def test_customized_regressor_metrics():
"""Check whether greater_is_better works for SymbolicRegressor."""
x_data = rng.uniform(-1, 1, 100).reshape(50, 2)
y_true = x_data[:, 0] ** 2 + x_data[:, 1] ** 2
est_gp = SymbolicRegressor(metric='mean absolute error',
stopping_criteria=0.000001, random_state=415,
parsimony_coefficient=0.001, init_method='full',
init_depth=(2, 4))
est_gp.fit(x_data, y_true)
formula = est_gp.__str__()
assert_equal('add(mul(X1, X1), mul(X0, X0))', formula, True)
def neg_mean_absolute_error(y, y_pred, sample_weight):
return -1 * mean_absolute_error(y, y_pred, sample_weight)
customized_fitness = make_fitness(neg_mean_absolute_error,
greater_is_better=True)
c_est_gp = SymbolicRegressor(metric=customized_fitness,
stopping_criteria=-0.000001, random_state=415,
parsimony_coefficient=0.001, verbose=0,
init_method='full', init_depth=(2, 4))
c_est_gp.fit(x_data, y_true)
c_formula = c_est_gp.__str__()
assert_equal('add(mul(X1, X1), mul(X0, X0))', c_formula, True)
def test_symbolic_regressor():
"""Check that SymbolicRegressor example works"""
rng = check_random_state(0)
X_train = rng.uniform(-1, 1, 100).reshape(50, 2)
y_train = X_train[:, 0] ** 2 - X_train[:, 1] ** 2 + X_train[:, 1] - 1
X_test = rng.uniform(-1, 1, 100).reshape(50, 2)
y_test = X_test[:, 0] ** 2 - X_test[:, 1] ** 2 + X_test[:, 1] - 1
est_gp = SymbolicRegressor(population_size=5000, generations=20,
stopping_criteria=0.01, p_crossover=0.7,
p_subtree_mutation=0.1, p_hoist_mutation=0.05,
p_point_mutation=0.1, max_samples=0.9,
parsimony_coefficient=0.01, random_state=0)
est_gp.fit(X_train, y_train)
assert_equal(len(est_gp._programs), 7)
expected = 'sub(add(-0.999, X1), mul(sub(X1, X0), add(X0, X1)))'
assert_equal(est_gp.__str__(), expected)
assert_almost_equal(est_gp.score(X_test, y_test), 0.99999, decimal=5)
dot_data = est_gp._program.export_graphviz()
expected = ('digraph program {\nnode [style=filled]\n0 [label="sub", '
'fillcolor="#136ed4"] ;\n1 [label="add", fillcolor="#136ed4"] '
';\n2 [label="-0.999", fillcolor="#60a6f6"] ;\n3 [label="X1", '
'fillcolor="#60a6f6"] ;\n1 -> 3 ;\n1 -> 2 ;\n4 [label="mul", '
'fillcolor="#136ed4"] ;\n5 [label="sub", fillcolor="#136ed4"] '
';\n6 [label="X1", fillcolor="#60a6f6"] ;\n7 [label="X0", '
'fillcolor="#60a6f6"] ;\n5 -> 7 ;\n5 -> 6 ;\n8 [label="add", '
'fillcolor="#136ed4"] ;\n9 [label="X0", fillcolor="#60a6f6"] '
';\n10 [label="X1", fillcolor="#60a6f6"] ;\n8 -> 10 ;\n8 -> 9 '
';\n4 -> 8 ;\n4 -> 5 ;\n0 -> 4 ;\n0 -> 1 ;\n}')
assert_equal(dot_data, expected)
assert_equal(est_gp._program.parents, {'method': 'Crossover',
'parent_idx': 1555,
'parent_nodes': range(1, 4),
'donor_idx': 78,
'donor_nodes': []})
idx = est_gp._program.parents['donor_idx']
fade_nodes = est_gp._program.parents['donor_nodes']
assert_equal(est_gp._programs[-2][idx].__str__(), 'add(-0.999, X1)')
assert_almost_equal(est_gp._programs[-2][idx].fitness_, 0.351803319075)
dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes)
expected = ('digraph program {\nnode [style=filled]\n0 [label="add", '
'fillcolor="#136ed4"] ;\n1 [label="-0.999", '
'fillcolor="#60a6f6"] ;\n2 [label="X1", fillcolor="#60a6f6"] '
';\n0 -> 2 ;\n0 -> 1 ;\n}')
assert_equal(dot_data, expected)
idx = est_gp._program.parents['parent_idx']
fade_nodes = est_gp._program.parents['parent_nodes']
assert_equal(est_gp._programs[-2][idx].__str__(),
'sub(sub(X1, 0.939), mul(sub(X1, X0), add(X0, X1)))')
assert_almost_equal(est_gp._programs[-2][idx].fitness_, 0.17080204042)
dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes)
expected = ('digraph program {\nnode [style=filled]\n0 [label="sub", '
'fillcolor="#136ed4"] ;\n1 [label="sub", fillcolor="#cecece"] '
';\n2 [label="X1", fillcolor="#cecece"] ;\n3 [label="0.939", '
'fillcolor="#cecece"] ;\n1 -> 3 ;\n1 -> 2 ;\n4 [label="mul", '
'fillcolor="#136ed4"] ;\n5 [label="sub", fillcolor="#136ed4"] '
';\n6 [label="X1", fillcolor="#60a6f6"] ;\n7 [label="X0", '
'fillcolor="#60a6f6"] ;\n5 -> 7 ;\n5 -> 6 ;\n8 [label="add", '
'fillcolor="#136ed4"] ;\n9 [label="X0", fillcolor="#60a6f6"] '
';\n10 [label="X1", fillcolor="#60a6f6"] ;\n8 -> 10 ;\n8 -> 9 '
';\n4 -> 8 ;\n4 -> 5 ;\n0 -> 4 ;\n0 -> 1 ;\n}')
assert_equal(dot_data, expected)
