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[WIP] add decision function to LogisticGAM, for compat with sklearn multiclass
closes https://github.com/dswah/pyGAM/issues/196
-
[x] add
decision_function
method to theLogisticGAM
class for compatibility with sklearn'sOneVsRestClassifier
class -
[x] test
-
[ ] refactor terms so that all argument processing and sanitation is done during the
fit()
method
Codecov Report
Merging #213 into master will increase coverage by
0.06%
. The diff coverage is100%
.
@@ Coverage Diff @@
## master #213 +/- ##
==========================================
+ Coverage 94.86% 94.92% +0.06%
==========================================
Files 22 22
Lines 3056 3074 +18
==========================================
+ Hits 2899 2918 +19
+ Misses 157 156 -1
Impacted Files | Coverage Δ | |
---|---|---|
pygam/tests/test_GAM_methods.py | 100% <100%> (ø) |
:arrow_up: |
pygam/pygam.py | 94.9% <100%> (+0.14%) |
:arrow_up: |
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Can't use predict_proba with #196 example
Can't use predict_proba with #196 example
@903124 ahh good point. this is because the predict_proba
api for pygam didnt conform to sklearn. i am making an update.
Predict_proba is now working properly but functional terms is (still) broken. It only returns the predicted class of the first item if a functional term is given.
@903124 hmm thats interesting, but i dont completely understand.
can you please show me an example?
base_estimator = LogisticGAM(n_splines=5)
ensemble = OneVsRestClassifier(base_estimator, n_jobs=1)
ensemble.fit(X_train, y_train)
OneVsRestClassifier(estimator=LogisticGAM(callbacks=['deviance', 'diffs', 'accuracy'],
fit_intercept=True, max_iter=100, n_splines=5, terms='auto',
tol=0.0001, verbose=False),
n_jobs=1)
ensemble.predict_proba(X_test)
array([[1.19215720e-01, 9.04500103e-02, 1.77326087e-05, ...,
2.42330347e-03, 1.32568439e-01, 2.24134228e-01],
[1.66961024e-01, 1.26811544e-01, 1.92006828e-04, ...,
2.29616707e-03, 1.00314986e-01, 7.13239544e-02],
[3.84291300e-02, 3.18707670e-02, 4.24383742e-10, ...,
2.49137619e-03, 4.38926722e-01, 3.26176537e-01],
...,
[9.50068415e-02, 8.09269049e-02, 2.80842523e-06, ...,
2.86022038e-03, 1.46092712e-01, 2.74045716e-01],
[1.46784609e-01, 1.06025123e-01, 2.14491296e-06, ...,
2.60290652e-03, 1.48957364e-01, 1.20364635e-02],
[1.52544943e-02, 1.36320357e-02, 1.01640876e-11, ...,
1.68127415e-03, 3.55414339e-01, 5.55598935e-01]])
base_estimator = LogisticGAM(s(0)+f(1)+f(2)+f(3)+f(4)+s(5), n_splines=5)
ensemble = OneVsRestClassifier(base_estimator, n_jobs=1)
ensemble.fit(X_train, y_train)
OneVsRestClassifier(estimator=LogisticGAM(callbacks=['deviance', 'diffs', 'accuracy'],
fit_intercept=True, max_iter=100, n_splines=5,
terms=s(0) + f(1) + f(2) + f(3) + f(4) + s(5), tol=0.0001,
verbose=False),
n_jobs=1)
ensemble.predict_proba(X_test)
array([[0.10159551, 0.08261063, 0.00094507, ..., 0.00239048, 0.21650545,
0.23143207],
[0.10159551, 0.08261063, 0.00094507, ..., 0.00239048, 0.21650545,
0.23143207],
[0.10159551, 0.08261063, 0.00094507, ..., 0.00239048, 0.21650545,
0.23143207],
...,
[0.10159551, 0.08261063, 0.00094507, ..., 0.00239048, 0.21650545,
0.23143207],
[0.10159551, 0.08261063, 0.00094507, ..., 0.00239048, 0.21650545,
0.23143207],
[0.10159551, 0.08261063, 0.00094507, ..., 0.00239048, 0.21650545,
0.23143207]])
@903124 thanks for the example. i see what you mean
@903124 thanks for your comments!
i took a look inside, and it appears to be a complex fix.
scikit-learn defers any processing of arguments until the fix()
method is called, but i violated that rule with pygam's terms
.
fixing this is going to require a little bit more effort to defer all processing of arguments to the terms.
Is this still going to be merged into master? Would love to try it out!