adelie.adelie_core.glm.GlmBinomialLogit64#

class adelie.adelie_core.glm.GlmBinomialLogit64#

Core GLM class for Binomial logit family.

Methods

`__init__`(self, arg0, arg1)
`gradient`(self, arg0, arg1)	Computes the gradient of the negative loss function.
`hessian`(self, arg0, arg1, arg2)	Computes a diagonal hessian majorization of the loss function.
`inv_hessian_gradient`(self, arg0, arg1, arg2, ...)	Computes the inverse hessian of the (negative) gradient of the loss function.
`loss`(self, arg0)	Computes the loss function.
`loss_full`(self)	Computes the loss function at the saturated model.

Attributes

`is_multi`	`True` if it defines a multi-response GLM family.
`name`	Name of the GLM family.

__init__(self: adelie.adelie_core.glm.GlmBinomialLogit64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n]]) → None#

gradient(self: adelie.adelie_core.glm.GlmBase64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes the gradient of the negative loss function.

Computes the (negative) gradient \(-\nabla \ell(\eta)\).

Parameters:

eta(n,) ndarray: Natural parameter.
grad(n,) ndarray: The gradient to store.

hessian(self: adelie.adelie_core.glm.GlmBase64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n]], arg2: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a diagonal hessian majorization of the loss function.

Computes a diagonal majorization of the hessian \(\nabla^2 \ell(\eta)\).

Note

Although the hessian is in general a fully dense matrix, we only require the user to output a diagonal matrix. It is recommended that the diagonal matrix dominates the full hessian. However, in some cases, the diagonal of the hessian suffices even when it does not majorize the hessian. Interestingly, most hessian computations become greatly simplified when evaluated using the gradient.

Parameters:

eta(n,) ndarray: Natural parameter.
grad(n,) ndarray: Gradient as in gradient() method.
hess(n,) ndarray: The hessian to store.

inv_hessian_gradient(self: adelie.adelie_core.glm.GlmBase64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n]], arg2: numpy.ndarray[numpy.float64[1, n]], arg3: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes the inverse hessian of the (negative) gradient of the loss function.

Computes \(-(\nabla^2 \ell(\eta))^{-1} \nabla \ell(\eta)\).

Note

Unlike the hessian() method, this function may use the full hessian matrix. The diagonal hessian majorization is provided in case it speeds-up computations, but it can be ignored. The default implementation simply computes grad / (hess + eps * (hess <= 0)) where eps is given by hessian_min.

Parameters:

eta(n,) ndarray: Natural parameter.
grad(n,) ndarray: Gradient as in gradient() method.
hess(n,) ndarray: Hessian as in hessian() method.
inv_hess_grad(n,) ndarray: The inverse hessian gradient to store.

loss(self: adelie.adelie_core.glm.GlmBase64, arg0: numpy.ndarray[numpy.float64[1, n]]) → float#

Computes the loss function.

Computes \(\ell(\eta)\).

Parameters:

eta(n,) ndarray: Natural parameter.

Returns:

lossfloat: Loss.

loss_full(self: adelie.adelie_core.glm.GlmBase64) → float#

Computes the loss function at the saturated model.

Computes \(\ell(\eta^\star)\) where \(\eta^\star\) is the minimizer.

Returns:

lossfloat: Loss at the saturated model.

is_multi#: True if it defines a multi-response GLM family. It is always False for this class.

name#: Name of the GLM family.