Linear generalized Huber estimator compatible with scikit-learn.

# ghlestimator

Linear Generalized Huber Regressor compatible with scikit-learn. A detailed explanation of the underlying generalized Huber objective function can be found here.

The Generalized Huber Regressor depends on the definition of a invertible link function g and optimizes a term proportional to (y - ginv(X'w))**2 for samples where |g(y) - (X'w)| <= epsilon and a term proportional to |y - ginv(X'w)| for samples where |g(y) - (X'w)| > epsilon, where w is to be optimized and ginv denotes the inverse of g.

## Parameters

class GeneralizedHuberRegressor(epsilon=1.0,max_iter=100,tol=1e-5, scale=10,


epsilon : float, default 1.0

The parameter epsilon defines the crossover between the rmse type of loss
and the mae type of loss.


max_iter : int, default 100

Maximum number of iterations that
scipy.optimize.minimize(method="L-BFGS-B") should run for.


fit_intercept : bool, default True

Whether or not to fit the intercept.


tol : float, default 1e-5

The iteration will stop when max{|proj g_i | i = 1, ..., n} <= tol
where pg_i is the i-th component of the projected gradient.


scale : float, default 10.0

Preconditioner for better numerical stability. Input array is internally
divided by scale.


The link function 'g', it's inverse 'ginv' and the derivative of the
latter 'ginvp' have to be specified as callables.
The default link function is g(x) = sign(x)log(1+|x|).


## Attributes

coef_ : array, shape (n_features,)

Fitted coefficients got by optimizing the generalized Huber loss.


intercept_ : float

The bias.


n_iter_ : int

Number of iterations that
scipy.optimize.minimize(method="L-BFGS-B") has run for.


## Methods

fit(self, X, y)

Fit the model to the given training features X and target y both given as
ndarrays.


predict(self, X)

Predict using the fitted linear model.


score(self, X, y)

Return the coefficient of determination R^2 of the prediction.


## Installation

Use the package manager pip to install ghlestimator.

pip install ghlestimator


## Usage

from ghlestimator import GeneralizedHuberRegressor

ghl = GeneralizedHuberRegressor() # initializes default ghl estimator
ghl.fit(X, y) # fit on features X and target y
ghl.score(X, y) # compute the R^2 score
ghl.predict(X) # make pedictions


MIT

## Project details

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