add exact estimation tests

src/tests/estimation/test_exact_estimation.py

@@ -0,0 +1,102 @@
+"""
+Pytest file for get_estimation.py
+
+It tests all the benchmark_mediation estimators :
+- for a certain tolerance
+- whether their effects satisfy "total = direct + indirect"
+- whether they support (n,1) and (n,) inputs
+
+To be robust to future updates, tests are adjusted with a smaller tolerance when possible.
+The test is skipped if estimator has not been implemented yet, i.e. if ValueError is raised.
+The test fails for any other unwanted behavior.
+"""
+
+from pprint import pprint
+import pytest
+import os
+import numpy as np
+
+from med_bench.get_estimation import get_estimation
+
+current_dir = os.path.dirname(__file__)
+true_estimations_file_path = os.path.join(current_dir, 'tests_results.npy')
+TRUE_ESTIMATIONS = np.load(true_estimations_file_path, allow_pickle=True)
+
+
+@pytest.fixture(params=range(TRUE_ESTIMATIONS.shape[0]))
+def tests_results_idx(request):
+    return request.param
+
+
+@pytest.fixture
+def data(tests_results_idx):
+    return TRUE_ESTIMATIONS[tests_results_idx]
+
+
+@pytest.fixture
+def estimator(data):
+    return data[0]
+
+
+@pytest.fixture
+def x(data):
+    return data[1]
+
+
+# t is raveled because some estimators fail with (n,1) inputs
+@pytest.fixture
+def t(data):
+    return data[2].ravel()
+
+
+@pytest.fixture
+def m(data):
+    return data[3]
+
+
+@pytest.fixture
+def y(data):
+    return data[4].ravel()  # same reason as t
+
+
+@pytest.fixture
+def config(data):
+    return data[5]
+
+
+@pytest.fixture
+def result(data):
+    return data[6]
+
+
+@pytest.fixture
+def effects_chap(x, t, m, y, estimator, config):
+    # try whether estimator is implemented or not
+    try:
+        res = get_estimation(x, t, m, y, estimator, config)[0:5]
+    except Exception as e:
+        if str(e) in (
+            "Estimator only supports 1D binary mediator.",
+            "Estimator does not support 1D binary mediator.",
+        ):
+            pytest.skip(f"{e}")
+
+        # We skip the test if an error with function from glmet rpy2 package occurs
+        elif "glmnet::glmnet" in str(e):
+            pytest.skip(f"{e}")
+
+        else:
+            pytest.fail(f"{e}")
+
+    # NaN situations
+    if np.all(np.isnan(res)):
+        pytest.xfail("all effects are NaN")
+    elif np.any(np.isnan(res)):
+        pprint("NaN found")
+
+    return res
+
+
+def test_estimation_exactness(result, effects_chap):
+
+    assert np.all(effects_chap == pytest.approx(result))