TransactionEncoder

TransactionEncoder()

Encoder class for transaction data in Python lists

Parameters

None

Attributes

columns_: list List of unique names in the X input list of lists

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/preprocessing/TransactionEncoder/

Methods


fit(X)

Learn unique column names from transaction DataFrame

Parameters


fit_transform(X, sparse=False)

Fit a TransactionEncoder encoder and transform a dataset.


get_params(deep=True)

Get parameters for this estimator.

Parameters

Returns


inverse_transform(array)

Transforms an encoded NumPy array back into transactions.

Parameters

    array([[True , False, True , True , False, True ],
    [True , False, True , False, False, True ],
    [True , False, True , False, False, False],
    [True , True , False, False, False, False],
    [False, False, True , True , True , True ],
    [False, False, True , False, True , True ],
    [False, False, True , False, True , False],
    [True , True , False, False, False, False]])
The corresponding column labels are available as self.columns_,
e.g., ['Apple', 'Bananas', 'Beer', 'Chicken', 'Milk', 'Rice']

Returns

    [['Apple', 'Beer', 'Rice', 'Chicken'],
    ['Apple', 'Beer', 'Rice'],
    ['Apple', 'Beer'],
    ['Apple', 'Bananas'],
    ['Milk', 'Beer', 'Rice', 'Chicken'],
    ['Milk', 'Beer', 'Rice'],
    ['Milk', 'Beer'],
    ['Apple', 'Bananas']]

set_params(params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Returns

self


transform(X, sparse=False)

Transform transactions into a one-hot encoded NumPy array.

Parameters

Returns