\!h0PSSKrSSKJr SSKJrJr SSKJrJ r "SS\\5r g)N) csr_matrix) BaseEstimatorTransformerMixin)_check_feature_names_incheck_is_fittedcD\rSrSrSrSrSrS SjrSrS Sjr Sr S r g ) TransactionEncoder a3Encoder 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/ cg)Nselfs {C:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\mlxtend/preprocessing/transactionencoder.py__init__TransactionEncoder.__init__ sc[5nUHnUHnURU5 M M [U5Ul0n[ UR5H updXeU'M XPlU$)aLearn unique column names from transaction DataFrame Parameters ------------ X : list of lists A python list of lists, where the outer list stores the n transactions and the inner list stores the items in each transaction. For example, [['Apple', 'Beer', 'Rice', 'Chicken'], ['Apple', 'Beer', 'Rice'], ['Apple', 'Beer'], ['Apple', 'Bananas'], ['Milk', 'Beer', 'Rice', 'Chicken'], ['Milk', 'Beer', 'Rice'], ['Milk', 'Beer'], ['Apple', 'Bananas']] )setaddsortedcolumns_ enumeratecolumns_mapping_)rX unique_items transactionitemcolumns_mappingcol_idxs rfitTransactionEncoder.fit#sh*u K#  &$|, &t}}5MG$+D !6 / rcU(a{S/n/nUHOn[U5H#nURUnURU5 M% UR[U55 MQ S/[U5-n[ XU4[ S9n U $[ R"[U5[UR54[ S9n [U5H#upUHnURUnSXU4'M M% U $)aTransform transactions into a one-hot encoded NumPy array. Parameters ------------ X : list of lists A python list of lists, where the outer list stores the n transactions and the inner list stores the items in each transaction. For example, [['Apple', 'Beer', 'Rice', 'Chicken'], ['Apple', 'Beer', 'Rice'], ['Apple', 'Beer'], ['Apple', 'Bananas'], ['Milk', 'Beer', 'Rice', 'Chicken'], ['Milk', 'Beer', 'Rice'], ['Milk', 'Beer'], ['Apple', 'Bananas']] sparse: bool (default=False) If True, transform will return Compressed Sparse Row matrix instead of the regular one. Returns ------------ array : NumPy array [n_transactions, n_unique_items] if sparse=False (default). Compressed Sparse Row matrix otherwise The one-hot encoded boolean array of the input transactions, where the columns represent the unique items found in the input array in alphabetic order. Exact representation depends on the sparse argument For example, array([[True , False, True , True , False, True ], [True , False, True , False, False, True ], [True , False, True , False, False, False], [True , True , False, False, False, False], [False, False, True , True , True , True ], [False, False, True , False, True , True ], [False, False, True , False, True , False], [True , True , False, False, False, False]]) The corresponding column labels are available as self.columns_, e.g., ['Apple', 'Bananas', 'Beer', 'Chicken', 'Milk', 'Rice'] rT)dtype) rrappendlenrboolnpzerosrr) rrsparseindptrindicesrrrnon_sparse_valuesarrayrow_idxs r transformTransactionEncoder.transformCs\ SFG  ,D"33D9GNN7+- c'l+ !"&W 5  1FC4PE HHc!fc$--&89FE(1! $'D"33D9G.2E7*+()5 rc UVVVs/sH9n[U5VVs/sHup4U(dMURUPM snnPM; snnn$s snnfs snnnf)aETransforms 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']] ``` )rr)rr-rowidxcells rinverse_transform$TransactionEncoder.inverse_transformsP\ 2;3 HIC4 T]]3  H  H sA A A A Ac>URU5RXS9$)z9Fit a TransactionEncoder encoder and transform a dataset.)r))r r/)rrr)s r fit_transform TransactionEncoder.fit_transformsxx{$$Q$66rc<[USS9 [XRS9$)aYUsed to get the column names of pandas output. This method combined with the `TransformerMixin` exposes the set_output API to the `TransactionEncoder`. This allows the user to set the transformed output to a `pandas.DataFrame` by default. See https://scikit-learn.org/stable/developers/develop.html#developer-api-set-output for more details. r) attributes) estimatorinput_features)rrrr s rget_feature_names_out(TransactionEncoder.get_feature_names_outs 4&mmTTr)rrN)F) __name__ __module__ __qualname____firstlineno____doc__rr r/r5r8r>__static_attributes__r rrr r s*$@@D/ b7 Urr ) numpyr' scipy.sparser sklearn.baserrsklearn.utils.validationrrr r rrrJs&#8MxU(8xUr