6.9.7. statsmodels.sandbox.tests.test_gam¶
Tests for gam.AdditiveModel and GAM with Polynomials compared to OLS and GLM
Created on Sat Nov 05 14:16:07 2011
Author: Josef Perktold License: BSD
6.9.7.1. Notes¶
- TODO: TestGAMGamma: has test failure (GLM looks good),
adding log-link didn’t help resolved: gamma doesn’t fail anymore after tightening the
convergence criterium (rtol=1e-6)- TODO: TestGAMNegativeBinomial: rvs generation doesn’t work,
- nbinom needs 2 parameters
- TODO: TestGAMGaussianLogLink: test failure,
but maybe precision issue, not completely off
but something is wrong, either the testcase or with the link >>> tt3.__class__ <class ‘__main__.TestGAMGaussianLogLink’> >>> tt3.res2.mu_pred.mean() 3.5616368292650766 >>> tt3.res1.mu_pred.mean() 3.6144278964707679 >>> tt3.mu_true.mean() 34.821904835958122 >>> >>> tt3.y_true.mean() 2.685225067611543 >>> tt3.res1.y_pred.mean() 0.52991541684645616 >>> tt3.res2.y_pred.mean() 0.44626406889363229
6.9.7.1.1. one possible change¶
- add average, integral based tests, instead of or additional to sup
- for example mean squared error for mu and eta (predict, fittedvalues) or mean absolute error, what’s the scale for this? required precision?
- this will also work for real non-parametric tests
example: Gamma looks good in average bias and average RMSE (RMISE)
>>> tt3 = _estGAMGamma()
>>> np.mean((tt3.res2.mu_pred - tt3.mu_true))/tt3.mu_true.mean()
-0.0051829977497423706
>>> np.mean((tt3.res2.y_pred - tt3.y_true))/tt3.y_true.mean()
0.00015255264651864049
>>> np.mean((tt3.res1.y_pred - tt3.y_true))/tt3.y_true.mean()
0.00015255538823786711
>>> np.mean((tt3.res1.mu_pred - tt3.mu_true))/tt3.mu_true.mean()
-0.0051937668989744494
>>> np.sqrt(np.mean((tt3.res1.mu_pred - tt3.mu_true)**2))/tt3.mu_true.mean()
0.022946118520401692
>>> np.sqrt(np.mean((tt3.res2.mu_pred - tt3.mu_true)**2))/tt3.mu_true.mean()
0.022953913332599746
>>> maxabs = lambda x: np.max(np.abs(x))
>>> maxabs((tt3.res1.mu_pred - tt3.mu_true))/tt3.mu_true.mean()
0.079540546242707733
>>> maxabs((tt3.res2.mu_pred - tt3.mu_true))/tt3.mu_true.mean()
0.079578857986784574
>>> maxabs((tt3.res2.y_pred - tt3.y_true))/tt3.y_true.mean()
0.016282852522951426
>>> maxabs((tt3.res1.y_pred - tt3.y_true))/tt3.y_true.mean()
0.016288391235613865
6.9.7.3. Classes¶
AdditiveModel(exog[, smoothers, weights, family]) |
additive model with non-parametric, smoothed components |
BaseAM() |
|
BaseGAM() |
|
CheckAM |
|
CheckGAM |
|
Dummy |
|
GAM |
alias of Model |
GLM(endog, exog[, family, offset, exposure, ...]) |
Generalized Linear Models class |
OLS(endog[, exog, missing, hasconst]) |
A simple ordinary least squares model. |
TestAdditiveModel() |
|
TestGAMBinomial() |
|
TestGAMGamma() |
|
TestGAMPoisson() |