fix: make vstack correctly handle stacking dataframes and series

modAL/expected_error.py

@@ -16,7 +16,7 @@
 
 
 def expected_error_reduction(learner: ActiveLearner, X: modALinput, loss: str = 'binary',
-                             p_subsample: np.float = 1.0, n_instances: int = 1,
+                             p_subsample: float = 1.0, n_instances: int = 1,
                              random_tie_break: bool = False) -> np.ndarray:
     """
     Expected error reduction query strategy.

modAL/utils/data.py

@@ -6,7 +6,7 @@
 
 try:
     import torch
-except:
+except ImportError:
     pass
 
 
@@ -23,22 +23,37 @@ def data_vstack(blocks: Sequence[modALinput]) -> modALinput:
     Returns:
         New sequence of vertically stacked elements.
     """
+
+    if not blocks:
+        return blocks
+
+    types = {type(block) for block in blocks}
+
     if any([sp.issparse(b) for b in blocks]):
         return sp.vstack(blocks)
-    elif isinstance(blocks[0], pd.DataFrame):
-        return blocks[0].append(blocks[1:])
-    elif isinstance(blocks[0], np.ndarray):
+    elif types - {pd.DataFrame, pd.Series} == set():
+        def _block_to_df(block):
+            if isinstance(block, pd.DataFrame):
+                return block
+            elif isinstance(block, pd.Series):
+                # interpret series as a row
+                return block.to_frame().T
+            else:
+                raise TypeError(f"Expected DataFrame or Series but encountered {type(block)}")
+
+        return pd.concat([_block_to_df(block) for block in blocks])
+    elif types == {np.ndarray}:
         return np.concatenate(blocks)
-    elif isinstance(blocks[0], list):
+    elif types == {list}:
         return np.concatenate(blocks).tolist()
 
     try:
-        if torch.is_tensor(blocks[0]):
+        if all(torch.is_tensor(block) for block in blocks):
             return torch.cat(blocks)
     except:
         pass
 
-    raise TypeError("%s datatype is not supported" % type(blocks[0]))
+    raise TypeError("%s datatype(s) not supported" % types)
 
 
 def data_hstack(blocks: Sequence[modALinput]) -> modALinput:
@@ -51,13 +66,19 @@ def data_hstack(blocks: Sequence[modALinput]) -> modALinput:
     Returns:
         New sequence of horizontally stacked elements.
     """
+
+    if not blocks:
+        return blocks
+
+    types = {type(block) for block in blocks}
+
     if any([sp.issparse(b) for b in blocks]):
         return sp.hstack(blocks)
-    elif isinstance(blocks[0], pd.DataFrame):
+    elif types == {pd.DataFrame}:
         pd.concat(blocks, axis=1)
-    elif isinstance(blocks[0], np.ndarray):
+    elif types == {np.ndarray}:
         return np.hstack(blocks)
-    elif isinstance(blocks[0], list):
+    elif types == {list}:
         return np.hstack(blocks).tolist()
 
     try:
@@ -66,7 +87,7 @@ def data_hstack(blocks: Sequence[modALinput]) -> modALinput:
     except:
         pass
 
-    TypeError("%s datatype is not supported" % type(blocks[0]))
+    raise TypeError("%s datatype(s) not supported" % types)
 
 
 def add_row(X: modALinput, row: modALinput):

rtd_requirements.txt

@@ -1,7 +1,8 @@
-numpy==1.20.0
+numpy
 scipy
 scikit-learn
 ipykernel
 nbsphinx
 pandas
 skorch
+torch