134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
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# mlxtend Machine Learning Library Extensions
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# Author: Steve Harenberg <harenbergsd@gmail.com>
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#
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# License: BSD 3 clause
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import math
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import itertools
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from ..frequent_patterns import fpcommon as fpc
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def fpgrowth(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0):
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"""Get frequent itemsets from a one-hot DataFrame
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Parameters
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-----------
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df : pandas DataFrame
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pandas DataFrame the encoded format. Also supports
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DataFrames with sparse data; for more info, please
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see (https://pandas.pydata.org/pandas-docs/stable/
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user_guide/sparse.html#sparse-data-structures)
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Please note that the old pandas SparseDataFrame format
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is no longer supported in mlxtend >= 0.17.2.
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The allowed values are either 0/1 or True/False.
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For example,
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```
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Apple Bananas Beer Chicken Milk Rice
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0 True False True True False True
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1 True False True False False True
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2 True False True False False False
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3 True True False False False False
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4 False False True True True True
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5 False False True False True True
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6 False False True False True False
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7 True True False False False False
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```
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min_support : float (default: 0.5)
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A float between 0 and 1 for minimum support of the itemsets returned.
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The support is computed as the fraction
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transactions_where_item(s)_occur / total_transactions.
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use_colnames : bool (default: False)
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If true, uses the DataFrames' column names in the returned DataFrame
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instead of column indices.
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max_len : int (default: None)
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Maximum length of the itemsets generated. If `None` (default) all
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possible itemsets lengths are evaluated.
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verbose : int (default: 0)
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Shows the stages of conditional tree generation.
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Returns
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-----------
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pandas DataFrame with columns ['support', 'itemsets'] of all itemsets
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that are >= `min_support` and < than `max_len`
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(if `max_len` is not None).
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Each itemset in the 'itemsets' column is of type `frozenset`,
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which is a Python built-in type that behaves similarly to
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sets except that it is immutable
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(For more info, see
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https://docs.python.org/3.6/library/stdtypes.html#frozenset).
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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/frequent_patterns/fpgrowth/
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"""
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fpc.valid_input_check(df)
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if min_support <= 0.:
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raise ValueError('`min_support` must be a positive '
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'number within the interval `(0, 1]`. '
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'Got %s.' % min_support)
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colname_map = None
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if use_colnames:
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colname_map = {idx: item for idx, item in enumerate(df.columns)}
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tree, _ = fpc.setup_fptree(df, min_support)
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minsup = math.ceil(min_support * len(df.index)) # min support as count
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generator = fpg_step(tree, minsup, colname_map, max_len, verbose)
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return fpc.generate_itemsets(generator, len(df.index), colname_map)
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def fpg_step(tree, minsup, colnames, max_len, verbose):
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"""
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Performs a recursive step of the fpgrowth algorithm.
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Parameters
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----------
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tree : FPTree
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minsup : int
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Yields
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------
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lists of strings
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Set of items that has occurred in minsup itemsets.
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"""
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count = 0
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items = tree.nodes.keys()
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if tree.is_path():
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# If the tree is a path, we can combinatorally generate all
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# remaining itemsets without generating additional conditional trees
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size_remain = len(items) + 1
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if max_len:
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size_remain = max_len - len(tree.cond_items) + 1
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for i in range(1, size_remain):
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for itemset in itertools.combinations(items, i):
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count += 1
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support = min([tree.nodes[i][0].count for i in itemset])
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yield support, tree.cond_items + list(itemset)
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elif not max_len or max_len > len(tree.cond_items):
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for item in items:
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count += 1
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support = sum([node.count for node in tree.nodes[item]])
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yield support, tree.cond_items + [item]
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if verbose:
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tree.print_status(count, colnames)
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# Generate conditional trees to generate frequent itemsets one item larger
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if not tree.is_path() and (not max_len or max_len > len(tree.cond_items)):
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for item in items:
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cond_tree = tree.conditional_tree(item, minsup)
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for sup, iset in fpg_step(cond_tree, minsup,
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colnames, max_len, verbose):
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yield sup, iset
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