Callback

gpflowopt/acquisition/acquisition.py

@@ -396,27 +396,31 @@ class MCMCAcquistion(AcquisitionSum):
     """
     Apply MCMC over the hyperparameters of an acquisition function (= over the hyperparameters of the contained models).
 
-    The models passed into an object of this class are optimized with MLE, and then further sampled with HMC.
-    These hyperparameter samples are then set in copies of the acquisition.
+    The models passed into an object of this class are optimized with MLE (fast burn-in), and then further sampled with
+    HMC. These hyperparameter samples are then set in copies of the acquisition.
 
     For evaluating the underlying acquisition function, the predictions of the acquisition copies are averaged.
     """
     def __init__(self, acquisition, n_slices, **kwargs):
         assert isinstance(acquisition, Acquisition)
         assert n_slices > 0
-
-        copies = [copy.deepcopy(acquisition) for _ in range(n_slices - 1)]
-        for c in copies:
-            c.optimize_restarts = 0
-
         # the call to the constructor of the parent classes, will optimize acquisition, so it obtains the MLE solution.
-        super(MCMCAcquistion, self).__init__([acquisition] + copies)
+        super(MCMCAcquistion, self).__init__([acquisition]*n_slices)
+        self._needs_new_copies = True
         self._sample_opt = kwargs
 
     def _optimize_models(self):
         # Optimize model #1
         self.operands[0]._optimize_models()
 
+        # Copy it again if needed due to changed free state
+        if self._needs_new_copies:
+            new_copies = [copy.deepcopy(self.operands[0]) for _ in range(len(self.operands) - 1)]
+            for c in new_copies:
+                c.optimize_restarts = 0
+            self.operands = ParamList([self.operands[0]] + new_copies)
+            self._needs_new_copies = False
+
         # Draw samples using HMC
         # Sample each model of the acquisition function - results in a list of 2D ndarrays.
         hypers = np.hstack([model.sample(len(self.operands), **self._sample_opt) for model in self.models])
@@ -440,3 +444,13 @@ def set_data(self, X, Y):
     def build_acquisition(self, Xcand):
         # Average the predictions of the copies.
         return 1. / len(self.operands) * super(MCMCAcquistion, self).build_acquisition(Xcand)
+
+    def _kill_autoflow(self):
+        """
+        Flag for recreation on next optimize.
+
+        Following the recompilation of models, the free state might have changed. This means updating the samples can
+        cause inconsistencies and errors.
+        """
+        super(MCMCAcquistion, self)._kill_autoflow()
+        self._needs_new_copies = True

gpflowopt/bo.py

@@ -16,12 +16,40 @@
 
 import numpy as np
 from scipy.optimize import OptimizeResult
+import tensorflow as tf
+from gpflow.gpr import GPR
 
 from .acquisition import Acquisition, MCMCAcquistion
-from .optim import Optimizer, SciPyOptimizer
-from .objective import ObjectiveWrapper
 from .design import Design, EmptyDesign
+from .objective import ObjectiveWrapper
+from .optim import Optimizer, SciPyOptimizer
 from .pareto import non_dominated_sort
+from .models import ModelWrapper
+
+
+def jitchol_callback(models):
+    """
+    Increase the likelihood in case of Cholesky failures.
+
+    This is similar to the use of jitchol in GPy. Default callback for BayesianOptimizer.
+    Only usable on GPR models, other types are ignored.
+    """
+    for m in np.atleast_1d(models):
+        if isinstance(m, ModelWrapper):
+            jitchol_callback(m.wrapped)  # pragma: no cover
+
+        if not isinstance(m, GPR):
+            continue
+
+        s = m.get_free_state()
+        eKdiag = np.mean(np.diag(m.kern.compute_K_symm(m.X.value)))
+        for e in [0] + [10**ex for ex in range(-6,-1)]:
+            try:
+                m.likelihood.variance = m.likelihood.variance.value + e * eKdiag
+                m.optimize(maxiter=5)
+                break
+            except tf.errors.InvalidArgumentError:  # pragma: no cover
+                m.set_state(s)
 
 
 class BayesianOptimizer(Optimizer):
@@ -32,7 +60,8 @@ class BayesianOptimizer(Optimizer):
     Additionally, it is configured with a separate optimizer for the acquisition function.
     """
 
