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 dict

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}}]

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

[Rb77e5952ab37-1]Contrast Coding Systems for Categorical Variables, from

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

[Rb77e5952ab37-2]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

Methods

fit(self, X[, y]) Fit encoder according to X and y.
fit_transform(self, X[, y]) Fit to data, then transform it.
get_feature_names(self) Returns the names of all transformed / added columns.
get_params(self[, deep]) Get parameters for this estimator.
inverse_transform(self, 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(self, \*\*params) Set the parameters of this estimator.
transform(self, X[, override_return_df]) Perform the transformation to new categorical data.
fit(self, X, y=None, **kwargs)[source]

Fit encoder according to X and y.

Parameters:
X : array-like, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples]

Target values.

Returns:
self : encoder

Returns self.

get_feature_names(self)[source]

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 are not included!

inverse_transform(self, 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_in : array-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.

transform(self, X, override_return_df=False)[source]

Perform the transformation to new categorical data.

Will use the mapping (if available) and the column list (if available, otherwise every column) to encode the data ordinarily.

Parameters:
X : array-like, shape = [n_samples, n_features]
Returns:
p : array, shape = [n_samples, n_numeric + N]

Transformed values with encoding applied.