reformatting

doubleml/tests/test_doubleml_external_prediction_implementation.py → doubleml/tests/test_doubleml_exceptions_ext_preds.py

@@ -1,10 +1,9 @@
-import numpy as np
 import pytest
 from doubleml import DoubleMLCVAR, DoubleMLQTE, DoubleMLData
 from doubleml.datasets import make_irm_data
 from doubleml.utils import dummy_regressor, dummy_classifier
 
-df_irm = make_irm_data(n_obs=500, dim_x=20, theta=0.5, return_type="DataFrame")
+df_irm = make_irm_data(n_obs=10, dim_x=2, theta=0.5, return_type="DataFrame")
 ext_predictions = {"d": {}}
 
 

doubleml/tests/test_pliv_external_predictions.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 import math
-from sklearn.linear_model import LinearRegression, LassoCV
+from sklearn.linear_model import LinearRegression
 from doubleml import DoubleMLPLIV, DoubleMLData
 from doubleml.datasets import make_pliv_CHS2015
 from doubleml.utils import dummy_regressor
@@ -32,6 +32,7 @@ def adapted_doubleml_fixture(score, dml_procedure, n_rep, dim_z):
     # IV-type score only allows dim_z = 1, so skip testcases with dim_z > 1 for IV-type score
     if dim_z > 1 and score == "IV-type":
         pytest.skip("IV-type score only allows dim_z = 1")
+        res_dict = None
     else:
         ext_predictions = {"d": {}}
 
@@ -86,7 +87,7 @@ def adapted_doubleml_fixture(score, dml_procedure, n_rep, dim_z):
 
         res_dict = {"coef_normal": DMLPLIV.coef, "coef_ext": DMLPLIV_ext.coef}
 
-        return res_dict
+    return res_dict
 
 
 @pytest.mark.ci

doubleml/tests/test_pq_external_predictions.py

@@ -4,7 +4,7 @@
 from sklearn.linear_model import LogisticRegression
 from doubleml import DoubleMLPQ, DoubleMLData
 from doubleml.datasets import make_irm_data
-from doubleml.utils import dummy_regressor, dummy_classifier
+from doubleml.utils import dummy_classifier
 from ._utils import draw_smpls
 
 
@@ -27,6 +27,7 @@ def normalize_ipw(request):
 def set_ml_m_ext(request):
     return request.param
 
+
 @pytest.fixture(scope="module", params=[True, False])
 def set_ml_g_ext(request):
     return request.param
@@ -36,7 +37,7 @@ def set_ml_g_ext(request):
 def doubleml_pq_fixture(dml_procedure, n_rep, normalize_ipw, set_ml_m_ext, set_ml_g_ext):
     ext_predictions = {"d": {}}
     np.random.seed(3141)
-    data = make_irm_data(theta=0.5, n_obs=1000, dim_x=5, return_type="DataFrame")
+    data = make_irm_data(theta=1, n_obs=500, dim_x=5, return_type="DataFrame")
 
     dml_data = DoubleMLData(data, "y", "d")
     all_smpls = draw_smpls(len(dml_data.y), 5, n_rep=n_rep, groups=None)
@@ -47,7 +48,7 @@ def doubleml_pq_fixture(dml_procedure, n_rep, normalize_ipw, set_ml_m_ext, set_m
         "n_rep": n_rep,
         "dml_procedure": dml_procedure,
         "normalize_ipw": normalize_ipw,
-        "draw_sample_splitting": False
+        "draw_sample_splitting": False,
     }
 
     ml_m = LogisticRegression(random_state=42)
@@ -63,24 +64,38 @@ def doubleml_pq_fixture(dml_procedure, n_rep, normalize_ipw, set_ml_m_ext, set_m
         ml_m = dummy_classifier()
     else:
         ml_m = LogisticRegression(random_state=42)
-        
+
     if set_ml_g_ext:
         ext_predictions["d"]["ml_g"] = DMLPQ.predictions["ml_g"][:, :, 0]
         ml_g = dummy_classifier()
     else:
         ml_g = LogisticRegression(random_state=42)
 
-    DMLPLQ_ext = DoubleMLPQ(ml_g = ml_g, ml_m = ml_m, **kwargs)
+    DMLPLQ_ext = DoubleMLPQ(ml_g=ml_g, ml_m=ml_m, **kwargs)
     DMLPLQ_ext.set_sample_splitting(all_smpls)
 
     np.random.seed(3141)
     DMLPLQ_ext.fit(external_predictions=ext_predictions)
 
-    res_dict = {"coef_normal": DMLPQ.coef, "coef_ext": DMLPLQ_ext.coef}
+    if set_ml_m_ext and not set_ml_g_ext:
+        # adjust tolerance for the case that ml_m is set to external predictions
+        # because no preliminary results are available for ml_m, the model use the (external) final predictions for ml_m
+        tol_rel = 0.1
+        tol_abs = 0.1
+    else:
+        tol_rel = 1e-9
+        tol_abs = 1e-4
+
+    res_dict = {"coef_normal": DMLPQ.coef, "coef_ext": DMLPLQ_ext.coef, "tol_rel": tol_rel, "tol_abs": tol_abs}
 
     return res_dict
 
 
 @pytest.mark.ci
 def test_doubleml_pq_coef(doubleml_pq_fixture):
-    assert math.isclose(doubleml_pq_fixture["coef_normal"], doubleml_pq_fixture["coef_ext"], rel_tol=1e-9, abs_tol=1e-4)
+    assert math.isclose(
+        doubleml_pq_fixture["coef_normal"],
+        doubleml_pq_fixture["coef_ext"],
+        rel_tol=doubleml_pq_fixture["tol_rel"],
+        abs_tol=doubleml_pq_fixture["tol_abs"],
+    )

doubleml/utils/dummy_learners.py

@@ -2,6 +2,23 @@
 
 
 class dummy_regressor(BaseEstimator):
+    """
+    A dummy regressor that raises an AttributeError when attempting to access
+    its fit, predict, or set_params methods.
+    Attributes
+    ----------
+    _estimator_type : str
+        Type of the estimator, set to "regressor".
+    Methods
+    -------
+    fit(*args)
+        Raises AttributeError: "Accessed fit method of DummyRegressor!"
+    predict(*args)
+        Raises AttributeError: "Accessed predict method of DummyRegressor!"
+    set_params(*args)
+        Raises AttributeError: "Accessed set_params method of DummyRegressor!"
+    """
+
     _estimator_type = "regressor"
 
     def fit(*args):
@@ -15,6 +32,25 @@ def set_params(*args):
 
 
 class dummy_classifier(BaseEstimator):
+    """
+    A dummy classifier that raises an AttributeError when attempting to access
+    its fit, predict, set_params, or predict_proba methods.
+    Attributes
+    ----------
+    _estimator_type : str
+        Type of the estimator, set to "classifier".
+    Methods
+    -------
+    fit(*args)
+        Raises AttributeError: "Accessed fit method of DummyClassifier!"
+    predict(*args)
+        Raises AttributeError: "Accessed predict method of DummyClassifier!"
+    set_params(*args)
+        Raises AttributeError: "Accessed set_params method of DummyClassifier!"
+    predict_proba(*args, **kwargs)
+        Raises AttributeError: "Accessed predict_proba method of DummyClassifier!"
+    """
+
     _estimator_type = "classifier"
 
     def fit(*args):