8000 Release 0.8.1 · scikit-optimize/scikit-optimize@de32b5f · GitHub
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Release 0.8.1
Merge changes from #943
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‎doc/templates/index.html

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@@ -109,6 +109,8 @@ <h4 class="sk-landing-call-header">News</h4>
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<li><strong>On-going development:</strong>
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<a href="https://scikit-optimize.github.io/dev/whats_new.html"><strong>What's new</strong> (Changelog)</a>
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</li>
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<li><strong>Sep 2020.</strong> scikit-optimize 0.8.1 (<a href 8000 ="whats_new/v0.8.html#version-0-8-1">Changelog</a>).
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<li><strong>Sep 2020.</strong> scikit-optimize 0.8 (<a href="whats_new/v0.8.html#version-0-8">Changelog</a>).
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<li><strong>Feb 2020.</strong> scikit-optimize 0.7.2 (<a href="whats_new/v0.7.html#version-0-7-2">Changelog</a>).
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<li><strong>Feb 2020.</strong> scikit-optimize 0.7.1 (<a href="whats_new/v0.7.html#version-0-7-1">Changelog</a>).
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<li><strong>Jan 2020.</strong> scikit-optimize 0.7 (<a href="whats_new/v0.7.html#version-0-7">Changelog</a>).

‎doc/whats_new/v0.8.rst

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.. currentmodule:: skopt
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.. _changes_0_8_1:
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Version 0.8.1
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=============
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**September 2020**
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- |Fix| GaussianProcessRegressor on sklearn 0.23 normalizes the
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variance to 1, which needs to reverted on predict.
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.. _changes_0_8:
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Version 0.8.0

‎skopt/__init__.py

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@@ -29,7 +29,7 @@
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# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
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# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
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#
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__version__ = "0.8.0"
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__version__ = "0.8.1"
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if __SKOPT_SETUP__:
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import sys

‎skopt/learning/gaussian_process/gpr.py

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@@ -224,10 +224,15 @@ def fit(self, X, y):
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self.K_inv_ = L_inv.dot(L_inv.T)
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# Fix deprecation warning #462
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if int(sklearn.__version__[2:4]) >= 19:
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if int(sklearn.__version__[2:4]) >= 23:
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self.y_train_std_ = self._y_train_std
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self.y_train_mean_ = self._y_train_mean
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elif int(sklearn.__version__[2:4]) >= 19:
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self.y_train_mean_ = self._y_train_mean
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self.y_train_std_ = 1
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else:
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self.y_train_mean_ = self.y_train_mean
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self.y_train_std_ = 1
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return self
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@@ -309,11 +314,14 @@ def predict(self, X, return_std=False, return_cov=False,
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else: # Predict based on GP posterior
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K_trans = self.kernel_(X, self.X_train_)
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y_mean = K_trans.dot(self.alpha_) # Line 4 (y_mean = f_star)
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y_mean = self.y_train_mean_ + y_mean # undo normal.
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# undo normalisation
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y_mean = self.y_train_std_ * y_mean + self.y_train_mean_
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if return_cov:
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v = cho_solve((self.L_, True), K_trans.T) # Line 5
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y_cov = self.kernel_(X) - K_trans.dot(v) # Line 6
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# undo normalisation
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y_cov = y_cov * self.y_train_std_**2
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return y_mean, y_cov
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elif return_std:
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warnings.warn("Predicted variances smaller than 0. "
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"Setting those variances to 0.")
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y_var[y_var_negative] = 0.0
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# undo normalisation
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y_var = y_var * self.y_train_std_**2
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y_std = np.sqrt(y_var)
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if return_mean_grad:
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grad = self.kernel_.gradient_x(X[0], self.X_train_)
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grad_mean = np.dot(grad.T, self.alpha_)
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# undo normalisation
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grad_mean = grad_mean * self.y_train_std_
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if return_std_grad:
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grad_std = np.zeros(X.shape[1])
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if not np.allclose(y_std, grad_std):
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grad_std = -np.dot(K_trans,
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np.dot(K_inv, grad))[0] / y_std
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# undo normalisation
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grad_std = grad_std * self.y_train_std_**2
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return y_mean, y_std, grad_mean, grad_std
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if return_std:

‎skopt/tests/test_acquisition.py

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check_gradient_correctness(X_new, gpr, acq_func, np.max(y))
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@pytest.mark.fast_test
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def test_acquisition_gradient_cookbook():
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rng = np.random.RandomState(0)
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X = rng.randn(20, 5)
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y = rng.randn(20)
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X_new = rng.randn(5)
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gpr = cook_estimator("GP", Space(((-5.0, 5.0),)), random_state=0)
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gpr.fit(X, y)
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for acq_func in ["LCB", "PI", "EI"]:
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check_gradient_correctness(X_new, gpr, acq_func, np.max(y))
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@pytest.mark.fast_test
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@pytest.mark.parametrize("acq_func", ["EIps", "PIps"])
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def test_acquisition_per_second(acq_func):

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