import unittest
import numpy as np
from pysr import sympy2jax, get_hof
import pandas as pd
from jax import numpy as jnp
from jax import random
from jax import grad
import sympy

class TestJAX(unittest.TestCase):
    def setUp(self):
        np.random.seed(0)

    def test_sympy2jax(self):
        x, y, z = sympy.symbols('x y z')
        cosx = 1.0 * sympy.cos(x) + y
        key = random.PRNGKey(0)
        X = random.normal(key, (1000, 2))
        true = 1.0 * jnp.cos(X[:, 0]) + X[:, 1]
        f, params = sympy2jax(cosx, [x, y, z])
        self.assertTrue(jnp.all(jnp.isclose(f(X, params), true)).item())
    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_jax_format=True,
                multioutput=False, nout=1, selection=[1, 2, 3])

        jformat = equations.iloc[-1].jax_format
        np.testing.assert_almost_equal(
                np.array(jformat['callable'](jnp.array(X), jformat['parameters'])),
                np.square(np.cos(X[:, 1])), # Select feature 1
                decimal=4
        )