Author: Ritchie Lee, Carnegie Mellon University Silicon Valley, email@example.com
In the face of big data, gaining insights by manually sifting through data is no longer practical. Machine learning methods typically rely on opaque statistical models. Although these may provide good input/output behavior, the results are not conducive to human understanding. We explore machine learning tasks guided by a grammar for interpretability. We learn expressions derived from the grammar, optimizing both fit to data and interpretability.
ExprSearch is a collection of algorithms for solving grammar-based expression discovery problems. These problems are traditionally tackled using Genetic Programming. Other methods also exist. The following algorithms are currently available:
A problem extends
ExprProblem and defines various specifics for a given grammar optimization problem, including:
The following example problems are available.
Julia 0.5 is required.
These packages are automatically fetched by the build script:
The algorithms all follow the same form. We will use Monte Carlo as an example.
using ExprSearch.MC #make MC algorithm available problem = Symbolic(...) #problem is defined by a type that extends ExprProblem p = MCESParams(...) #choose the algorithm by populating its input params object that extends SearchParams result = exprsearch(p::SearchParams, problem::ExprProblem) #algorithm dispatched on p::SearchParams #result is of type MCSearchResult that extends SearchResult
To learn a decision tree using grammar-based expression search as a subroutine, first set the params for the expression search algorithm, then pass that as an argument into the gbdt params object.
data = dataset("MyDataset") grammar, symtable, _, _ = Grammars.time_series_realonly1(...) fitness_function = FitnessFunctions.Gini_NumNodes(w_metric, w_num_nodes) problem = GBDMProblem(data, grammar, fitness_function, symtable) gp_params = GPESParams(...) gbdt_params = GBDTParams(problem, length(data), gp_params, max_gbdt_depth, ...) result = induce_tree(gbdt_params)
over 3 years ago