chainladder.BootstrapODPSample

class chainladder.BootstrapODPSample(n_sims=1000, n_periods=-1, hat_adj=True, drop=None, drop_high=None, drop_low=None, drop_valuation=None, random_state=None)

Class to generate bootstrap samples of triangles. Currently this Only supports ‘single’ triangles (single index and single column).

Parameters:

n_sims: int (default=1000)

Number of simulations to generate

n_periods: integer, optional (default=-1)

number of origin periods to be used in the ldf average calculation. For all origin periods, set n_periods=-1

hat_adj: bool (default=False)

Adjust standardized Pearson residuals with the hat matrix adjustment factor. If false, Degree of Freedom adjustment is used.

drop: tuple or list of tuples

Drops specific origin/development combination(s) from residual sample

drop_high: bool or list of bool (default=None)

Drops highest link ratio(s) from residual sample

drop_low: bool or list of bool (default=None)

Drops lowest link ratio(s) from residual sample

drop_valuation: str or list of str (default=None)

Drops specific valuation periods from residual sample. str must be date convertible.

random_state: int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

resampled_triangles_: Triangle

A set of triangles represented by each simulation

scale_:

The scale parameter to be used in generating process risk

Methods

fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_backend(backend[, inplace, deep])	Converts triangle array_backend.
set_fit_request(*[, sample_weight])	Request metadata passed to the fit method.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
to_json()	Serializes triangle object to json format
to_pickle(path[, protocol])	Serializes triangle object to pickle.
transform(X)	If X and self are of different shapes, align self to X, else return self.

fit
pipe