-    def __init__(self, domain, acquisition, optimizer=None, initial=None, scaling=True, hyper_draws=None):
+    def __init__(self, domain, acquisition, optimizer=None, initial=None, scaling=True, hyper_draws=None,
+                 callback=jitchol_callback):
         """
         :param Domain domain: The optimization space.
         :param Acquisition acquisition: The acquisition function to optimize over the domain.
@@ -51,6 +80,12 @@ def __init__(self, domain, acquisition, optimizer=None, initial=None, scaling=Tr
             are obtained using Hamiltonian MC.
             (see `GPflow documentation <https://gpflow.readthedocs.io/en/latest//>`_ for details) for each model.
             The acquisition score is computed for each draw, and averaged.
+        :param callable callback: (optional) this function or object will be called, after the
+            data of all models has been updated with all models as retrieved by acquisition.models as argument without
+            the wrapping model handling any scaling . This allows custom model optimization strategies to be implemented.
+            All manipulations of GPflow models are permitted. Combined with the optimize_restarts parameter of
+            :class:`~.Acquisition` this allows several scenarios: do the optimization manually from the callback
+            (optimize_restarts equals 0), or choose the starting point + some random restarts (optimize_restarts > 0).
         """
         assert isinstance(acquisition, Acquisition)
         assert hyper_draws is None or hyper_draws > 0
@@ -69,6 +104,8 @@ def __init__(self, domain, acquisition, optimizer=None, initial=None, scaling=Tr
         initial = initial or EmptyDesign(domain)
         self.set_initial(initial.generate())
 
+        self._model_callback = callback
+
     @Optimizer.domain.setter
     def domain(self, dom):
         assert self.domain.size == dom.size
@@ -86,6 +123,8 @@ def _update_model_data(self, newX, newY):
         assert self.acquisition.data[0].shape[1] == newX.shape[-1]
         assert self.acquisition.data[1].shape[1] == newY.shape[-1]
         assert newX.shape[0] == newY.shape[0]
+        if newX.size == 0:
+            return
         X = np.vstack((self.acquisition.data[0], newX))
         Y = np.vstack((self.acquisition.data[1], newY))
         self.acquisition.set_data(X, Y)
@@ -174,7 +213,6 @@ def _optimize(self, fx, n_iter):
         :param n_iter: number of iterations to run
         :return: OptimizeResult object
         """
-
         assert isinstance(fx, ObjectiveWrapper)
 
         # Evaluate and add the initial design (if any)
@@ -190,6 +228,10 @@ def inverse_acquisition(x):
 
         # Optimization loop
         for i in range(n_iter):
+            # If a callback is specified, and acquisition has the setup flag enabled (indicating an upcoming
+            # compilation), run the callback.
+            if self._model_callback and self.acquisition._needs_setup:
+                self._model_callback([m.wrapped for m in self.acquisition.models])
             result = self.optimizer.optimize(inverse_acquisition)
             self._update_model_data(result.x, fx(result.x))
 

testing/test_acquisition.py

@@ -146,7 +146,6 @@ def test_object_integrity(self, acquisition):
         for oper in acquisition.operands:
             self.assertTrue(isinstance(oper, gpflowopt.acquisition.Acquisition),
                             msg="All operands should be an acquisition object")
-
         self.assertTrue(all(isinstance(m, gpflowopt.models.ModelWrapper) for m in acquisition.models))
 
     @parameterized.expand(list(zip(aggregations)))
@@ -218,9 +217,23 @@ def test_marginalized_score(self, acquisition):
         ei_mcmc = acquisition.evaluate(Xt)
         np.testing.assert_almost_equal(ei_mle, ei_mcmc, decimal=5)
 
-    @parameterized.expand(list(zip([aggregations[2]])))
-    def test_mcmc_acq_models(self, acquisition):
+    def test_mcmc_acq(self):
+        acquisition = gpflowopt.acquisition.MCMCAcquistion(
+            gpflowopt.acquisition.ExpectedImprovement(create_parabola_model(domain)), 10)
+        for oper in acquisition.operands:
+            self.assertListEqual(acquisition.models, oper.models)
+            self.assertEqual(acquisition.operands[0], oper)
+        self.assertTrue(acquisition._needs_new_copies)
+        acquisition._optimize_models()
         self.assertListEqual(acquisition.models, acquisition.operands[0].models)
+        for oper in acquisition.operands[1:]:
+            self.assertNotEqual(acquisition.operands[0], oper)
+        self.assertFalse(acquisition._needs_new_copies)
+        acquisition._setup()
+        Xt = np.random.rand(20, 2) * 2 - 1
+        ei_mle = acquisition.operands[0].evaluate(Xt)
+        ei_mcmc = acquisition.evaluate(Xt)
+        np.testing.assert_almost_equal(ei_mle, ei_mcmc, decimal=5)
 
