move IPW integration test to better test file

Author: drbenvincent

causalpy/tests/test_integration_pymc_examples.py

@@ -11,6 +11,7 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
+import arviz as az
 import numpy as np
 import pandas as pd
 import pytest
@@ -597,6 +598,56 @@ def test_iv_reg():
     assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"]
 
 
+@pytest.mark.integration
+def test_inverse_prop():
+    """Test the InversePropensityWeighting class."""
+    df = cp.load_data("nhefs")
+    sample_kwargs = {
+        "tune": 100,
+        "draws": 500,
+        "chains": 2,
+        "cores": 2,
+        "random_seed": 100,
+    }
+    result = cp.InversePropensityWeighting(
+        df,
+        formula="trt ~ 1 + age + race",
+        outcome_variable="outcome",
+        weighting_scheme="robust",
+        model=cp.pymc_models.PropensityScore(sample_kwargs=sample_kwargs),
+    )
+    assert isinstance(result.idata, az.InferenceData)
+    ps = result.idata.posterior["p"].mean(dim=("chain", "draw"))
+    w1, w2, _, _ = result.make_doubly_robust_adjustment(ps)
+    assert isinstance(w1, pd.Series)
+    assert isinstance(w2, pd.Series)
+    w1, w2, n1, nw = result.make_raw_adjustments(ps)
+    assert isinstance(w1, pd.Series)
+    assert isinstance(w2, pd.Series)
+    w1, w2, n1, n2 = result.make_robust_adjustments(ps)
+    assert isinstance(w1, pd.Series)
+    assert isinstance(w2, pd.Series)
+    w1, w2, n1, n2 = result.make_overlap_adjustments(ps)
+    assert isinstance(w1, pd.Series)
+    assert isinstance(w2, pd.Series)
+    ate_list = result.get_ate(0, result.idata)
+    assert isinstance(ate_list, list)
+    ate_list = result.get_ate(0, result.idata, method="raw")
+    assert isinstance(ate_list, list)
+    ate_list = result.get_ate(0, result.idata, method="robust")
+    assert isinstance(ate_list, list)
+    ate_list = result.get_ate(0, result.idata, method="overlap")
+    assert isinstance(ate_list, list)
+    fig, axs = result.plot_ate(prop_draws=1, ate_draws=10)
+    assert isinstance(fig, plt.Figure)
+    assert isinstance(axs, list)
+    assert all(isinstance(ax, plt.Axes) for ax in axs)
+    fig, axs = result.plot_balance_ecdf("age")
+    assert isinstance(fig, plt.Figure)
+    assert isinstance(axs, list)
+    assert all(isinstance(ax, plt.Axes) for ax in axs)
+
+
 # DEPRECATION WARNING TESTS ============================================================
 
 

causalpy/tests/test_misc.py

@@ -15,10 +15,6 @@
 Miscellaneous unit tests
 """
 
-import arviz as az
-import pandas as pd
-from matplotlib import pyplot as plt
-
 import causalpy as cp
 
 sample_kwargs = {"tune": 20, "draws": 20, "chains": 2, "cores": 2}
@@ -41,52 +37,3 @@ def test_regression_kink_gradient_change():
     assert cp.RegressionKink._eval_gradient_change(0, 0, -2, 1) == -2.0
     assert cp.RegressionKink._eval_gradient_change(-1, -1, -2, 1) == -1.0
     assert cp.RegressionKink._eval_gradient_change(1, 0, -2, 1) == -1.0
-
-
-def test_inverse_prop():
-    """Test the InversePropensityWeighting class."""
-    df = cp.load_data("nhefs")
-    sample_kwargs = {
-        "tune": 100,
-        "draws": 500,
-        "chains": 2,
-        "cores": 2,
-        "random_seed": 100,
-    }
-    result = cp.InversePropensityWeighting(
-        df,
-        formula="trt ~ 1 + age + race",
-        outcome_variable="outcome",
-        weighting_scheme="robust",
-        model=cp.pymc_models.PropensityScore(sample_kwargs=sample_kwargs),
-    )
-    assert isinstance(result.idata, az.InferenceData)
-    ps = result.idata.posterior["p"].mean(dim=("chain", "draw"))
-    w1, w2, _, _ = result.make_doubly_robust_adjustment(ps)
-    assert isinstance(w1, pd.Series)
-    assert isinstance(w2, pd.Series)
-    w1, w2, n1, nw = result.make_raw_adjustments(ps)
-    assert isinstance(w1, pd.Series)
-    assert isinstance(w2, pd.Series)
-    w1, w2, n1, n2 = result.make_robust_adjustments(ps)
-    assert isinstance(w1, pd.Series)
-    assert isinstance(w2, pd.Series)
-    w1, w2, n1, n2 = result.make_overlap_adjustments(ps)
-    assert isinstance(w1, pd.Series)
-    assert isinstance(w2, pd.Series)
-    ate_list = result.get_ate(0, result.idata)
-    assert isinstance(ate_list, list)
-    ate_list = result.get_ate(0, result.idata, method="raw")
-    assert isinstance(ate_list, list)
-    ate_list = result.get_ate(0, result.idata, method="robust")
-    assert isinstance(ate_list, list)
-    ate_list = result.get_ate(0, result.idata, method="overlap")
-    assert isinstance(ate_list, list)
-    fig, axs = result.plot_ate(prop_draws=1, ate_draws=10)
-    assert isinstance(fig, plt.Figure)
-    assert isinstance(axs, list)
-    assert all(isinstance(ax, plt.Axes) for ax in axs)
-    fig, axs = result.plot_balance_ecdf("age")
-    assert isinstance(fig, plt.Figure)
-    assert isinstance(axs, list)
-    assert all(isinstance(ax, plt.Axes) for ax in axs)