183 lines
6.4 KiB
Python
183 lines
6.4 KiB
Python
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# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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import numpy as np
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from scipy.sparse import csr_matrix
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from sklearn.base import BaseEstimator, TransformerMixin
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class TransactionEncoder(BaseEstimator, TransformerMixin):
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"""Encoder class for transaction data in Python lists
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Parameters
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------------
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None
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Attributes
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------------
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columns_: list
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List of unique names in the `X` input list of lists
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Examples
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------------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/preprocessing/TransactionEncoder/
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"""
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def __init__(self):
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return None
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def fit(self, X):
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"""Learn unique column names from transaction DataFrame
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Parameters
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------------
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X : list of lists
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A python list of lists, where the outer list stores the
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n transactions and the inner list stores the items in each
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transaction.
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For example,
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[['Apple', 'Beer', 'Rice', 'Chicken'],
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['Apple', 'Beer', 'Rice'],
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['Apple', 'Beer'],
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['Apple', 'Bananas'],
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['Milk', 'Beer', 'Rice', 'Chicken'],
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['Milk', 'Beer', 'Rice'],
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['Milk', 'Beer'],
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['Apple', 'Bananas']]
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"""
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unique_items = set()
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for transaction in X:
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for item in transaction:
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unique_items.add(item)
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self.columns_ = sorted(unique_items)
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columns_mapping = {}
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for col_idx, item in enumerate(self.columns_):
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columns_mapping[item] = col_idx
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self.columns_mapping_ = columns_mapping
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return self
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def transform(self, X, sparse=False):
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"""Transform transactions into a one-hot encoded NumPy array.
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Parameters
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------------
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X : list of lists
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A python list of lists, where the outer list stores the
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n transactions and the inner list stores the items in each
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transaction.
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For example,
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[['Apple', 'Beer', 'Rice', 'Chicken'],
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['Apple', 'Beer', 'Rice'],
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['Apple', 'Beer'],
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['Apple', 'Bananas'],
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['Milk', 'Beer', 'Rice', 'Chicken'],
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['Milk', 'Beer', 'Rice'],
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['Milk', 'Beer'],
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['Apple', 'Bananas']]
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sparse: bool (default=False)
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If True, transform will return Compressed Sparse Row matrix
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instead of the regular one.
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Returns
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------------
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array : NumPy array [n_transactions, n_unique_items]
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if sparse=False (default).
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Compressed Sparse Row matrix otherwise
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The one-hot encoded boolean array of the input transactions,
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where the columns represent the unique items found in the input
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array in alphabetic order. Exact representation depends
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on the sparse argument
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For example,
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array([[True , False, True , True , False, True ],
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[True , False, True , False, False, True ],
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[True , False, True , False, False, False],
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[True , True , False, False, False, False],
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[False, False, True , True , True , True ],
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[False, False, True , False, True , True ],
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[False, False, True , False, True , False],
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[True , True , False, False, False, False]])
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The corresponding column labels are available as self.columns_, e.g.,
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['Apple', 'Bananas', 'Beer', 'Chicken', 'Milk', 'Rice']
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"""
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if sparse:
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indptr = [0]
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indices = []
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for transaction in X:
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# set is necessary because conversion to SparseDataFrame
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# will fail if there are duplicate items
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for item in set(transaction):
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col_idx = self.columns_mapping_[item]
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indices.append(col_idx)
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indptr.append(len(indices))
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non_sparse_values = [True]*len(indices)
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array = csr_matrix((non_sparse_values, indices, indptr),
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dtype=bool)
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else:
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array = np.zeros((len(X), len(self.columns_)), dtype=bool)
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for row_idx, transaction in enumerate(X):
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for item in transaction:
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col_idx = self.columns_mapping_[item]
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array[row_idx, col_idx] = True
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return array
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def inverse_transform(self, array):
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"""Transforms an encoded NumPy array back into transactions.
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Parameters
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------------
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array : NumPy array [n_transactions, n_unique_items]
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The NumPy one-hot encoded boolean array of the input transactions,
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where the columns represent the unique items found in the input
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array in alphabetic order
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For example,
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```
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array([[True , False, True , True , False, True ],
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[True , False, True , False, False, True ],
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[True , False, True , False, False, False],
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[True , True , False, False, False, False],
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[False, False, True , True , True , True ],
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[False, False, True , False, True , True ],
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[False, False, True , False, True , False],
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[True , True , False, False, False, False]])
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```
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The corresponding column labels are available as self.columns_,
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e.g., ['Apple', 'Bananas', 'Beer', 'Chicken', 'Milk', 'Rice']
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Returns
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------------
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X : list of lists
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A python list of lists, where the outer list stores the
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n transactions and the inner list stores the items in each
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transaction.
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For example,
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```
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[['Apple', 'Beer', 'Rice', 'Chicken'],
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['Apple', 'Beer', 'Rice'],
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['Apple', 'Beer'],
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['Apple', 'Bananas'],
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['Milk', 'Beer', 'Rice', 'Chicken'],
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['Milk', 'Beer', 'Rice'],
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['Milk', 'Beer'],
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['Apple', 'Bananas']]
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```
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"""
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return [[self.columns_[idx]
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for idx, cell in enumerate(row) if cell]
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for row in array]
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def fit_transform(self, X, sparse=False):
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"""Fit a TransactionEncoder encoder and transform a dataset."""
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return self.fit(X).transform(X, sparse=sparse)
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