cox_regression.client module

Client module for cox regression

class cox_regression.client.Client(pool, max_iter=25, server_name='server')

Bases: object

The client class, representing data owning clients in the learning process. Based on logistic regression client.

__init__(pool, max_iter=25, server_name='server')

Initializes the client.

Parameters:

• pool (Pool) – The communication pool.

• max_iter (int) – The max number of epochs

• server_name (str) – The name of the server in the communication pool

async compute_statistics(include_bins=False)

Compute statistics for each coefficient: standard error, z-value and p-value.

Parameters:

include_bins (bool) – Whether to include parameters for the time bins.

Return type:

list[dict[str, float]]

Returns:

A list containing a dictionary for each covariate. The dictionary contains three values: ‘se’ containing the standard error, ‘z’ containing z-value (Wald statistic) ‘p’ containing the p-value.

property model_: ndarray[tuple[int, ...], dtype[float64]]

Return the fitted model.

Returns:

the fitted model

Raises:

ValueError – when model is not yet computed.

async run(covariates, times, events)

Perform the learning process.

Parameters:

• covariates (ndarray[tuple[int, ...], dtype[floating[Any]]]) – The covariates of the patients. Can have multiple columns.

• times (ndarray[tuple[int, ...], dtype[floating[Any]]]) – The failure/censoring times.

• events (ndarray[tuple[int, ...], dtype[bool]]) – The event indicators. Should contain boolean values.

Return type:

ndarray[tuple[int, ...], dtype[float64]]

Returns:

The resulting model.

async run_time_varying(ids, covariates, start_times, end_times, events)

Perform the learning process.

Parameters:

• ids (ndarray[tuple[int, ...], dtype[int64]]) – The patient ids. Can be used to specify time-varying covariates. The id is unique per patient and a patient can have multiple rows. However, a patient id can have only one failure.

• covariates (ndarray[tuple[int, ...], dtype[floating[Any]]]) – The covariates of the patients. Can have multiple columns.

• start_times (ndarray[tuple[int, ...], dtype[floating[Any]]]) – The start time of the interval.

• end_times (ndarray[tuple[int, ...], dtype[floating[Any]]]) – The end time of the interval.

• events (ndarray[tuple[int, ...], dtype[bool]]) – The event indicators. Should contain boolean values.

Return type:

ndarray[tuple[int, ...], dtype[float64]]

Returns:

The resulting model.

property stacked_data_: ndarray[tuple[int, ...], dtype[floating[Any]]]

Return the stacked data.

Returns:

the stacked data.

Raises:

ValueError – when stacked data is not set.

property stacked_target_: ndarray[tuple[int, ...], dtype[bool]]

Return the stacked target vector.

Returns:

the stacked target vector.

Raises:

ValueError – when stacked target is not set.