expected_error_reduction fixed for multidimensional data

Author: cosmic-cortex

modAL/expected_error.py

@@ -61,8 +61,8 @@ def expected_error_reduction(learner: ActiveLearner, X: modALinput, loss: str =
         if np.random.rand() <= p_subsample:
             # estimate the expected error
             for y_idx, y in enumerate(possible_labels):
-                X_new = data_vstack((learner.X_training, x.reshape(1, -1)))
-                y_new = data_vstack((learner.y_training, np.array(y).reshape(1, )))
+                X_new = data_vstack((learner.X_training, np.expand_dims(x, axis=0)))
+                y_new = data_vstack((learner.y_training, np.array(y).reshape(1,)))
 
                 cloned_estimator.fit(X_new, y_new)
                 refitted_proba = cloned_estimator.predict_proba(X)
@@ -73,6 +73,7 @@ def expected_error_reduction(learner: ActiveLearner, X: modALinput, loss: str =
 
                 expected_error[x_idx] += np.sum(loss)*X_proba[x_idx, y_idx]
 
+
         else:
             expected_error[x_idx] = np.inf
 

tests/example_tests/multidimensional_data.py

@@ -1,11 +1,13 @@
 import numpy as np
+from sklearn.base import BaseEstimator
+
 from modAL.models import ActiveLearner
 from modAL.uncertainty import margin_sampling, entropy_sampling
 from modAL.batch import uncertainty_batch_sampling
 from modAL.expected_error import expected_error_reduction
 
 
-class MockClassifier:
+class MockClassifier(BaseEstimator):
     def __init__(self, n_classes=2):
         self.n_classes = n_classes
 
@@ -21,11 +23,11 @@ def predict_proba(self, X):
 
 if __name__ == '__main__':
     X_train = np.random.rand(10, 5, 5)
-    y_train = np.random.rand(10, 1)
+    y_train = np.random.randint(0, 2, size=10)
     X_pool = np.random.rand(10, 5, 5)
-    y_pool = np.random.rand(10, 1)
+    y_pool = np.random.randint(0, 2, size=10)
 
-    strategies = [margin_sampling, entropy_sampling, uncertainty_batch_sampling]
+    strategies = [margin_sampling, entropy_sampling, uncertainty_batch_sampling, expected_error_reduction]
 
     for query_strategy in strategies:
         print("testing %s..." % query_strategy.__name__)