[MNT] rename MeanNormalisationScaler public name to british spelling

README.md

@@ -128,7 +128,7 @@ Please share your story by answering 1 quick question
 * ArcSinhTransformer
 
 ### Variable Scaling methods
-* MeanNormalizationScaler
+* MeanNormalisationScaler
 
 ### Variable Creation:
  * MathFeatures

docs/api_doc/scaling/MeanNormalisationScaler.rst

@@ -0,0 +1,6 @@
+MeanNormalisationScaler
+=======================
+
+.. autoclass:: feature_engine.scaling.MeanNormalisationScaler
+    :members:
+

docs/api_doc/scaling/index.rst

@@ -9,4 +9,4 @@ given columns.
 .. toctree::
    :maxdepth: 1
 
-   MeanNormalizationScaler
+   MeanNormalisationScaler

docs/index.rst

@@ -328,7 +328,7 @@ procedures to a subset of the variables only, check out the :doc:`api_doc/wrappe
 
 Feature-engine extends scikit-learn's scaling functionality with the following transformer:
 
-- :doc:`api_doc/scaling/MeanNormalizationScaler`: scale variables using mean normalisation
+- :doc:`api_doc/scaling/MeanNormalisationScaler`: scale variables using mean normalisation
 
 
 Scikit-learn Wrapper:

docs/user_guide/scaling/MeanNormalizationScaler.rst → docs/user_guide/scaling/MeanNormalisationScaler.rst

@@ -2,12 +2,12 @@
 
 .. currentmodule:: feature_engine.scaling
 
-MeanNormalizationScaler
+MeanNormalisationScaler
 =======================
 
 .. attention::
 
-    **New in version 2.0:** When `variables` is `None`, :class:`MeanNormalizationScaler()` used to
+    **New in version 2.0:** When `variables` is `None`, :class:`MeanNormalisationScaler()` used to
     raise an error if the dataframe contained no numerical variables. You can now
     set the new parameter `return_empty` to `True` to make the transformer return an
     empty list of variables and skip the scaling instead, leaving the dataframe
@@ -28,23 +28,23 @@ Mean normalisation is given by the following formula:
 
     X' = (X - Mean(X)) / (Max(X) - Min(X))
 
-:class:`MeanNormalizationScaler()` scales variables using mean normalisation.
+:class:`MeanNormalisationScaler()` scales variables using mean normalisation.
 
 .. note::
 
-    :class:`MeanNormalizationScaler()` only works with non-constant numerical variables.
+    :class:`MeanNormalisationScaler()` only works with non-constant numerical variables.
     If the variable is constant, the scaler will raise an error.
 
 Python implementation
 ---------------------
 
-We'll show how to use :class:`MeanNormalizationScaler()` through a toy dataset. Let's create
+We'll show how to use :class:`MeanNormalisationScaler()` through a toy dataset. Let's create
 a toy dataset:
 
 .. code:: python
 
     import pandas as pd
-    from feature_engine.scaling import MeanNormalizationScaler
+    from feature_engine.scaling import MeanNormalisationScaler
 
     df = pd.DataFrame.from_dict(
         {
@@ -81,12 +81,12 @@ First, let's make a list with the variable names:
       'Height',
     ]
 
-Now, let's set up :class:`MeanNormalizationScaler()`:
+Now, let's set up :class:`MeanNormalisationScaler()`:
 
 .. code:: python
 
     # set up the scaler
-    scaler = MeanNormalizationScaler(variables = vars)
+    scaler = MeanNormalisationScaler(variables = vars)
 
     # fit the scaler
     scaler.fit(df)
@@ -155,4 +155,4 @@ For tutorials about this and other feature engineering methods check out these r
 
 Both our book and courses are suitable for beginners and more advanced data scientists
 alike. By purchasing them you are supporting `Sole <https://linkedin.com/in/soledad-galli>`_,
-the main developer of feature-engine.
+the main developer of feature-engine.

docs/user_guide/scaling/index.rst

@@ -59,4 +59,4 @@ Scalers
 .. toctree::
    :maxdepth: 1
 
-   MeanNormalizationScaler
+   MeanNormalisationScaler

feature_engine/scaling/__init__.py

@@ -3,8 +3,9 @@
 scaling methods.
 """
 
