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"""Code for exporting discovered expressions to numpy""" | |
import numpy as np | |
import pandas as pd | |
from sympy import lambdify | |
import warnings | |
class CallableEquation: | |
"""Simple wrapper for numpy lambda functions built with sympy""" | |
def __init__(self, sympy_symbols, eqn, selection=None, variable_names=None): | |
self._sympy = eqn | |
self._sympy_symbols = sympy_symbols | |
self._selection = selection | |
self._variable_names = variable_names | |
def __repr__(self): | |
return f"PySRFunction(X=>{self._sympy})" | |
def __call__(self, X): | |
expected_shape = (X.shape[0],) | |
if isinstance(X, pd.DataFrame): | |
# Lambda function takes as argument: | |
return self._lambda( | |
**{k: X[k].values for k in self._variable_names} | |
) * np.ones(expected_shape) | |
if self._selection is not None: | |
if X.shape[1] != len(self._selection): | |
warnings.warn( | |
"`X` should be of shape (n_samples, len(self._selection)). " | |
"Automatically filtering `X` to selection. " | |
"Note: Filtered `X` column order may not match column order in fit " | |
"this may lead to incorrect predictions and other errors." | |
) | |
X = X[:, self._selection] | |
return self._lambda(*X.T) * np.ones(expected_shape) | |
def _lambda(self): | |
return lambdify(self._sympy_symbols, self._sympy) | |