Refactor tests for compatibility with logp dispatch and RandomVariables

Author: brandonwillard

pymc3/model.py

@@ -1463,7 +1463,7 @@ def fastfn(outs, mode=None, model=None):
     return model.fastfn(outs, mode)
 
 
-def Point(*args, **kwargs):
+def Point(*args, filter_model_vars=True, **kwargs):
     """Build a point. Uses same args as dict() does.
     Filters out variables not in the model. All keys are strings.
 
@@ -1481,7 +1481,7 @@ def Point(*args, **kwargs):
     return {
         get_var_name(k): np.array(v)
         for k, v in d.items()
-        if get_var_name(k) in map(get_var_name, model.vars)
+        if not filter_model_vars or (get_var_name(k) in map(get_var_name, model.vars))
     }
 
 

pymc3/tests/models.py

@@ -30,7 +30,7 @@ def simple_model():
     mu = -2.1
     tau = 1.3
     with Model() as model:
-        Normal("x", mu, tau=tau, shape=2, testval=aet.ones(2) * 0.1)
+        Normal("x", mu, tau=tau, size=2, testval=np.ones(2) * 0.1)
 
     return model.test_point, model, (mu, tau ** -0.5)
 
@@ -39,7 +39,7 @@ def simple_categorical():
     p = floatX_array([0.1, 0.2, 0.3, 0.4])
     v = floatX_array([0.0, 1.0, 2.0, 3.0])
     with Model() as model:
-        Categorical("x", p, shape=3, testval=[1, 2, 3])
+        Categorical("x", p, size=3, testval=[1, 2, 3])
 
     mu = np.dot(p, v)
     var = np.dot(p, (v - mu) ** 2)
@@ -50,7 +50,7 @@ def multidimensional_model():
     mu = -2.1
     tau = 1.3
     with Model() as model:
-        Normal("x", mu, tau=tau, shape=(3, 2), testval=0.1 * aet.ones((3, 2)))
+        Normal("x", mu, tau=tau, size=(3, 2), testval=0.1 * np.ones((3, 2)))
 
     return model.test_point, model, (mu, tau ** -0.5)
 
@@ -93,7 +93,7 @@ def simple_2model_continuous():
     with Model() as model:
         x = pm.Normal("x", mu, tau=tau, testval=0.1)
         pm.Deterministic("logx", aet.log(x))
-        pm.Beta("y", alpha=1, beta=1, shape=2)
+        pm.Beta("y", alpha=1, beta=1, size=2)
     return model.test_point, model
 
 
@@ -106,7 +106,7 @@ def mv_simple():
             "x",
             aet.constant(mu),
             tau=aet.constant(tau),
-            shape=3,
+            size=3,
             testval=floatX_array([0.1, 1.0, 0.8]),
         )
     H = tau
@@ -123,7 +123,7 @@ def mv_simple_coarse():
             "x",
             aet.constant(mu),
             tau=aet.constant(tau),
-            shape=3,
+            size=3,
             testval=floatX_array([0.1, 1.0, 0.8]),
         )
     H = tau
@@ -140,7 +140,7 @@ def mv_simple_very_coarse():
             "x",
             aet.constant(mu),
             tau=aet.constant(tau),
-            shape=3,
+            size=3,
             testval=floatX_array([0.1, 1.0, 0.8]),
         )
     H = tau
@@ -153,7 +153,7 @@ def mv_simple_discrete():
     n = 5
     p = floatX_array([0.15, 0.85])
     with pm.Model() as model:
-        pm.Multinomial("x", n, aet.constant(p), shape=d, testval=np.array([1, 4]))
+        pm.Multinomial("x", n, aet.constant(p), size=d, testval=np.array([1, 4]))
         mu = n * p
         # covariance matrix
         C = np.zeros((d, d))
@@ -186,28 +186,28 @@ def mv_prior_simple():
     std_post = (K - np.dot(v.T, v)).diagonal() ** 0.5
 
     with pm.Model() as model:
-        x = pm.Flat("x", shape=n)
-        x_obs = pm.MvNormal("x_obs", observed=obs, mu=x, cov=noise * np.eye(n), shape=n)
+        x = pm.Flat("x", size=n)
+        x_obs = pm.MvNormal("x_obs", observed=obs, mu=x, cov=noise * np.eye(n), size=n)
 
     return model.test_point, model, (K, L, mu_post, std_post, noise)
 