 
 class TestJointAcquisition(unittest.TestCase):

testing/test_optimizers.py

@@ -214,8 +214,8 @@ def test_optimize_multi_objective(self):
         result = optimizer.optimize(vlmop2, n_iter=2)
         self.assertTrue(result.success)
         self.assertEqual(result.nfev, 2, "Only 2 evaluations permitted")
-        self.assertTupleEqual(result.x.shape, (9, 2))
-        self.assertTupleEqual(result.fun.shape, (9, 2))
+        self.assertTupleEqual(result.x.shape, (7, 2))
+        self.assertTupleEqual(result.fun.shape, (7, 2))
         _, dom = gpflowopt.pareto.non_dominated_sort(result.fun)
         self.assertTrue(np.all(dom==0))
 
@@ -288,6 +288,71 @@ def test_mcmc(self):
         self.assertTrue(np.allclose(result.x, 0), msg="Optimizer failed to find optimum")
         self.assertTrue(np.allclose(result.fun, 0), msg="Incorrect function value returned")
 
+    def test_callback(self):
+        class DummyCallback(object):
+            def __init__(self):
+                self.counter = 0
+
+            def __call__(self, models):
+                self.counter += 1
+
+        c = DummyCallback()
+        optimizer = gpflowopt.BayesianOptimizer(self.domain, self.acquisition, callback=c)
+        result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=2)
+        self.assertEqual(c.counter, 2)
+
+    def test_callback_recompile(self):
+        class DummyCallback(object):
+            def __init__(self):
+                self.recompile = False
+
+            def __call__(self, models):
+                c = np.random.randint(2, 10)
+                models[0].kern.variance.prior = gpflow.priors.Gamma(c, 1./c)
+                self.recompile = models[0]._needs_recompile
+
+        c = DummyCallback()
+        optimizer = gpflowopt.BayesianOptimizer(self.domain, self.acquisition, callback=c)
+        self.acquisition.evaluate(np.zeros((1,2))) # Make sure its run and setup to skip
+        result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1)
+        self.assertFalse(c.recompile)
+        result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1)
+        self.assertTrue(c.recompile)
+        self.assertFalse(self.acquisition.models[0]._needs_recompile)
+
+    def test_callback_recompile_mcmc(self):
+        class DummyCallback(object):
+            def __init__(self):
+                self.no_models = 0
+
+            def __call__(self, models):
+                c = np.random.randint(2, 10)
+                models[0].kern.variance.prior = gpflow.priors.Gamma(c, 1. / c)
+                self.no_models = len(models)
+
+        c = DummyCallback()
+        optimizer = gpflowopt.BayesianOptimizer(self.domain, self.acquisition, hyper_draws=5, callback=c)
+        opers = optimizer.acquisition.operands
+        result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1)
+        self.assertEqual(c.no_models, 1)
+        self.assertEqual(id(opers[0]), id(optimizer.acquisition.operands[0]))
+        for op1, op2 in zip(opers[1:], optimizer.acquisition.operands[1:]):
+            self.assertNotEqual(id(op1), id(op2))
+        opers = optimizer.acquisition.operands
+        result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1)
+        self.assertEqual(id(opers[0]), id(optimizer.acquisition.operands[0]))
+        for op1, op2 in zip(opers[1:], optimizer.acquisition.operands[1:]):
+            self.assertNotEqual(id(op1), id(op2))
+
+    def test_nongpr_model(self):
+        design = gpflowopt.design.LatinHyperCube(16, self.domain)
+        X, Y = design.generate(), parabola2d(design.generate())[0]
+        m = gpflow.vgp.VGP(X, Y, gpflow.kernels.RBF(2, ARD=True), likelihood=gpflow.likelihoods.Gaussian())
+        acq = gpflowopt.acquisition.ExpectedImprovement(m)
+        optimizer = gpflowopt.BayesianOptimizer(self.domain, acq)
+        result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1)
+        self.assertTrue(result.success)
+
 
 class TestSilentOptimization(unittest.TestCase):
     @contextmanager
@@ -323,3 +388,4 @@ def _optimize(self, objective):
                 opt.optimize(None)
                 output = out.getvalue().strip()
                 self.assertEqual(output, '')
+