module mlmodel.quantile_regression

Inheritance diagram of mlinsights.mlmodel.quantile_regression

Short summary

module mlinsights.mlmodel.quantile_regression

Implements a quantile linear regression.

source on GitHub

Classes

class truncated documentation
QuantileLinearRegression Quantile Linear Regression or linear regression trained with norm L1. This class inherits from sklearn.linear_models.LinearRegression. …

Static Methods

staticmethod truncated documentation
_epsilon  

Methods

method truncated documentation
__init__ Parameters ———- fit_intercept: boolean, optional, default True whether to calculate the …
fit Fits a linear model with L1 norm which is equivalent to a quantile regression. Parameters …
score Returns Mean absolute error regression loss. Parameters ———- X : array-like, shape = (n_samples, …

Documentation

Implements a quantile linear regression.

source on GitHub

class mlinsights.mlmodel.quantile_regression.QuantileLinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1, delta=0.0001, max_iter=10, quantile=0.5, verbose=False)[source]

Bases: sklearn.linear_model.base.LinearRegression

Quantile Linear Regression or linear regression trained with norm L1. This class inherits from sklearn.linear_models.LinearRegression. See notebook Quantile Regression.

Norm L1 is chosen if quantile=0.5, otherwise, for quantile=\rho, the following error is optimized:

\sum_i \rho |f(X_i) - Y_i|^- + (1-\rho) |f(X_i) - Y_i|^+

where |f(X_i) - Y_i|^-= \max(Y_i - f(X_i), 0) and |f(X_i) - Y_i|^+= \max(f(X_i) - Y_i, 0). f(i) is the prediction, Y_i the expected value.

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Parameters:
  • fit_intercept (boolean, optional, default True) – whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (e.g. data is expected to be already centered).
  • normalize (boolean, optional, default False) – This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False.
  • copy_X (boolean, optional, default True) – If True, X will be copied; else, it may be overwritten.
  • n_jobs (int, optional, default 1) – The number of jobs to use for the computation. If -1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems.
  • max_iter (int, optional, default 1) – The number of iteration to do at training time. This parameter is specific to the quantile regression.
  • delta (float, optional, default 0.0001) – Used to ensure matrices has an inverse (M + delta*I).
  • quantile (float, by default 0.5,) – determines which quantile to use to estimate the regression.
  • verbose (bool, optional, default False) – Prints error at each iteration of the optimisation.

source on GitHub

__abstractmethods__ = frozenset()
__init__(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1, delta=0.0001, max_iter=10, quantile=0.5, verbose=False)[source]
Parameters:
  • fit_intercept (boolean, optional, default True) – whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (e.g. data is expected to be already centered).
  • normalize (boolean, optional, default False) – This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False.
  • copy_X (boolean, optional, default True) – If True, X will be copied; else, it may be overwritten.
  • n_jobs (int, optional, default 1) – The number of jobs to use for the computation. If -1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems.
  • max_iter (int, optional, default 1) – The number of iteration to do at training time. This parameter is specific to the quantile regression.
  • delta (float, optional, default 0.0001) – Used to ensure matrices has an inverse (M + delta*I).
  • quantile (float, by default 0.5,) – determines which quantile to use to estimate the regression.
  • verbose (bool, optional, default False) – Prints error at each iteration of the optimisation.

source on GitHub

_abc_impl = <_abc_data object>
static _epsilon(y_true, y_pred, quantile, sample_weight=None)[source]
fit(X, y, sample_weight=None)[source]

Fits a linear model with L1 norm which is equivalent to a quantile regression.

Parameters:
  • X (numpy array or sparse matrix of shape [n_samples,n_features]) – Training data
  • y (numpy array of shape [n_samples, n_targets]) – Target values. Will be cast to X’s dtype if necessary
  • sample_weight (numpy array of shape [n_samples]) – Individual weights for each sample
Returns:

  • self (returns an instance of self.)
  • The training produces the following attributes
  • as results of the training.
  • The implementation is not the most efficient
  • as it calls multiple times method fit
  • from sklearn.linear_models.LinearRegression.
  • Data gets checked and rescaled each time.
  • The optimization follows the algorithm
  • `Iteratively reweighted least squares <https (//en.wikipedia.org/wiki/Iteratively_reweighted_least_squares>`_.)
  • It is described in French at
  • `Régression quantile <http (//www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx3/notebooks/td_note_2017_2.html>`_.)
coef_

Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.

Type:array, shape (n_features, ) or (n_targets, n_features)
intercept_

Independent term in the linear model.

Type:array
n_iter_

Number of iterations at training time.

Type:int

source on GitHub

score(X, y, sample_weight=None)[source]

Returns Mean absolute error regression loss.

Parameters:
  • X (array-like, shape = (n_samples, n_features)) – Test samples.
  • y (array-like, shape = (n_samples) or (n_samples, n_outputs)) – True values for X.
  • sample_weight (array-like, shape = [n_samples], optional) – Sample weights.
Returns:

score – mean absolute error regression loss
Return type:

float

source on GitHub