CatBoost Encoder
- class category_encoders.cat_boost.CatBoostEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value', random_state=None, sigma=None, a=1)[source]
CatBoost Encoding for categorical features.
Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.
CatBoostEncoder is the variation of target encoding. It supports time-aware encoding, regularization, and online learning.
This implementation is time-aware (similar to CatBoost’s parameter ‘has_time=True’), so no random permutations are used. It makes this encoder sensitive to ordering of the data and suitable for time series problems. If your data does not have time dependency, it should still work just fine, assuming sorting of the data won’t leak any information outside the training scope (i.e., no data leakage). When data leakage is a possibility, it is wise to eliminate it first (for example, shuffle or resample the data).
NOTE: behavior of the transformer would differ in transform and fit_transform methods depending if y values are passed. If no target is passed, then encoder will map the last value of the running mean to each category. If y is passed then it will map all values of the running mean to each category’s occurrences.
- 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 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 ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target mean.
- handle_unknown: str
options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target mean.
- sigma: float
adds normal (Gaussian) distribution noise into training data in order to decrease overfitting (testing data are untouched). sigma gives the standard deviation (spread or “width”) of the normal distribution.
- a: float
additive smoothing (it is the same variable as “m” in m-probability estimate). By default set to 1.
References
[1]Transforming categorical features to numerical features, from
https://tech.yandex.com/catboost/doc/dg/concepts/algorithm-main-stages_cat-to-numberic-docpage/
[2]CatBoost: unbiased boosting with categorical features, from
https://arxiv.org/abs/1706.09516
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_params
([deep])Get parameters for this estimator.
set_output
(*[, transform])Set output container.
set_params
(**params)Set the parameters of this estimator.
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_params
([deep])Get parameters for this estimator.
set_output
(*[, transform])Set output container.
set_params
(**params)Set the parameters of this estimator.
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_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.
- 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.