add tests

pymc3/tests/test_bart.py

@@ -0,0 +1,69 @@
+import numpy as np
+
+import pymc3 as pm
+
+
+def test_split_node():
+    split_node = pm.distributions.tree.SplitNode(index=5, idx_split_variable=2, split_value=3.0)
+    assert split_node.index == 5
+    assert split_node.idx_split_variable == 2
+    assert split_node.split_value == 3.0
+    assert split_node.depth == 2
+    assert split_node.get_idx_parent_node() == 2
+    assert split_node.get_idx_left_child() == 11
+    assert split_node.get_idx_right_child() == 12
+
+
+def test_leaf_node():
+    leaf_node = pm.distributions.tree.LeafNode(index=5, value=3.14, idx_data_points=[1, 2, 3])
+    assert leaf_node.index == 5
+    assert np.array_equal(leaf_node.idx_data_points, [1, 2, 3])
+    assert leaf_node.value == 3.14
+    assert leaf_node.get_idx_parent_node() == 2
+    assert leaf_node.get_idx_left_child() == 11
+    assert leaf_node.get_idx_right_child() == 12
+
+
+def test_model():
+    X = np.linspace(7, 15, 100)
+    Y = np.sin(np.random.normal(X, 0.2)) + 3
+    X = X[:, None]
+
+    with pm.Model() as model:
+        sigma = pm.HalfNormal("sigma", 1)
+        mu = pm.BART("mu", X, Y, m=50)
+        y = pm.Normal("y", mu, sigma, observed=Y)
+        idata = pm.sample()
+        mean = idata.posterior["mu"].stack(samples=("chain", "draw")).mean("samples")
+
+    np.testing.assert_allclose(mean, Y, 0.5)
+
+    Y = np.repeat([0, 1], 50)
+    with pm.Model() as model:
+        mu_ = pm.BART("mu_", X, Y, m=50)
+        mu = pm.Deterministic("mu", pm.math.invlogit(mu_))
+        y = pm.Bernoulli("y", mu, observed=Y)
+        idata = pm.sample()
+        mean = idata.posterior["mu"].stack(samples=("chain", "draw")).mean("samples")
+
+    np.testing.assert_allclose(mean, Y, atol=0.5)
+
+
+def test_bart_vi():
+    X = np.random.normal(0, 1, size=(3, 250)).T
+    Y = np.random.normal(0, 1, size=250)
+    X[:, 0] = np.random.normal(Y, 0.1)
+
+    with pm.Model() as model:
+        mu = pm.BART("mu", X, Y, m=10)
+        sigma = pm.HalfNormal("sigma", 1)
+        y = pm.Normal("y", mu, sigma, observed=Y)
+        idata = pm.sample(random_seed=3415, chains=1)
+        var_imp = (
+            idata.sample_stats["variable_inclusion"]
+            .stack(samples=("chain", "draw"))
+            .mean("samples")
+        )
+        var_imp /= var_imp.sum()
+        assert var_imp[0] > var_imp[1:].sum()
+        np.testing.assert_almost_equal(var_imp.sum(), 1)

pymc3/tests/test_sampling.py

@@ -174,25 +174,6 @@ def test_trace_report(self, step_cls, discard):
             assert trace.report.n_draws == 100
             assert isinstance(trace.report.t_sampling, float)
 
-    def test_bart_vi(self):
-        X = np.random.normal(0, 1, size=(3, 250)).T
-        Y = np.random.normal(0, 1, size=250)
-        X[:, 0] = np.random.normal(Y, 0.1)
-
-        with pm.Model() as model:
-            mu = pm.BART("mu", X, Y, m=10)
-            sigma = pm.HalfNormal("sigma", 1)
-            y = pm.Normal("y", mu, sigma, observed=Y)
-            idata = pm.sample(random_seed=3415, chains=1)
-            var_imp = (
-                idata.sample_stats["variable_inclusion"]
-                .stack(samples=("chain", "draw"))
-                .mean("samples")
-            )
-            var_imp /= var_imp.sum()
-            assert var_imp[0] > var_imp[1:].sum()
-            npt.assert_almost_equal(var_imp.sum(), 1)
-
     def test_return_inferencedata(self):
         with self.model:
             kwargs = dict(draws=100, tune=50, cores=1, chains=2, step=pm.Metropolis())