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)