8000 MNT: fix plot_evaluation when skipping dimension, add test by QuentinSoubeyran · Pull Request #1066 · scikit-optimize/scikit-optimize · GitHub
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MNT: fix plot_evaluation when skipping dimension, add test #1066

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MNT: fix plot_evaluation when skipping dimension, add test #1066

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9c6ec62
fix plot_evaluation when skipping dimension, add test
QuentinSoubeyran Sep 30, 2021
4f36bc7
fix linting
QuentinSoubeyran Sep 30, 2021
414d057
updated whats new
QuentinSoubeyran Oct 1, 2021
6ae2104
fixed another wrong indexing
QuentinSoubeyran Oct 1, 2021
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9 changes: 9 additions & 0 deletions doc/whats_new/v0.9.rst
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Expand Up @@ -26,3 +26,12 @@ Version 0.9.0
for scikit-learn >= 1.0. :pr:`1063`
- Minor documentation improvements.
- Various small bugs and fixes.
:pr:`988`

Version 0.9.1
=============

- |Fix| Fix `skopt.plots.plot_evaluations` incorrectly handling dimensions
as categorical dimensions when constant dimension are ignored due to wrong
indexing.
:pr:`1066`
4 changes: 2 additions & 2 deletions skopt/plots.py
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Expand Up @@ -827,7 +827,7 @@ def plot_evaluations(result, bins=20, dimensions=None,
for j in range(n_dims):
if i == j:
index, dim = plot_dims[i]
if iscat[j]:
if iscat[index]:
bins_ = len(dim.categories)
elif dim.prior == 'log-uniform':
low, high = space.bounds[index]
Expand All @@ -839,7 +839,7 @@ def plot_evaluations(result, bins=20, dimensions=None,
else:
ax_ = ax[i, i]
ax_.hist(samples[:, index], bins=bins_,
range=None if iscat[j] else dim.bounds)
range=None if iscat[index] else dim.bounds)

# lower triangle
elif i > j:
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22 changes: 22 additions & 0 deletions skopt/tests/test_plots.py
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Expand Up @@ -132,6 +132,28 @@ def objective(params):
# Look into how matplotlib does this.


def test_plots_skip_constant():
"""Test that constant dimension are properly skipped"""
SPACE = [
Categorical([0], name="dummy"),
Categorical([0], name="dummy"),
Integer(1, 20, name='max_depth'),
Integer(1, 20, name='max_depth'),
]
X, y = load_breast_cancer(return_X_y=True)

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, X, y))
res = gp_minimize(objective, SPACE, n_calls=10, random_state=3)
print(res.space)
print(plots._map_categories(res.space, res.x_iters, res.x))
plots.plot_evaluations(res)


@pytest.mark.fast_test
def test_evaluate_min_params():
res = gp_minimize(bench3,
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0