BaseN

class category_encoders.basen.BaseNEncoder(verbose=0, cols=None, mapping=None, drop_invariant=False, return_df=True, base=2, handle_unknown='value', handle_missing='value')[source]

Base-N encoder encodes the categories into arrays of their base-N representation. A base of 1 is equivalent to one-hot encoding (not really base-1, but useful), a base of 2 is equivalent to binary encoding. N=number of actual categories is equivalent to vanilla ordinal encoding.

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).

base: int

when the downstream model copes well with nonlinearities (like decision tree), use higher base.

handle_unknown: str

options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’. Warning: if indicator is used, an extra column will be added in if the transform matrix has unknown categories. This can cause unexpected changes in dimension in some cases.

handle_missing: str

options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’. Warning: if indicator is used, an extra column will be added in if the transform matrix has nan values. This can cause unexpected changes in dimension in some cases.

Methods

basen_encode(self, X_in[, cols]) Basen encoding encodes the integers as basen code with one column per digit.
basen_to_integer(self, X, cols, base) Convert basen code as integers.
col_transform(self, col, digits) The lambda body to transform the column values
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.
set_params(self, \*\*params) Set the parameters of this estimator.
transform(self, X[, override_return_df]) Perform the transformation to new categorical data.
calc_required_digits  
fit_base_n_encoding  
number_to_base  
basen_encode(self, X_in, cols=None)[source]

Basen encoding encodes the integers as basen code with one column per digit.

Parameters:
X_in: DataFrame
cols: list-like, default None

Column names in the DataFrame to be encoded

Returns:
dummies : DataFrame
basen_to_integer(self, X, cols, base)[source]

Convert basen code as integers.

Parameters:
X : DataFrame

encoded data

cols : list-like

Column names in the DataFrame that be encoded

base : int

The base of transform

Returns:
numerical: DataFrame
col_transform(self, col, digits)[source]

The lambda body to transform the column values

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.

Parameters:
X_in : array-like, shape = [n_samples, n_features]
Returns:
p: array, the same size of X_in
transform(self, X, override_return_df=False)[source]

Perform the transformation to new categorical data.

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

Transformed values with encoding applied.