DevelopmentConstant

class chainladder.DevelopmentConstant(patterns=None, style='ldf', callable_axis=0, groupby=None)

An 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: Literal[‘cdf’, ‘ldf’], optional (default=’ldf’)

Type of pattern given to the Estimator. cdf for cumulative development factors and ldf for (non-cumulative) loss development factors.

callable_axis: 0 or 1

If a callable is supplied, the axis, index (0) or column (1) 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

Examples

Commonly, we will have to use a pattern from another source, such as a pattern populated by the regulator to develop our triangle.

Once we have the pattern, we can use the DevelopmentConstant estimator to develop our triangle. Further IBNR models will use these patterns to develop triangles to ultimate.

xyz = cl.load_sample("xyz")
reported_patterns = {
    12: 4.0,
    24: 2.9,
    36: 1.8,
    48: 1.4,
    60: 1.2,
    72: 1.1,
    84: 1.03,
    96: 1.02,
    108: 1.005,
}
print(np.round(cl.DevelopmentConstant(patterns=reported_patterns, style="cdf").fit_transform(
    xyz["Incurred"]
).ldf_,4))
print(np.round(cl.DevelopmentConstant(patterns=reported_patterns, style="cdf").fit_transform(
    xyz["Incurred"]
).cdf_,4))

        12-24   24-36   36-48   48-60   60-72  72-84   84-96  96-108  108-120  120-132
(All)  1.3793  1.6111  1.2857  1.1667  1.0909  1.068  1.0098  1.0149    1.005      1.0
       12-Ult  24-Ult  36-Ult  48-Ult  60-Ult  72-Ult  84-Ult  96-Ult  108-Ult  120-Ult
(All)     4.0     2.9     1.8     1.4     1.2     1.1    1.03    1.02    1.005      1.0

When patterns vary by triangle index (such as LOBs), pass a callable to patterns and set callable_axis=0 to apply it along the index.

clrd = cl.load_sample("clrd")
agway = clrd.loc["Agway Ins Co", "CumPaidLoss"]
cdfs = {
    "comauto": [3.832, 1.874, 1.386, 1.181, 1.085, 1.043, 1.022, 1.013, 1.007, 1],
    "medmal": [24.168, 4.127, 2.103, 1.528, 1.275, 1.161, 1.088, 1.047, 1.018, 1],
    "othliab": [10.887, 3.416, 1.957, 1.433, 1.231, 1.119, 1.06, 1.031, 1.011, 1],
    "ppauto": [2.559, 1.417, 1.181, 1.084, 1.04, 1.019, 1.009, 1.004, 1.001, 1],
    "prodliab": [13.703, 5.613, 2.92, 1.765, 1.385, 1.177, 1.072, 1.034, 1.008, 1],
    "wkcomp": [4.106, 1.865, 1.418, 1.234, 1.141, 1.09, 1.056, 1.03, 1.01, 1],
}
patterns = pd.DataFrame(cdfs, index=range(12, 132, 12)).T
model = cl.DevelopmentConstant(
    patterns=lambda x: patterns.loc[x.loc["LOB"]].to_dict(),
    callable_axis=0,
    style="cdf",
)
print(model.fit_transform(agway).cdf_.loc["comauto"])

       12-Ult  24-Ult  36-Ult  48-Ult  60-Ult  72-Ult  84-Ult  96-Ult  108-Ult  120-Ult
(All)   3.832   1.874   1.386   1.181   1.085   1.043   1.022   1.013    1.007      1.0

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

Fit the model with X.

Parameters:

XTriangle-like

Set of LDFs to which the munich adjustment will be applied.

yIgnored

sample_weightIgnored

Returns:

selfobject

Returns the instance itself.

transform(X)

If X and self are of different shapes, align self to X, else return self.

Parameters:

XTriangle

The triangle to be transformed

Returns:

X_newNew triangle with transformed attributes.

Inherited Methods

DevelopmentConstant.fit_transform	Fit to data, then transform it.
DevelopmentConstant.get_metadata_routing	Get metadata routing of this object.
DevelopmentConstant.get_params	Get parameters for this estimator.
DevelopmentConstant.pipe	Apply func(self, *args, **kwargs).
DevelopmentConstant.set_backend	Converts triangle array_backend.
DevelopmentConstant.set_output	Set output container.
DevelopmentConstant.set_params	Set the parameters of this estimator.
DevelopmentConstant.to_json	Serializes triangle object to json format
DevelopmentConstant.to_pickle	Serializes triangle object to pickle.