Est.', 'SE' ,'t(Est/SE)', 'p-value')
summary += "%-31s %10s %10s %10s %10s\n" % ('-'*31, '-'*10 ,'-'*10, '-'*10,'-'*10)
for i in range(self.k):
summary += "%-31s %10.3f %10.3f %10.3f %10.3f\n" % (XNames[i], glm_rslt.params[i], glm_rslt.bse[i], glm_rslt.tvalues[i], glm_rslt.pvalues[i])
summary += "\n"
return summary

def summaryGWR(self):
XNames = ["X"+str(i) for i in range(self.k)]

summary = "%s\n" %('Geographically Weighted Regression (GWR) Results')
summary += '-' * 75 + '\n'

if self.model.fixed:
summary += "%-50s %20s\n" % ('Spatial kernel:', 'Fixed ' + self.model.kernel)
else:
summary += "%-54s %20s\n" % ('Spatial kernel:', 'Adaptive ' + self.model.kernel)

summary += "%-62s %12.3f\n" % ('Bandwidth used:', self.model.bw)

summary += "\n%s\n" % ('Diagnostic information')
summary += '-' * 75 + '\n'

if isinstance(self.family, Gaussian):

summary += "%-62s %12.3f\n" % ('Residual sum of squares:', self.resid_ss)
summary += "%-62s %12.3f\n" % ('Effective number of parameters (trace(S)):', self.tr_S)
summary += "%-62s %12.3f\n" % ('Degree of freedom (n - trace(S)):', self.df_model)
summary += "%-62s %12.3f\n" % ('Sigma estimate:', np.sqrt(self.sigma2))
summary += "%-62s %12.3f\n" % ('Log-likelihood:', self.llf)
summary += "%-62s %12.3f\n" % ('AIC:', self.aic)
summary += "%-62s %12.3f\n" % ('AICc:', self.aicc)
summary += "%-62s %12.3f\n" % ('BIC:', self.bic)
summary += "%-62s %12.3f\n" % ('R2:', self.R2)
else:
summary += "%-62s %12.3f\n" % ('Effective number of parameters (trace(S)):', self.tr_S)
summary += "%-62s %12.3f\n" % ('Degree of freedom (n - trace(S)):', self.df_model)
summary += "%-62s %12.3f\n" % ('Log-likelihood:', self.llf)
summary += "%-62s %12.3f\n" % ('AIC:', self.aic)
summary += "%-62s %12.3f\n" % ('AICc:', self.aicc)
summary += "%-62s %12.3f\n" % ('BIC:', self.bic)
#summary += "%-60s %12.6f\n" % ('Percent deviance explained:', 0)


summary += "%-62s %12.3f\n" % ('Adj. alpha (95%):', self.adj_alpha[1])
summary += "%-62s %12.3f\n" % ('Adj. critical t value (95%):', self.critical_tval(self.adj_alpha[1]))

summary += "\n%s\n" % ('Summary Statistics For GWR Parameter Estimates')
summary += '-' * 75 + '\n'
summary += "%-20s %10s %10s %10s %10s %10s\n" % ('Variable', 'Mean' ,'STD', 'Min' ,'Median', 'Max')
summary += "%-20s %10s %10s %10s %10s %10s\n" % ('-'*20, '-'*10 ,'-'*10, '-'*10 ,'-'*10, '-'*10)
for i in range(self.k):
summary += "%-20s %10.3f %10.3f %10.3f %10.3f %10.3f\n" % (XNames[i], np.mean(self.params[:,i]) ,np.std(self.params[:,i]),np.min(self.params[:,i]) ,np.median(self.params[:,i]), np.max(self.params[:,i]))

summary += '=' * 75 + '\n'

return summary



def summaryMGWR(self):

XNames = ["X"+str(i) for i in range(self.k)]

summary = ''
summary += "%s\n" %('Multi-Scale Geographically Weighted Regression (MGWR) Results')
summary += '-' * 75 + '\n'

if self.model.fixed:
summary += "%-50s %20s\n" % ('Spatial kernel:', 'Fixed ' + self.model.kernel)
else:
summary += "%-54s %20s\n" % ('Spatial kernel:', 'Adaptive ' + self.model.kernel)

summary += "%-54s %20s\n" % ('Criterion for optimal bandwidth:', self.model.selector.criterion)

if self.model.selector.rss_score:
summary += "%-54s %20s\n" % ('Score of Change (SOC) type:', 'RSS')
else:
summary += "%-54s %20s\n" % ('Score of Change (SOC) type:', 'Smoothing f')

summary += "%-54s %20s\n\n" % ('Termination criterion for MGWR:', self.model.selector.tol_multi)

summary += "%s\n" %('MGWR bandwidths')
summary += '-' * 75 + '\n'
summary += "%-15s %14s %10s %16s %16s\n" % ('Variable', 'Bandwidth', 'ENP_j','Adj t-val(95%)','Adj alpha(95%)')
for j in range(self.k):
summary += "%-14s %15.3f %10.3f %16.3f %16.3f\n" % (XNames[j], self.model.bw[j], self.ENP_j[j],self.critical_tval()[j],self.adj_alpha_j[j,1])

summary += "\n%s\n" % ('Diagnostic information')
summary += '-' * 75 + '\n'

summary += "%-62s %12.3f\n" % ('Residual sum of squares:', self.resid_ss)
summary += "%-62s %12.3f\n" % ('Effective number of parameters (trace(S)):', self.tr_S)
summary += "%-62s %12.3f\n" % ('Degree of freedom (n - trace(S)):', self.df_model)

summary += "%-62s %12.3f\n" % ('Sigma estimate:', np.sqrt(self.sigma2))
summary += "%-62s %12.3f\n" % ('Log-likelihood:', self.llf)
summary += "%-62s %12.3f\n" % ('AIC:', self.aic)
summary += "%-62s %12.3f\n" % ('AICc:', self.aicc)
summary += "%-62s %12.3f\n" % ('BIC:', self.bic)

summary += "\n%s\n" % ('Summary Statistics For MGWR Parameter Estimates')
summary += '-' * 75 + '\n'
summary += "%-20s %10s %10s %10s %10s %10s\n" % ('Variable', 'Mean' ,'STD', 'Min' ,'Median', 'Max')
summary += "%-20s %10s %10s %10s %10s %10s\n" % ('-'*20, '-'*10 ,'-'*10, '-'*10 ,'-'*10, '-'*10)
for i in range(self.k):
summary += "%-20s %10.3f %10.3f %10.3f %10.3f %10.3f\n" % (XNames[i], np.mean(self.params[:,i]) ,np.std(self.params[:,i]),np.min(self.params[:,i]) ,np.median(self.params[:,i]), np.max(self.params[:,i]))

summary += '=' * 75 + '\n'
return summary