# Common Options

You likely don't need to tune the hyperparameters yourself,
but if you would like, you can use `hyperparamopt.py` as an example.

Common options to `PySR` include:
- `binary_operators`, `unary_operators`
- `niterations`
- `procs`
- `populations`
- `weights`
- `maxsize`, `maxdepth`
- `batching`, `batchSize`
- `variable_names` (or pandas input)
- SymPy output

These are described below

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.

## Operators

A list of operators can be found on the operators page.
One can define custom operators in Julia by passing a string:
```python
equations = pysr.pysr(X, y, niterations=100,
    binary_operators=["mult", "plus", "special(x, y) = x^2 + y"],
    extra_sympy_mappings={'special': lambda x, y: x**2 + y},
    unary_operators=["cos"])
```

Now, the symbolic regression code can search using this `special` function
that squares its left argument and adds it to its right. Make sure
all passed functions are valid Julia code, and take one (unary)
or two (binary) float32 scalars as input, and output a float32. This means if you
write any real constants in your operator, like `2.5`, you have to write them
instead as `2.5f0`, which defines it as `Float32`.
Operators are automatically vectorized.

One should also define `extra_sympy_mappings`,
so that the SymPy code can understand the output equation from Julia,
when constructing a useable function. This step is optional, but
is necessary for the `lambda_format` to work.

One can also edit `operators.jl`.

## Iterations

This is the total number of generations that `pysr` will run for.
I usually set this to a large number, and exit when I am satisfied
with the equations.

## Processors

One can adjust the number of workers used by Julia with the
`procs` option. You should set this equal to the number of cores
you want `pysr` to use. This will also run `procs` number of
populations simultaneously by default.

## Populations

By default, `populations=procs`, but you can set a different
number of populations with this option. More populations may increase
the diversity of equations discovered, though will take longer to train.
However, it may be more efficient to have `populations>procs`,
as there are multiple populations running
on each core.

## Weighted data

Here, we assign weights to each row of data
using inverse uncertainty squared. We also use 10 processes
instead of the usual 4, which creates more populations
(one population per thread).
```python
sigma = ...
weights = 1/sigma**2

equations = pysr.pysr(X, y, weights=weights, procs=10)
```

## Max size

`maxsize` controls the maximum size of equation (number of operators,
constants, variables). `maxdepth` is by default not used, but can be set
to control the maximum depth of an equation. These will make processing
faster, as longer equations take longer to test.


## Batching
One can turn on mini-batching, with the `batching` flag,
and control the batch size with `batchSize`. This will make
evolution faster for large datasets. Equations are still evaluated
on the entire dataset at the end of each iteration to compare to the hall
of fame, but only on a random subset during mutations and annealing.

## Variable Names

You can pass a list of strings naming each column of `X` with
`variable_names`. Alternatively, you can pass `X` as a pandas dataframe
and the columns will be used as variable names. Make sure only
alphabetical characters and `_` are used in these names.

## SymPy output

The `pysr` command will return a pandas dataframe. The `sympy_format`
column gives sympy equations. You can use this to get LaTeX format, with,
e.g.,

```python
simplified = equations.iloc[-1]['sympy_format'].simplify()
print(sympy.latex(simplified))
```

If you have set variable names with `variable_names` or a Pandas
dataframe as input for `X`, this will use the same names for each
input column instead of `x0`.