def _evaluate_min_params(result, params='result', n_minimum_search=None, random_state=None): """Returns the minimum based on `params`""" x_vals = None space = result.space if isinstance(params, str): if params == 'result': # Using the best observed result x_vals = result.x elif params == 'expected_minimum': if result.space.is_partly_categorical: # space is also categorical raise ValueError('expected_minimum does not support any' 'categorical values') # Do a gradient based minimum search using scipys own minimizer if n_minimum_search: # If a value for # expected_minimum_samples has been parsed x_vals, _ = expected_minimum(result, n_random_starts=n_minimum_search, random_state=random_state) else: # Use standard of 20 random starting points x_vals, _ = expected_minimum(result, n_random_starts=20, random_state=random_state) elif params == 'expected_minimum_random': # Do a minimum search by evaluating the function with # n_samples sample values if n_minimum_search: # If a value for # n_minimum_samples has been parsed x_vals, _ = expected_minimum_random_sampling( result, n_random_starts=n_minimum_search, random_state=random_state) else: # Use standard of 10^n_parameters. Note this # becomes very slow for many parameters x_vals, _ = expected_minimum_random_sampling( result, n_random_starts=10**len(result.x), random_state=random_state) else: raise ValueError('Argument ´eval_min_params´ must be a valid' 'string (´result´)') elif isinstance(params, list): assert len(params) == len(result.x), 'Argument' \ '´eval_min_params´ of type list must have same length as' \ 'number of features' # Using defined x_values x_vals = params else: raise ValueError('Argument ´eval_min_params´ must' 'be a string or a list') return x_vals
def test_expected_minimum_random_sampling(): res = gp_minimize(bench3, [(-2.0, 2.0)], x0=[0.], noise=1e-8, n_calls=8, n_random_starts=3, random_state=1) x_min, f_min = expected_minimum_random_sampling(res, random_state=1) x_min2, f_min2 = expected_minimum_random_sampling(res, random_state=1) assert f_min <= res.fun # true since noise ~= 0.0 assert x_min == x_min2 assert f_min == f_min2
def test_evaluate_min_params(): res = gp_minimize(bench3, [(-2.0, 2.0)], x0=[0.], noise=1e-8, n_calls=8, n_random_starts=3, random_state=1) x_min, f_min = expected_minimum(res, random_state=1) x_min2, f_min2 = expected_minimum_random_sampling(res, n_random_starts=1000, random_state=1) plots.plot_gaussian_process(res) assert _evaluate_min_params(res, params='result') == res.x assert _evaluate_min_params(res, params=[1.]) == [1.] assert _evaluate_min_params(res, params='expected_minimum', random_state=1) == x_min assert _evaluate_min_params(res, params='expected_minimum', n_minimum_search=20, random_state=1) == x_min assert _evaluate_min_params(res, params='expected_minimum_random', n_minimum_search=1000, random_state=1) == x_min2
def test_plots_work(): """Basic smoke tests to make sure plotting doesn't crash.""" SPACE = [ Integer(1, 20, name='max_depth'), Integer(2, 100, name='min_samples_split'), Integer(5, 30, name='min_samples_leaf'), Integer(1, 30, name='max_features'), Categorical(['gini', 'entropy'], name='criterion'), Categorical(list('abcdefghij'), name='dummy'), ] def objective(params): clf = DecisionTreeClassifier(random_state=3, **{ dim.name: val for dim, val in zip(SPACE, params) if dim.name != 'dummy' }) return -np.mean(cross_val_score(clf, *load_breast_cancer(True))) res = gp_minimize(objective, SPACE, n_calls=10, random_state=3) x_min, f_min = expected_minimum_random_sampling(res, random_state=1) x_min2, f_min2 = expected_minimum(res, random_state=1) assert x_min == x_min2 assert f_min == f_min2 plots.plot_convergence(res) plots.plot_evaluations(res) plots.plot_objective(res) plots.plot_objective(res, minimum='expected_minimum_random') plots.plot_objective(res, sample_source='expected_minimum_random', n_minimum_search=10000) plots.plot_objective(res, sample_source='result') plots.plot_regret(res)
def test_plots_work(): """Basic smoke tests to make sure plotting doesn't crash.""" SPACE = [ Integer(1, 20, name='max_depth'), Integer(2, 100, name='min_samples_split'), Integer(5, 30, name='min_samples_leaf'), Integer(1, 30, name='max_features'), Categorical(['gini', 'entropy'], name='criterion'), Categorical(list('abcdefghij'), name='dummy'), ] def objective(params): clf = DecisionTreeClassifier(random_state=3, **{ dim.name: val for dim, val in zip(SPACE, params) if dim.name != 'dummy' }) return -np.mean(cross_val_score(clf, *load_breast_cancer(True))) res = gp_minimize(objective, SPACE, n_calls=10, random_state=3) x = [[11, 52, 8, 14, 'entropy', 'f'], [14, 90, 10, 2, 'gini', 'a'], [7, 90, 6, 14, 'entropy', 'f']] samples = res.space.transform(x) xi_ = [1., 10.5, 20.] yi_ = [-0.9240883492576596, -0.9240745890422687, -0.9240586402439884] xi, yi = partial_dependence_1D(res.space, res.models[-1], 0, samples, n_points=3) assert_array_almost_equal(xi, xi_) assert_array_almost_equal(yi, yi_, 1e-3) xi_ = [0, 1] yi_ = [-0.9241087603770617, -0.9240188905968352] xi, yi = partial_dependence_1D(res.space, res.models[-1], 4, samples, n_points=3) assert_array_almost_equal(xi, xi_) assert_array_almost_equal(yi, yi_, 1e-3) xi_ = [0, 1] yi_ = [1., 10.5, 20.] zi_ = [[-0.92412562, -0.92403575], [-0.92411186, -0.92402199], [-0.92409591, -0.92400604]] xi, yi, zi = partial_dependence_2D(res.space, res.models[-1], 0, 4, samples, n_points=3) assert_array_almost_equal(xi, xi_) assert_array_almost_equal(yi, yi_) assert_array_almost_equal(zi, zi_, 1e-3) x_min, f_min = expected_minimum_random_sampling(res, random_state=1) x_min2, f_min2 = expected_minimum(res, random_state=1) x_min, f_min = expected_minimum_random_sampling(res, random_state=1) x_min2, f_min2 = expected_minimum(res, random_state=1) assert x_min == x_min2 assert f_min == f_min2 plots.plot_convergence(res) plots.plot_evaluations(res) plots.plot_objective(res) plots.plot_objective(res, dimensions=["a", "b", "c", "d", "e", "f"]) plots.plot_objective(res, minimum='expected_minimum_random') plots.plot_objective(res, sample_source='expected_minimum_random', n_minimum_search=10000) plots.plot_objective(res, sample_source='result') plots.plot_regret(res) plots.plot_objective_2D(res, 0, 4) plots.plot_histogram(res, 0, 4)