import unittest
import numpy as np
import pandas as pd
from pysr import sympy2torch, get_hof
import torch
import sympy

class TestTorch(unittest.TestCase):
    def test_sympy2torch(self):
        x, y, z = sympy.symbols('x y z')
        cosx = 1.0 * sympy.cos(x) + y
        X = torch.randn((1000, 3))
        true = 1.0 * torch.cos(X[:, 0]) + X[:, 1]
        torch_module = sympy2torch(cosx, [x, y, z])
        self.assertTrue(
                np.all(np.isclose(torch_module(X).detach().numpy(), true.detach().numpy()))
        )
    def test_pipeline(self):
        X = np.random.randn(100, 10)
        equations = pd.DataFrame({
            'Equation': ['1.0', 'cos(x0)', 'square(cos(x0))'],
            'MSE': [1.0, 0.1, 1e-5],
            'Complexity': [1, 2, 3]
            })

        equations['Complexity MSE Equation'.split(' ')].to_csv(
                'equation_file.csv.bkup', sep='|')

        equations = get_hof(
                'equation_file.csv', n_features=2, variables_names='x1 x2 x3'.split(' '),
                extra_sympy_mappings={}, output_torch_format=True,
                multioutput=False, nout=1, selection=[1, 2, 3])

        tformat = equations.iloc[-1].torch_format
        np.testing.assert_almost_equal(
                tformat(torch.tensor(X)).detach().numpy(),
                np.square(np.cos(X[:, 1])) #Selection 1st feature
        )