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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