Bayesian optimization example added to tests

Author: cosmic-cortex

tests/core_tests.py

@@ -749,6 +749,7 @@ def test_examples(self):
         import example_tests.stream_based_sampling
         import example_tests.custom_query_strategies
         import example_tests.information_density
+        import example_tests.bayesian_optimization
 
 
 if __name__ == '__main__':

tests/example_tests/bayesian_optimization.py

@@ -0,0 +1,40 @@
+import numpy as np
+from functools import partial
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import Matern
+from modAL.models import BayesianOptimizer
+from modAL.acquisition import PI, EI, UCB, max_PI, max_EI, max_UCB
+
+
+# generating the data
+X = np.linspace(0, 20, 1000).reshape(-1, 1)
+y = np.sin(X)/2 - ((10 - X)**2)/50 + 2
+
+# assembling initial training set
+X_initial, y_initial = X[150].reshape(1, -1), y[150].reshape(1, -1)
+
+# defining the kernel for the Gaussian process
+kernel = Matern(length_scale=1.0)
+
+tr = 0.1
+PI_tr = partial(PI, tradeoff=tr)
+PI_tr.__name__ = 'PI, tradeoff = %1.1f' % tr
+max_PI_tr = partial(max_PI, tradeoff=tr)
+
+acquisitions = zip(
+    [PI_tr, EI, UCB],
+    [max_PI_tr, max_EI, max_UCB],
+)
+
+for acquisition, query_strategy in acquisitions:
+
+    # initializing the optimizer
+    optimizer = BayesianOptimizer(
+        estimator=GaussianProcessRegressor(kernel=kernel),
+        X_training=X_initial, y_training=y_initial,
+        query_strategy=query_strategy
+    )
+
+    for n_query in range(5):
+        query_idx, query_inst = optimizer.query(X)
+        optimizer.teach(X[query_idx].reshape(1, -1), y[query_idx].reshape(1, -1))