Update PyTensor dependency

Author: tvwenger

conda-envs/environment-dev.yml

@@ -14,7 +14,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.16.1,<2.17
+- pytensor>=2.17.0,<2.18
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/environment-docs.yml

@@ -12,7 +12,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.16.1,<2.17
+- pytensor>=2.17.0,<2.18
 - python-graphviz
 - scipy>=1.4.1
 - typing-extensions>=3.7.4

conda-envs/environment-test.yml

@@ -17,7 +17,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.16.1,<2.17
+- pytensor>=2.17.0,<2.18
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/windows-environment-dev.yml

@@ -14,7 +14,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.16.1,<2.17
+- pytensor>=2.17.0,<2.18
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/windows-environment-test.yml

@@ -17,7 +17,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.16.1,<2.17
+- pytensor>=2.17.0,<2.18
 - python-graphviz
 - networkx
 - scipy>=1.4.1

pymc/distributions/continuous.py

@@ -36,10 +36,10 @@
 from pytensor.tensor.math import tanh
 from pytensor.tensor.random.basic import (
     BetaRV,
+    _gamma,
     cauchy,
     chisquare,
     exponential,
-    gamma,
     gumbel,
     halfcauchy,
     halfnormal,
@@ -2201,16 +2201,17 @@ class Gamma(PositiveContinuous):
     sigma : tensor_like of float, optional
         Alternative scale parameter (sigma > 0).
     """
-    rv_op = gamma
+    # gamma is temporarily a deprecation wrapper in PyTensor
+    rv_op = _gamma
 
     @classmethod
     def dist(cls, alpha=None, beta=None, mu=None, sigma=None, **kwargs):
         alpha, beta = cls.get_alpha_beta(alpha, beta, mu, sigma)
         alpha = pt.as_tensor_variable(floatX(alpha))
         beta = pt.as_tensor_variable(floatX(beta))
-
-        # The PyTensor `GammaRV` `Op` will invert the `beta` parameter itself
-        return super().dist([alpha, beta], **kwargs)
+        # PyTensor gamma op is parametrized in terms of scale (1/beta)
+        scale = pt.reciprocal(beta)
+        return super().dist([alpha, scale], **kwargs)
 
     @classmethod
     def get_alpha_beta(cls, alpha=None, beta=None, mu=None, sigma=None):
@@ -2232,15 +2233,14 @@ def get_alpha_beta(cls, alpha=None, beta=None, mu=None, sigma=None):
 
         return alpha, beta
 
-    def moment(rv, size, alpha, inv_beta):
-        # The PyTensor `GammaRV` `Op` inverts the `beta` parameter itself
-        mean = alpha * inv_beta
+    def moment(rv, size, alpha, scale):
+        mean = alpha * scale
         if not rv_size_is_none(size):
             mean = pt.full(size, mean)
         return mean
 
-    def logp(value, alpha, inv_beta):
-        beta = pt.reciprocal(inv_beta)
+    def logp(value, alpha, scale):
+        beta = pt.reciprocal(scale)
         res = -pt.gammaln(alpha) + logpow(beta, alpha) - beta * value + logpow(value, alpha - 1)
         res = pt.switch(pt.ge(value, 0.0), res, -np.inf)
         return check_parameters(
@@ -2250,14 +2250,13 @@ def logp(value, alpha, inv_beta):
             msg="alpha > 0, beta > 0",
         )
 
-    def logcdf(value, alpha, inv_beta):
-        beta = pt.reciprocal(inv_beta)
+    def logcdf(value, alpha, scale):
+        beta = pt.reciprocal(scale)
         res = pt.switch(
             pt.lt(value, 0),
             -np.inf,
             pt.log(pt.gammainc(alpha, beta * value)),
         )
-
         return check_parameters(res, 0 < alpha, 0 < beta, msg="alpha > 0, beta > 0")
 
