Chainladder

Chainladder#

class chainladder.Chainladder[source]#

The basic deterministic chainladder method.

Parameters:

None

Attributes:

X_:: returns X used to fit the triangle
ultimate_:: The ultimate losses per the method
ibnr_:: The IBNR per the method
full_expectation_:: The ultimates back-filled to each development period in X replacing the known data
full_triangle_:: The ultimates back-filled to each development period in X retaining the known data

Examples

Fit the chainladder method to a loss triangle and inspect the projected ultimates.

tr = cl.load_sample('ukmotor')
model = cl.Chainladder().fit(tr)
print(model.ultimate_)

              2261
12690.000000
13096.902024
14030.536767
13137.859861
13880.404483
16812.150646
20679.919151

The ibnr_ attribute is ultimate_ - latest_diagonal. The 2007 origin is fully developed in the data, so its IBNR is NaN.

print(model.ibnr_)

              2261
         NaN
  350.902024
 1037.536767
 2044.859861
 3663.404483
 7162.150646
14396.919151

full_triangle_ projects each origin to ultimate while preserving the known cells. Showing the last three origins and the first five development periods makes the data-to-projection boundary visible: whole-number cells are observed, decimal cells are projected.

print(model.full_triangle_.iloc[..., -3:, :5])

          12            24            36            48            60
4150.0   7897.000000  10217.000000  11719.970266  12853.969769
5102.0   9650.000000  12374.981102  14195.400857  15568.917781
6283.0  11870.058983  15221.943585  17461.165333  19150.670711

full_expectation_ is similar but replaces every cell, including the known ones, with the model’s expectation. Compare the 12 column above against the same slice below: the observed values have been overwritten.

print(model.full_expectation_.iloc[..., -3:, :5])

               12            24            36            48            60
4217.162588   7967.208127  10217.000000  11719.970266  12853.969769
5107.889530   9650.000000  12374.981102  14195.400857  15568.917781
6283.000000  11870.058983  15221.943585  17461.165333  19150.670711

fit(X, y=None, sample_weight=None)[source]#

Fit the model with X.

Parameters:

X: Triangle-like: Data to which the model will be applied.
y: Ignored
sample_weightIgnored

Returns:

self: object: Returns the instance itself.

Examples

Fitting returns the estimator itself, so it can be chained with attribute access.

tr = cl.load_sample('ukmotor')
cl.Chainladder().fit(tr)

Chainladder()

predict(X, sample_weight=None)[source]#

Predicts the chainladder ultimate on a new triangle X

Parameters:

X: Triangle: Loss data to which the model will be applied.
sample_weight: Triangle: Required exposure to be used in the calculation.

Returns:

X_new: Triangle: Loss data with chainladder ultimate applied

Examples

predict applies the fitted development patterns to a different Triangle. A common workflow is to fit on a prior-period view of the data (one diagonal removed) and then apply that model to the current Triangle. The ultimates differ from a freshly-fit model because the patterns reflect the older view.

tr = cl.load_sample('ukmotor')
tr_prior = tr[tr.valuation < tr.valuation_date]
model = cl.Chainladder().fit(tr_prior)
print(model.predict(tr).ultimate_)

              2261
12690.000000
12746.000000
13641.379750
12719.871218
13485.986574
16296.783586
20040.175415

Inherited Methods

`Chainladder.fit_predict`
`Chainladder.get_metadata_routing`	Get metadata routing of this object.
`Chainladder.get_params`	Get parameters for this estimator.
`Chainladder.intersection`	Given two Triangles with mismatched indices, this method aligns their indices
`Chainladder.pipe`	Apply `func(self, args, *kwargs)`.
`Chainladder.set_backend`	Converts triangle array_backend.
`Chainladder.set_params`	Set the parameters of this estimator.
`Chainladder.to_json`	Serializes triangle object to json format
`Chainladder.to_pickle`	Serializes triangle object to pickle.
`Chainladder.validate_X`
`Chainladder.validate_weight`

Chainladder

Contents

Chainladder#