tick.survival.ModelCoxRegPartialLik¶

class tick.survival.ModelCoxRegPartialLik[source]¶

Partial likelihood of the Cox regression model (proportional hazards). This class gives first order information (gradient and loss) for this model.

Attributes

features : numpy.ndarray, shape=(n_samples, n_features), (read-only)

The features matrix

times : numpy.ndarray, shape = (n_samples,), (read-only)

Obverved times

censoring : numpy.ndarray, shape = (n_samples,), (read-only)

Boolean indicator of censoring of each sample. True means true failure, namely non-censored time

n_samples : int (read-only)

Number of samples

n_features : int (read-only)

Number of features

n_failures : int (read-only)

Number of true failure times

n_coeffs : int (read-only)

Total number of coefficients of the model

censoring_rate : float

The censoring_rate (percentage of ???)

Notes

There is no intercept in this model

__init__()[source]¶

Initialize self. See help(type(self)) for accurate signature.

fit(features: numpy.ndarray, times: numpy.array, censoring: numpy.array) → tick.base_model.model.Model[source]¶

Set the data into the model object

Parameters

features : numpy.ndarray, shape=(n_samples, n_features)

The features matrix

times : numpy.array, shape = (n_samples,)

Observed times

censoring : numpy.array, shape = (n_samples,)

Indicator of censoring of each sample. True means true failure, namely non-censored time. dtype must be unsigned short

Returns

output : ModelCoxRegPartialLik

The current instance with given data

grad(coeffs: numpy.ndarray, out: numpy.ndarray = None) → numpy.ndarray¶

Computes the gradient of the model at coeffs

Parameters

coeffs : numpy.ndarray

Vector where gradient is computed

out : numpy.ndarray or None

If None a new vector containing the gradient is returned, otherwise, the result is saved in out and returned

Returns

output : numpy.ndarray

The gradient of the model at coeffs

Notes

The fit method must be called to give data to the model, before using grad. An error is raised otherwise.

loss(coeffs: numpy.ndarray) → float¶

Computes the value of the goodness-of-fit at coeffs

Parameters

coeffs : numpy.ndarray

The loss is computed at this point

Returns

output : float

The value of the loss

Notes

The fit method must be called to give data to the model, before using loss. An error is raised otherwise.

loss_and_grad(coeffs: numpy.ndarray, out: numpy.ndarray = None) → tuple¶

Computes the value and the gradient of the function at coeffs

Parameters

coeffs : numpy.ndarray

Vector where the loss and gradient are computed

out : numpy.ndarray or None

If None a new vector containing the gradient is returned, otherwise, the result is saved in out and returned

Returns

loss : float

The value of the loss

grad : numpy.ndarray

The gradient of the model at coeffs

Notes

The fit method must be called to give data to the model, before using loss_and_grad. An error is raised otherwise.