VotingChainladder

class chainladder.VotingChainladder(estimators, *, weights=None, default_weighting=None, n_jobs=None, verbose=False)

Prediction voting chainladder method for unfitted estimators.

A voting chainladder is an ensemble meta-estimator that fits several base chainladder methods, each on the whole triangle. Then it combines the individual predictions based on a matrix of weights to form a final prediction.

Read more in the User Guide.

Added in version 0.8.0.

Parameters:

estimators: list of (str, estimator) tuples

Invoking the fit method on the VotingChainladder will fit clones of those original estimators that will be stored in the class attribute self.estimators_. An estimator can be set to 'drop' using set_params.

weights: array callable or dict, default=None

array: Numpy array of shape (index, columns, origin, n_estimators). Minimum shape required is (origin, n_estimators). Lower dimensional weight arrays will have missing dimensions repeated to match the shape of the triangle. list: List of weights where each weight is a list of length n_estimators. dict: A dictionary where the origin is mapped to a weighting tuple. Missing origin periods will be given default_weighting. callable: A callable that returns weighting tuples. None uses default_weighting.

default_weighting: tuple of shape (n_estimators, ), default=None

Default weighting to use where a weight was not provided or if weights is None. None uses a typle of all ones which is equivalent to averaging the predictions of the estimators.

n_jobs: int, default=None

The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. for more details.

verbose: bool, default=False

If True, the time elapsed while fitting will be printed as it is completed.

Attributes:

estimators_: list of chainladder estimators

The collection of fitted sub-estimators as defined in estimators that are not ‘drop’.

named_estimators_: Bunch

Attribute to access any fitted sub-estimators by name.

Examples

import numpy as np

raa = cl.load_sample('RAA')
cl_ult = cl.Chainladder().fit(raa).ultimate_  # Chainladder Ultimate
apriori = cl_ult * 0 + (float(cl_ult.sum()) / 10)  # Mean Chainladder Ultimate
bcl = cl.Chainladder()
bf = cl.BornhuetterFerguson()
cc = cl.CapeCod()
estimators = [('bcl', bcl), ('bf', bf), ('cc', cc)]
weights = np.array([[0.6, 0.2, 0.2]] * 4 + [[0, 0.5, 0.5]] * 3 + [[0, 0, 1]] * 3)
vot = cl.VotingChainladder(estimators=estimators, weights=weights)
vot.fit(raa, sample_weight=apriori)
print(vot.ultimate_)

              2261
1981  18834.000000
1982  16875.500226
1983  24058.534810
1984  28542.580970
1985  28236.843134
1986  19905.317262
1987  18947.245455
1988  23106.943030
1989  20004.502125
1990  21605.832631

fit(X, y=None, sample_weight=None)

Fit the estimators.

Parameters:

XTriangle

Loss data to which the voting will be applied.

yNone

Ignored

sample_weightTriangle, default=None

Exposure to be used in the calculation. Required if any of the estimators are exposure based.

Returns:

selfobject

Fitted estimator.

fit_transform(X, y=None, sample_weight=None)

Fit and return predictions for VotingChainladder

Parameters:

XTriangle

Loss data to which the model will be applied.

yNone

Ignored

sample_weightTriangle, default=None

Exposure to be used in the calculation. Required if any of the estimators are exposure based.

Returns:

X_new: Triangle

Loss data with VotingChainladder ultimate applied

predict(X, sample_weight=None)

Predicts the voting chainladder ultimate on a new triangle X

Predicts the ultimate for each of the estimators and combines them into a single ultimate based on the weights given.

Parameters:

XTriangle

Loss data to which the model will be applied.

sample_weightTriangle, default=None

Exposure to be used in the calculation. Required if any of the estimators are exposure based.

Returns:

X_new: Triangle

Loss data with VotingChainladder ultimate applied

transform(X, sample_weight=None)

Return predictions for VotingChainladder

Parameters:

XTriangle

Loss data to which the model will be applied.

sample_weightTriangle, default=None

Exposure to be used in the calculation. Required if any of the estimators are exposure based.

Returns:

X_new: Triangle

Loss data with VotingChainladder ultimate applied

Inherited Methods

VotingChainladder.fit_predict
VotingChainladder.get_metadata_routing	Get metadata routing of this object.
VotingChainladder.get_params	Get the parameters of an estimator from the ensemble.
VotingChainladder.intersection	Given two Triangles with mismatched indices, this method aligns their indices
VotingChainladder.pipe
VotingChainladder.set_backend	Converts triangle array_backend.
VotingChainladder.set_output	Set output container.
VotingChainladder.set_params	Set the parameters of an estimator from the ensemble.
VotingChainladder.to_json	Serializes triangle object to json format
VotingChainladder.to_pickle	Serializes triangle object to pickle.
VotingChainladder.validate_X
VotingChainladder.validate_weight