A standard for assumption vectors

Many assumptions/hypterparameters of Estimators take on a vector-like feel along an axis of a Triangle. However, there is a great deal of inconsistency in implementation across estimators with at least five different approaches being used.

For example:

import chainladder as cl
import numpy as np
raa = cl.load_sample('raa')

# List-style approach
cl.Development(n_periods=[3]*5+[-1]*4).fit(raa)
# Dict-style approach
cl.DevelopmentConstant(
    patterns={k: 3.0**(12/k) for k in range(12, 120, 12)},
    style='cdf').fit(raa) 
# tuple-style approach
cl.TailCurve(fit_period=(3, None)).fit(raa)
# callable approach
cl.DevelopmentConstant(
    patterns=lambda x : {k: 3.0**(12/k) for k in range(12, 120, 12)},
    style='cdf').fit(raa)
#  numpy array approach
bcl, bf, cc = cl.Chainladder(), cl.BornhuetterFerguson(), cl.CapeCod()
estimators = [('bcl', bcl), ('bf', bf), ('cc', cc)]
weights = np.array([[1, 0, 0]] * 4 + [[0, 1, 0]] * 3 + [[0, 0, 1]] * 3)
cl.VotingChainladder(estimators=estimators, weights=weights) 

We need to determine a flexible standard that can can be used by all estimators. Multiple approaches should be fine but should be implemented everywhere so that users can use the style that matches their mental model. We will also need to retain backward compatibility of any deprecated approaches until the next major release.

[cbalona]

In that case I think MethodBase should just have a private function that coerces the inputs to an array, and then each estimator can do with the array what it needs to after having been refactored to handle the array.

[jbogaardt]

makes sense