Spaces:
Sleeping
Sleeping
File size: 2,379 Bytes
1858a22 f755fc9 1858a22 f20792e 1858a22 90d24f5 1858a22 f755fc9 1858a22 90d24f5 1858a22 90d24f5 1858a22 f755fc9 1858a22 90d24f5 1858a22 90d24f5 1858a22 f755fc9 1858a22 90d24f5 1858a22 90d24f5 1858a22 f755fc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
# Examples
### Preamble
```python
import numpy as np
from pysr import *
```
We'll also set up some default options that will
make these simple searches go faster (but are less optimal
for more complex searches).
```python
kwargs = dict(populations=5, niterations=5, annealing=True)
```
## 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=["+", "-", "*", "/"], **kwargs)
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=["plus", "mult"],
unary_operators=["inv(x) = 1/x"],
**kwargs
)
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=["plus", "mult"],
unary_operators=["inv(x) = 1/x"],
**kwargs
)
model.fit(X, y)
```
## 4. Plotting an expression
Here, let's use the same equations, but get a format we can actually
use and test. We can add this option after a search via the `set_params`
function:
```python
model.set_params(extra_sympy_mappings={"inv": lambda x: 1/x})
model.sympy()
```
If you look at the lists of expressions before and after, you will
see that the sympy format now has replaced `inv` with `1/`.
For now, let's consider the expressions for output 0:
```python
expressions = expressions[0]
```
This is a pandas table, which we can filter:
```python
best_expression = expressions.iloc[expressions.MSE.argmin()]
```
We can see the LaTeX version of this with:
```python
import sympy
sympy.latex(best_expression.sympy_format)
```
We can access the numpy version with:
```python
f = best_expression.lambda_format
print(f)
```
Which shows a PySR object on numpy code:
```
>> PySRFunction(X=>1/x0)
```
Let's plot this against the truth:
```python
from matplotlib import pyplot as plt
plt.scatter(y[:, 0], f(X))
plt.xlabel('Truth')
plt.ylabel('Prediction')
plt.show()
```
Which gives us:
|