SymRegg - Equality graph Assisted Search Technique for Equation Discovery (Symbolic Regression)
A Python package for symbolic regression using e-graphs. PySymRegg is built on top of the SymRegg algorithm and provides a scikit-learn compatible API for symbolic regression tasks.
This repository provides a CLI and a Python package for SymRegg with a scikit-learn compatible API for symbolic regression.
Instructions:
CLI
How to use
SymRegg - symbolic regression with e-graphs.
Usage: egraphSearch (-d|--dataset INPUT-FILE) [-t|--test ARG]
[-g|--generations GENS] (-a|--algorithm ALG)
(-s|--maxSize ARG) [-k|--folds ARG]
[--trace] [--loss ARG] [--opt-iter ARG]
[--opt-retries ARG] [--non-terminals ARG] [--dump-to ARG]
[--load-from ARG]
Symbolic Regression search algorithm exploiting the potentials of equality
saturation and e-graphs.
Available options:
-d,--dataset INPUT-FILE CSV dataset.
-t,--test ARG test data (default: "")
-g,--generations GENS Number of generations. (default: 100)
-a,--algorithm ALG Algorithm.
-s,--maxSize ARG max-size.
-k,--folds ARG k-split ratio training-validation (default: 1)
--trace print all evaluated expressions.
--loss ARG distribution of the data. (default: Gaussian)
--opt-iter ARG number of iterations in parameter optimization.
(default: 30)
--opt-retries ARG number of retries of parameter fitting. (default: 1)
--non-terminals ARG set of non-terminals to use in the search.
(default: "Add,Sub,Mul,Div,PowerAbs,Recip")
--dump-to ARG dump final e-graph to a file. (default: "")
--load-from ARG load initial e-graph from a file. (default: "")
-h,--help Show this help text
The dataset file must contain a header with each features name, and the --dataset and --test arguments can be accompanied by arguments separated by ':' following the format:
filename.ext:start_row:end_row:target:features:ynoise
where each ':' field is optional. The fields are:
- start_row:end_row is the range of the training rows (default 0:nrows-1). every other row not included in this range will be used as validation
- target is either the name of the (if the datafile has headers) or the index of the target variable
- features is a comma separated list of names or indices to be used as input variables of the regression model.
- ynoise is either the name or the index of the noise / uncertainty information of the target.
Example of valid names: dataset.csv, mydata.tsv, dataset.csv:20:100, dataset.tsv:20:100:price:m2,rooms,neighborhood, dataset.csv:::5:0,1,2.
The format of the file will be determined by the extension (e.g., csv, tsv,...).
Installation
To install SymRegg you'll need:
libzlibnloptlibgmpghc-9.6.6or highercabal
Method 1: PIP
Simply run:
pip install symregg
under your Python environment.
Method 2: cabal
After installing the dependencies (e.g., apt install libz libnlopt libgmp), install ghcup
For Linux, macOS, FreeBSD or WSL2:
curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh
For Windows, run the following in a PowerShell:
Set-ExecutionPolicy Bypass -Scope Process -Force;[System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; try { & ([ScriptBlock]::Create((Invoke-WebRequest https://www.haskell.org/ghcup/sh/bootstrap-haskell.ps1 -UseBasicParsing))) -Interactive -DisableCurl } catch { Write-Error $_ }
After the installation, run ghcup tui and install the latest stack or cabal together with ghc-9.6.6 (select the items and press i).
To install symregg simply run:
cabal install
Python
Installation
pip install pysymregg
Features
- Scikit-learn compatible API with
fit()andpredict()methods - Support for multiple optimization algorithms
- Flexible function set selection
- Various loss functions for different problem types
- Parameter optimization with multiple restarts
- Ability to save and load e-graphs
Usage
Basic Example
from pysymregg import PySymRegg
import numpy as np
# Create sample data
X = np.linspace(-10, 10, 100).reshape(-1, 1)
y = 2 * X.ravel() + 3 * np.sin(X.ravel()) + np.random.normal(0, 1, 100)
# Create and fit the model
model = PySymRegg(gen=100, nonterminals="add,sub,mul,div,sin,cos")
model.fit(X, y)
# Make predictions
y_pred = model.predict(X)
# Examine the results
print(model.results)
Integration with scikit-learn
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from symregg import SymRegg
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create and fit model
model = SymRegg(gen=150, optIter=100)
model.fit(X_train, y_train)
# Evaluate on test set
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Test MSE: {mse}")
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
gen |
int | 100 | Number of generations to run |
alg |
str | "BestFirst" | Algorithm type: "BestFirst" or "OnlyRandom" |
maxSize |
int | 15 | Maximum allowed size for expressions (max 100) |
nonterminals |
str | "add,sub,mul,div" | Comma-separated list of allowed functions |
loss |
str | "MSE" | Loss function: "MSE", "Gaussian", "Bernoulli", or "Poisson" |
optIter |
int | 50 | Number of iterations for parameter optimization |
optRepeat |
int | 2 | Number of restarts for parameter optimization |
nParams |
int | -1 | Maximum number of parameters (-1 for unlimited) |
folds |
int | 1 | Data splitting ratio for validation |
trace |
bool | False | Whether to return all visited expressions instead of the Pareto front |
dumpTo |
str | "" | Filename to save the final e-graph |
loadFrom |
str | "" | Filename to load an e-graph to resume search |
Available Functions
The following functions can be used in the nonterminals parameter:
- Basic operations:
add,sub,mul,div - Powers:
power,powerabs,square,cube - Roots:
sqrt,sqrtabs,cbrt - Trigonometric:
sin,cos,tan,asin,acos,atan - Hyperbolic:
sinh,cosh,tanh,asinh,acosh,atanh - Others:
abs,log,logabs,exp,recip,aq(analytical quotient)
Methods
fit(X, y, Xerr, yerr): Fits the symbolic regression model with optional uncertainty information forX, y.fit_mvsr(Xs, ys, Xerrs, yerrs): Fits the symbolic regression model using multi-view SR where each argument is a list of numpy arrays describing multiple samples.predict(X): Generates predictions using the best modelpredict_mvsr(X, view): counter part ofpredictfor multi-view. Must specify the view.score(X, y): Computes R² score of the best modelevaluate_best_model(X): Evaluates the best model on the given dataevaluate_best_model_mvsr(X, view): Counterpart for multi-view. Must specify the view.evaluate_model(ix, X): Evaluates the model with indexixon the given dataevaluate_model_mvsr(ix, X, view): Counterpart for multi-view. Must specify the view.
Results
After fitting, the results attribute contains a pandas DataFrame with details about the discovered models, including:
- Mathematical expressions
- Model complexity
- Parameter values
- Error metrics
- NumPy-compatible expressions
License
[LICENSE]
Citation
If you use PySymRegg in your research, please cite:
TBD
Acknowledgments
The bindings were created following the amazing example written by wenkokke
Fabricio Olivetti de Franca is supported by Conselho Nacional de Desenvolvimento Cient'{i}fico e Tecnol'{o}gico (CNPq) grant 301596/2022-0.
Gabriel Kronberger is supported by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Federal Ministry for Labour and Economy, and the regional government of Upper Austria within the COMET project ProMetHeus (904919) supported by the Austrian Research Promotion Agency (FFG).
1 maintainer
Metadata
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