chainladder.DevelopmentConstant

chainladder.DevelopmentConstant#

class chainladder.DevelopmentConstant(patterns=None, style='ldf', callable_axis=0, groupby=None)#
A Estimator that allows for including of external patterns into a

Development style model. When this estimator is fit against a triangle, only the grain of the existing triangle is retained.

Parameters:
patterns: dict or callable

A dictionary key:value representation of age(in months):value. If callable is supplied, callable must return a dict for each element of the callable axis

style: string, optional (default=’ldf’)

Type of pattern given to the Estimator. Options include ‘cdf’ or ‘ldf’.

callable_axis: 0 or 1

If a callable is supplied, the axis (index or column) along which to apply the callable. If patterns is not a callable, then this parameter is ignored.

groupby:

option to group levels of the triangle index together for the purposes estimating patterns. If omitted, each level of the triangle index will receive its own patterns.

Attributes:
ldf_: Triangle

The estimated loss development patterns

cdf_: Triangle

The estimated cumulative development patterns

Methods

fit(X[, y, sample_weight])

Fit the model with X.

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.

pipe