Ordinal

class category_encoders.ordinal.OrdinalEncoder(verbose=0, mapping=None, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value')[source]

Encodes categorical features as ordinal, in one ordered feature.

Ordinal encoding uses a single column of integers to represent the classes. An optional mapping dict can be passed in; in this case, we use the knowledge that there is some true order to the classes themselves. Otherwise, the classes are assumed to have no true order and integers are selected at random.

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).

mapping: list of dicts

a mapping of class to label to use for the encoding, optional. the dict contains the keys ‘col’ and ‘mapping’. the value of ‘col’ should be the feature name. the value of ‘mapping’ should be a dictionary of ‘original_label’ to ‘encoded_label’. example mapping: [

{‘col’: ‘col1’, ‘mapping’: {None: 0, ‘a’: 1, ‘b’: 2}}, {‘col’: ‘col2’, ‘mapping’: {None: 0, ‘x’: 1, ‘y’: 2}}

]

handle_unknown: str

options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which will impute the category -1.

handle_missing: str

options are ‘error’, ‘return_nan’, and ‘value, default to ‘value’, which treat nan as a category at fit time, or -2 at transform time if nan is not a category during fit.

References

1

Contrast Coding Systems for Categorical Variables, from

https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/

2

Gregory Carey (2003). Coding Categorical Variables, from

http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf

Attributes
category_mapping
feature_names

Methods

fit(X[, y])

Fits the encoder according to X and y.

fit_transform(X[, y])

Fit to data, then transform it.

get_feature_names()

Returns the names of all transformed / added columns.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X_in)

Perform the inverse transformation to encoded data.

ordinal_encoding(X_in[, mapping, cols, ...])

Ordinal encoding uses a single column of integers to represent the classes.

set_params(**params)

Set the parameters of this estimator.

transform(X[, override_return_df])

Perform the transformation to new categorical data.

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
category_mapping
feature_names

Methods

fit(X[, y])

Fits the encoder according to X and y.

fit_transform(X[, y])

Fit to data, then transform it.

get_feature_names()

Returns the names of all transformed / added columns.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X_in)

Perform the inverse transformation to encoded data.

ordinal_encoding(X_in[, mapping, cols, ...])

Ordinal encoding uses a single column of integers to represent the classes.

set_params(**params)

Set the parameters of this estimator.

transform(X[, override_return_df])

Perform the transformation to new categorical data.

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)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

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() 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.

inverse_transform(X_in)[source]

Perform the inverse transformation to encoded data. Will attempt best case reconstruction, which means it will return nan for handle_missing and handle_unknown settings that break the bijection. We issue warnings when some of those cases occur.

Parameters
X_inarray-like, shape = [n_samples, n_features]
Returns
p: array, the same size of X_in
static ordinal_encoding(X_in, mapping=None, cols=None, handle_unknown='value', handle_missing='value')[source]

Ordinal encoding uses a single column of integers to represent the classes. An optional mapping dict can be passed in, in this case we use the knowledge that there is some true order to the classes themselves. Otherwise, the classes are assumed to have no true order and integers are selected at random.

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, override_return_df=False)

Perform the transformation to new categorical data.

Parameters
Xarray-like, shape = [n_samples, n_features]
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.