datascienceinc/Skater: Enable Interpretability via Rule Extraction(BRL)

Skater till now has been an interpretation engine to enable post-hoc model evaluation and interpretation. With this PR Skater starts its journey to support interpretable models. Rule List algorithms are highly popular in the space of Interpretable Models because the trained models are represented as simple decision lists. In the latest release, we enable support for Bayesian Rule Lists(BRL). The probabilistic classifier( estimating P(Y=1|X) for each X ) optimizes the posterior of a Bayesian hierarchical model over the pre-mined rules.

Usage Example:

 from skater.core.global_interpretation.interpretable_models.brlc import BRLC
 import pandas as pd
 from sklearn.datasets.mldata import fetch_mldata
 input_df = fetch_mldata("diabetes")
 ...
 Xtrain, Xtest, ytrain, ytest = train_test_split(input_df, y, test_size=0.20, random_state=0)

 sbrl_model = BRLC(min_rule_len=1, max_rule_len=10, iterations=10000, n_chains=20, drop_features=True)
 # Train a model, by default discretizer is enabled. So, you wish to exclude features then exclude them using
 # the undiscretize_feature_list parameter
 model = sbrl_model.fit(Xtrain, ytrain, bin_labels="default")