Yes, I actually mentioned that on the roadmap thread. It should
definitely be added.
On 09/19/2018 06:17 PM, Guillaume Lemaître wrote:
Actually I don't see anything mentioning it in the road map currently.
Should it be added?
Sent from my phone - sorry to be brief and potential misspell.
*From:* luiz...@gmail.com
*Sent:* 19 September 2018 7:12 pm
*To:* scikit-learn@python.org
*Reply to:* scikit-learn@python.org
*Subject:* Re: [scikit-learn] Issues with clone for ensemble of,
classifiers
Guillaume - thank you for the comments. Indeed, an approach to
"freeze" a fitted classifier would solve our problem. The Github issue
seems to be inactive for a while, but I will check if anyone else is
working on it.
Luiz Gustavo
On Wed, Sep 19, 2018 at 12:02 PM <scikit-learn-requ...@python.org
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Today's Topics:
1. Re: Issues with clone for ensemble of classifiers
(Guillaume Lema?tre)
----------------------------------------------------------------------
Message: 1
Date: Wed, 19 Sep 2018 17:38:46 +0200
From: Guillaume Lema?tre <g.lemaitr...@gmail.com
<mailto:g.lemaitr...@gmail.com>>
To: Scikit-learn user and developer mailing list
<scikit-learn@python.org <mailto:scikit-learn@python.org>>
Subject: Re: [scikit-learn] Issues with clone for ensemble of
classifiers
Message-ID:
<CACDxx9gyszjJP-5ZB_bvH4nCkdn-sb6CCb=k2j_koonfpbq...@mail.gmail.com
<mailto:k2j_koonfpbq...@mail.gmail.com>>
Content-Type: text/plain; charset="UTF-8"
However, there is some issue to frozen a fitted classifier. You
can refer to:
https://github.com/scikit-learn/scikit-learn/issues/8370
with the associated discussion.
On Wed, 19 Sep 2018 at 17:34, Guillaume Lema?tre
<g.lemaitr...@gmail.com <mailto:g.lemaitr...@gmail.com>> wrote:
>
> Ups I misread your comment. I don't think that we have currently a
> mechanism to avoid cloning classifier internally.
> On Wed, 19 Sep 2018 at 17:31, Guillaume Lema?tre
<g.lemaitr...@gmail.com <mailto:g.lemaitr...@gmail.com>> wrote:
> >
> > You don't have anywhere in your class MyClassifier where you are
> > calling base_classifier.fit <http://classifier.fit>(...)
therefore when calling
> > base_classifier.predict <http://classifier.predict>(...) it
will let you know that you did not fit
> > it.
> >
> > On Wed, 19 Sep 2018 at 16:43, Luiz Gustavo Hafemann
<luiz...@gmail.com <mailto:luiz...@gmail.com>> wrote:
> > >
> > > Hello,
> > >
> > > I am one of the developers of a library for Dynamic Ensemble
Selection (DES) methods (the library is called DESlib), and we are
currently working to get the library fully compatible with
scikit-learn (to submit it to scikit-learn-contrib). We have
"check_estimator" working for most of the classes, but now I am
having problems to make the classes compatible with GridSearch /
other CV functions.
> > >
> > > One of the main use cases of this library is to facilitate
research on this field, and this led to a design decision that the
base classifiers are fit by the user, and the DES methods receive
a pool of base classifiers that were already fit (this allow users
to compare many DES techniques with the same base classifiers).
This is creating an issue with GridSearch, since the clone method
(defined in sklearn.base <http://sklearn.base>) is not cloning the
classes as we would like. It does a shallow (non-deep) copy of the
parameters, but we would like the pool of base classifiers to be
deep-copied.
> > >
> > > I analyzed this issue and I could not find a solution that
does not require changes on the scikit-learn code. Here is the
sequence of steps that cause the problem:
> > >
> > > GridSearchCV calls "clone" on the DES estimator (link)
> > > The clone function calls the "get_params" function of the
DES estimator (link, line 60). We don't re-implement this
function, so it gets all the parameters, including the pool of
classifiers (at this point, they are still "fitted")
> > > The clone function then clones each parameter with
safe=False (line 62). When cloning the pool of classifiers, the
result is a pool that is not "fitted" anymore.
> > >
> > > The problem is that, to my knowledge, there is no way for my
classifier to inform "clone" that a parameter should be always
deep copied. I see that other ensemble methods in sklearn always
fit the base classifiers within the "fit" method of the ensemble,
so this problem does not happen there. I would like to know if
there is a solution for this problem while having the base
classifiers fitted elsewhere.
> > >
> > > Here is a short code that reproduces the issue:
> > >
> > > ---------------------------
> > >
> > > from sklearn.model_selection import GridSearchCV,
train_test_split
> > > from sklearn.base <http://sklearn.base> import
BaseEstimator, ClassifierMixin
> > > from sklearn.ensemble <http://sklearn.ensemble> import
BaggingClassifier
> > > from sklearn.datasets <http://sklearn.datasets> import load_iris
> > >
> > >
> > > class MyClassifier(BaseEstimator, ClassifierMixin):
> > > def __init__(self, base_classifiers, k):
> > > self.base_classifiers = base_classifiers # Base
classifiers that are already trained
> > > self.k = k # Simulate a parameter that we want to
do a grid search on
> > >
> > > def fit(self, X_dsel, y_dsel):
> > > pass # Here we would fit any parameters for the
Dynamic selection method, not the base classifiers
> > >
> > > def predict(self, X):
> > > return self.base_classifiers.predict
<http://classifiers.predict>(X) # In practice the methods would do
something with the predictions of each classifier
> > >
> > >
> > > X, y = load_iris(return_X_y=True)
> > > X_train, X_dsel, y_train, y_dsel = train_test_split(X, y,
test_size=0.5)
> > >
> > > base_classifiers = BaggingClassifier()
> > > base_classifiers.fit <http://classifiers.fit>(X_train, y_train)
> > >
> > > clf = MyClassifier(base_classifiers, k=1)
> > >
> > > params = {'k': [1, 3, 5, 7]}
> > > grid = GridSearchCV(clf, params)
> > >
> > > grid.fit <http://grid.fit>(X_dsel, y_dsel) # Raises error
that the bagging classifiers are not fitted
> > >
> > > ---------------------------
> > >
> > > Btw, here is the branch that we are using to make the
library compatible with sklearn:
https://github.com/Menelau/DESlib/tree/sklearn-estimators. The
failing test related to this issue is in
https://github.com/Menelau/DESlib/blob/sklearn-estimators/deslib/tests/test_des_integration.py#L36
> > >
> > > Thanks in advance for any help on this case,
> > >
> > > Luiz Gustavo Hafemann
> > >
> > > _______________________________________________
> > > scikit-learn mailing list
> > > scikit-learn@python.org <mailto:scikit-learn@python.org>
> > > https://mail.python.org/mailman/listinfo/scikit-learn
> >
> >
> >
> > --
> > Guillaume Lemaitre
> > INRIA Saclay - Parietal team
> > Center for Data Science Paris-Saclay
> > https://glemaitre.github.io/
>
>
>
> --
> Guillaume Lemaitre
> INRIA Saclay - Parietal team
> Center for Data Science Paris-Saclay
> https://glemaitre.github.io/
--
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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