Invalid test value type by Bernoulli distribution

[michaelosthege]

Description of your problem

While locally running tests, a bug was surfaced by a recently added validity check (see #4214 (comment)) from Theano-PyMC:

________________________________________________________________ TestSamplePPC.test_model_shared_variable _________________________________________________________________

self = <pymc3.tests.test_sampling.TestSamplePPC object at 0x000001EF2100D748>

    def test_model_shared_variable(self):
        x = np.random.randn(100)
        y = x > 0
        x_shared = theano.shared(x)
        y_shared = theano.shared(y)
        with pm.Model() as model:
            coeff = pm.Normal("x", mu=0, sd=1)
            logistic = pm.Deterministic("p", pm.math.sigmoid(coeff * x_shared))

>           obs = pm.Bernoulli("obs", p=logistic, observed=y_shared)

pymc3\tests\test_sampling.py:572:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pymc3\distributions\distribution.py:98: in __new__
    return model.Var(name, dist, data, total_size, dims=dims)
pymc3\model.py:1166: in Var
    model=self,
pymc3\model.py:1800: in __init__
    self.tag.test_value = theano.compile.view_op(data).tag.test_value
..\theano-pymc\theano\gof\utils.py:277: in __setattr__
    obj = self.attr_filter(obj)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = TensorType(bool, vector)
data = array([1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,
       1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,...0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1,
       1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1], dtype=int64)
strict = False, allow_downcast = None

    def filter(self, data, strict=False, allow_downcast=None):
        """
        Convert `data` to something which can be associated to a
        `TensorVariable`.

        This function is not meant to be called in user code. It is for
        `Linker` instances to use when running a compiled graph.

        """
        # Explicit error message when one accidentally uses a Variable as
        # input (typical mistake, especially with shared variables).
        if isinstance(data, Variable):
            raise TypeError(
                "Expected an array-like object, but found a Variable: "
                "maybe you are trying to call a function on a (possibly "
                "shared) variable instead of a numeric array?"
            )

        if (type(data) is np.ndarray) and (data.dtype == self.numpy_dtype):
            if data.dtype.num != self.numpy_dtype.num:
                data = theano._asarray(data, dtype=self.dtype)
            # -- now fall through to ndim check
        elif (type(data) is np.memmap) and (data.dtype == self.numpy_dtype):
            # numpy.memmap is a "safe" subclass of ndarray,
            # so we can use it wherever we expect a base ndarray.
            # however, casting it would defeat the purpose of not
            # loading the whole data into memory
            pass
        elif strict:
            # If any of the two conditions above was not met,
            # we raise a meaningful TypeError.
            if not (type(data) is np.ndarray):
                raise TypeError(
                    "%s expected a ndarray object." % self, data, type(data)
                )
            if data.dtype != self.numpy_dtype:
                raise TypeError(
                    ("%s expected a ndarray object with " "dtype = %s (got %s).")
                    % (self, self.numpy_dtype, data.dtype)
                )
            raise AssertionError("This point should never be reached.")
        else:
            if allow_downcast:
                # Convert to self.dtype, regardless of the type of data
                data = theano._asarray(data, dtype=self.dtype)
                # TODO: consider to pad shape with ones to make it consistent
                # with self.broadcastable... like vector->row type thing
            else:
                if isinstance(data, np.ndarray):
                    # Check if self.dtype can accurately represent data
                    # (do not try to convert the data)
                    up_dtype = scal.upcast(self.dtype, data.dtype)
                    if up_dtype == self.dtype:
                        # Bug in the following line when data is a
                        # scalar array, see
                        # http://projects.scipy.org/numpy/ticket/1611
                        # data = data.astype(self.dtype)
                        data = theano._asarray(data, dtype=self.dtype)
                    if up_dtype != self.dtype:
                        err_msg = (
                            "%s cannot store a value of dtype %s without "
                            "risking loss of precision. If you do not mind "
                            "this loss, you can: "
                            "1) explicitly cast your data to %s, or "
                            '2) set "allow_input_downcast=True" when calling '
                            '"function". Value: "%s"'
                            % (self, data.dtype, self.dtype, repr(data))
                        )
>                       raise TypeError(err_msg)
E                       TypeError: TensorType(bool, vector) cannot store a value of dtype int64 without risking loss of precision. If you do not mind this loss, you can: 1) explicitly cast your data to bool, or 2) set "allow_input_downcast=True" when calling "function". Value: "array([1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,
E                              1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0,
E                              0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0,
E                              1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1,
E                              1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1], dtype=int64)"

..\theano-pymc\theano\tensor\type.py:145: TypeError

The problem does not surface in CI pipelines, because they don't test against latest Theano-PyMC !

Versions and main components

• PyMC3 Version: recent master
• Theano Version: recent Theano-PyMC master