enh: convert TensorFlow to numpy in histplots

jonas-eschle · jonas-eschle · commit 82fd35de3692 · 2024-04-15T13:15:41.000-04:00
diff --git a/lib/matplotlib/cbook.py b/lib/matplotlib/cbook.py
@@ -2381,6 +2381,20 @@ def _is_jax_array(x):
         # may have arbitrary user code, so we deliberately catch all exceptions
         return False
 
+def _is_tensorflow_array(x):
+    """Check if 'x' is a TensorFlow Tensor or Variable."""
+    try:
+        # we're intentionally not attempting to import TensorFlow. If somebody
+        # has created a TensorFlow array, TensorFlow should already be in sys.modules
+        # we use `is_tensor` to not depend on the class structure of TensorFlow
+        # arrays, as `tf.Variables` are not instances of `tf.Tensor`
+        # (but convert the same way)
+        return isinstance(x, sys.modules['tensorflow'].is_tensor(x))
+    except Exception:  # TypeError, KeyError, AttributeError, maybe others?
+        # we're attempting to access attributes on imported modules which
+        # may have arbitrary user code, so we deliberately catch all exceptions
+        return False
+
 
 def _unpack_to_numpy(x):
     """Internal helper to extract data from e.g. pandas and xarray objects."""
@@ -2396,10 +2410,14 @@ def _unpack_to_numpy(x):
         # so in this case we do not want to return a function
         if isinstance(xtmp, np.ndarray):
             return xtmp
-    if _is_torch_array(x) or _is_jax_array(x):
-        xtmp = x.__array__()
-
-        # In case __array__() method does not return a numpy array in future
+    if _is_torch_array(x) or _is_jax_array(x) or _is_tensorflow_array(x):
+        # using np.asarray() instead of explicitly __array__(), as the latter is
+        # only _one_ of many methods, and it's the last resort, see also
+        # https://numpy.org/devdocs/user/basics.interoperability.html#using-arbitrary-objects-in-numpy
+        # therefore, let arrays do better if they can
+        xtmp = np.asarray(x)
+
+        # In case np.asarray method does not return a numpy array in future
         if isinstance(xtmp, np.ndarray):
             return xtmp
     return x
diff --git a/lib/matplotlib/tests/test_cbook.py b/lib/matplotlib/tests/test_cbook.py
@@ -963,7 +963,10 @@ def __array__(self):
     torch_tensor = torch.Tensor(data)
 
     result = cbook._unpack_to_numpy(torch_tensor)
-    assert result is torch_tensor.__array__()
+    # compare results, do not check for identity: the latter would fail
+    # if not mocked, and the implementation does not guarantee it
+    # is the same Python object, just the same values.
+    assert_array_equal(result, data)
 
 
 def test_unpack_to_numpy_from_jax():
@@ -988,4 +991,35 @@ def __array__(self):
     jax_array = jax.Array(data)
 
     result = cbook._unpack_to_numpy(jax_array)
-    assert result is jax_array.__array__()
+    # compare results, do not check for identity: the latter would fail
+    # if not mocked, and the implementation does not guarantee it
+    # is the same Python object, just the same values.
+    assert_array_equal(result, data)
+
+def test_unpack_to_numpy_from_tensorflow():
+    """
+    Test that tensorflow arrays are converted to NumPy arrays.
+
+    We don't want to create a dependency on tensorflow in the test suite, so we mock it.
+    """
+    class Tensor:
+        def __init__(self, data):
+            self.data = data
+
+        def __array__(self):
+            return self.data
+
+    tensorflow = ModuleType('tensorflow')
+    tensorflow.is_tensor = lambda x: isinstance(x, Tensor)
+    tensorflow.Tensor = Tensor
+
+    sys.modules['tensorflow'] = tensorflow
+
+    data = np.arange(10)
+    tf_tensor = tensorflow.Tensor(data)
+
+    result = cbook._unpack_to_numpy(tf_tensor)
+    # compare results, do not check for identity: the latter would fail
+    # if not mocked, and the implementation does not guarantee it
+    # is the same Python object, just the same values.
+    assert_array_equal(result, data)
