slope::Logistic Class Reference

The Logistic class represents a logistic loss function. More...

#include <logistic.h>

Inheritance diagram for slope::Logistic:

Collaboration diagram for slope::Logistic:

Public Member Functions
double	loss (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y) override
	Calculates the loss for the logistic loss function.
double	loss (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y, const Eigen::VectorXd &observation_weights) override
	Calculates the loss averaged by observation weights.
double	dual (const Eigen::MatrixXd &theta, const Eigen::MatrixXd &y, const Eigen::VectorXd &w) override
	Calculates the dual for the logistic loss function.
Eigen::MatrixXd	dualPoint (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y, bool fit_intercept) override
	Constructs a dual candidate from a linear predictor.
Eigen::MatrixXd	dualPoint (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y, bool fit_intercept, const Eigen::VectorXd &observation_weights) override
	Constructs a dual candidate with weighted intercept stationarity.
Eigen::MatrixXd	hessianDiagonal (const Eigen::MatrixXd &eta) override
	Calculates hessian diagonal.
Eigen::MatrixXd	preprocessResponse (const Eigen::MatrixXd &y) override
	Preprocesses the response for the quadratic model.
Eigen::MatrixXd	link (const Eigen::MatrixXd &mu) override
	The link function.
Eigen::MatrixXd	inverseLink (const Eigen::MatrixXd &eta) override
	The inverse link function, also known as the mean function.
Eigen::MatrixXd	predict (const Eigen::MatrixXd &eta) override
	Return predicted response, that is 0 or 1 depending on the predicted probabilities.
Public Member Functions inherited from slope::Loss
virtual	~Loss ()=default
	Destructor for the Loss class.
Eigen::MatrixXd	residual (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y)
	Calculates the generalized residual.
virtual void	updateWeightsAndWorkingResponse (Eigen::MatrixXd &w, Eigen::MatrixXd &z, const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y)
	Updates weights and working response.
virtual void	updateWeightsAndWorkingResponse (Eigen::MatrixXd &w, Eigen::MatrixXd &z, const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y, const Eigen::VectorXd &observation_weights)
	Updates the IRLS approximation including observation weights.
virtual void	updateIntercept (Eigen::VectorXd &beta0, const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y)
	Updates the intercept with a gradient descent update.
virtual double	deviance (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y)
	Computes deviance, which is 2 times the difference between the loglikelihood of the model and the loglikelihood of the null (intercept-only) model.
virtual double	deviance (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y, const Eigen::VectorXd &observation_weights)
	Computes deviance averaged by observation weights.

Additional Inherited Members
Protected Member Functions inherited from slope::Loss
Eigen::MatrixXd	residualOnSupport (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y, const Eigen::VectorXd &observation_weights)
	Evaluates family residuals only on positive-weight observations.
	Loss (double lipschitz_constant)
	Constructs an loss function with specified Lipschitz constant.
Eigen::MatrixXd	domainSafePoint (const Eigen::MatrixXd &mean, const Eigen::MatrixXd &y, double lower, double upper) const
	Enforces scalar intercept stationarity without leaving a mean domain.
Eigen::MatrixXd	domainSafePoint (const Eigen::MatrixXd &mean, const Eigen::MatrixXd &y, double lower, double upper, const Eigen::VectorXd &observation_weights) const

Detailed Description

The Logistic class represents a logistic loss function.

The logistic loss function is used for binary classification problems. It calculates the loss, dual, residual, and updates weights and working response.

Definition at line 18 of file logistic.h.

Constructor & Destructor Documentation

Logistic()

slope::Logistic::Logistic ( )

inlineexplicit

Definition at line 21 of file logistic.h.

Member Function Documentation

dual()

double slope::Logistic::dual ( const Eigen::MatrixXd & theta, const Eigen::MatrixXd & y, const Eigen::VectorXd & w )

overridevirtual

Calculates the dual for the logistic loss function.

Parameters

theta	The dual variables.
y	The true labels.
w	Weights

Returns

The dual value.

Implements slope::Loss.

dualPoint() [1/2]

Eigen::MatrixXd slope::Logistic::dualPoint ( const Eigen::MatrixXd & eta, const Eigen::MatrixXd & y, bool fit_intercept )

overridevirtual

Constructs a dual candidate from a linear predictor.

When an intercept is fitted, the candidate must satisfy the corresponding zero-sum constraint. Losses with restricted conjugate domains override this method to preserve those domains while enforcing the constraint.

Parameters

eta	Linear predictor.
y	Response.
fit_intercept	Whether the model has an unpenalized intercept.

Returns

A candidate dual point before regularizer scaling.

Reimplemented from slope::Loss.

dualPoint() [2/2]

Eigen::MatrixXd slope::Logistic::dualPoint ( const Eigen::MatrixXd & eta, const Eigen::MatrixXd & y, bool fit_intercept, const Eigen::VectorXd & observation_weights )

overridevirtual

Constructs a dual candidate with weighted intercept stationarity.

Reimplemented from slope::Loss.

hessianDiagonal()

Eigen::MatrixXd slope::Logistic::hessianDiagonal ( const Eigen::MatrixXd & eta )

overridevirtual

Calculates hessian diagonal.

Parameters

eta	Linear predictor

Returns

A matrix of ones (n x m)

Implements slope::Loss.

inverseLink()

Eigen::MatrixXd slope::Logistic::inverseLink ( const Eigen::MatrixXd & eta )

overridevirtual

The inverse link function, also known as the mean function.

Parameters

eta

Mean

Returns

\( \frac{1}{1 + \exp(-\eta)} \)

Implements slope::Loss.

link()

Eigen::MatrixXd slope::Logistic::link ( const Eigen::MatrixXd & mu )

overridevirtual

The link function.

Parameters

mu

Mean

Returns

\( \log(\frac{\mu}{1 - \mu}) \)

Implements slope::Loss.

loss() [1/2]

double slope::Logistic::loss ( const Eigen::MatrixXd & eta, const Eigen::MatrixXd & y )

overridevirtual

Calculates the loss for the logistic loss function.

Parameters

eta	The predicted values.
y	The true labels.

Returns

The loss value.

Implements slope::Loss.

loss() [2/2]

double slope::Logistic::loss ( const Eigen::MatrixXd & eta, const Eigen::MatrixXd & y, const Eigen::VectorXd & observation_weights )

overridevirtual

Calculates the loss averaged by observation weights.

Reimplemented from slope::Loss.

predict()

Eigen::MatrixXd slope::Logistic::predict ( const Eigen::MatrixXd & eta )

overridevirtual

Return predicted response, that is 0 or 1 depending on the predicted probabilities.

Parameters

eta	The linear predictor

Returns

The predicted response

Implements slope::Loss.

preprocessResponse()

Eigen::MatrixXd slope::Logistic::preprocessResponse ( const Eigen::MatrixXd & y )

overridevirtual

Preprocesses the response for the quadratic model.

Checks if the response is in {0, 1} and converts it otherwise

Parameters

y	Response vector (in {0,1})

Returns

Modified response.

Implements slope::Loss.

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The documentation for this class was generated from the following file:

• include/slope/losses/logistic.h