add exception tests

Author: SvenKlaassen

doubleml/irm/iivm.py

@@ -570,6 +570,8 @@ def robust_confset(self, level=0.95):
             bounds of an interval. The union of this intervals forms the confidence set.
         """
 
+        if self.framework is None:
+            raise ValueError("Apply fit() before robust_confset().")
         if not isinstance(level, float):
             raise TypeError(f"The confidence level must be of float type. {str(level)} of type {str(type(level))} was passed.")
         if (level <= 0) | (level >= 1):

doubleml/irm/tests/test_iivm_unif_confset.py

@@ -53,3 +53,36 @@ def test_coverage_robust_confset():
     # Calculate coverage rate
     coverage_rate = np.mean(coverage)
     assert coverage_rate >= 0.9, f"Coverage rate {coverage_rate} is below 0.9"
+
+
+@pytest.mark.ci
+def test_exceptions_robust_confset():
+    # Test parameters
+    true_ATE = 0.5
+    instrument_size = 0.005
+    n_samples = 1000
+
+    np.random.seed(3141)
+    data = generate_weak_iv_data(n_samples, instrument_size, true_ATE)
+
+    # create new model
+    learner_g = LinearRegression()
+    classifier_m = LogisticRegression()
+    classifier_r = RandomForestClassifier(n_estimators=20, max_depth=5)
+    dml_iivm_obj = dml.DoubleMLIIVM(data, learner_g, classifier_m, classifier_r)
+
+    # Check if the robust_confset method raises an exception when called before fitting
+    msg = r"Apply fit\(\) before robust_confset\(\)."
+    with pytest.raises(ValueError, match=msg):
+        dml_iivm_obj.robust_confset()
+
+    # Fit the model
+    dml_iivm_obj.fit()
+
+    # Check invalid inputs
+    msg = "The confidence level must be of float type. 0.95 of type <class 'str'> was passed."
+    with pytest.raises(TypeError, match=msg):
+        dml_iivm_obj.robust_confset(level="0.95")
+    msg = r"The confidence level must be in \(0,1\). 1.5 was passed."
+    with pytest.raises(ValueError, match=msg):
+        dml_iivm_obj.robust_confset(level=1.5)