[no ci] minor fixes to RandomSubsample transform

Author: vpratz

bayesflow/adapters/transforms/random_subsample.py

@@ -8,8 +8,10 @@ class RandomSubsample(ElementwiseTransform):
     """
     A transform that takes a random subsample of the data within an axis.
 
-    Example: adapter.random_subsample("x", sample_size = 3, axis = -1)
+    Examples
+    --------
 
+    >>> adapter = bf.Adapter().random_subsample("x", sample_size=3, axis=-1)
     """
 
     def __init__(
@@ -20,23 +22,22 @@ def __init__(
         super().__init__()
         if isinstance(sample_size, float):
             if sample_size <= 0 or sample_size >= 1:
-                ValueError("Sample size as a percentage must be a float between 0 and 1 exclusive. ")
+                raise ValueError("Sample size as a percentage must be a float between 0 and 1 exclusive. ")
         self.sample_size = sample_size
         self.axis = axis
 
     def forward(self, data: np.ndarray, **kwargs) -> np.ndarray:
-        axis = self.axis
-        max_sample_size = data.shape[axis]
+        max_sample_size = data.shape[self.axis]
 
         if isinstance(self.sample_size, int):
             sample_size = self.sample_size
         else:
             sample_size = np.round(self.sample_size * max_sample_size)
 
         # random sample without replacement
-        sample_indices = np.random.permutation(max_sample_size)[0 : sample_size - 1]
+        sample_indices = np.random.permutation(max_sample_size)[:sample_size]
 
-        return np.take(data, sample_indices, axis)
+        return np.take(data, sample_indices, self.axis)
 
     def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
         # non invertible transform