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dswah / pyGAM

[HELP REQUESTED] Generalized Additive Models in Python
https://pygam.readthedocs.io
Apache License 2.0
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AttributeError: 'NoneType' object has no attribute 'get_params' #273

Open michaeljfazio opened 4 years ago

michaeljfazio commented 4 years ago

I'm attempting to run a LogisticGAM for multiple data sets in a loop as such:

for key, vessel_df in subset_df.groupby(level=0):
    vessel_df = vessel_df.copy()
    X = vessel_df[['latitude', 'longitude', 'day']].values
    y = vessel_df['seen'].values
    gam = LogisticGAM(te(0,1) + f(2)).gridsearch(X,y)
    ...

My X values are in the form:

215057000
[[35.9498     14.8061      4.        ]
 [35.9498     14.8061      4.        ]
 [35.9501     14.8062      4.        ]
 ...
 [35.9493     14.80875     1.        ]
 [35.94968889 14.80867778  1.        ]
 [35.94997273 14.80865455  1.        ]]

and my y values are in the form:

[ True False  True False False False False  True False  True  True  True
  True  True  True  True  True  True  True False False  True False  True
  True  True  True  True  True  True  True  True  True  True  True  True
  True  True  True  True  True  True  True  True  True  True  True False
 False False False False False ...]

Most datasets process without issue. I hit one dataset in particular however which results in the following error:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-252-3a4de86de893> in <module>()
     15     X = vessel_df[['latitude', 'longitude', 'day']].values
     16     y = vessel_df['seen'].values
---> 17     gam = LogisticGAM(te(0,1) + f(2)).gridsearch(X,y, keep_best=True, progress=False)

~/anaconda3/envs/python3/lib/python3.6/site-packages/pygam/pygam.py in gridsearch(self, X, y, weights, return_scores, keep_best, objective, progress, **param_grids)
   1921             self.set_params(deep=True,
   1922                             force=True,
-> 1923                             **best_model.get_params(deep=True))
   1924         if return_scores:
   1925             return OrderedDict(zip(models, scores))

AttributeError: 'NoneType' object has no attribute 'get_params'

It appears that best_model is under some circumstances None. Thus, this operation throws an unrecoverable Error and bails out of processing the rest of the data in my data set.

I can prevent the Error from happening by using:

gam = LogisticGAM(te(0,1) + f(2)).gridsearch(X,y, keep_best=False)

Though I'm not entirely sure what effect that has on the gridsearch operation itself.

I believe the offending line of code is here:

https://github.com/dswah/pyGAM/blob/29425798e13651f03c1fd3cc1096071cd752403a/pygam/pygam.py#L1946

michaeljfazio commented 4 years ago

A more general question for @dswah is if this project is still being actively developed by yourself? It's hard to tell what could be human errors vs potential bugs 🤦

shyamcody commented 4 years ago

Can you provide a small sample from where the error can be reproduced? Currently, I can not reproduce this error. Pygam == 0.8.0 numpy == 1.18.1 python == 3.6.9 scikit-learn == 0.22.1 scipy == 1.4.1

alessioarena commented 3 years ago

I had a similar issue, trying to fit a GammaGAM using gridsearch. Turned out few of the models did not converge actually converge, but all of the ones that did converge had score=np.inf.

I am reporting one of the models, but my very limited knowledge of this type of statistics tells me that is simply not the right model to fit.

Maybe would be worth capturing this case of all np.inf scores and provide an informative message to the user?

 'm_features': 14,
 'edof_per_coef': array([9.93304195e-01, 9.73450354e-01, 8.37501105e-01, 1.24155502e-02,
        9.73335983e-01, 6.62577638e-01, 4.62581498e-01, 1.79131924e-02,
        4.52513971e-02, 3.17708351e-01, 1.26445528e-02, 2.84219452e-01,
        9.68476716e-01, 2.95486749e-02, 3.05148555e-01, 5.29838552e-01,
        1.91864774e-01, 2.17500491e-03, 1.44377102e-02, 8.09849215e-01,
        9.50971019e-03, 9.23589282e-01, 7.37266469e-03, 8.70123566e-01,
        3.30357987e-03, 3.52345050e-01, 4.12186917e-03, 2.08808998e-02,
        9.54180736e-01, 1.97330286e-22]),
 'edof': 11.589669830831653,
 'scale': 3.898812179664143,
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 'AIC': -inf,
 'AICc': -inf,
 'pseudo_r2': OrderedDict([('explained_deviance', nan),
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 'GCV': inf,
 'UBRE': None,
 'loglikelihood': inf,
 'deviance': inf,
 'p_values': [0.00016579268901795174,
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