Fixes #43: Remove kwargs from base transformer. init (#56)

* removed **kwargs arg from BaseTransformer.__init__

* added test for unexpected kwarg handling to test_BaseTransformer.py
added similar test to example child class test module
test_DataFrameMethodTransformer.py

* fix broken tests with incorrect kwargs

tests/base/test_BaseTransformer.py

@@ -227,6 +227,17 @@ def test_y_no_rows_error(self):
 
             x.fit(X=df, y=pd.Series(name="b", dtype=object))
 
+    def test_unexpected_kwarg_error(self):
+
+        with pytest.raises(
+            TypeError,
+            match=re.escape(
+                "__init__() got an unexpected keyword argument 'unexpected_kwarg'"
+            ),
+        ):
+
+            BaseTransformer(columns="a", unexpected_kwarg="spanish inquisition")
+
 
 class TestTransform(object):
     """Tests for BaseTransformer.transform()."""

tests/base/test_DataFrameMethodTransformer.py

@@ -3,6 +3,7 @@
 import tests.test_data as d
 import pandas as pd
 import numpy as np
+import re
 
 import tubular
 from tubular.base import DataFrameMethodTransformer
@@ -167,6 +168,23 @@ def test_attributes_set(self):
             msg="Attributes for DataFrameMethodTransformer set in init",
         )
 
+    def test_unexpected_kwarg_error(self):
+
+        with pytest.raises(
+            TypeError,
+            match=re.escape(
+                "__init__() got an unexpected keyword argument 'unexpected_kwarg'"
+            ),
+        ):
+
+            DataFrameMethodTransformer(
+                new_column_name="a",
+                pd_method_name="sum",
+                columns=["b", "c"],
+                drop_original=True,
+                unexpected_kwarg="spanish inquisition",
+            )
+
 
 class TestTransform(object):
     """Tests for DataFrameMethodTransformer.transform()."""

tests/imputers/test_NearestMeanResponseImputer.py

@@ -23,7 +23,7 @@ def test_arguments(self):
     def test_class_methods(self):
         """Test that NearestMeanResponseImputer has fit and transform methods."""
 
-        x = NearestMeanResponseImputer(response_column="c", columns=None)
+        x = NearestMeanResponseImputer(columns=None)
 
         ta.classes.test_object_method(obj=x, expected_method="fit", msg="fit")
 

tests/nominal/test_GroupRareLevelsTransformer.py

@@ -220,7 +220,7 @@ def test_learnt_values_weight(self):
 
         df = d.create_df_6()
 
-        x = GroupRareLevelsTransformer(columns=["b"], cut_off_percent=0.3, weights="a")
+        x = GroupRareLevelsTransformer(columns=["b"], cut_off_percent=0.3, weight="a")
 
         x.fit(df)
 
@@ -235,7 +235,7 @@ def test_learnt_values_weight_2(self):
 
         df = d.create_df_6()
 
-        x = GroupRareLevelsTransformer(columns=["c"], cut_off_percent=0.2, weights="a")
+        x = GroupRareLevelsTransformer(columns=["c"], cut_off_percent=0.2, weight="a")
 
         x.fit(df)
 

tests/nominal/test_MeanResponseTransformer.py

@@ -29,7 +29,7 @@ def test_arguments(self):
     def test_class_methods(self):
         """Test that MeanResponseTransformer has fit and transform methods."""
 
-        x = MeanResponseTransformer(response_column="a")
+        x = MeanResponseTransformer()
 
         ta.classes.test_object_method(obj=x, expected_method="fit", msg="fit")
 
@@ -40,7 +40,7 @@ def test_class_methods(self):
     def test_inheritance(self):
         """Test that NominalToIntegerTransformer inherits from BaseNominalTransformer."""
 
-        x = MeanResponseTransformer(response_column="a")
+        x = MeanResponseTransformer()
 
         ta.classes.assert_inheritance(x, tubular.nominal.BaseNominalTransformer)
 
@@ -127,7 +127,7 @@ def test_check_is_fitted_called(self, mocker):
 
         expected_call_args = {0: {"args": (["global_mean"],), "kwargs": {}}}
 
-        x = MeanResponseTransformer(response_column="target")
+        x = MeanResponseTransformer()
 
         x.fit(pd.DataFrame({"a": ["1", "2"]}), pd.Series([2, 3]))
 
@@ -612,7 +612,7 @@ def test_learnt_values_not_modified(self):
     def test_expected_output(self, df, expected):
         """Test that the output is expected from transform."""
 
