fix the tests

Author: williambdean

tests/test_data.py

@@ -13,11 +13,9 @@
 #   limitations under the License.
 
 import io
-import itertools as it
 
 from os import path
 
-import cloudpickle
 import numpy as np
 import pytensor
 import pytensor.tensor as pt
@@ -29,7 +27,7 @@
 import pymc as pm
 
 from pymc.data import MinibatchOp
-from pymc.pytensorf import GeneratorOp, floatX
+from pymc.pytensorf import floatX
 
 
 class TestData:
@@ -495,83 +493,6 @@ def integers_ndim(ndim):
         i += 1
 
 
-@pytest.mark.usefixtures("strict_float32")
-class TestGenerator:
-    def test_basic(self):
-        generator = pm.GeneratorAdapter(integers())
-        gop = GeneratorOp(generator)()
-        assert gop.tag.test_value == np.float32(0)
-        f = pytensor.function([], gop)
-        assert f() == np.float32(0)
-        assert f() == np.float32(1)
-        for _ in range(2, 100):
-            f()
-        assert f() == np.float32(100)
-
-    def test_ndim(self):
-        for ndim in range(10):
-            res = list(it.islice(integers_ndim(ndim), 0, 2))
-            generator = pm.GeneratorAdapter(integers_ndim(ndim))
-            gop = GeneratorOp(generator)()
-            f = pytensor.function([], gop)
-            assert ndim == res[0].ndim
-            np.testing.assert_equal(f(), res[0])
-            np.testing.assert_equal(f(), res[1])
-
-    def test_cloning_available(self):
-        gop = pm.generator(integers())
-        res = gop**2
-        shared = pytensor.shared(pm.floatX(10))
-        res1 = pytensor.clone_replace(res, {gop: shared})
-        f = pytensor.function([], res1)
-        assert f() == np.float32(100)
-
-    def test_default_value(self):
-        def gen():
-            for i in range(2):
-                yield pm.floatX(np.ones((10, 10)) * i)
-
-        gop = pm.generator(gen(), np.ones((10, 10)) * 10)
-        f = pytensor.function([], gop)
-        np.testing.assert_equal(np.ones((10, 10)) * 0, f())
-        np.testing.assert_equal(np.ones((10, 10)) * 1, f())
-        np.testing.assert_equal(np.ones((10, 10)) * 10, f())
-        with pytest.raises(ValueError):
-            gop.set_default(1)
-
-    def test_set_gen_and_exc(self):
-        def gen():
-            for i in range(2):
-                yield pm.floatX(np.ones((10, 10)) * i)
-
-        gop = pm.generator(gen())
-        f = pytensor.function([], gop)
-        np.testing.assert_equal(np.ones((10, 10)) * 0, f())
-        np.testing.assert_equal(np.ones((10, 10)) * 1, f())
-        with pytest.raises(StopIteration):
-            f()
-        gop.set_gen(gen())
-        np.testing.assert_equal(np.ones((10, 10)) * 0, f())
-        np.testing.assert_equal(np.ones((10, 10)) * 1, f())
-
-    def test_pickling(self, datagen):
-        gen = pm.generator(datagen)
-        cloudpickle.loads(cloudpickle.dumps(gen))
-        bad_gen = pm.generator(integers())
-        with pytest.raises(TypeError):
-            cloudpickle.dumps(bad_gen)
-
-    def test_gen_cloning_with_shape_change(self, datagen):
-        gen = pm.generator(datagen)
-        gen_r = pt.random.normal(size=gen.shape).T
-        X = gen.dot(gen_r)
-        res, _ = pytensor.scan(lambda x: x.sum(), X, n_steps=X.shape[0])
-        assert res.eval().shape == (50,)
-        shared = pytensor.shared(datagen.data.astype(gen.dtype))
-        res2 = pytensor.clone_replace(res, {gen: shared**2})
-        assert res2.eval().shape == (1000,)
-
-
 def gen1():
     i = 0
     while True:

tests/test_pytensorf.py

@@ -27,7 +27,6 @@
 from pytensor.graph.basic import Variable, equal_computations
 from pytensor.tensor.random.basic import normal, uniform
 from pytensor.tensor.subtensor import AdvancedIncSubtensor
-from pytensor.tensor.variable import TensorVariable
 
 import pymc as pm
 
@@ -37,19 +36,16 @@
 from pymc.exceptions import NotConstantValueError
 from pymc.logprob.utils import ParameterValueError
 from pymc.pytensorf import (
-    GeneratorOp,
     collect_default_updates,
     compile,
     constant_fold,
     convert_data,
-    convert_generator_data,
     extract_obs_data,
     hessian,
     hessian_diag,
     replace_rng_nodes,
     replace_vars_in_graphs,
     reseed_rngs,
-    smarttypeX,
     walk_model,
 )
 from pymc.vartypes import int_types
@@ -265,32 +261,6 @@ def test_convert_data(input_dtype):
         assert pytensor_output.dtype == intX
 
 
-@pytest.mark.parametrize("input_dtype", ["int32", "int64", "float32", "float64"])
-def test_convert_generator_data(input_dtype):
-    # Create a generator object producing NumPy arrays with the intended dtype.
-    # This is required to infer the correct dtype.
-    square_generator = (np.array([i**2], dtype=input_dtype) for i in range(100))
-
-    # Output is NOT wrapped with `pm.floatX`/`intX`,
-    # but produced from calling a special Op.
-    with pytest.warns(DeprecationWarning, match="get in touch"):
-        result = convert_generator_data(square_generator)
-    apply = result.owner
-    op = apply.op
-    # Make sure the returned object is a PyTensor TensorVariable
-    assert isinstance(result, TensorVariable)
-    assert isinstance(op, GeneratorOp), f"It's a {type(apply)}"
-    # There are no inputs - because it generates...
-    assert apply.inputs == []
-
-    # Evaluation results should have the correct* dtype!
-    # (*intX/floatX will be enforced!)
-    evaled = result.eval()
-    expected_dtype = smarttypeX(np.array(1, dtype=input_dtype)).dtype
-    assert result.type.dtype == expected_dtype
-    assert evaled.dtype == np.dtype(expected_dtype)
-
-
 def test_pandas_to_array_pandas_index():
     data = pd.Index([1, 2, 3])
     result = convert_data(data)