MeanEncoder#
- class feature_engine.encoding.MeanEncoder(variables=None, missing_values='raise', ignore_format=False, unseen='ignore', smoothing=0.0)[source]#
The MeanEncoder() replaces categories by the mean value of the target for each category.
For example in the variable colour, if the mean of the target for blue, red and grey is 0.5, 0.8 and 0.1 respectively, blue is replaced by 0.5, red by 0.8 and grey by 0.1.
For rare categories, i.e., those with few observations, the mean target value might be less reliable. To mitigate poor estimates returned for rare categories, the mean target value can be determined as a mixture of the target mean value for the entire data set (also called the prior) and the mean target value for the category (the posterior), weighted by the number of observations:
\[mapping = (w_i) posterior + (1-w_i) prior\]where the weight is calculated as:
\[w_i = n_i t / (s + n_i t)\]In the previous equation, t is the target variance in the entire dataset, s is the target variance within the category and n is the number of observations for the category.
The encoder will encode only categorical variables by default (type ‘object’ or ‘categorical’). You can pass a list of variables to encode. Alternatively, the encoder will find and encode all categorical variables (type ‘object’ or ‘categorical’).
With
ignore_format=True
you have the option to encode numerical variables as well. The procedure is identical, you can either enter the list of variables to encode, or the transformer will automatically select all variables.The encoder first maps the categories to the numbers for each variable (fit). The encoder then replaces the categories with those numbers (transform).
More details in the User Guide.
- Parameters
- variables: list, default=None
The list of categorical variables that will be encoded. If None, the encoder will find and transform all variables of type object or categorical by default. You can also make the transformer accept numerical variables, see the parameter
ignore_format
.- missing_values: string, default=’raise’
Indicates if missing values should be ignored or raised. If
'raise'
the transformer will return an error if the the datasets tofit
ortransform
contain missing values. If'ignore'
, missing data will be ignored when learning parameters or performing the transformation.- ignore_format: bool, default=False
This transformer operates only on variables of type object or categorical. To override this behaviour and allow the transformer to transform numerical variables as well, set to
True
.If
ignore_format
isFalse
, the encoder will automatically select variables of type object or categorical, or check that the variables entered by the user are of type object or categorical. IfTrue
, the encoder will select all variables or accept all variables entered by the user, including those cast as numeric.In short, set to
True
when you want to encode numerical variables.- unseen: string, default=’ignore’
Indicates what to do when categories not present in the train set are encountered during transform. If
'raise'
, then unseen categories will raise an error. If'ignore'
, then unseen categories will be encoded as NaN and a warning will be raised instead. If'encode'
, unseen categories will be encoded with the prior.- smoothing: int, float, str, default=0.0
Smoothing factor. Should be >= 0. If 0 then no smoothing is applied, and the mean target value per category is returned without modification. If ‘auto’ then wi is calculated as described above and the category is encoded as the blended values of the prior and the posterior. If int or float, then the wi is calculated as ni / (ni+smoothing). Higher values lead to stronger smoothing (higher weight of prior).
- Attributes
- encoder_dict_:
Dictionary with the target mean value per category per variable.
- variables_:
The group of variables that will be transformed.
- feature_names_in_:
List with the names of features seen during
fit
.- n_features_in_:
The number of features in the train set used in fit.
See also
feature_engine.encoding.RareLabelEncoder
category_encoders.target_encoder.TargetEncoder
category_encoders.m_estimate.MEstimateEncoder
Notes
NAN are introduced when encoding categories that were not present in the training dataset. If this happens, try grouping infrequent categories using the RareLabelEncoder().
Check also the related transformers in the the open-source package Category encoders
References
- 1
Micci-Barreca D. “A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems”. ACM SIGKDD Explorations Newsletter, 2001. https://dl.acm.org/citation.cfm?id=507538
Examples
>>> import pandas as pd >>> from feature_engine.encoding import MeanEncoder >>> X = pd.DataFrame(dict(x1 = [1,2,3,4,5], x2 = ["c", "c", "c", "b", "a"])) >>> y = pd.Series([0,1,1,1,0]) >>> me = MeanEncoder() >>> me.fit(X,y) >>> me.transform(X) x1 x2 0 1 0.666667 1 2 0.666667 2 3 0.666667 3 4 1.000000 4 5 0.000000
Methods
fit:
Learn the target mean value per category, per variable.
fit_transform:
Fit to data, then transform it.
get_feature_names_out:
Get output feature names for transformation.
get_params:
Get parameters for this estimator.
set_params:
Set the parameters of this estimator.
inverse_transform:
Convert the data back to the original representation.
transform:
Encode the categories to numbers.
- fit(X, y)[source]#
Learn the mean value of the target for each category of the variable.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The training input samples. Can be the entire dataframe, not just the variables to be encoded.
- y: pandas series
The target.
- fit_transform(X, y=None, **fit_params)[source]#
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_feature_names_out(input_features=None)[source]#
Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.
- Parameters
- input_featuresarray or list, default=None
This parameter exits only for compatibility with the Scikit-learn pipeline.
If
None
, thenfeature_names_in_
is used as feature names in.If an array or list, then
input_features
must matchfeature_names_in_
.
- Returns
- feature_names_out: list
Transformed feature names.
- get_params(deep=True)[source]#
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- inverse_transform(X)[source]#
Convert the encoded variable back to the original values.
Note that if unseen was set to ‘encode’, then this method is not implemented.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features].
The transformed dataframe.
- Returns
- X_tr: pandas dataframe of shape = [n_samples, n_features].
The un-transformed dataframe, with the categorical variables containing the original values.
- rtype
DataFrame
..
- set_params(**params)[source]#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.
- transform(X)[source]#
Replace categories with the learned parameters.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features].
The dataset to transform.
- Returns
- X_new: pandas dataframe of shape = [n_samples, n_features].
The dataframe containing the categories replaced by numbers.
- rtype
DataFrame
..