Eureqa.jl

Symbolic regression built on Julia, and interfaced by Python. Uses regularized evolution and simulated annealing.

Backstory: we used the original eureqa in our paper to convert a graph neural network into an analytic equation describing dark matter overdensity. However, eureqa is GUI-only, doesn't allow for user-defined operators, has no distributed capabilities, and has become proprietary. Thus, the goal of this package is to have an open-source symbolic regression tool as efficient as eureqa, while also exposing a configurable python interface.

The algorithms here implement regularized evolution, as in AutoML-Zero, but with additional algorithmic changes such as simulated annealing, and classical optimization of constants.

Installation

Install Julia. Then, at the command line, install the Optim package via: julia -e 'import Pkg; Pkg.add("Optim")'. For python, you need to have Python 3, numpy, and pandas installed.

Running:

Quickstart

import numpy as np
from eureqa import eureqa

# Dataset
X = 2*np.random.randn(100, 5)
y = 2*np.cos(X[:, 3]) + X[:, 0]**2 - 2

# Learn equations
equations = eureqa(X, y, niterations=5)

...

print(equations)

which gives:

   Complexity       MSE                                                Equation
0           5  1.947431                          plus(-1.7420927, mult(x0, x0))
1           8  0.486858           plus(-1.8710494, plus(cos(x3), mult(x0, x0)))
2          11  0.000000  plus(plus(mult(x0, x0), cos(x3)), plus(-2.0, cos(x3)))

API

What follows is the API reference for running the numpy interface. Note that most parameters here have been tuned with ~1000 trials over several example equations, so you don't need to tune them yourself. However, you should adjust threads, niterations, binary_operators, unary_operators to your requirements.

The program will output a pandas DataFrame containing the equations, mean square error, and complexity. It will also dump to a csv at the end of every iteration, which is hall_of_fame.csv by default. It also prints the equations to stdout.

You can add more operators in operators.jl, or use default Julia ones. Make sure all operators are defined for scalar Float32. Then just specify the operator names in your call, as above. You can also change the dataset learned on by passing in X and y as numpy arrays to eureqa(...).

eureqa(X=None, y=None, threads=4, niterations=20,
   ncyclesperiteration=int(default_ncyclesperiteration),
   binary_operators=["plus", "mult"], unary_operators=["cos", "exp", "sin"],
   alpha=default_alpha, annealing=True, fractionReplaced=default_fractionReplaced,
   fractionReplacedHof=default_fractionReplacedHof, npop=int(default_npop),
   parsimony=default_parsimony, migration=True, hofMigration=True
   shouldOptimizeConstants=True, topn=int(default_topn),
   weightAddNode=default_weightAddNode, weightDeleteNode=default_weightDeleteNode,
   weightDoNothing=default_weightDoNothing,
   weightMutateConstant=default_weightMutateConstant,
   weightMutateOperator=default_weightMutateOperator,
   weightRandomize=default_weightRandomize, weightSimplify=default_weightSimplify,
   timeout=None, equation_file='hall_of_fame.csv', test='simple1', maxsize=20)

Run symbolic regression to fit f(X[i, :]) ~ y[i] for all i.

Arguments:

X: np.ndarray, 2D array. Rows are examples, columns are features.
y: np.ndarray, 1D array. Rows are examples.
threads: int, Number of threads (=number of populations running). You can have more threads than cores - it actually makes it more efficient.
niterations: int, Number of iterations of the algorithm to run. The best equations are printed, and migrate between populations, at the end of each.
ncyclesperiteration: int, Number of total mutations to run, per 10 samples of the population, per iteration.
binary_operators: list, List of strings giving the binary operators in Julia's Base, or in operator.jl.
unary_operators: list, Same but for operators taking a single Float32.
alpha: float, Initial temperature.
annealing: bool, Whether to use annealing. You should (and it is default).
fractionReplaced: float, How much of population to replace with migrating equations from other populations.
fractionReplacedHof: float, How much of population to replace with migrating equations from hall of fame.
npop: int, Number of individuals in each population
parsimony: float, Multiplicative factor for how much to punish complexity.
migration: bool, Whether to migrate.
hofMigration: bool, Whether to have the hall of fame migrate.
shouldOptimizeConstants: bool, Whether to numerically optimize constants (Nelder-Mead/Newton) at the end of each iteration.
topn: int, How many top individuals migrate from each population.
weightAddNode: float, Relative likelihood for mutation to add a node
weightDeleteNode: float, Relative likelihood for mutation to delete a node
weightDoNothing: float, Relative likelihood for mutation to leave the individual
weightMutateConstant: float, Relative likelihood for mutation to change the constant slightly in a random direction.
weightMutateOperator: float, Relative likelihood for mutation to swap an operator.
weightRandomize: float, Relative likelihood for mutation to completely delete and then randomly generate the equation
weightSimplify: float, Relative likelihood for mutation to simplify constant parts by evaluation
timeout: float, Time in seconds to timeout search
equation_file: str, Where to save the files (.csv separated by |)
test: str, What test to run, if X,y not passed.
maxsize: int, Max size of an equation.

Returns:

pd.DataFrame, Results dataframe, giving complexity, MSE, and equations (as strings).

TODO

Consider adding mutation for constant<->variable
Use NN to generate weights over all probability distribution conditional on error and existing equation, and train on some randomly-generated equations
Performance:
- Use an enum for functions instead of storing them?
- Current most expensive operations:
  - Calculating the loss function - there is duplicate calculations happening.
  - Declaration of the weights array every iteration
Write our own tree copy operation; deepcopy() is the slowest operation by far.
Hyperparameter tune
Create a benchmark for accuracy
Add interface for either defining an operation to learn, or loading in arbitrary dataset.
- Could just write out the dataset in julia, or load it.
Create a Python interface
Explicit constant optimization on hall-of-fame
- Create method to find and return all constants, from left to right
- Create method to find and set all constants, in same order
- Pull up some optimization algorithm and add it. Keep the package small!
Create a benchmark for speed
Simplify subtrees with only constants beneath them. Or should I? Maybe randomly simplify sometimes?
Record hall of fame
Optionally (with hyperparameter) migrate the hall of fame, rather than current bests
Test performance of reduced precision integers
- No effect
Create struct to pass through all hyperparameters, instead of treating as constants
- Make sure doesn't affect performance