TuringBot Python Library

Python interface for TuringBot, a symbolic regression software that discovers mathematical formulas from data.

TuringBot uses simulated annealing to search the space of possible mathematical expressions, finding accurate and compact formulas that describe your data. It supports 16 search metrics (including custom metrics), train/test splits, custom base functions, custom constants, and a wide range of mathematical functions.

Installation

pip install turingbot

You also need the TuringBot desktop application installed. Download it from turingbotsoftware.com/download.html.

Quick start

import time
from turingbot import simulation

sim = simulation()

# Start a symbolic regression search
# TuringBot is detected automatically if installed in the default location
sim.start_process(
    input_file="your_data.csv",  # CSV with input columns + target column
    search_metric=4,             # RMS error
    threads=4,
)

# Let TuringBot search for formulas
time.sleep(60)

# Read the best formulas found so far
sim.refresh_functions()
for f in sim.functions:
    print(f)

# Stop the search
sim.terminate_process()

Or using the context manager:

import time
from turingbot import simulation

with simulation() as sim:
    sim.start_process(
        input_file="your_data.csv",
        search_metric=4,
        threads=4,
    )

    time.sleep(60)
    sim.refresh_functions()

    for f in sim.functions:
        print(f)

If TuringBot is installed in a non-default location, pass the path explicitly:

sim.start_process(
    input_file="your_data.csv",
    path="/custom/path/to/TuringBot",
)

Default paths checked automatically:

Using numpy arrays and pandas DataFrames

You can pass numpy arrays or pandas DataFrames directly instead of file paths:

import numpy as np
from turingbot import simulation

# Generate some data: y = 2*x1 + x2^2
data = np.column_stack([
    x1 := np.random.rand(100),
    x2 := np.random.rand(100),
    2 * x1 + x2 ** 2,
])

with simulation() as sim:
    sim.start_process(
        input_file=data,
        column_names=["x1", "x2", "y"],
    )
    # ...

If column_names is not provided, TuringBot will use its default names (col1, col2, ...).

Pandas DataFrames work the same way, and column names are preserved automatically:

import pandas as pd
from turingbot import simulation

df = pd.read_csv("your_data.csv")

with simulation() as sim:
    sim.start_process(input_file=df)
    # ...

Input format

The input file should be a CSV or TXT file where the last column is the target variable. Example:

x1,x2,y
0.1,0.2,0.24
0.3,0.4,0.55
0.5,0.6,0.81

Search metrics

Parameters

See the full list of parameters in the start_process docstring, including:

• threads -- Number of threads
• train_test_split -- Train/test split ratio
• maximum_formula_complexity -- Max complexity of formulas
• integer_constants -- Restrict to integer constants
• normalize_dataset -- Normalize input data
• allowed_functions -- Restrict which math functions to use
• custom_formula -- Provide a formula template
• custom_constants -- Define custom constants (e.g. "g=9.81,c=299792458")
• custom_functions -- Define custom base functions (e.g. "bump(x)=exp(-x*x)")
• custom_metric -- Define a custom error metric (e.g. "mean(pow(actual-predicted,2))")
• function_size -- Override base function sizes (e.g. "sin:3 cos:3 pow:5")

Documentation

Full documentation: turingbotsoftware.com/documentation.html