"""Scikit-optimize plotting tests."""
import numpy as np
import pytest
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score
from numpy.testing import assert_raises
from numpy.testing import assert_array_almost_equal
from skopt.space import Integer, Categorical
from skopt import plots, gp_minimize
import matplotlib.pyplot as plt
from skopt.benchmarks import bench3
from skopt import expected_minimum, expected_minimum_random_sampling
from skopt.plots import _evaluate_min_params, partial_dependence
from skopt.plots import partial_dependence_1D, partial_dependence_2D
from skopt import Optimizer
def save_axes(ax, filename):
"""Save matplotlib axes `ax` to an image `filename`."""
fig = plt.gcf()
fig.add_axes(ax)
fig.savefig(filename)
@pytest.mark.slow_test
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)
# TODO: Compare plots to known good results?
# Look into how matplotlib does this.
@pytest.mark.slow_test
def test_plots_work_without_cat():
"""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'),
]
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)
plots.plot_convergence(res)
plots.plot_evaluations(res)
plots.plot_objective(res)
plots.plot_objective(res,
minimum='expected_minimum')
plots.plot_objective(res,
sample_source='expected_minimum',
n_minimum_search=10)
plots.plot_objective(res, sample_source='result')
plots.plot_regret(res)
# TODO: Compare plots to known good results?
# Look into how matplotlib does this.
@pytest.mark.fast_test
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_names_dimensions():
# Define objective
def objective(x, noise_level=0.1):
return np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) +\
np.random.randn() * noise_level
# Initialize Optimizer
opt = Optimizer([(-2.0, 2.0)], n_initial_points=1)
# Optimize
for i in range(2):
next_x = opt.ask()
f_val = objective(next_x)
res = opt.tell(next_x, f_val)
# Plot results
plots.plot_objective(res)