 
 def non_normal(n=2):
     with pm.Model() as model:
-        pm.Beta("x", 3, 3, shape=n, transform=None)
+        pm.Beta("x", 3, 3, size=n, transform=None)
     return model.test_point, model, (np.tile([0.5], n), None)
 
 
 def exponential_beta(n=2):
     with pm.Model() as model:
-        pm.Beta("x", 3, 1, shape=n, transform=None)
-        pm.Exponential("y", 1, shape=n, transform=None)
+        pm.Beta("x", 3, 1, size=n, transform=None)
+        pm.Exponential("y", 1, size=n, transform=None)
     return model.test_point, model, None
 
 
 def beta_bernoulli(n=2):
     with pm.Model() as model:
-        pm.Beta("x", 3, 1, shape=n, transform=None)
+        pm.Beta("x", 3, 1, size=n, transform=None)
         pm.Bernoulli("y", 0.5)
     return model.test_point, model, None
 

pymc3/tests/test_coords.py

@@ -1,8 +1,10 @@
 import numpy as np
+import pytest
 
 import pymc3 as pm
 
 
+@pytest.mark.xfail("Arviz incompatibilities")
 def test_coords():
     chains = 2
     n_features = 3

pymc3/tests/test_data_container.py

@@ -20,7 +20,7 @@
 
 import pymc3 as pm
 
-from pymc3.aesaraf import floatX
+from pymc3.distributions import logpt
 from pymc3.tests.helpers import SeededTest
 
 
@@ -32,6 +32,7 @@ def test_deterministic(self):
             pm.Normal("y", 0, 1, observed=X)
             model.logp(model.test_point)
 
+    @pytest.mark.xfail(reason="Competence hasn't been updated")
     def test_sample(self):
         x = np.random.normal(size=100)
         y = x + np.random.normal(scale=1e-2, size=100)
@@ -105,7 +106,7 @@ def test_shared_data_as_index(self):
         with pm.Model() as model:
             index = pm.Data("index", [2, 0, 1, 0, 2])
             y = pm.Data("y", [1.0, 2.0, 3.0, 2.0, 1.0])
-            alpha = pm.Normal("alpha", 0, 1.5, shape=3)
+            alpha = pm.Normal("alpha", 0, 1.5, size=3)
             pm.Normal("obs", alpha[index], np.sqrt(1e-2), observed=y)
 
             prior_trace = pm.sample_prior_predictive(1000, var_names=["alpha"])
@@ -150,15 +151,15 @@ def test_shared_scalar_as_rv_input(self):
             v = pm.Normal("v", mu=shared_var, shape=1)
 
         np.testing.assert_allclose(
-            v.logp({"v": [5.0]}),
+            logpt(v, 5.0).eval(),
             -0.91893853,
             rtol=1e-5,
         )
 
         shared_var.set_value(10.0)
 
         np.testing.assert_allclose(
-            v.logp({"v": [10.0]}),
+            logpt(v, 10.0).eval(),
             -0.91893853,
             rtol=1e-5,
         )
@@ -179,6 +180,7 @@ def test_set_data_to_non_data_container_variables(self):
             pm.set_data({"beta": [1.1, 2.2, 3.3]}, model=model)
         error.match("defined as `pymc3.Data` inside the model")
 
+    @pytest.mark.xfail(reason="Depends on ModelGraph")
     def test_model_to_graphviz_for_model_with_data_container(self):
         with pm.Model() as model:
             x = pm.Data("x", [1.0, 2.0, 3.0])

pymc3/tests/test_dist_math.py

@@ -132,6 +132,7 @@ def logp(self, value):
         )
 
 
+@pytest.mark.xfail(reason="This test relies on the deprecated Distribution interface")
 def test_multinomial_bound():
 
     x = np.array([1, 5])
@@ -150,6 +151,7 @@ def test_multinomial_bound():
     )
 
 
+@pytest.mark.xfail(reason="MvNormal not implemented")
 class TestMvNormalLogp:
     def test_logp(self):
         np.random.seed(42)

pymc3/tests/test_distribution_defaults.py

@@ -18,6 +18,8 @@
 from pymc3.distributions import Categorical, Continuous, DiscreteUniform
 from pymc3.model import Model
 
+pytestmark = pytest.mark.xfail(reason="This test relies on the deprecated Distribution interface")
+
 
 class DistTest(Continuous):
     def __init__(self, a, b, *args, **kwargs):