[MRG+1] Implement greedy A-optimal acquisition function for pure exploration

skopt/acquisition.py

@@ -105,10 +105,12 @@ def gaussian_lcb(X, model, kappa=1.96, return_grad=False):
         It should have a ``return_std`` parameter that returns the standard
         deviation.
 
-    * `kappa`: [float, default 1.96]:
+    * `kappa`: [float, default 1.96 or 'inf']:
         Controls how much of the variance in the predicted values should be
         taken into account. If set to be very high, then we are favouring
         exploration over exploitation and vice versa.
+        If set to 'inf', the acquisition function will only use the variance
+        which is useful in a pure exploration setting.
         Useless if ``method`` is set to "LCB".
 
     * `return_grad`: [boolean, optional]:
@@ -132,10 +134,14 @@ def gaussian_lcb(X, model, kappa=1.96, return_grad=False):
                 X, return_std=True, return_mean_grad=True,
                 return_std_grad=True)
 
+            if kappa == "inf":
+                return -std, -std_grad
             return mu - kappa * std, mu_grad - kappa * std_grad
 
         else:
             mu, std = model.predict(X, return_std=True)
+            if kappa == "inf":
+                return -std
             return mu - kappa * std
 
 

skopt/tests/test_acquisition.py

@@ -71,6 +71,14 @@ def test_acquisition_pi_correctness():
 
 
 @pytest.mark.fast_test
+def test_acquisition_variance_correctness():
+    # check that it works with a vector as well
+    X = 10 * np.ones((4, 2))
+    var = gaussian_lcb(X, ConstSurrogate(), kappa='inf')
+    assert_array_almost_equal(var, [-1.0] * 4)
+
+
+@pytest.mark.fast_test
 def test_acquisition_lcb_correctness():
     # check that it works with a vector as well
     X = 10 * np.ones((4, 2))