 

requirements-dev.txt

@@ -18,7 +18,7 @@ numpydoc
 pandas>=0.24.0
 polyagamma
 pre-commit>=2.8.0
-pytensor>=2.16.1,<2.17
+pytensor>=2.17.0,<2.18
 pytest-cov>=2.5
 pytest>=3.0
 scipy>=1.4.1

requirements.txt

@@ -4,6 +4,6 @@ cloudpickle
 fastprogress>=0.2.0
 numpy>=1.15.0
 pandas>=0.24.0
-pytensor>=2.16.1,<2.17
+pytensor>=2.17.0,<2.18
 scipy>=1.4.1
 typing-extensions>=3.7.4

tests/distributions/test_continuous.py

@@ -2199,7 +2199,7 @@ class TestHalfCauchy(BaseTestDistributionRandom):
 class TestGamma(BaseTestDistributionRandom):
     pymc_dist = pm.Gamma
     pymc_dist_params = {"alpha": 2.0, "beta": 5.0}
-    expected_rv_op_params = {"alpha": 2.0, "beta": 1 / 5.0}
+    expected_rv_op_params = {"shape": 2.0, "scale": 1 / 5.0}
     reference_dist_params = {"shape": 2.0, "scale": 1 / 5.0}
     reference_dist = seeded_numpy_distribution_builder("gamma")
     checks_to_run = [

tests/logprob/test_mixture.py

@@ -62,16 +62,14 @@
 
 
 def test_mixture_basics():
-    srng = pt.random.RandomStream(29833)
-
     def create_mix_model(size, axis):
-        X_rv = srng.normal(0, 1, size=size, name="X")
-        Y_rv = srng.gamma(0.5, 0.5, size=size, name="Y")
+        X_rv = pt.random.normal(0, 1, size=size, name="X")
+        Y_rv = pt.random.gamma(0.5, scale=2.0, size=size, name="Y")
 
         p_at = pt.scalar("p")
         p_at.tag.test_value = 0.5
 
-        I_rv = srng.bernoulli(p_at, size=size, name="I")
+        I_rv = pt.random.bernoulli(p_at, size=size, name="I")
         i_vv = I_rv.clone()
         i_vv.name = "i"
 
@@ -119,15 +117,13 @@ def create_mix_model(size, axis):
     ],
 )
 def test_compute_test_value(op_constructor):
-    srng = pt.random.RandomStream(29833)
-
-    X_rv = srng.normal(0, 1, name="X")
-    Y_rv = srng.gamma(0.5, 0.5, name="Y")
+    X_rv = pt.random.normal(0, 1, name="X")
+    Y_rv = pt.random.gamma(0.5, scale=2.0, name="Y")
 
     p_at = pt.scalar("p")
     p_at.tag.test_value = 0.3
 
-    I_rv = srng.bernoulli(p_at, name="I")
+    I_rv = pt.random.bernoulli(p_at, name="I")
 
     i_vv = I_rv.clone()
     i_vv.name = "i"
@@ -160,20 +156,18 @@ def test_compute_test_value(op_constructor):
     ],
 )
 def test_hetero_mixture_binomial(p_val, size, supported):
-    srng = pt.random.RandomStream(29833)
-
-    X_rv = srng.normal(0, 1, size=size, name="X")
-    Y_rv = srng.gamma(0.5, 0.5, size=size, name="Y")
+    X_rv = pt.random.normal(0, 1, size=size, name="X")
+    Y_rv = pt.random.gamma(0.5, scale=2.0, size=size, name="Y")
 
     if np.ndim(p_val) == 0:
         p_at = pt.scalar("p")
         p_at.tag.test_value = p_val
-        I_rv = srng.bernoulli(p_at, size=size, name="I")
+        I_rv = pt.random.bernoulli(p_at, size=size, name="I")
         p_val_1 = p_val
     else:
         p_at = pt.vector("p")
         p_at.tag.test_value = np.array(p_val, dtype=pytensor.config.floatX)
-        I_rv = srng.categorical(p_at, size=size, name="I")
+        I_rv = pt.random.categorical(p_at, size=size, name="I")
         p_val_1 = p_val[1]
 
