multi_argmax and max_std_sampling test case coverage increased

Author: cosmic-cortex

tests/core_tests.py

@@ -164,7 +164,7 @@ def test_data_vstack(self):
 
     def test_multi_argmax(self):
         for n_pool in range(2, 100):
-            for n_instances in range(1, n_pool):
+            for n_instances in range(1, n_pool+1):
                 utility = np.zeros(n_pool)
                 max_idx = np.random.choice(range(n_pool), size=n_instances, replace=False)
                 utility[max_idx] = 1e-10 + np.random.rand(n_instances, )
@@ -1087,3 +1087,4 @@ def test_examples(self):
 
 if __name__ == '__main__':
     unittest.main(verbosity=2)
+0

tests/example_tests/active_regression.py

@@ -6,16 +6,10 @@
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import WhiteKernel, RBF
 from modAL.models import ActiveLearner
+from modAL.disagreement import max_std_sampling
 
 np.random.seed(0)
 
-
-# query strategy for regression
-def GP_regression_std(regressor, X):
-    _, std = regressor.predict(X, return_std=True)
-    query_idx = np.argmax(std)
-    return query_idx, X[query_idx]
-
 # generating the data
 X = np.random.choice(np.linspace(0, 20, 10000), size=200, replace=False).reshape(-1, 1)
 y = np.sin(X) + np.random.normal(scale=0.3, size=X.shape)
@@ -32,7 +26,7 @@ def GP_regression_std(regressor, X):
 # initializing the active learner
 regressor = ActiveLearner(
     estimator=GaussianProcessRegressor(kernel=kernel),
-    query_strategy=GP_regression_std,
+    query_strategy=max_std_sampling,
     X_training=X_initial.reshape(-1, 1), y_training=y_initial.reshape(-1, 1)
 )