TransactionEncoder: Convert item lists into transaction data for frequent itemset mining

Encoder class for transaction data in Python lists

from mlxtend.preprocessing import TransactionEncoder

Overview

Encodes database transaction data in form of a Python list of lists into a NumPy array.

Example 1

Suppose we have the following transaction data:

from mlxtend.preprocessing import TransactionEncoder

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

Using and TransactionEncoder object, we can transform this dataset into an array format suitable for typical machine learning APIs. Via the fit method, the TransactionEncoder learns the unique labels in the dataset, and via the transform method, it transforms the input dataset (a Python list of lists) into a one-hot encoded NumPy boolean array:

te = TransactionEncoder()
te_ary = te.fit(dataset).transform(dataset)
te_ary

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]], dtype=bool)

The NumPy array is boolean for the sake of memory efficiency when working with large datasets. If a classic integer representation is desired instead, we can just convert the array to the appropriate type: 

te_ary.astype("int")

array([[1, 0, 1, 1, 0, 1],
       [1, 0, 1, 0, 0, 1],
       [1, 0, 1, 0, 0, 0],
       [1, 1, 0, 0, 0, 0],
       [0, 0, 1, 1, 1, 1],
       [0, 0, 1, 0, 1, 1],
       [0, 0, 1, 0, 1, 0],
       [1, 1, 0, 0, 0, 0]])

After fitting, the unique column names that correspond to the data array shown above can be accessed via the columns_ attribute:

te.columns_

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

For our convenience, we can turn theencoded array into a pandas DataFrame:

import pandas as pd

pd.DataFrame(te_ary, columns=te.columns_)
Apple Bananas Beer Chicken Milk Rice
0 True False True True False True
1 True False True False False True
2 True False True False False False
3 True True False False False False
4 False False True True True True
5 False False True False True True
6 False False True False True False
7 True True False False False False

If we desire, we can turn the one-hot encoded array back into a transaction list of lists via the inverse_transform function:

first4 = te_ary[:4]
te.inverse_transform(first4)

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

API

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 https://rasbt.github.io/mlxtend/user_guide/preprocessing/TransactionEncoder/

Methods

fit(X)

Learn unique column names from transaction DataFrame

Parameters

  • X : list of lists

    A python list of lists, where the outer list stores the n transactions and the inner list stores the items in each transaction.

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

fit_transform(X, sparse=False)

Fit a TransactionEncoder encoder and transform a dataset.

get_params(deep=True)

Get parameters for this estimator.

Parameters

  • deep : boolean, optional

    If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

  • params : mapping of string to any

    Parameter names mapped to their values.

inverse_transform(array)

Transforms an encoded NumPy array back into transactions.

Parameters

  • array : NumPy array [n_transactions, n_unique_items]

    The NumPy one-hot encoded boolean array of the input transactions, where the columns represent the unique items found in the input array in alphabetic order

    For example,

    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

  • X : list of lists

    A python list of lists, where the outer list stores the n transactions and the inner list stores the items in each transaction.

    For example,

    [['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

  • X : list of lists

    A python list of lists, where the outer list stores the n transactions and the inner list stores the items in each transaction.

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

    sparse: bool (default=False) If True, transform will return Compressed Sparse Row matrix instead of the regular one.

Returns

  • array : NumPy array [n_transactions, n_unique_items]

    if sparse=False (default). Compressed Sparse Row matrix otherwise The one-hot encoded boolean array of the input transactions, where the columns represent the unique items found in the input array in alphabetic order. Exact representation depends on the sparse argument

    For example, 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']

ython