-        x = MeanResponseTransformer(response_column="a", columns=["b", "d", "f"])
+        x = MeanResponseTransformer(columns=["b", "d", "f"])
 
         # set the impute values dict directly rather than fitting x on df so test works with helpers
         x.mappings = {

tests/nominal/test_OrdinalEncoderTransformer.py

@@ -22,7 +22,7 @@ def test_arguments(self):
     def test_class_methods(self):
         """Test that OrdinalEncoderTransformer has fit and transform methods."""
 
-        x = OrdinalEncoderTransformer(response_column="a")
+        x = OrdinalEncoderTransformer()
 
         ta.classes.test_object_method(obj=x, expected_method="fit", msg="fit")
 
@@ -33,7 +33,7 @@ def test_class_methods(self):
     def test_inheritance(self):
         """Test that NominalToIntegerTransformer inherits from BaseNominalTransformer."""
 
-        x = OrdinalEncoderTransformer(response_column="a")
+        x = OrdinalEncoderTransformer()
 
         ta.classes.assert_inheritance(x, tubular.nominal.BaseNominalTransformer)
         ta.classes.assert_inheritance(x, tubular.mapping.BaseMappingTransformMixin)

tests/numeric/test_LogTransformer.py

@@ -32,7 +32,6 @@ def test_base_type_error(self):
             LogTransformer(
                 columns=["a"],
                 base="a",
-                new_column_name="b",
             )
 
     def test_base_not_strictly_positive_error(self):
@@ -46,7 +45,6 @@ def test_base_not_strictly_positive_error(self):
             LogTransformer(
                 columns=["a"],
                 base=0,
-                new_column_name="b",
             )
 
     def test_class_methods(self):

tests/test_transformers.py

@@ -46,7 +46,7 @@ def ListOfTransformers():
             imputers.MedianImputer(columns="a"),
             imputers.MeanImputer(columns="a"),
             imputers.ModeImputer(columns="a"),
-            imputers.NearestMeanResponseImputer(response_column="a"),
+            imputers.NearestMeanResponseImputer(columns="a"),
             imputers.NullIndicator(columns="a"),
             mapping.BaseMappingTransformer(mappings={"a": {1: 2, 3: 4}}),
             mapping.BaseMappingTransformMixin(),
@@ -64,8 +64,8 @@ def ListOfTransformers():
             nominal.BaseNominalTransformer(),
             nominal.NominalToIntegerTransformer(columns="a"),
             nominal.GroupRareLevelsTransformer(columns="a"),
-            nominal.MeanResponseTransformer(columns="a", response_column="b"),
-            nominal.OrdinalEncoderTransformer(columns="a", response_column="b"),
+            nominal.MeanResponseTransformer(columns="a"),
+            nominal.OrdinalEncoderTransformer(columns="a"),
             nominal.OneHotEncodingTransformer(columns="a"),
             numeric.LogTransformer(columns="a"),
             numeric.CutTransformer(column="a", new_column_name="b"),
@@ -96,3 +96,11 @@ def test_clone(self, transformer):
         """
 
         b.clone(transformer)
+
+    @pytest.mark.parametrize("transformer", ListOfTransformers())
+    def test_unexpected_kwarg(self, transformer):
+        """
+        Test that transformer can be used in sklearn.base.clone function.
+        """
+
+        b.clone(transformer)

tubular/base.py

@@ -30,9 +30,6 @@ class BaseTransformer(TransformerMixin, BaseEstimator):
     verbose : bool, default = False
         Should statements be printed when methods are run?
 
-    **kwds
-        Arbitrary keyword arguments.
-
     Attributes
     ----------
     columns : list or None
@@ -54,7 +51,7 @@ def classname(self):
         """Method that returns the name of the current class when called"""
         return type(self).__name__
 
-    def __init__(self, columns=None, copy=True, verbose=False, **kwargs):
+    def __init__(self, columns=None, copy=True, verbose=False):
 
         self.version_ = __version__