     i_vv = I_rv.clone()
@@ -203,7 +197,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
 
     bern_sp = sp.bernoulli(p_val_1)
     norm_sp = sp.norm(loc=0, scale=1)
-    gamma_sp = sp.gamma(0.5, scale=1.0 / 0.5)
+    gamma_sp = sp.gamma(0.5, scale=2.0)
 
     for i in range(10):
         i_val = bern_sp.rvs(size=size, random_state=test_val_rng)
@@ -230,7 +224,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -251,7 +245,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array([0.5], dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array([100], dtype=pytensor.config.floatX),
@@ -272,7 +266,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array([0.5], dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array([100], dtype=pytensor.config.floatX),
@@ -293,7 +287,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -314,7 +308,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -335,7 +329,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -361,7 +355,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -384,7 +378,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -405,7 +399,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -426,7 +420,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -447,7 +441,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array([0.5], dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array([100], dtype=pytensor.config.floatX),
@@ -468,7 +462,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array([0.5, 1], dtype=pytensor.config.floatX),
-                np.array([0.5, 1], dtype=pytensor.config.floatX),
+                np.array([2.0, 1], dtype=pytensor.config.floatX),
             ),
             (
                 np.array([100, 1000], dtype=pytensor.config.floatX),
@@ -489,7 +483,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array([0.5, 1], dtype=pytensor.config.floatX),
-                np.array([0.5, 1], dtype=pytensor.config.floatX),
+                np.array([2.0, 1], dtype=pytensor.config.floatX),
             ),
             (
                 np.array([100, 1000], dtype=pytensor.config.floatX),
@@ -510,7 +504,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -531,7 +525,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -552,7 +546,7 @@ def test_hetero_mixture_binomial(p_val, size, supported):
             ),
             (
                 np.array(0.5, dtype=pytensor.config.floatX),
-                np.array(0.5, dtype=pytensor.config.floatX),
+                np.array(2.0, dtype=pytensor.config.floatX),
             ),
             (
                 np.array(100, dtype=pytensor.config.floatX),
@@ -570,16 +564,14 @@ def test_hetero_mixture_binomial(p_val, size, supported):
 def test_hetero_mixture_categorical(
     X_args, Y_args, Z_args, p_val, comp_size, idx_size, extra_indices, join_axis, supported
 ):
-    srng = pt.random.RandomStream(29833)
-
-    X_rv = srng.normal(*X_args, size=comp_size, name="X")
-    Y_rv = srng.gamma(*Y_args, size=comp_size, name="Y")
-    Z_rv = srng.normal(*Z_args, size=comp_size, name="Z")
+    X_rv = pt.random.normal(*X_args, size=comp_size, name="X")
+    Y_rv = pt.random.gamma(Y_args[0], scale=Y_args[1], size=comp_size, name="Y")
+    Z_rv = pt.random.normal(*Z_args, size=comp_size, name="Z")
 
     p_at = pt.as_tensor(p_val).type()
     p_at.name = "p"
     p_at.tag.test_value = np.array(p_val, dtype=pytensor.config.floatX)
-    I_rv = srng.categorical(p_at, size=idx_size, name="I")
+    I_rv = pt.random.categorical(p_at, size=idx_size, name="I")
 
     i_vv = I_rv.clone()
     i_vv.name = "i"
@@ -612,7 +604,7 @@ def test_hetero_mixture_categorical(
     test_val_rng = np.random.RandomState(3238)
 
     norm_1_sp = sp.norm(loc=X_args[0], scale=X_args[1])
-    gamma_sp = sp.gamma(Y_args[0], scale=1 / Y_args[1])
+    gamma_sp = sp.gamma(Y_args[0], scale=Y_args[1])
     norm_2_sp = sp.norm(loc=Z_args[0], scale=Z_args[1])
 
     # Handle scipy annoying squeeze of random draws