Remove sklearn<=1.6 compatibility code

feature_engine/tags.py

@@ -1,9 +1,3 @@
-import sklearn
-from sklearn.utils.fixes import parse_version
-
-sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
-
-
 def _return_tags():
     tags = {
         "preserves_dtype": [],
@@ -32,14 +26,13 @@ def _return_tags():
         },
     }
 
-    if sklearn_version > parse_version("1.6"):
-        msg1 = "against Feature-engines design."
-        msg2 = "Our transformers do not preserve dtype."
-        all_fail = {
-            "check_do_not_raise_errors_in_init_or_set_params": msg1,
-            "check_transformer_preserve_dtypes": msg2,
-            # TODO: investigate this test further.
-            "check_n_features_in_after_fitting": "not sure why it fails, we do check.",
-        }
-        tags["_xfail_checks"].update(all_fail)  # type: ignore
+    msg1 = "against Feature-engines design."
+    msg2 = "Our transformers do not preserve dtype."
+    all_fail = {
+        "check_do_not_raise_errors_in_init_or_set_params": msg1,
+        "check_transformer_preserve_dtypes": msg2,
+        # TODO: investigate this test further.
+        "check_n_features_in_after_fitting": "not sure why it fails, we do check.",
+    }
+    tags["_xfail_checks"].update(all_fail)  # type: ignore
     return tags

tests/test_creation/test_check_estimator_creation.py

@@ -1,9 +1,7 @@
 import pandas as pd
 import pytest
-import sklearn
 from sklearn.pipeline import Pipeline
 from sklearn.utils.estimator_checks import check_estimator
-from sklearn.utils.fixes import parse_version
 
 from feature_engine.creation import (
     CyclicalFeatures,
@@ -14,8 +12,6 @@
 )
 from tests.estimator_checks.estimator_checks import check_feature_engine_estimator
 
-sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
-
 # Estimators for sklearn's check_estimator
 # Note: GeoDistanceFeatures is not included here because it requires 4 specific
 # named coordinate columns, but sklearn's check_estimator generates test data
@@ -29,20 +25,13 @@
     DecisionTreeFeatures(regression=False),
 ]
 
-if sklearn_version > parse_version("1.6"):
-
-    @pytest.mark.parametrize("estimator", _estimators)
-    def test_check_estimator_from_sklearn(estimator):
-        return check_estimator(
-            estimator=estimator,
-            expected_failed_checks=estimator._more_tags()["_xfail_checks"],
-        )
-
-else:
 
-    @pytest.mark.parametrize("estimator", _estimators)
-    def test_check_estimator_from_sklearn(estimator):
-        return check_estimator(estimator)
+@pytest.mark.parametrize("estimator", _estimators)
+def test_check_estimator_from_sklearn(estimator):
+    return check_estimator(
+        estimator=estimator,
+        expected_failed_checks=estimator._more_tags()["_xfail_checks"],
+    )
 
 
 _estimators = [

tests/test_creation/test_geo_features.py

@@ -356,7 +356,5 @@ def test_more_tags_and_sklearn_tags():
         == "transformer has mandatory parameters"
     )
 
-    # basic check for sklearn tags if available (new sklearn versions)
-    if hasattr(transformer, "__sklearn_tags__"):
-        tags = transformer.__sklearn_tags__()
-        assert tags is not None
+    tags = transformer.__sklearn_tags__()
+    assert tags is not None

tests/test_discretisation/test_check_estimator_discretisers.py

@@ -1,10 +1,8 @@
 import numpy as np
 import pandas as pd
 import pytest
-import sklearn
 from sklearn.pipeline import Pipeline
 from sklearn.utils.estimator_checks import check_estimator
-from sklearn.utils.fixes import parse_version
 
 from feature_engine.discretisation import (
     ArbitraryDiscretiser,
@@ -15,9 +13,6 @@
 )
 from tests.estimator_checks.estimator_checks import check_feature_engine_estimator
 
-sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
-
-
 _estimators = [
     DecisionTreeDiscretiser(regression=False),
     EqualFrequencyDiscretiser(),
@@ -26,20 +21,13 @@
     GeometricWidthDiscretiser(),
 ]
 
-if sklearn_version < parse_version("1.6"):
-
-    @pytest.mark.parametrize("estimator", _estimators)
-    def test_check_estimator_from_sklearn(estimator):
-        return check_estimator(estimator)
 
-else:
-
-    @pytest.mark.parametrize("estimator", _estimators)
-    def test_check_estimator_from_sklearn(estimator):
-        return check_estimator(
-            estimator=estimator,
-            expected_failed_checks=estimator._more_tags()["_xfail_checks"],
-        )
+@pytest.mark.parametrize("estimator", _estimators)
+def test_check_estimator_from_sklearn(estimator):
+    return check_estimator(
+        estimator=estimator,
+        expected_failed_checks=estimator._more_tags()["_xfail_checks"],
+    )
 
 
 @pytest.mark.parametrize("estimator", _estimators)

tests/test_encoding/test_check_estimator_encoders.py

@@ -1,12 +1,10 @@
 import pandas as pd
 import pytest
-import sklearn
 from numpy import nan
 from sklearn import clone
 from sklearn.exceptions import NotFittedError
 from sklearn.pipeline import Pipeline
 from sklearn.utils.estimator_checks import check_estimator
-from sklearn.utils.fixes import parse_version
 
 from feature_engine.encoding import (
     CountEncoder,
@@ -25,8 +23,6 @@
     test_df,
 )
 
-sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
-
 _estimators = [
     CountEncoder(ignore_format=True),
     CountFrequencyEncoder(ignore_format=True),
@@ -46,22 +42,16 @@
 ]
 
 
-if sklearn_version < parse_version("1.6"):
-
-    @pytest.mark.parametrize("estimator", _estimators)
-    def test_check_estimator_from_sklearn(estimator):
-        return check_estimator(estimator)
+expected_fails = _return_tags()["_xfail_checks"]
+expected_fails.update({"check_estimators_nan_inf": "transformer allows NA"})
 
-else:
-    expected_fails = _return_tags()["_xfail_checks"]
-    expected_fails.update({"check_estimators_nan_inf": "transformer allows NA"})
 
-    @pytest.mark.parametrize("estimator", _estimators)
-    def test_check_estimator_from_sklearn(estimator):
-        if estimator.__class__.__name__ != "WoEEncoder":
-            return check_estimator(
-                estimator=estimator, expected_failed_checks=expected_fails
-            )
+@pytest.mark.parametrize("estimator", _estimators)
+def test_check_estimator_from_sklearn(estimator):
+    if estimator.__class__.__name__ != "WoEEncoder":
+        return check_estimator(
+            estimator=estimator, expected_failed_checks=expected_fails
+        )
 
 
 _estimators = [