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)
