def fit_models(self, estimation_settings={}, pool_map=map):
if not estimation_settings:
estimation_settings = self.estimation_settings
self.models = fit_models(self.models,
self.task.data,
self.task.target_variable_index,
time_index=self.task.time_index,
task_type=self.task.task_type,
pool_map=pool_map,
verbosity=self.verbosity,
estimation_settings=estimation_settings)
return self.models
def test_parameter_estimation():
np.random.seed(1)
def f(x):
return 2.0 * (x[:,0] + 0.3)
X = np.linspace(-1, 1, 20).reshape(-1,1)
Y = f(X).reshape(-1,1)
data = np.hstack((X, Y))
grammar = GeneratorGrammar("""S -> S '+' T [0.4] | T [0.6]
T -> 'C' [0.6] | T "*" V [0.4]
V -> 'x' [0.5] | 'y' [0.5]""")
symbols = {"x":['x'], "start":"S", "const":"C"}
N = 2
models = generate_models(grammar, symbols, strategy_settings = {"N":N})
models = fit_models(models, data, target_variable_index = -1, task_type="algebraic")
assert np.abs(models[0].get_error() - 0.36) < 1e-6
assert np.abs(models[1].get_error() - 1.4736842) < 1e-6
def test_parameter_estimation_ODE():
B = -2.56; a = 0.4; ts = np.linspace(0.45, 0.87, 5)
ys = (ts+B)*np.exp(a*ts); xs = np.exp(a*ts)
data = np.hstack((ts.reshape(-1, 1), xs.reshape(-1, 1), ys.reshape(-1, 1)))
grammar = GeneratorGrammar("""S -> S '+' T [0.4] | T [0.6]
T -> V [0.6] | 'C' "*" V [0.4]
V -> 'x' [0.5] | 'y' [0.5]""")
symbols = {"x":['y', 'x'], "start":"S", "const":"C"}
np.random.seed(2)
models = generate_models(grammar, symbols, strategy_settings={"N":5})
models = fit_models(models, data, target_variable_index=-1, time_index=0, task_type="differential")
print("\n", models, "\n\nFinal score:")
for m in models:
print(f"model: {str(m.get_full_expr()):<30}; error: {m.get_error():<15}")
def assert_line(models, i, expr, error, tol=1e-9, n=100):
assert str(models[i].get_full_expr())[:n] == expr[:n]
assert abs(models[i].get_error() - error) < tol
assert_line(models, 0, "y", 0.7321678286712089)
assert_line(models, 1, "x", 0.06518775248116751)
assert_line(models, 2, "x + 0.40026612522043*y", 2.5265334439915307e-09, n=8)
return
V -> 'x' [0.5] | 'y' [0.5]""")
symbols = {"x": ['y', 'x'], "start": "S", "const": "C"}
# models = generate_models(grammar, symbols, strategy_settings={"N":20})
models = generate_models(grammar, symbols, strategy_settings={"N": 4})
# 3.) discover the right equation
data = np.hstack((T.reshape(-1, 1), X, Y))
models = fit_models(
models,
data,
target_variable_index=-1,
time_index=0,
task_type="differential",
# estimation_settings={"optimizer": 'metamodel', "verbosity": 4}
# estimation_settings={"optimizer": 'differential_evolution', "verbosity": 1}
# hyperopt:
estimation_settings={
"optimizer": 'hyperopt',
# "hyperopt_space_fn": hp.quniform,
"hyperopt_space_fn": hp.qnormal,
# "hyperopt_space_args": (-13, 13, 1/100), # 1/100 ... ne razlikuje 0.123 in 0.124
"hyperopt_space_args":
(0.4, 2, 1 / 1000), # 1/100 ... ne razlikuje 0.123 in 0.124
})
# 4.) print models' results
print("\n", models, "\n\nFinal score:")
for m in models:
print(f"model: {str(m.get_full_expr()):<30}; error: {m.get_error():<15}")
print("\n While dataset was generated by diff. eq. ẏ = 0.4·y + x ")