Merge branch 'j-external-predictions' of https://github.com/DoubleML/…

…doubleml-for-py into j-external-predictions

doubleml/double_ml_lpq.py

@@ -188,7 +188,7 @@ def __init__(
                 stratify=strata,
             )
             self._smpls = obj_dml_resampling.split_samples()
-            
+
         self._external_predictions_implemented = True
 
     @property
@@ -385,9 +385,9 @@ def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=Fa
 
                 # preliminary propensity for z
                 ml_m_z_prelim = clone(fitted_models["ml_m_z"][i_fold])
-                m_z_hat_prelim = _dml_cv_predict(ml_m_z_prelim, x_train_1, z_train_1, method="predict_proba", smpls=smpls_prelim)[
-                    "preds"
-                ]
+                m_z_hat_prelim = _dml_cv_predict(
+                    ml_m_z_prelim, x_train_1, z_train_1, method="predict_proba", smpls=smpls_prelim
+                )["preds"]
 
                 m_z_hat_prelim = _trimm(m_z_hat_prelim, self.trimming_rule, self.trimming_threshold)
                 if self._normalize_ipw:

doubleml/double_ml_pq.py

@@ -1,5 +1,4 @@
 import numpy as np
-import copy
 from sklearn.base import clone
 from sklearn.utils import check_X_y
 from sklearn.model_selection import StratifiedKFold, train_test_split
@@ -182,7 +181,10 @@ def __init__(
                 stratify=self._dml_data.d,
             )
             self._smpls = obj_dml_resampling.split_samples()
-
+<<<<<<< HEAD
+=======
+
+>>>>>>> 5d59ac29a02b6034a9e550709069398ea177a30d
         self._external_predictions_implemented = True
 
     @property

doubleml/tests/test_did_external_predictions.py

@@ -1,12 +1,13 @@
 import numpy as np
 import pytest
 import math
-from sklearn.linear_model import LinearRegression, LassoCV, LogisticRegression
-from doubleml import DoubleMLData, DoubleMLDID
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from doubleml import DoubleMLDID
 from doubleml.datasets import make_did_SZ2020
 from doubleml.utils import dummy_regressor, dummy_classifier
 from ._utils import draw_smpls
 
+
 @pytest.fixture(scope="module", params=["observational", "experimental"])
 def did_score(request):
     return request.param
@@ -32,7 +33,7 @@ def doubleml_did_fixture(did_score, dml_procedure, n_rep):
         "score": did_score,
         "n_rep": n_rep,
         "dml_procedure": dml_procedure,
-        "draw_sample_splitting": False
+        "draw_sample_splitting": False,
     }
     DMLDID = DoubleMLDID(ml_g=LinearRegression(), ml_m=LogisticRegression(), **kwargs)
     DMLDID.set_sample_splitting(all_smpls)

doubleml/tests/test_didcs_external_predictions.py

@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 import math
-from sklearn.linear_model import LinearRegression, LassoCV, LogisticRegression
-from doubleml import DoubleMLData, DoubleMLDIDCS
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from doubleml import DoubleMLDIDCS
 from doubleml.datasets import make_did_SZ2020
 from doubleml.utils import dummy_regressor, dummy_classifier
 from ._utils import draw_smpls
@@ -34,7 +34,7 @@ def doubleml_didcs_fixture(did_score, dml_procedure, n_rep):
         "n_rep": n_rep,
         "n_folds": 5,
         "dml_procedure": dml_procedure,
-        "draw_sample_splitting": False
+        "draw_sample_splitting": False,
     }
     DMLDIDCS = DoubleMLDIDCS(ml_g=LinearRegression(), ml_m=LogisticRegression(), **kwargs)
     DMLDIDCS.set_sample_splitting(all_smpls)

doubleml/tests/test_doubleml_exceptions.py

@@ -2,7 +2,7 @@
 import pandas as pd
 import numpy as np
 
-from doubleml import DoubleMLPLR, DoubleMLIRM, DoubleMLIIVM, DoubleMLPLIV, DoubleMLData,\
+from doubleml import DoubleMLPLR, DoubleMLIRM, DoubleMLIIVM, DoubleMLPLIV, DoubleMLData, \
     DoubleMLClusterData, DoubleMLPQ, DoubleMLLPQ, DoubleMLCVAR, DoubleMLQTE, DoubleMLDID, DoubleMLDIDCS
 from doubleml.datasets import make_plr_CCDDHNR2018, make_irm_data, make_pliv_CHS2015, make_iivm_data, \
     make_pliv_multiway_cluster_CKMS2021, make_did_SZ2020

doubleml/tests/test_doubleml_external_prediction_implementation.py → doubleml/tests/test_doubleml_exceptions_external_pred.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=10, theta=0.5, return_type="DataFrame")
 ext_predictions = {"d": {}}
 
 

doubleml/tests/test_dummy_learners.py

@@ -42,5 +42,5 @@ def test_clone(dl_fixture):
     try:
         _ = clone(dl_fixture["dummy_regressor"])
         _ = clone(dl_fixture["dummy_classifier"])
-    except Error as e:
+    except Exception as e:
         pytest.fail(f"clone() raised an exception:\n{str(e)}\n")

doubleml/tests/test_iivm_external_predictions.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 import math
-from sklearn.linear_model import LinearRegression, LassoCV, LogisticRegression
+from sklearn.linear_model import LinearRegression, LogisticRegression
 from doubleml import DoubleMLIIVM, DoubleMLData
 from doubleml.datasets import make_iivm_data
 from doubleml.utils import dummy_regressor, dummy_classifier
@@ -21,9 +21,7 @@ def n_rep(request):
 def adapted_doubleml_fixture(dml_procedure, n_rep):
     ext_predictions = {"d": {}}
 
-    data = make_iivm_data(
-        n_obs=500, dim_x=20, theta=0.5, alpha_x=1.0, return_type="DataFrame"
-    )
+    data = make_iivm_data(n_obs=500, dim_x=20, theta=0.5, alpha_x=1.0, return_type="DataFrame")
 
     np.random.seed(3141)
 
@@ -45,14 +43,13 @@ def adapted_doubleml_fixture(dml_procedure, n_rep):
     np.random.seed(3141)
 
     DMLIIVM.fit(store_predictions=True)
-    
+
     ext_predictions["d"]["ml_g0"] = DMLIIVM.predictions["ml_g0"][:, :, 0]
     ext_predictions["d"]["ml_g1"] = DMLIIVM.predictions["ml_g1"][:, :, 0]
     ext_predictions["d"]["ml_m"] = DMLIIVM.predictions["ml_m"][:, :, 0]
     ext_predictions["d"]["ml_r0"] = DMLIIVM.predictions["ml_r0"][:, :, 0]
     ext_predictions["d"]["ml_r1"] = DMLIIVM.predictions["ml_r1"][:, :, 0]
 
-
     DMLIIVM_ext = DoubleMLIIVM(
         ml_g=dummy_regressor(), ml_m=dummy_classifier(), ml_r=dummy_classifier(), **kwargs
     )

doubleml/tests/test_lpq_external_predictions.py

@@ -4,7 +4,7 @@
 from sklearn.linear_model import LogisticRegression
 from doubleml import DoubleMLLPQ, DoubleMLData
 from doubleml.datasets import make_iivm_data
-from doubleml.utils import dummy_regressor, dummy_classifier
+from doubleml.utils import dummy_classifier
 from ._utils import draw_smpls
 
 

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
@@ -32,12 +32,11 @@ 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": {}}
 
-        data = make_pliv_CHS2015(
-            n_obs=500, dim_x=20, alpha=0.5, dim_z=dim_z, return_type="DataFrame"
-        )
+        data = make_pliv_CHS2015(n_obs=500, dim_x=20, alpha=0.5, dim_z=dim_z, return_type="DataFrame")
 
         np.random.seed(3141)
 
@@ -77,16 +76,14 @@ def adapted_doubleml_fixture(score, dml_procedure, n_rep, dim_z):
                 ml_m_key = "ml_m_" + "Z" + str(instr + 1)
                 ext_predictions["d"][ml_m_key] = DMLPLIV.predictions[ml_m_key][:, :, 0]
 
-        DMLPLIV_ext = DoubleMLPLIV(
-            ml_m=dummy_regressor(), ml_l=dummy_regressor(), ml_r=dummy_regressor(), **kwargs
-        )
+        DMLPLIV_ext = DoubleMLPLIV(ml_m=dummy_regressor(), ml_l=dummy_regressor(), ml_r=dummy_regressor(), **kwargs)
 
         np.random.seed(3141)
         DMLPLIV_ext.fit(external_predictions=ext_predictions)
 
         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
 
 
@@ -79,7 +79,8 @@ def doubleml_pq_fixture(dml_procedure, n_rep, normalize_ipw, set_ml_m_ext, set_m
 
     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
+        # because no preliminary results are available for ml_m,
+        # the model use the (external) final predictions for ml_m for calculating the ipw estimate
         tol_rel = 0.1
         tol_abs = 0.1
     else:

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 +32,21 @@ 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".
+    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):