# Toy Examples with Code

## Preamble

```python
import numpy as np
from pysr import *
```

## 1. Simple search

Here's a simple example where we
find the expression `2 cos(x3) + x0^2 - 2`.

```python
X = 2 * np.random.randn(100, 5)
y = 2 * np.cos(X[:, 3]) + X[:, 0] ** 2 - 2
model = PySRRegressor(binary_operators=["+", "-", "*", "/"])
model.fit(X, y)
print(model)
```

## 2. Custom operator

Here, we define a custom operator and use it to find an expression:

```python
X = 2 * np.random.randn(100, 5)
y = 1 / X[:, 0]
model = PySRRegressor(
    binary_operators=["+", "*"],
    unary_operators=["inv(x) = 1/x"],
    extra_sympy_mappings={"inv": lambda x: 1/x},
)
model.fit(X, y)
print(model)
```

## 3. Multiple outputs

Here, we do the same thing, but with multiple expressions at once,
each requiring a different feature.

```python
X = 2 * np.random.randn(100, 5)
y = 1 / X[:, [0, 1, 2]]
model = PySRRegressor(
    binary_operators=["+", "*"],
    unary_operators=["inv(x) = 1/x"],
    extra_sympy_mappings={"inv": lambda x: 1/x},
)
model.fit(X, y)
```

## 4. Plotting an expression

For now, let's consider the expressions for output 0.
We can see the LaTeX version of this with:

```python
model.latex()[0]
```

or output 1 with `model.latex()[1]`.

Let's plot the prediction against the truth:

```python
from matplotlib import pyplot as plt
plt.scatter(y[:, 0], model.predict(X)[:, 0])
plt.xlabel('Truth')
plt.ylabel('Prediction')
plt.show()
```

Which gives us:

![Truth vs Prediction](images/example_plot.png)

We may also plot the output of a particular expression
by passing the index of the expression to `predict` (or
`sympy` or `latex` as well)

## 5. Feature selection

PySR and evolution-based symbolic regression in general performs
very poorly when the number of features is large.
Even, say, 10 features might be too much for a typical equation search.

If you are dealing with high-dimensional data with a particular type of structure,
you might consider using deep learning to break the problem into
smaller "chunks" which can then be solved by PySR, as explained in the paper
[2006.11287](https://arxiv.org/abs/2006.11287).

For tabular datasets, this is a bit trickier. Luckily, PySR has a built-in feature
selection mechanism. Simply declare the parameter `select_k_features=5`, for selecting
the most important 5 features.

Here is an example. Let's say we have 30 input features and 300 data points, but only 2
of those features are actually used:

```python
X = np.random.randn(300, 30)
y = X[:, 3]**2 - X[:, 19]**2 + 1.5
```

Let's create a model with the feature selection argument set up:

```python
model = PySRRegressor(
    binary_operators=["+", "-", "*", "/"],
    unary_operators=["exp"],
    select_k_features=5,
)
```

Now let's fit this:

```python
model.fit(X, y)
```

Before the Julia backend is launched, you can see the string:

```text
Using features ['x3', 'x5', 'x7', 'x19', 'x21']
```

which indicates that the feature selection (powered by a gradient-boosting tree)
has successfully selected the relevant two features.

This fit should find the solution quickly, whereas with the huge number of features,
it would have struggled.

This simple preprocessing step is enough to simplify our tabular dataset,
but again, for more structured datasets, you should try the deep learning
approach mentioned above.

## 6. Denoising

Many datasets, especially in the observational sciences,
contain intrinsic noise. PySR is noise robust itself, as it is simply optimizing a loss function,
but there are still some additional steps you can take to reduce the effect of noise.

One thing you could do, which we won't detail here, is to create a custom log-likelihood
given some assumed noise model. By passing weights to the fit function, and
defining a custom loss function such as `loss="myloss(x, y, w) = w * (x - y)^2"`,
you can define any sort of log-likelihood you wish. (However, note that it must be bounded at zero)

However, the simplest thing to do is preprocessing, just like for feature selection. To do this,
set the parameter `denoise=True`. This will fit a Gaussian process (containing a white noise kernel)
to the input dataset, and predict new targets (which are assumed to be denoised) from that Gaussian process.

For example:

```python
X = np.random.randn(100, 5)
noise = np.random.randn(100) * 0.1
y = np.exp(X[:, 0]) + X[:, 1] + X[:, 2] + noise
```

Let's create and fit a model with the denoising argument set up:

```python
model = PySRRegressor(
    binary_operators=["+", "-", "*", "/"],
    unary_operators=["exp"],
    denoise=True,
)
model.fit(X, y)
print(model)
```

If all goes well, you should find that it predicts the correct input equation, without the noise term!

## 7. Julia packages and types

PySR uses [SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl)
as its search backend. This is a pure Julia package, and so can interface easily with any other
Julia package.
For some tasks, it may be necessary to load such a package.

For example, let's consider an example where we wish to find the following relationship:

$$ y = p_{3x + 1} - 5, $$

where $p_i$ is the $i$th prime number, and $x$ is the input feature.

Let's see if we can discover this relationship between $x$ and $y$, using
the [Primes.jl](https://github.com/JuliaMath/Primes.jl) package.

First, let's manually initialize the Julia backend
(here, with 8 threads and `-O3`):

```python
import pysr
jl = pysr.julia_helpers.init_julia(julia_kwargs={"threads": 8, "optimize": 3})
```

`jl` stores the Julia runtime.

Now, let's run some Julia code to add the Primes.jl
package to the PySR environment:

```python
jl.eval("""
import Pkg
Pkg.add("Primes")
""")
```

This imports the Julia package manager, and uses it to install
`Primes.jl`. Now let's import `Primes.jl`:

Now, let's import it

```python
jl.eval("import Primes")
```

Now, let's define a custom operator. We can then pass this
to PySR later on.

```python
jl.eval("""
function p(i::T) where T
    if (0.5 < i < 1000)
        return T(Primes.prime(round(Int, i)))
    else
        return T(NaN)
    end
end
""")
```

We have created a custom operator `p`, which takes an arbitrary number as input.
It then checks whether the input is between 0.5 and 1000.
If out-of-bounds, it returns `NaN`.
If in-bounds, it rounds it to the nearest integer, and returns the corresponding prime number, mapped to the same type as input.

Now, let's generate some test data, using the first 100 primes.
Since we are using PyJulia, we can pass data back and forth
to our custom Julia operator:

```python
primes = {i: jl.p(i*1.0) for i in range(1, 999)}
```

And let's create a dataset:

```python
import numpy as np

X = np.random.randint(0, 100, 100)[:, None]
y = [primes[3*X[i, 0] + 1] - 5 for i in range(100)]
```

Finally, let's create a PySR model, and pass the custom operator. We also need to define the sympy equivalent, which we can leave as a placeholder for now:

```python
from pysr import PySRRegressor
import sympy

class sympy_p(sympy.Function):
    pass

model = PySRRegressor(
    binary_operators=["+", "-", "*", "/"],
    unary_operators=["p"],
    niterations=1000,
    extra_sympy_mappings={"p": sympy_p}
)
```

We are all set to go! Let's see if we can find the true relation:

```python
model.fit(X, y)
```

if all works out, you should be able to see the true relation (note that the constant offset might not be exactly 1, since it is allowed to round to the nearest integer).
You can get the sympy version of the last row with:

```python
model.sympy(index=-1)
```

## 8. Additional features

For the many other features available in PySR, please
read the [Options section](options.md).