Generalized Linear Mixed Model Encoder
- class category_encoders.glmm.GLMMEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value', random_state=None, randomized=False, sigma=0.05, binomial_target=None)[source]
Generalized linear mixed model.
Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.
This is a supervised encoder similar to TargetEncoder or MEstimateEncoder, but there are some advantages:
Solid statistical theory behind the technique. Mixed effects models are a mature branch of statistics.
2. No hyper-parameters to tune. The amount of shrinkage is automatically determined through the estimation process. In short, the less observations a category has and/or the more the outcome varies for a category then the higher the regularization towards “the prior” or “grand mean”. 3. The technique is applicable for both continuous and binomial targets. If the target is continuous, the encoder returns regularized difference of the observation’s category from the global mean.
If the target is binomial, the encoder returns regularized log odds per category.
In comparison to JamesSteinEstimator, this encoder utilizes generalized linear mixed models from statsmodels library.
Note: This is an alpha implementation. The API of the method may change in the future.
- Parameters:
- verbose: int
integer indicating verbosity of the output. 0 for none.
- cols: list
a list of columns to encode, if None, all string columns will be encoded.
- drop_invariant: bool
boolean for whether or not to drop encoded columns with 0 variance.
- return_df: bool
boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array).
- handle_missing: str
options are ‘return_nan’, ‘error’ and ‘value’, defaults to ‘value’, which returns 0.
- handle_unknown: str
options are ‘return_nan’, ‘error’ and ‘value’, defaults to ‘value’, which returns 0.
- randomized: bool,
adds normal (Gaussian) distribution noise into training data in order to decrease overfitting (testing data are untouched).
- sigma: float
standard deviation (spread or “width”) of the normal distribution.
- binomial_target: bool
if True, the target must be binomial with values {0, 1} and Binomial mixed model is used. If False, the target must be continuous and Linear mixed model is used. If None (the default), a heuristic is applied to estimate the target type.
References
[1]Data Analysis Using Regression and Multilevel/Hierarchical Models, page 253, from
https://faculty.psau.edu.sa/filedownload/doc-12-pdf-a1997d0d31f84d13c1cdc44ac39a8f2c-original.pdf
Methods
fit
(X[, y])Fits the encoder according to X and y.
fit_transform
(X[, y])Encoders that utilize the target must make sure that the training data are transformed with:
Returns the names of all input columns present when fitting.
get_feature_names_out
([input_features])Returns the names of all transformed / added columns.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_output
(*[, transform])Set output container.
set_params
(**params)Set the parameters of this estimator.
set_transform_request
(*[, override_return_df])Request metadata passed to the
transform
method.transform
(X[, y, override_return_df])Perform the transformation to new categorical data.
get_feature_names
- Parameters:
- verbose: int
integer indicating verbosity of output. 0 for none.
- cols: list
a list of columns to encode, if None, all string and categorical columns will be encoded.
- drop_invariant: bool
boolean for whether or not to drop columns with 0 variance.
- return_df: bool
boolean for whether to return a pandas DataFrame from transform and inverse transform (otherwise it will be a numpy array).
- handle_missing: str
how to handle missing values at fit time. Options are ‘error’, ‘return_nan’, and ‘value’. Default ‘value’, which treat NaNs as a countable category at fit time.
- handle_unknown: str, int or dict of {columnoption, …}.
how to handle unknown labels at transform time. Options are ‘error’ ‘return_nan’, ‘value’ and int. Defaults to None which uses NaN behaviour specified at fit time. Passing an int will fill with this int value.
- kwargs: dict.
additional encoder specific parameters like regularisation.
Methods
fit
(X[, y])Fits the encoder according to X and y.
fit_transform
(X[, y])Encoders that utilize the target must make sure that the training data are transformed with:
Returns the names of all input columns present when fitting.
get_feature_names_out
([input_features])Returns the names of all transformed / added columns.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_output
(*[, transform])Set output container.
set_params
(**params)Set the parameters of this estimator.
set_transform_request
(*[, override_return_df])Request metadata passed to the
transform
method.transform
(X[, y, override_return_df])Perform the transformation to new categorical data.
get_feature_names
- fit(X, y=None, **kwargs)
Fits the encoder according to X and y.
- Parameters:
- Xarray-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
- yarray-like, shape = [n_samples]
Target values.
- Returns:
- selfencoder
Returns self.
- fit_transform(X, y=None, **fit_params)
- Encoders that utilize the target must make sure that the training data are transformed with:
transform(X, y)
- and not with:
transform(X)
- get_feature_names_in() List[str]
Returns the names of all input columns present when fitting. These columns are necessary for the transform step.
- get_feature_names_out(input_features=None) ndarray
Returns the names of all transformed / added columns.
Note that in sklearn the get_feature_names_out function takes the feature_names_in as an argument and determines the output feature names using the input. A fit is usually not necessary and if so a NotFittedError is raised. We just require a fit all the time and return the fitted output columns.
- Returns:
- feature_names: np.ndarray
A numpy array with all feature names transformed or added. Note: potentially dropped features (because the feature is constant/invariant) are not included!
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)
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.
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns:
- selfestimator instance
Estimator instance.
- set_params(**params)
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.
- set_transform_request(*, override_return_df: bool | None | str = '$UNCHANGED$') GLMMEncoder
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- override_return_dfstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
override_return_df
parameter intransform
.
- Returns:
- selfobject
The updated object.
- transform(X, y=None, override_return_df=False)
Perform the transformation to new categorical data.
Some encoders behave differently on whether y is given or not. This is mainly due to regularisation in order to avoid overfitting. On training data transform should be called with y, on test data without.
- Parameters:
- Xarray-like, shape = [n_samples, n_features]
- yarray-like, shape = [n_samples] or None
- override_return_dfbool
override self.return_df to force to return a data frame
- Returns:
- parray or DataFrame, shape = [n_samples, n_features_out]
Transformed values with encoding applied.