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View Code? Open in Web Editor NEW[HELP REQUESTED] Generalized Additive Models in Python
Home Page: https://pygam.readthedocs.io
License: Apache License 2.0
[HELP REQUESTED] Generalized Additive Models in Python
Home Page: https://pygam.readthedocs.io
License: Apache License 2.0
pg 164 in Wood introduces this concept. elaborated in appendix A.5
pg 177
H is any positive semidefinite
matrix, which may be zero, but may also be used to allow lower bounds to
be imposed on the smoothing parameters, or to regularize an ill-conditioned problem.
For example, if it is required that λ1 ≥ 0.1 and λ2 ≥ 10, then we could set H =
0.1S1 + 10S2.
this will make it easy to make custom GAMs!
ensure that AIC is computed right:
no constants missing from log likelihood, and deviance is defined correctly.
this is importnat to be able to compare models when data has different scales.
Hello,
I was hoping to try out your implementation of GAM. However I am not sure how I can do that.
Thanks,
Vinod
need to find optimal lambda vector somehow :)
I want to make predictions of time series. Can this project process time series data ?
see how jacques implemented his versioning in
https://github.com/jwkvam/bowtie/blob/master/bowtie/__init__.py
any distribution might have a known scale, and the API should be abstract enough to allow this.
there are some threads where people specifically request GAMs.
want quick way of checking model.
for the 2nd order difference penalty to make sense, the knots have to be evenly spaced.
easy.
for example, logistic GAM shouldnt be allowed to fit on y in whole real line
add these puppies.
eliminate corresponding columns and rows from U, D, Vt
however, would like models to stay in main module
lol.
we dont need to compute the whole A (influence, hat) matrix for the edof estimation.
try doing just the diagonal.
create a few standard penalty matrices to chose from:
this is a thing that people do.
the edges are wrong
X_train = np.array([1,2,3,4])
y_train = np.array([2,3,4,5])
gam = LogisticGAM()
gam.fit(X_train, y_train)
I run the above codes and raised an error"AssertionError: y data is not in domain of link function"
I don't know how to use your API.
it'll be more clear this way
maybe someone wants to use their own penalty
should not be allowed to fit n basis functions with less than n data-points.
woohoo!
needed for generating knots, basis functions, penalty matrices
estimated scale should go here otherwise no one will know where to find it.
its almost free.
you should be able to penalize each feature differently, duh.
Using piecewise constant splines, no difference penalty
right now its buried in the penalty matrix method.
it should be somewhere else.
class will read better if all properties that are specific to glms are self.glm_X_
not self.X_glm_
maybe even QR decomposition ?
kind of already do it...
one should be able to specify multiple penalties per feature, and each penalty should get its own lambda
would like to be able to see these per feature function
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