Implement icdf for Univariate distribution (#6528)

Also extended testing.check_icdf with skip_paradomain_outside_edge_test param

---------

Co-authored-by: Michal Raczycki <[email protected]>

Author: michaelraczycki

pymc/distributions/continuous.py

@@ -345,6 +345,11 @@ def logcdf(value, lower, upper):
             msg="lower <= upper",
         )
 
+    def icdf(value, lower, upper):
+        res = lower + (upper - lower) * value
+        res = check_icdf_value(res, value)
+        return check_icdf_parameters(res, lower < upper)
+
 
 @_default_transform.register(Uniform)
 def uniform_default_transform(op, rv):

pymc/testing.py

@@ -526,6 +526,7 @@ def check_icdf(
     pymc_dist: Distribution,
     paramdomains: Dict[str, Domain],
     scipy_icdf: Callable,
+    skip_paramdomain_outside_edge_test=False,
     decimal: Optional[int] = None,
     n_samples: int = 100,
 ) -> None:
@@ -548,7 +549,7 @@ def check_icdf(
     paramdomains : Dictionary of Parameter : Domain pairs
         Supported domains of distribution parameters
     scipy_icdf : Scipy icdf method
-        Scipy icdf (ppp) method of equivalent pymc_dist distribution
+        Scipy icdf (ppf) method of equivalent pymc_dist distribution
     decimal : int, optional
         Level of precision with which pymc_dist and scipy_icdf are compared.
         Defaults to 6 for float64 and 3 for float32
@@ -557,6 +558,9 @@ def check_icdf(
         are compared between pymc and scipy methods. If n_samples is below the
         total number of combinations, a random subset is evaluated. Setting
         n_samples = -1, will return all possible combinations. Defaults to 100
+    skip_paradomain_outside_edge_test : Bool
+        Whether to run test 2., which checks that pymc distribution icdf
+        returns nan for invalid parameter values outside the supported domain edge
 
     """
     if decimal is None:
@@ -586,19 +590,20 @@ def check_icdf(
     valid_params = {param: paramdomain.vals[0] for param, paramdomain in paramdomains.items()}
     valid_params["q"] = valid_value
 
-    # Test pymc distribution raises ParameterValueError for parameters outside the
-    # supported domain edges (excluding edges)
-    invalid_params = find_invalid_scalar_params(paramdomains)
-    for invalid_param, invalid_edges in invalid_params.items():
-        for invalid_edge in invalid_edges:
-            if invalid_edge is None:
-                continue
+    if not skip_paramdomain_outside_edge_test:
+        # Test pymc distribution raises ParameterValueError for parameters outside the
+        # supported domain edges (excluding edges)
+        invalid_params = find_invalid_scalar_params(paramdomains)
+        for invalid_param, invalid_edges in invalid_params.items():
+            for invalid_edge in invalid_edges:
+                if invalid_edge is None:
+                    continue
 
-            point = valid_params.copy()
-            point[invalid_param] = invalid_edge
-            with pytest.raises(ParameterValueError):
-                pymc_icdf(**point)
-                pytest.fail(f"test_params={point}")
+                point = valid_params.copy()
+                point[invalid_param] = invalid_edge
+                with pytest.raises(ParameterValueError):
+                    pymc_icdf(**point)
+                    pytest.fail(f"test_params={point}")
 
     # Test that values below 0 or above 1 evaluate to nan
     invalid_values = find_invalid_scalar_params({"q": domain})["q"]

tests/distributions/test_continuous.py

@@ -26,9 +26,9 @@
 
 import pymc as pm
 
-from pymc.distributions.continuous import Normal, get_tau_sigma, interpolated
+from pymc.distributions.continuous import Normal, Uniform, get_tau_sigma, interpolated
 from pymc.distributions.dist_math import clipped_beta_rvs
-from pymc.logprob.abstract import logcdf
+from pymc.logprob.abstract import icdf, logcdf
 from pymc.logprob.joint_logprob import logp
 from pymc.logprob.utils import ParameterValueError
 from pymc.pytensorf import floatX
@@ -176,13 +176,21 @@ def test_uniform(self):
             lambda value, lower, upper: st.uniform.logcdf(value, lower, upper - lower),
             skip_paramdomain_outside_edge_test=True,
         )
+        check_icdf(
+            pm.Uniform,
+            {"lower": -Rplusunif, "upper": Rplusunif},
+            lambda q, lower, upper: st.uniform.ppf(q=q, loc=lower, scale=upper - lower),
+            skip_paramdomain_outside_edge_test=True,
+        )
         # Custom logp / logcdf check for invalid parameters
         invalid_dist = pm.Uniform.dist(lower=1, upper=0)
         with pytensor.config.change_flags(mode=Mode("py")):
             with pytest.raises(ParameterValueError):
                 logp(invalid_dist, np.array(0.5)).eval()
             with pytest.raises(ParameterValueError):
                 logcdf(invalid_dist, np.array(0.5)).eval()
+            with pytest.raises(ParameterValueError):
+                icdf(invalid_dist, np.array(0.5)).eval()
 
     def test_triangular(self):
         check_logp(