Make RDD Tests Optional (again?)

doubleml/rdd/rdd.py

@@ -4,7 +4,11 @@
 from collections.abc import Callable
 
 from scipy.stats import norm
-from rdrobust import rdrobust, rdbwselect
+try:
+    from rdrobust import rdrobust, rdbwselect
+    _rdrobust_available = True
+except ImportError:
+    _rdrobust_available = False
 
 from sklearn.base import clone
 from sklearn.utils.multiclass import type_of_target
@@ -105,6 +109,9 @@ def __init__(self,
                  fs_kernel="triangular",
                  **kwargs):
 
+        if not _rdrobust_available:
+            raise ImportError("rdrobust is not installed. Please install it using 'pip install DoubleML[rdd]'")
+
         self._check_data(obj_dml_data, cutoff)
         self._dml_data = obj_dml_data
 

doubleml/rdd/tests/conftest.py

@@ -7,7 +7,11 @@
 from doubleml import DoubleMLData
 from doubleml.rdd import RDFlex
 
-from rdrobust import rdrobust
+try:
+    from rdrobust import rdrobust
+    _rdrobust_available = True
+except ImportError:
+    _rdrobust_available = False
 
 from sklearn.dummy import DummyRegressor, DummyClassifier
 
@@ -25,6 +29,9 @@ def predict_dummy():
     - make predictions using rdrobust as a reference
     """
     def _predict_dummy(data: DoubleMLData, cutoff, alpha, n_rep, p, fs_specification, ml_g=ml_g_dummy):
+        if not _rdrobust_available:
+            raise ImportError("rdrobust is not installed. Please install it using 'pip install DoubleML[rdd]'")
+
         dml_rdflex = RDFlex(
             data,
             ml_g=ml_g,

doubleml/rdd/tests/test_rdd_classifier.py

@@ -24,6 +24,6 @@
 dml_rdflex = RDFlex(dml_data, ml_g=LogisticRegression(), ml_m=LogisticRegression(), fuzzy=True)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_classifier():
     dml_rdflex.fit()

doubleml/rdd/tests/test_rdd_classifier_fuzzy.py

@@ -62,37 +62,37 @@ def predict_nonplacebo(predict_dummy, data, cutoff, alpha, p, n_rep, fs_specific
     )
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_coef(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_coef(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_se(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_se(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_ci(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_ci(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)

doubleml/rdd/tests/test_rdd_classifier_fuzzy_left.py

@@ -62,37 +62,37 @@ def predict_nonplacebo(predict_dummy, data, cutoff, alpha, p, n_rep, fs_specific
     )
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_coef(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_coef(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_se(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_se(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_ci(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_ci(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)

doubleml/rdd/tests/test_rdd_classifier_fuzzy_right.py

@@ -62,37 +62,37 @@ def predict_nonplacebo(predict_dummy, data, cutoff, alpha, p, n_rep, fs_specific
     )
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_coef(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_coef(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_se(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_se(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_ci(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_ci(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)

doubleml/rdd/tests/test_rdd_classifier_sharp.py

@@ -62,37 +62,37 @@ def predict_nonplacebo(predict_dummy, data, cutoff, alpha, p, n_rep, fs_specific
     )
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_coef(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_coef(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_se(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_se(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_ci(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_ci(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)

doubleml/rdd/tests/test_rdd_default_values.py

@@ -30,6 +30,6 @@ def _assert_resampling_default_settings(dml_obj):
     assert dml_obj.fuzzy is False
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_defaults():
     _assert_resampling_default_settings(dml_rdflex)

doubleml/rdd/tests/test_rdd_exceptions.py

@@ -57,7 +57,7 @@ def predict_proba(self, X):
         )
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_exception_data():
     # DoubleMLData
     msg = r"The data must be of DoubleMLData type. \[\] of type <class 'list'> was passed."
@@ -101,7 +101,7 @@ def test_rdd_exception_data():
         _ = RDFlex(tmp_dml_data, ml_g)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_exception_cutoff():
     msg = "Cutoff value has to be a float or int. Object of type <class 'list'> passed."
     with pytest.raises(TypeError, match=msg):
@@ -112,14 +112,14 @@ def test_rdd_exception_cutoff():
         _ = RDFlex(dml_data, ml_g, cutoff=200)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_warning_fuzzy():
     msg = 'A sharp RD design is being estimated, but the data indicate that the design is fuzzy.'
     with pytest.warns(UserWarning, match=msg):
         _ = RDFlex(dml_data, ml_g, cutoff=0.1)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_warning_treatment_assignment():
     msg = ("Treatment probability within bandwidth left from cutoff higher than right from cutoff.\n"
            "Treatment assignment might be based on the wrong side of the cutoff.")
@@ -129,7 +129,7 @@ def test_rdd_warning_treatment_assignment():
         _ = RDFlex(tmp_dml_data, ml_g, ml_m, fuzzy=True)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_exception_learner():
 
     # ml_g
@@ -163,7 +163,7 @@ def test_rdd_exception_learner():
         _ = RDFlex(tmp_dml_data, ml_g, ml_m, fuzzy=False)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_exception_resampling():
     # n_folds
     msg = r"The number of folds must be of int type. \[1\] of type <class 'list'> was passed."
@@ -182,7 +182,7 @@ def test_rdd_exception_resampling():
         _ = RDFlex(dml_data, ml_g, ml_m, n_rep=0)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_exception_kernel():
     msg = "fs_kernel must be either a string or a callable. 2 of type <class 'int'> was passed."
     with pytest.raises(TypeError, match=msg):
@@ -192,14 +192,14 @@ def test_rdd_exception_kernel():
         _ = RDFlex(dml_data, ml_g, ml_m, fs_kernel='rbf')
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_exception_h_fs():
     msg = "Initial bandwidth 'h_fs' has to be a float. Object of type <class 'int'> passed."
     with pytest.raises(TypeError, match=msg):
         _ = RDFlex(dml_data, ml_g, ml_m, h_fs=1)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_exception_fs_specification():
     msg = "fs_specification must be a string. 1 of type <class 'int'> was passed."
     with pytest.raises(TypeError, match=msg):
@@ -211,7 +211,7 @@ def test_rdd_exception_fs_specification():
         _ = RDFlex(dml_data, ml_g, ml_m, fs_specification='local_constant')
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_exception_fit():
     rdd_model = RDFlex(dml_data, ml_g, ml_m)
     msg = (r"The number of iterations for the iterative bandwidth fitting must be of int type. \[0\] of type <class 'list'> "

doubleml/rdd/tests/test_rdd_fuzzy.py

@@ -59,37 +59,37 @@ def predict_nonplacebo(predict_dummy, data, cutoff, alpha, p, n_rep, fs_specific
     )
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_coef(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_coef(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['coef'], reference['coef'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_se(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_se(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['se'], reference['se'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_placebo_ci(predict_placebo):
     reference, actual = predict_placebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)
 
 
-@pytest.mark.ci
+@pytest.mark.ci_rdd
 def test_rdd_nonplacebo_ci(predict_nonplacebo):
     reference, actual = predict_nonplacebo
     assert np.allclose(actual['ci'], reference['ci'], rtol=1e-9, atol=1e-4)