Support imputations with ndarray data

RELEASE-NOTES.md

@@ -1,10 +1,11 @@
 # Release Notes
 
-## PyMC3 vNext (on deck)
+## PyMC3 vNext (3.11.1)
 
 ### Breaking Changes
 
 ### New Features
++ Automatic imputations now also work with `ndarray` data, not just `pd.Series` or `pd.DataFrame` (see[#4439](https://github.com/pymc-devs/pymc3/pull/4439)).
 
 ### Maintenance
 - `math.log1mexp_numpy` no longer raises RuntimeWarning when given very small inputs. These were commonly observed during NUTS sampling (see [#4428](https://github.com/pymc-devs/pymc3/pull/4428)).

pymc3/model.py

@@ -1695,16 +1695,31 @@ def pandas_to_array(data):
     XXX: When `data` is a generator, this will return a Theano tensor!
 
     """
-    if hasattr(data, "values"):  # pandas
-        if data.isnull().any().any():  # missing values
-            ret = np.ma.MaskedArray(data.values, data.isnull().values)
+    if hasattr(data, "to_numpy") and hasattr(data, "isnull"):
+        # typically, but not limited to pandas objects
+        vals = data.to_numpy()
+        mask = data.isnull().to_numpy()
+        if mask.any():
+            # there are missing values
+            ret = np.ma.MaskedArray(vals, mask)
         else:
-            ret = data.values
-    elif hasattr(data, "mask"):
-        if data.mask.any():
-            ret = data
-        else:  # empty mask
-            ret = data.filled()
+            ret = vals
+    elif isinstance(data, np.ndarray):
+        if isinstance(data, np.ma.MaskedArray):
+            if not data.mask.any():
+                # empty mask
+                ret = data.filled()
+            else:
+                # already masked and rightly so
+                ret = data
+        else:
+            # already a ndarray, but not masked
+            mask = np.isnan(data)
+            if np.any(mask):
+                ret = np.ma.MaskedArray(data, mask)
+            else:
+                # no masking required
+                ret = data
     elif isinstance(data, theano.graph.basic.Variable):
         ret = data
     elif sps.issparse(data):

pymc3/tests/test_model_helpers.py

@@ -41,9 +41,8 @@ def test_pandas_to_array(self, input_dtype):
         pandas_input = pd.DataFrame(dense_input)
 
         # All the even numbers are replaced with NaN
-        missing_pandas_input = pd.DataFrame(
-            np.array([[np.nan, 1, np.nan], [3, np.nan, 5], [np.nan, 7, np.nan]])
-        )
+        missing_numpy_input = np.array([[np.nan, 1, np.nan], [3, np.nan, 5], [np.nan, 7, np.nan]])
+        missing_pandas_input = pd.DataFrame(missing_numpy_input)
         masked_array_input = ma.array(dense_input, mask=(np.mod(dense_input, 2) == 0))
 
         # Create a generator object. Apparently the generator object needs to
@@ -72,7 +71,7 @@ def test_pandas_to_array(self, input_dtype):
 
         # Check function behavior when using masked array inputs and pandas
         # objects with missing data
-        for input_value in [masked_array_input, missing_pandas_input]:
+        for input_value in [missing_numpy_input, masked_array_input, missing_pandas_input]:
             func_output = func(input_value)
             assert isinstance(func_output, ma.core.MaskedArray)
             assert func_output.shape == input_value.shape