Refactoring the ChiSquared distribution

pymc3/distributions/continuous.py

@@ -28,6 +28,7 @@
     BetaRV,
     WeibullRV,
     cauchy,
+    chisquare,
     exponential,
     gamma,
     gumbel,
@@ -2548,7 +2549,7 @@ def logcdf(value, alpha, beta):
         )
 
 
-class ChiSquared(Gamma):
+class ChiSquared(PositiveContinuous):
     r"""
     :math:`\chi^2` log-likelihood.
 
@@ -2583,13 +2584,48 @@ class ChiSquared(Gamma):
 
     Parameters
     ----------
-    nu: int
+    nu: float
         Degrees of freedom (nu > 0).
     """
+    rv_op = chisquare
 
-    def __init__(self, nu, *args, **kwargs):
-        self.nu = nu = at.as_tensor_variable(floatX(nu))
-        super().__init__(alpha=nu / 2.0, beta=0.5, *args, **kwargs)
+    @classmethod
+    def dist(cls, nu, *args, **kwargs):
+        nu = at.as_tensor_variable(floatX(nu))
+        return super().dist([nu], *args, **kwargs)
+
+    def logp(value, nu):
+        """
+        Calculate log-probability of ChiSquared distribution at specified value.
+
+        Parameters
+        ----------
+        value: numeric
+            Value(s) for which log-probability is calculated. If the log probabilities for multiple
+            values are desired the values must be provided in a numpy array or Aesara tensor
+
+        Returns
+        -------
+        TensorVariable
+        """
+        return Gamma.logp(value, nu / 2, 2)
+
+    def logcdf(value, nu):
+        """
+        Compute the log of the cumulative distribution function for ChiSquared distribution
+        at the specified value.
+
+        Parameters
+        ----------
+        value: numeric or np.ndarray or `TensorVariable`
+            Value(s) for which log CDF is calculated. If the log CDF for
+            multiple values are desired the values must be provided in a numpy
+            array or `TensorVariable`.
+        Returns
+        -------
+        TensorVariable
+        """
+        return Gamma.logcdf(value, nu / 2, 2)
 
 
 # TODO: Remove this once logpt for multiplication is working!

pymc3/tests/test_distributions.py

@@ -1030,15 +1030,26 @@ def test_half_normal(self):
             lambda value, sigma: sp.halfnorm.logcdf(value, scale=sigma),
         )
 
-    @pytest.mark.xfail(reason="Distribution not refactored yet")
-    def test_chi_squared(self):
+    def test_chisquared_logp(self):
         self.check_logp(
             ChiSquared,
             Rplus,
-            {"nu": Rplusdunif},
+            {"nu": Rplus},
             lambda value, nu: sp.chi2.logpdf(value, df=nu),
         )
 
+    @pytest.mark.xfail(
+        condition=(aesara.config.floatX == "float32"),
+        reason="Fails on float32 due to numerical issues",
+    )
+    def test_chisquared_logcdf(self):
+        self.check_logcdf(
+            ChiSquared,
+            Rplus,
+            {"nu": Rplus},
+            lambda value, nu: sp.chi2.logcdf(value, df=nu),
+        )
+
     @pytest.mark.xfail(reason="Distribution not refactored yet")
     def test_wald_logp(self):
         self.check_logp(

pymc3/tests/test_distributions_random.py

@@ -277,12 +277,6 @@ class TestAsymmetricLaplace(BaseTestCases.BaseTestCase):
     params = {"kappa": 1.0, "b": 1.0, "mu": 0.0}
 
 
-@pytest.mark.xfail(reason="This distribution has not been refactored for v4")
-class TestChiSquared(BaseTestCases.BaseTestCase):
-    distribution = pm.ChiSquared
-    params = {"nu": 2.0}
-
-
 @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
 class TestExGaussian(BaseTestCases.BaseTestCase):
     distribution = pm.ExGaussian
@@ -753,6 +747,19 @@ class TestInverseGammaMuSigma(BaseTestDistribution):
     tests_to_run = ["check_pymc_params_match_rv_op"]
 
 
+class TestChiSquared(BaseTestDistribution):
+    pymc_dist = pm.ChiSquared
+    pymc_dist_params = {"nu": 2.0}
+    expected_rv_op_params = {"nu": 2.0}
+    reference_dist_params = {"df": 2.0}
+    reference_dist = seeded_numpy_distribution_builder("chisquare")
+    tests_to_run = [
+        "check_pymc_params_match_rv_op",
+        "check_pymc_draws_match_reference",
+        "check_rv_size",
+    ]
+
+
 class TestBinomial(BaseTestDistribution):
     pymc_dist = pm.Binomial
     pymc_dist_params = {"n": 100, "p": 0.33}