Quantile Encoder
- class category_encoders.quantile_encoder.QuantileEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', quantile=0.5, m=1.0)[source]
Quantile Encoding for categorical features.
This a statistically modified version of target MEstimate encoder where selected features are replaced by the statistical quantile instead of the mean. Replacing with the median is a particular case where self.quantile = 0.5. In comparison to MEstimateEncoder it has two tunable parameter m and quantile
- Parameters:
- verbose: int
integer indicating verbosity of the output. 0 for none.
- quantile: float
float indicating statistical quantile. ´0.5´ for median.
- m: float
this is the “m” in the m-probability estimate. Higher value of m results into stronger shrinking. M is non-negative. 0 for no smoothing.
- 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 quantile.
- handle_unknown: str
options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target quantile.
References
[1]Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems, https://link.springer.com/chapter/10.1007%2F978-3-030-85529-1_14
[2]A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems, equation 7, from https://dl.acm.org/citation.cfm?id=507538
[3]On estimating probabilities in tree pruning, equation 1, from https://link.springer.com/chapter/10.1007/BFb0017010
[4]Additive smoothing, from https://en.wikipedia.org/wiki/Additive_smoothing#Generalized_to_the_case_of_known_incidence_rates
[5]Target encoding done the right way https://maxhalford.github.io/blog/target-encoding/
- Attributes:
- feature_names_out_
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.
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.
fit_quantile_encoding
get_feature_names
quantile_encode
- 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.
- Attributes:
- feature_names_out_
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.
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.
fit_quantile_encoding
get_feature_names
quantile_encode
- 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() List[str]
Returns the names of all transformed / added columns.
- Returns:
- feature_names: list
A list 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.