Operators

Pre-defined

First, note that pretty much any valid Julia function which takes one or two scalars as input, and returns on scalar as output, is likely to be a valid operator^[1]. A selection of these and other valid operators are stated below.

Also, note that it's a good idea to not use too many operators, since it can exponentially increase the search space.

Unary Operators

Basic	Exp/Log	Trig	Hyperbolic	Special	Rounding
neg	exp	sin	sinh	erf	round
square	log	cos	cosh	erfc	floor
cube	log10	tan	tanh	gamma	ceil
cbrt	log2	asin	asinh	relu
sqrt	log1p	acos	acosh	sinc
abs		atan	atanh
sign
inv

Binary Operators

Arithmetic	Comparison	Logic
+	max	logical_or[2]
-	min	logical_and[3]
*	>[4]
/	>=
^	<
	<=
	cond[5]
	mod

Higher Arity Operators

Ternary
clamp
fma / muladd
max
min

Note that to use operators with arity 3 or more, you must use the operators parameter instead of the *ary_operators parameters, and pass operators as a dictionary with the arity as key:

operators={
    1: ["sin"], 2: ["+", "-", "*"], 3: ["clamp"]
},

Custom

Instead of passing a predefined operator as a string, you can just define a custom function as Julia code. For example:

    PySRRegressor(
        ...,
        unary_operators=["myfunction(x) = x^2"],
        binary_operators=["myotherfunction(x, y) = x^2*y"],
        extra_sympy_mappings={
            "myfunction": lambda x: x**2,
            "myotherfunction": lambda x, y: x**2 * y,
        },
    )

Make sure that it works with Float32 as a datatype (for default precision, or Float64 if you set precision=64). That means you need to write 1.5f3 instead of 1.5e3, if you write any constant numbers, or simply convert a result to Float64(...).

PySR expects that operators not throw an error for any input value over the entire real line from -3.4e38 to +3.4e38. Thus, for invalid inputs, such as negative numbers to a sqrt function, you may simply return a NaN of the same type as the input. For example,

my_sqrt(x) = x >= 0 ? sqrt(x) : convert(typeof(x), NaN)

would be a valid operator. The genetic algorithm will preferentially selection expressions which avoid any invalid values over the training dataset.

---

1. However, you will need to define a sympy equivalent in extra_sympy_mapping if you want to use a function not in the above list. ↩︎

2. logical_or is equivalent to (x, y) -> (x > 0 || y > 0) ? 1 : 0 ↩︎

3. logical_and is equivalent to (x, y) -> (x > 0 && y > 0) ? 1 : 0 ↩︎

4. > is equivalent to (x, y) -> x > y ? 1 : 0 ↩︎

5. cond is equivalent to (x, y) -> x > 0 ? y : 0 ↩︎