Bayesian optimization fixed for multidimensional functions

examples/bayesian_optimization_multidim.py

@@ -0,0 +1,26 @@
+import numpy as np
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import Matern
+from modAL.models import BayesianOptimizer
+from modAL.acquisition import max_EI
+
+
+# generating the data
+x1, x2 = np.linspace(0, 10, 11).reshape(-1, 1), np.linspace(0, 10, 11).reshape(-1, 1)
+x1, x2 = np.meshgrid(x1, x2)
+X = np.concatenate((x1.reshape(-1, 1), x2.reshape(-1, 1)), axis=1)
+
+y = np.sin(np.linalg.norm(X, axis=1))/2 - ((10 - np.linalg.norm(X, axis=1))**2)/50 + 2
+
+# assembling initial training set
+X_initial, y_initial = X[:10], y[:10]
+
+# defining the kernel for the Gaussian process
+kernel = Matern(length_scale=1.0)
+
+optimizer = BayesianOptimizer(estimator=GaussianProcessRegressor(kernel=kernel),
+                              X_training=X_initial, y_training=y_initial,
+                              query_strategy=max_EI)
+
+query_idx, query_inst = optimizer.query(X)
+optimizer.teach(X[query_idx].reshape(1, -1), y[query_idx])

modAL/acquisition.py

@@ -47,7 +47,7 @@ def optimizer_PI(optimizer: BaseLearner, X: modALinput, tradeoff: float = 0) ->
     """
     try:
         mean, std = optimizer.predict(X, return_std=True)
-        std = std.reshape(-1, 1)
+        mean, std = mean.reshape(-1, ), std.reshape(-1, )
     except NotFittedError:
         mean, std = np.zeros(shape=(X.shape[0], 1)), np.ones(shape=(X.shape[0], 1))
 
@@ -68,7 +68,7 @@ def optimizer_EI(optimizer: BaseLearner, X: modALinput, tradeoff: float = 0) ->
     """
     try:
         mean, std = optimizer.predict(X, return_std=True)
-        std = std.reshape(-1, 1)
+        mean, std = mean.reshape(-1, ), std.reshape(-1, )
     except NotFittedError:
         mean, std = np.zeros(shape=(X.shape[0], 1)), np.ones(shape=(X.shape[0], 1))
 
@@ -89,7 +89,7 @@ def optimizer_UCB(optimizer: BaseLearner, X: modALinput, beta: float = 1) -> np.
     """
     try:
         mean, std = optimizer.predict(X, return_std=True)
-        std = std.reshape(-1, 1)
+        mean, std = mean.reshape(-1, ), std.reshape(-1, )
     except NotFittedError:
         mean, std = np.zeros(shape=(X.shape[0], 1)), np.ones(shape=(X.shape[0], 1))
 

tests/core_tests.py

@@ -210,8 +210,8 @@ def test_acquisition_functions(self):
 
     def test_optimizer_PI(self):
         for n_samples in range(1, 100):
-            mean = np.random.rand(n_samples, 1)
-            std = np.random.rand(n_samples, 1)
+            mean = np.random.rand(n_samples, )
+            std = np.random.rand(n_samples, )
             tradeoff = np.random.rand()
             max_val = np.random.rand()
 
@@ -239,8 +239,8 @@ def test_optimizer_PI(self):
 
     def test_optimizer_EI(self):
         for n_samples in range(1, 100):
-            mean = np.random.rand(n_samples, 1)
-            std = np.random.rand(n_samples, 1)
+            mean = np.random.rand(n_samples, )
+            std = np.random.rand(n_samples, )
             tradeoff = np.random.rand()
             max_val = np.random.rand()
 
@@ -272,8 +272,8 @@ def test_optimizer_EI(self):
 
     def test_optimizer_UCB(self):
         for n_samples in range(1, 100):
-            mean = np.random.rand(n_samples, 1)
-            std = np.random.rand(n_samples, 1)
+            mean = np.random.rand(n_samples, )
+            std = np.random.rand(n_samples, )
             beta = np.random.rand()
 
             # 1. fitted estimator