-from .mean_normalization import MeanNormalizationScaler
+from .mean_normalization import MeanNormalisationScaler, MeanNormalizationScaler
 
 __all__ = [
+    "MeanNormalisationScaler",
     "MeanNormalizationScaler",
 ]

feature_engine/scaling/mean_normalization.py

@@ -1,6 +1,7 @@
 # Authors: Vasco Schiavo <vasco.schiavo@protonmail.com>
 # License: BSD 3 clause
 
+import warnings
 from typing import List, Optional, Union
 
 import pandas as pd
@@ -37,9 +38,9 @@
     fit_transform=_fit_transform_docstring,
     inverse_transform=_inverse_transform_docstring,
 )
-class MeanNormalizationScaler(BaseNumericalTransformer):
+class MeanNormalisationScaler(BaseNumericalTransformer):
     """
-    MeanNormalizationScaler() applies mean normalisation, which consists of subtracting
+    MeanNormalisationScaler() applies mean normalisation, which consists of subtracting
     the mean of each feature and then dividing the result by the value range, that is,
     the difference between its maximum and minimum value. The method aims to center the
     variables at 0, and rescale the distribution between -1 and 1.
@@ -51,7 +52,6 @@ class MeanNormalizationScaler(BaseNumericalTransformer):
 
     More details in the :ref:`User Guide <mean_normalisation_scaler>`.
 
-
     Parameters
     ----------
     {variables}
@@ -89,10 +89,10 @@ class MeanNormalizationScaler(BaseNumericalTransformer):
 
     >>> import numpy as np
     >>> import pandas as pd
-    >>> from feature_engine.scaling import MeanNormalizationScaler
+    >>> from feature_engine.scaling import MeanNormalisationScaler
     >>> np.random.seed(42)
     >>> X = pd.DataFrame(dict(x = np.random.lognormal(size = 100)))
-    >>> mns = MeanNormalizationScaler()
+    >>> mns = MeanNormalisationScaler()
     >>> mns.fit(X)
     >>> X = mns.transform(X)
     >>> X.head()
@@ -189,3 +189,21 @@ def inverse_transform(self, X: pd.DataFrame) -> pd.DataFrame:
         X[self.variables_] = X[self.variables_] * self.range_ + self.mean_
 
         return X
+
+
+# TODO: remove in version 2.1.0
+class MeanNormalizationScaler(MeanNormalisationScaler):
+    def __init__(
+        self,
+        variables: Union[None, int, str, List[Union[str, int]]] = None,
+        return_empty: bool = False,
+    ) -> None:
+        warnings.warn(
+            "MeanNormalizationScaler was deprecated in favour of "
+            "MeanNormalisationScaler in version 2.0.0 and will be removed in "
+            "version 2.1.0. To silence this warning, use MeanNormalisationScaler "
+            "instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        super().__init__(variables=variables, return_empty=return_empty)

tests/test_scaling/test_mean_normalization.py

@@ -4,11 +4,37 @@
 import pytest
 from sklearn.exceptions import NotFittedError
 
-from feature_engine.scaling import MeanNormalizationScaler
+from feature_engine.scaling import MeanNormalisationScaler, MeanNormalizationScaler
 from tests.estimator_checks.fit_functionality_checks import check_return_empty
 
+DEPRECATION_WARNING = (
+    "MeanNormalizationScaler was deprecated in favour of "
+    "MeanNormalisationScaler in version 2.0.0 and will be removed in version 2.1.0. "
+    "To silence this warning, use MeanNormalisationScaler instead."
+)
 
-def test_transforming_int_vars():
+
+@pytest.fixture(
+    params=[MeanNormalisationScaler, MeanNormalizationScaler],
+    ids=["MeanNormalisationScaler", "MeanNormalizationScaler"],
+)
+def transformer_class(request):
+    return request.param
+
+
+def make_transformer(transformer_class, **kwargs):
+    if transformer_class is MeanNormalizationScaler:
+        with pytest.warns(FutureWarning, match=re.escape(DEPRECATION_WARNING)):
+            return transformer_class(**kwargs)
+    return transformer_class(**kwargs)
+
+
+def test_mean_normalization_scaler_raises_future_warning():
+    with pytest.warns(FutureWarning, match=re.escape(DEPRECATION_WARNING)):
+        MeanNormalizationScaler()
+
+
+def test_transforming_int_vars(transformer_class):
     # input test case
     df = pd.DataFrame(
         {
@@ -26,7 +52,7 @@ def test_transforming_int_vars():
         }
     )
 
