Merge branch 'v4' into reintro_shape

pymc3/aesaraf.py

@@ -88,7 +88,13 @@ def pandas_to_array(data):
     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()
+        null_data = data.isnull()
+        if hasattr(null_data, "to_numpy"):
+            # pandas Series
+            mask = null_data.to_numpy()
+        else:
+            # pandas Index
+            mask = null_data
         if mask.any():
             # there are missing values
             ret = np.ma.MaskedArray(vals, mask)

pymc3/tests/test_aesaraf.py

@@ -52,7 +52,7 @@ def test_change_rv_size():
     loc = at.as_tensor_variable([1, 2])
     rv = normal(loc=loc)
     assert rv.ndim == 1
-    assert rv.eval().shape == (2,)
+    assert tuple(rv.shape.eval()) == (2,)
 
     with pytest.raises(ShapeError, match="must be ≤1-dimensional"):
         change_rv_size(rv, new_size=[[2, 3]])
@@ -61,7 +61,7 @@ def test_change_rv_size():
 
     rv_new = change_rv_size(rv, new_size=(3,), expand=True)
     assert rv_new.ndim == 2
-    assert rv_new.eval().shape == (3, 2)
+    assert tuple(rv_new.shape.eval()) == (3, 2)
 
     # Make sure that the shape used to determine the expanded size doesn't
     # depend on the old `RandomVariable`.
@@ -71,7 +71,7 @@ def test_change_rv_size():
 
     rv_newer = change_rv_size(rv_new, new_size=(4,), expand=True)
     assert rv_newer.ndim == 3
-    assert rv_newer.eval().shape == (4, 3, 2)
+    assert tuple(rv_newer.shape.eval()) == (4, 3, 2)
 
     # Make sure we avoid introducing a `Cast` by converting the new size before
     # constructing the new `RandomVariable`
@@ -80,7 +80,19 @@ def test_change_rv_size():
     rv_newer = change_rv_size(rv, new_size=new_size, expand=False)
     assert rv_newer.ndim == 2
     assert isinstance(rv_newer.owner.inputs[1], Constant)
-    assert rv_newer.eval().shape == (4, 3)
+    assert tuple(rv_newer.shape.eval()) == (4, 3)
+
+    rv = normal(0, 1)
+    new_size = at.as_tensor(np.array([4, 3], dtype="int32"))
+    rv_newer = change_rv_size(rv, new_size=new_size, expand=True)
+    assert rv_newer.ndim == 2
+    assert tuple(rv_newer.shape.eval()) == (4, 3)
+
+    rv = normal(0, 1)
+    new_size = at.as_tensor(2, dtype="int32")
+    rv_newer = change_rv_size(rv, new_size=new_size, expand=True)
+    assert rv_newer.ndim == 1
+    assert tuple(rv_newer.shape.eval()) == (2,)
 
 
 class TestBroadcasting:
@@ -436,6 +448,13 @@ def test_pandas_to_array(input_dtype):
     assert isinstance(wrapped, TensorVariable)
 
 
+def test_pandas_to_array_pandas_index():
+    data = pd.Index([1, 2, 3])
+    result = pandas_to_array(data)
+    expected = np.array([1, 2, 3])
+    np.testing.assert_array_equal(result, expected)
+
+
 def test_walk_model():
     d = at.vector("d")
     b = at.vector("b")

pymc3/tests/test_sampling.py

@@ -688,10 +688,10 @@ def test_deterministic_of_observed_modified_interface(self):
         meas_in_1 = pm.aesaraf.floatX(2 + 4 * np.random.randn(100))
         meas_in_2 = pm.aesaraf.floatX(5 + 4 * np.random.randn(100))
         with pm.Model() as model:
-            mu_in_1 = pm.Normal("mu_in_1", 0, 1)
-            sigma_in_1 = pm.HalfNormal("sd_in_1", 1)
-            mu_in_2 = pm.Normal("mu_in_2", 0, 1)
-            sigma_in_2 = pm.HalfNormal("sd__in_2", 1)
+            mu_in_1 = pm.Normal("mu_in_1", 0, 1, testval=0)
+            sigma_in_1 = pm.HalfNormal("sd_in_1", 1, testval=1)
+            mu_in_2 = pm.Normal("mu_in_2", 0, 1, testval=0)
+            sigma_in_2 = pm.HalfNormal("sd__in_2", 1, testval=1)
 
             in_1 = pm.Normal("in_1", mu_in_1, sigma_in_1, observed=meas_in_1)
             in_2 = pm.Normal("in_2", mu_in_2, sigma_in_2, observed=meas_in_2)

requirements.txt

@@ -1,4 +1,4 @@
-aesara>=2.0.8
+aesara>=2.0.9
 arviz>=0.11.2
 cachetools>=4.2.1
 dill