Closed
Description
Describe the issue:
In basic.py, we have this definition of the icdf helper function:
def icdf(rv: TensorVariable, value: TensorLike, **kwargs) -> TensorVariable:
"""Create a graph for the inverse CDF of a Random Variable."""
value = pt.as_tensor_variable(value, dtype=rv.dtype)
try:
return _icdf_helper(rv, value, **kwargs)
except NotImplementedError:
# Try to rewrite rv
fgraph, rv_values, _ = construct_ir_fgraph({rv: value})
[ir_rv] = fgraph.outputs
return _icdf_helper(ir_rv, value, **kwargs)
I specifically have a concern about value = pt.as_tensor_variable(value, dtype=rv.dtype)
Should not the input type to icdf function be a floating point type? (Type of probability value).
rv.dtype can be integer too, such as for discrete distributions and is not the right type for the probability value input to the icdf function.
Reproduceable code example:
import pytensor as pt
import pymc as pm
from pymc.logprob.basic import icdf
from pymc.pytensorf import inputvars
dist = pm.Geometric.dist(p=0.1)
dist_icdf = icdf(dist, dist.type())
dist_icdf_fn = pt.function(list(inputvars(dist_icdf)), dist_icdf)
dist_icdf_fn(0.1)Error message:
TensorType(int64, ()) cannot store accurately value 0.1, it would be represented as 0. If you do not mind this precision loss, you can: 1) explicitly convert your data to a numpy array of dtype int64, or 2) set "allow_input_downcast=True" when calling "function".PyMC version information:
pytensor: 2.10.1
Python: 3.10.10
PyMC: latest commit b7764dd
Context for the issue:
I would like to implement a util function for a self consistency test for ICDF and logcdf of discrete distributions.
That is, to check if:
value = icdf(dist, exp(logcdf(dist, value)))