reflect review

obp/policy/linear.py

@@ -146,7 +146,7 @@ def select_action(self, context: np.ndarray) -> np.ndarray:
         """
         check_array(array=context, name="context", expected_dim=2)
         if context.shape[0] != 1:
-            raise ValueError("Expected `context.shape[1] == 1`, but found it False")
+            raise ValueError("Expected `context.shape[0] == 1`, but found it False")
 
         if self.random_.rand() > self.epsilon:
             self.theta_hat = np.concatenate(
@@ -222,7 +222,7 @@ def select_action(self, context: np.ndarray) -> np.ndarray:
         """
         check_array(array=context, name="context", expected_dim=2)
         if context.shape[0] != 1:
-            raise ValueError("Expected `context.shape[1] == 1`, but found it False")
+            raise ValueError("Expected `context.shape[0] == 1`, but found it False")
 
         self.theta_hat = np.concatenate(
             [

obp/policy/logistic.py

@@ -54,7 +54,7 @@ def __post_init__(self) -> None:
             raise ValueError(f"`alpha_`= {self.alpha_}, must be > 0.0.")
 
         check_scalar(self.lambda_, "lambda_", float)
-        if self.alpha_ <= 0.0:
+        if self.lambda_ <= 0.0:
             raise ValueError(f"`lambda_`= {self.lambda_}, must be > 0.0.")
 
         self.alpha_list = self.alpha_ * np.ones(self.n_actions)

tests/policy/test_logistic.py

@@ -7,6 +7,55 @@
 from obp.policy.logistic import MiniBatchLogisticRegression
 
 
+def test_logistic_base_exception():
+    # invalid dim
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=2, dim=-3)
+
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=2, dim=0)
+
+    with pytest.raises(TypeError):
+        LogisticEpsilonGreedy(n_actions=2, dim="3")
+
+    # invalid n_actions
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=-3, dim=2)
+
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=1, dim=2)
+
+    with pytest.raises(TypeError):
+        LogisticEpsilonGreedy(n_actions="2", dim=2)
+
+    # invalid len_list
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=2, dim=2, len_list=-3)
+
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=2, dim=2, len_list=0)
+
+    with pytest.raises(TypeError):
+        LogisticEpsilonGreedy(n_actions=2, dim=2, len_list="3")
+
+    # invalid batch_size
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=2, dim=2, batch_size=-2)
+
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=2, dim=2, batch_size=0)
+
+    with pytest.raises(TypeError):
+        LogisticEpsilonGreedy(n_actions=2, dim=2, batch_size="10")
+
+    # invalid relationship between n_actions and len_list
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=5, len_list=10, dim=2)
+
+    with pytest.raises(ValueError):
+        LogisticEpsilonGreedy(n_actions=2, len_list=3, dim=2)
+
+
 def test_logistic_epsilon_normal_epsilon():
 
     policy1 = LogisticEpsilonGreedy(n_actions=2, dim=2)