chainladder-python

Enhancements:

• Added predict functionality IBNR methods

Bug fixes:

• Fixed mutation in TriangleGroupBy aggregate methods
• Fixed bug in Triangle.grain method
• Fixed issue #43 User-Specified columns should be honored
• Fixed issue #44 cdf_ labels should be 'xx-Ult'
• Fixed issue #45 MackChainladder.total_mack_std_error_ TypeError

Enhancements:

• Added ability to_pickle and read_pickle functionality to Triangles and Estimators.

Bug Fix:

• Minor bug in valuation slicing that restated the valuation_date of triangle incorrectly.

Enhancements:

• #34 added a decay period to TailConstant to allow for run-off patterns beyond the end of the triangle for Actual vs. Expected analysis
• #41 Added Triangle.dev_to_val() and Triangle.val_to_dev() to allow switching between a left-aligned (development) and right-aligned (valuation) Triangle

Bug fixes:

• #40 Fixed bug that did not replace column in Triangle when assigned to existing column name

Enhancements:

• Triangle.grain works with incremental and cumulative triangles now
• Triangle.trend has been changed to trend along valuation period
• Added substantial functionality to Development allowing end user to omit any specified link ratios.
• Converted origin and valuation from DateTimeIndex to PeriodIndex so that they work better with pandas datetime functionality.

Bug Fixes:

• 'regression' and 'simple' averages were swapped in Development, this has been corrected
• Fixed bug that didn't allow TailCurve to be fit directly to a Triangle.

Other:

• Refactored WeightedRegression class to be in sklearn style

Enhancements

• Added Triangle.valuation accessor to be used similar to origin and development. This allows for slicing valuations (e.g. diagonals).
• Made triangle arithmetic more robust.
• Extended tail patterns from a point estimate to a one year run-off plus a point estimate. This is to facilitate Actual Vs Expected analysis for the year following an analysis.
• Created Triangle.values property to align with pandas-style syntax.
• Backward compatible to python 3.5

Enhancements:

• Created BootstrapODPSample class to perform ODP Bootstrap sampling of triangles
• Added more aggregate functions to Triangle including: mean, median, max, min, prod, var, std
• Added several more pandas passthrough methods including: to_dict, unstack, pivot, drop_duplicates, describe, melt
• Added functionality to the Triangle.to_frame method to allow any 4D Triangle to be recast as a DataFrame as long as any two of its axes are of length=1.

Bug Fixes:

• Bug fix in Gridsearch that didn't allow for passing of a sample_weight when used in conjunction with Pipeline
• Altered Triangle.rename method to be more consistent with the pandas implementation

• hotfix to CapeCod to make it work properly
• CapeCod Unit Test
• added sample_weight to GridSearch.fit()
• Lot's o' documentation

• converted sample datasets to csv format from pkl format

minor bug fixes

What's New

• Modifed core.Triangle to use parameter index in place of keys and columns in place of values. This is done to promote consistency with the pandas API as well as allowing for values property to be used in extended functionality of the Triangle
• Added warnings to development.Development to warn on failure of Mack Standard Error when only one period is used in the estimation of LDFs
• Added workflow.Pipelne and workflow.GridSearch for scikit-learnesque scenario testing.

Significantly overhauled API to be more consistent with pandas as scikit-learn.

Added Munich Chainladder functionality.

Initial release with appropriate setup.py configuration

This release includes the Mack Chainladder model.