[BUG] min binary function raises errors during prediction

Describe the bug When I use “min” and “max” in my binary operators, fitting goes well but prediction raises an error.

Version (please include the following information):

• OS: ubuntu 20.04
• Julia version 1.8.0
• Python version 3.8
• PySR version 0.10.1
• Does the bug still appear with the latest version of PySR? Yes

Configuration

import numpy as np
from pysr import PySRRegressor

data_x = np.random.randint(-100, 100, (200, 2))
data_y = np.minimum(data_x[:,0], 8)

model = PySRRegressor(
    model_selection="best",  # Result is mix of simplicity+accuracy
    niterations=40,
    binary_operators=[
        "min", "max"
    ],
    unary_operators=[
    ],
    extra_sympy_mappings={
    },
    # ^ Define operator for SymPy as well
    loss="loss(x, y) = (x - y)^2",
    warm_start=False,
)
print('Fitting data')
model.fit(data_x, data_y)
print(model)
print(model.get_best())

print(model.predict(data_x))

Error message ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Happens in predict() call. The fit result is correct.

Additional context I tried adding {‘min’: np.minimum}, or {‘min’: min}, or {‘min’: sympy.Min} to extra_sympy_mappings. None worked.

I found a workaround :

for row in data_x: 
    model.predict(row)

Not very satisfying though.

[gdelab]

Thanks for the amazing package by the way !

[gdelab]

Thanks for the thourough answer. Yes, sure for the PR !