NormodOLS#

class sknormod.NormodOLS(*, fit_intercept=True, copy_X=True, n_jobs=None, positive=False)#

Normod in OLS. Fits a homoskedastic Gaussian using ordinary least squares. It is just scikit-learn LinearRegression, so the parameters are the same as to LinearRegression, but the fitted model is DistributionalRegressor, i.e., it has methods to get centiles or z-scores etc.

Parameters:

fit_interceptbool, default=True: Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations.
copy_Xbool, default=True: Whether to copy the input data; if False, it may be overwritten.
n_jobsint or None, default=None: The number of jobs to use for the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.
positivebool, default=False: When set to True, forces the coefficients to be positive.

Examples

>>> from sknormod.base import NormodOLS
>>> import numpy as np
>>> X = np.arange(100).reshape(100, 1)
>>> y = np.zeros((100, ))
>>> normod = NormodOLS()
>>> normod.fit(X, y)
NormodOLS()

Methods

`fit`(X, y)	Fit linear model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict_distr_p`(X, p)	Predict the percentiles of the distribution.
`predict_distr_params`(X)	Predict distribution parameters for given input samples.
`score`(X, y)	Compute the logarithmic score of the predicted distribution parameters against the true values.
`set_params`(**params)	Set the parameters of this estimator.
`transform_to_p`(X, y)	Transform the target values to percentiles.
`transform_to_z`(X, y)	Transform the target values to z-scores.

fit(X, y)#

Fit linear model.

Parameters:

Xarray-like or sparse matrix of shape (n_samples, n_features): Training data.
yarray-like of shape (n_samples,): Target values.

Returns:

selfobject: Fitted estimator.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict_distr_p(X, p)#

Predict the percentiles of the distribution.

Parameters:

Xarray-like or sparse matrix of shape (n_samples, n_features): The input samples.
parray-like of shape (n_percentiles,): The percentiles to predict.

Returns:

percentileslist of floats: The predicted percentiles.

predict_distr_params(X)#

Predict distribution parameters for given input samples.

Parameters:

Xarray-like or sparse matrix of shape (n_samples, n_features): Input samples.

Returns:

paramsarray-like of shape (n_samples, 2): Predicted distribution parameters where each row represents [mu, sigma].

score(X, y)#: Compute the logarithmic score of the predicted distribution parameters against the true values.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: