update docstrings acc. to Refactor Data Generators #306

Author: JanTeichertKluge

doubleml/irm/apos.py

@@ -673,7 +673,7 @@ def set_sample_splitting(self, all_smpls, all_smpls_cluster=None):
         --------
         >>> import numpy as np
         >>> import doubleml as dml
-        >>> from doubleml.datasets import make_plr_CCDDHNR2018
+        >>> from doubleml.plm.datasets import make_plr_CCDDHNR2018
         >>> from sklearn.ensemble import RandomForestRegressor
         >>> from sklearn.base import clone
         >>> np.random.seed(3141)

doubleml/irm/cvar.py

@@ -82,7 +82,7 @@ class DoubleMLCVAR(LinearScoreMixin, DoubleML):
     --------
     >>> import numpy as np
     >>> import doubleml as dml
-    >>> from doubleml.datasets import make_irm_data
+    >>> from doubleml.irm.datasets import make_irm_data
     >>> from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
     >>> np.random.seed(3141)
     >>> ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

doubleml/irm/lpq.py

@@ -83,7 +83,7 @@ class DoubleMLLPQ(NonLinearScoreMixin, DoubleML):
     --------
     >>> import numpy as np
     >>> import doubleml as dml
-    >>> from doubleml.datasets import make_iivm_data
+    >>> from doubleml.irm.datasets import make_iivm_data
     >>> from sklearn.ensemble import RandomForestClassifier
     >>> np.random.seed(3141)
     >>> ml_g = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

doubleml/irm/pq.py

@@ -90,7 +90,7 @@ class DoubleMLPQ(NonLinearScoreMixin, DoubleML):
     --------
     >>> import numpy as np
     >>> import doubleml as dml
-    >>> from doubleml.datasets import make_irm_data
+    >>> from doubleml.irm.datasets import make_irm_data
     >>> from sklearn.ensemble import RandomForestClassifier
     >>> np.random.seed(3141)
     >>> ml_g = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

doubleml/irm/qte.py

@@ -72,7 +72,7 @@ class DoubleMLQTE:
     --------
     >>> import numpy as np
     >>> import doubleml as dml
-    >>> from doubleml.datasets import make_irm_data
+    >>> from doubleml.irm.datasets import make_irm_data
     >>> from sklearn.ensemble import RandomForestClassifier
     >>> np.random.seed(3141)
     >>> ml_g = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)
@@ -499,7 +499,7 @@ def set_sample_splitting(self, all_smpls, all_smpls_cluster=None):
         --------
         >>> import numpy as np
         >>> import doubleml as dml
-        >>> from doubleml.datasets import make_plr_CCDDHNR2018
+        >>> from doubleml.plm.datasets import make_plr_CCDDHNR2018
         >>> from sklearn.ensemble import RandomForestRegressor
         >>> from sklearn.base import clone
         >>> np.random.seed(3141)