fix: default query strategy for BayesianOptimizer fixed

Author: cosmic-cortex

modAL/acquisition.py

@@ -8,7 +8,6 @@
 from scipy.special import ndtr
 from sklearn.exceptions import NotFittedError
 
-from modAL.models import BayesianOptimizer
 from modAL.utils.selection import multi_argmax
 from modAL.utils.data import modALinput
 
@@ -33,7 +32,7 @@ def UCB(mean, std, beta):
 """
 
 
-def optimizer_PI(optimizer: BayesianOptimizer, X: modALinput, tradeoff: float = 0) -> np.ndarray:
+def optimizer_PI(optimizer, X: modALinput, tradeoff: float = 0) -> np.ndarray:
     """
     Probability of improvement acquisition function for Bayesian optimization.
 
@@ -54,7 +53,7 @@ def optimizer_PI(optimizer: BayesianOptimizer, X: modALinput, tradeoff: float =
     return PI(mean, std, optimizer.y_max, tradeoff)
 
 
-def optimizer_EI(optimizer: BayesianOptimizer, X: modALinput, tradeoff: float = 0) -> np.ndarray:
+def optimizer_EI(optimizer, X: modALinput, tradeoff: float = 0) -> np.ndarray:
     """
     Expected improvement acquisition function for Bayesian optimization.
 
@@ -75,7 +74,7 @@ def optimizer_EI(optimizer: BayesianOptimizer, X: modALinput, tradeoff: float =
     return EI(mean, std, optimizer.y_max, tradeoff)
 
 
-def optimizer_UCB(optimizer: BayesianOptimizer, X: modALinput, beta: float = 1) -> np.ndarray:
+def optimizer_UCB(optimizer, X: modALinput, beta: float = 1) -> np.ndarray:
     """
     Upper confidence bound acquisition function for Bayesian optimization.
 
@@ -103,7 +102,7 @@ def optimizer_UCB(optimizer: BayesianOptimizer, X: modALinput, beta: float = 1)
 """
 
 
-def max_PI(optimizer: BayesianOptimizer, X: modALinput, tradeoff: float = 0,
+def max_PI(optimizer, X: modALinput, tradeoff: float = 0,
            n_instances: int = 1) -> Tuple[np.ndarray, modALinput]:
     """
     Maximum PI query strategy. Selects the instance with highest probability of improvement.
@@ -123,7 +122,7 @@ def max_PI(optimizer: BayesianOptimizer, X: modALinput, tradeoff: float = 0,
     return query_idx, X[query_idx]
 
 
-def max_EI(optimizer: BayesianOptimizer, X: modALinput, tradeoff: float = 0,
+def max_EI(optimizer, X: modALinput, tradeoff: float = 0,
            n_instances: int = 1) -> Tuple[np.ndarray, modALinput]:
     """
     Maximum EI query strategy. Selects the instance with highest expected improvement.
@@ -143,7 +142,7 @@ def max_EI(optimizer: BayesianOptimizer, X: modALinput, tradeoff: float = 0,
     return query_idx, X[query_idx]
 
 
-def max_UCB(optimizer: BayesianOptimizer, X: modALinput, beta: float = 1,
+def max_UCB(optimizer, X: modALinput, beta: float = 1,
             n_instances: int = 1) -> Tuple[np.ndarray, modALinput]:
     """
     Maximum UCB query strategy. Selects the instance with highest upper confidence bound.

modAL/models.py

@@ -17,6 +17,7 @@
 from modAL.utils.data import data_vstack, modALinput
 from modAL.uncertainty import uncertainty_sampling
 from modAL.disagreement import vote_entropy_sampling, max_std_sampling
+from modAL.acquisition import max_EI
 
 
 if sys.version_info >= (3, 4):
@@ -323,8 +324,15 @@ class BayesianOptimizer(BaseLearner):
         ...         optimizer.teach(X[query_idx].reshape(1, -1), y[query_idx].reshape(1, -1))
 
     """
-    def __init__(self, *args, **kwargs) -> None:
-        super(BayesianOptimizer, self).__init__(*args, **kwargs)
+    def __init__(self,
+                 estimator: BaseEstimator,
+                 query_strategy: Callable = max_EI,
+                 X_training: Optional[modALinput] = None,
+                 y_training: Optional[modALinput] = None,
+                 bootstrap_init: bool = False,
+                 **fit_kwargs) -> None:
+        super(BayesianOptimizer, self).__init__(estimator, query_strategy,
+                                                X_training, y_training, bootstrap_init, **fit_kwargs)
         # setting the maximum value
         if self.y_training is not None:
             max_idx = np.argmax(self.y_training)