-    transformer = MeanNormalizationScaler(variables=None)
+    transformer = make_transformer(transformer_class, variables=None)
     X = transformer.fit_transform(df)
 
     pd.testing.assert_frame_equal(X, expected_df)
@@ -37,9 +63,11 @@ def test_transforming_int_vars():
     pd.testing.assert_frame_equal(Xit, df)
 
 
-def test_mean_normalization_plus_automatically_find_variables(df_vartypes):
+def test_mean_normalization_plus_automatically_find_variables(
+    df_vartypes, transformer_class
+):
     # test case 1: automatically select variables
-    transformer = MeanNormalizationScaler(variables=None)
+    transformer = make_transformer(transformer_class, variables=None)
     X = transformer.fit_transform(df_vartypes)
 
     # expected output
@@ -66,9 +94,9 @@ def test_mean_normalization_plus_automatically_find_variables(df_vartypes):
     pd.testing.assert_frame_equal(Xit, df_vartypes, rtol=10e-3)
 
 
-def test_mean_normalization_plus_user_passes_var_list(df_vartypes):
+def test_mean_normalization_plus_user_passes_var_list(df_vartypes, transformer_class):
     # test case 2: user passes variables
-    transformer = MeanNormalizationScaler(variables="Age")
+    transformer = make_transformer(transformer_class, variables="Age")
     X = transformer.fit_transform(df_vartypes)
 
     # expected output
@@ -93,28 +121,28 @@ def test_mean_normalization_plus_user_passes_var_list(df_vartypes):
     pd.testing.assert_frame_equal(Xit, df_vartypes, rtol=10e-3)
 
 
-def test_fit_raises_error_if_na_in_df(df_na):
+def test_fit_raises_error_if_na_in_df(df_na, transformer_class):
     # test case 3: when dataset contains na, fit method
-    transformer = MeanNormalizationScaler()
+    transformer = make_transformer(transformer_class)
     with pytest.raises(ValueError):
         transformer.fit(df_na)
 
 
-def test_transform_raises_error_if_na_in_df(df_vartypes, df_na):
+def test_transform_raises_error_if_na_in_df(df_vartypes, df_na, transformer_class):
     # test case 4: when dataset contains na, transform method
-    transformer = MeanNormalizationScaler()
+    transformer = make_transformer(transformer_class)
     transformer.fit(df_vartypes)
     with pytest.raises(ValueError):
         transformer.transform(df_na[["Name", "City", "Age", "Marks", "dob"]])
 
 
-def test_non_fitted_error(df_vartypes):
-    transformer = MeanNormalizationScaler()
+def test_non_fitted_error(df_vartypes, transformer_class):
+    transformer = make_transformer(transformer_class)
     with pytest.raises(NotFittedError):
         transformer.transform(df_vartypes)
 
 
-def test_constant_columns_error():
+def test_constant_columns_error(transformer_class):
     # input test case
     df = pd.DataFrame(
         {
@@ -124,10 +152,15 @@ def test_constant_columns_error():
         }
     )
 
-    transformer = MeanNormalizationScaler()
+    transformer = make_transformer(transformer_class)
     with pytest.raises(ValueError, match=re.escape("Division by zero is not allowed")):
         transformer.fit(df)
 
 
-def test_check_return_empty():
-    check_return_empty(MeanNormalizationScaler())
+def test_check_return_empty(transformer_class):
+    transformer = make_transformer(transformer_class)
+    if transformer_class is MeanNormalizationScaler:
+        with pytest.warns(FutureWarning, match=re.escape(DEPRECATION_WARNING)):
+            check_return_empty(transformer)
+    else:
+        check_return_empty(transformer)