Can I conclude that you are looking to implement a transformer? Note that
scikit learn transformers only act on X data, not on y data at the moment.
If this is what you need, then you need to implement a transform method for
your class. fit will still be necessary though, as the pipeline calls it
always.
HTH,
Michael
On Mon, Feb 16, 2015 at 4:20 PM, Pagliari, Roberto <rpagli...@appcomsci.com>
wrote:
> Broadly speaking, I would like to add my own custom function into a
> pipeline.
> However, my function is not really a classifier, nor a regressor.
> What do you think would be the best way to go about it? Is there a
> shortcut that does not require implementing the functions below?
>
>
> Thank you,
>
>
> -----Original Message-----
> From: Gael Varoquaux [mailto:gael.varoqu...@normalesup.org]
> Sent: Monday, February 16, 2015 12:56 AM
> To: scikit-learn-general@lists.sourceforge.net
> Subject: Re: [Scikit-learn-general] which methods do I need to implement
> for a regressor?
>
> You need fit, predict, and set_params. But set_params you can get by
> inheriting sklearn.base.BaseEstimator
>
> G
>
> On Mon, Feb 16, 2015 at 05:50:24AM +0000, Pagliari, Roberto wrote:
> > I'd like to implement my own regressor/classificator and possibly use
> > it in a pipeline.
>
> > do I need to implement all methods below or can some of them be missing?
>
> >
> ┌───────────────────────────────────┬─────────────────────────────────────────┐
> > │decision_function(X) │Predict using the linear model
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │densify() │Convert coefficient matrix to dense
> array│
> > │ │format.
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │fit(X, y │Fit linear model with Stochastic
> Gradient│
> > │[, coef_init, intercept_init, ...])│Descent.
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │fit_transform(X[, y]) │Fit to data, then transform it.
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │get_params([deep]) │Get parameters for this estimator.
> │
> > ├───────────────────────────────────┼─────────────────────────────────
> > ────────┤ │partial_fit(X, y[, sample_weight]) │Fit linear model with
> > Stochastic Gradient│
> > │ │Descent.
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │predict(X) │Predict using the linear model
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │score(X, y[, sample_weight]) │Returns the coefficient of
> determination │
> > │ │R^2 of the prediction.
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │set_params(*args, **kwargs) │
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │sparsify() │Convert coefficient matrix to
> sparse │
> > │ │format.
> │
> >
> ├───────────────────────────────────┼─────────────────────────────────────────┤
> > │transform(X[, threshold]) │Reduce X to its most important
> features. │
> > └───────────────────────────────────┴─────────────────────────────────
> > ────────┘
>
> > ----------------------------------------------------------------------
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>
> --
> Gael Varoquaux
> Researcher, INRIA Parietal
> Laboratoire de Neuro-Imagerie Assistee par Ordinateur
> NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
> Phone: ++ 33-1-69-08-79-68
> http://gael-varoquaux.info http://twitter.com/GaelVaroquaux
>
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