Improve tests (#757)

* start moving tests

* modifications

* Replace numpy.round_ with numpy.round

* Keep porting tests..

* Add testcategorical

* Finalize test migration

* fixes

bambi/priors/prior.py

@@ -65,7 +65,7 @@ def __repr__(self):
 
 def format_arg(value, decimals):
     try:
-        outcome = np.round_(value, decimals)
+        outcome = np.round(value, decimals)
     except:  # pylint: disable = bare-except
         try:
             outcome = value.name

tests/test_aliases.py

@@ -1,6 +1,8 @@
 import pytest
 
 import bambi as bmb
+import numpy as np
+import pandas as pd
 
 
 @pytest.fixture(scope="module")
@@ -13,6 +15,26 @@ def anes():
     return bmb.load_data("ANES")
 
 
+@pytest.fixture(scope="module")
+def data_100():
+    size = 100
+    rng = np.random.default_rng(121195)
+    data = pd.DataFrame(
+        {
+            "n1": rng.normal(size=size),
+            "n2": rng.normal(size=size),
+            "n3": rng.normal(size=size),
+            "b0": rng.binomial(n=1, p=0.5, size=size),
+            "b1": rng.choice(["a", "b"], size=size),
+            "count1": rng.poisson(lam=2, size=size),
+            "count2": rng.poisson(lam=2, size=size),
+            "cat1": rng.choice(list("MNOP"), size=size),
+            "cat2": rng.choice(list("FGHIJK"), size=size),
+        }
+    )
+    return data
+
+
 def test_non_distributional_model(my_data):
     # Plain model
     formula = bmb.Formula("y ~ x")
@@ -126,3 +148,19 @@ def test_set_alias_warnings(my_data):
             print(model.constant_components)
         assert len(record) == 1
         assert str(record[0].message) == expected_warning
+
+
+# FIXME: Move somewhere
+def test_set_alias(data_100):
+    model = bmb.Model("n1 ~ n2 + (n2|cat1)", data_100)
+    aliases = {
+        "Intercept": "α",
+        "n2": "β",
+        "1|cat1": "α_group",
+        "n2|cat1": "β_group",
+        "sigma": "σ",
+    }
+    model.set_alias(aliases)
+    model.build()
+    new_names = set(["α", "β", "α_group", "α_group_σ", "β_group", "β_group_σ", "σ"])
+    assert new_names.issubset(set(model.backend.model.named_vars))

tests/test_alternative_samplers.py

@@ -0,0 +1,78 @@
+import bambi as bmb
+import numpy as np
+import pandas as pd
+
+import pytest
+
+
+@pytest.fixture(scope="module")
+def data_n100():
+    size = 100
+    rng = np.random.default_rng(121195)
+    data = pd.DataFrame(
+        {
+            "b1": rng.binomial(n=1, p=0.5, size=size),
+            "n1": rng.poisson(lam=2, size=size),
+            "n2": rng.poisson(lam=2, size=size),
+            "y1": rng.normal(size=size),
+            "y2": rng.normal(size=size),
+            "y3": rng.normal(size=size),
+            "cat2": rng.choice(["a", "b"], size=size),
+            "cat4": rng.choice(list("MNOP"), size=size),
+            "cat5": rng.choice(list("FGHIJK"), size=size),
+        }
+    )
+    return data
+
+
+def test_laplace():
+    data = pd.DataFrame(np.repeat((0, 1), (30, 60)), columns=["w"])
+    priors = {"Intercept": bmb.Prior("Uniform", lower=0, upper=1)}
+    model = bmb.Model("w ~ 1", data=data, family="bernoulli", priors=priors, link="identity")
+    results = model.fit(inference_method="laplace")
+    mode_n = results.posterior["Intercept"].mean().item()
+    std_n = results.posterior["Intercept"].std().item()
+    mode_a = data.mean()
+    std_a = data.std() / len(data) ** 0.5
+    np.testing.assert_array_almost_equal((mode_n, std_n), (mode_a.item(), std_a.item()), decimal=2)
+
+
+def test_vi():
+    data = pd.DataFrame(np.repeat((0, 1), (30, 60)), columns=["w"])
+    priors = {"Intercept": bmb.Prior("Uniform", lower=0, upper=1)}
+    model = bmb.Model("w ~ 1", data=data, family="bernoulli", priors=priors, link="identity")
+    results = model.fit(inference_method="vi", method="advi")
+    samples = results.sample(1000).posterior["Intercept"]
+    mode_n = samples.mean()
+    std_n = samples.std()
+    mode_a = data.mean()
+    std_a = data.std() / len(data) ** 0.5
+    np.testing.assert_array_almost_equal(
+        (mode_n.item(), std_n.item()), (mode_a.item(), std_a.item()), decimal=2
+    )
+
+
+@pytest.mark.parametrize(
+    "args",
+    [
+        ("mcmc", {}),
+        ("nuts_numpyro", {"chain_method": "vectorized"}),
+        ("nuts_blackjax", {"chain_method": "vectorized"}),
+    ],
+)
+def test_logistic_regression_categoric_alternative_samplers(data_n100, args):
+    model = bmb.Model("b1 ~ n1", data_n100, family="bernoulli")
+    model.fit(tune=50, draws=50, inference_method=args[0], **args[1])
+
+
+@pytest.mark.parametrize(
+    "args",
+    [
+        ("mcmc", {}),
+        ("nuts_numpyro", {"chain_method": "vectorized"}),
+        ("nuts_blackjax", {"chain_method": "vectorized"}),
+    ],
+)
+def test_regression_alternative_samplers(data_n100, args):
+    model = bmb.Model("n1 ~ n2", data_n100)
+    model.fit(tune=50, draws=50, inference_method=args[0], **args[1])