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Add: FP Growth Algorithm (#10746)
* Add: FP Growth Algorithm
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* Update frequent_pattern_growth.py
---------
Co-authored-by: Jeel Gajera <jeelgajera00@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
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Jeel Gajera
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@ -541,6 +541,7 @@
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* [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
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* Forecasting
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* [Run](machine_learning/forecasting/run.py)
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* [Frequent Pattern Growth Algorithm](machine_learning/frequent_pattern_growth.py)
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* [Gradient Descent](machine_learning/gradient_descent.py)
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* [K Means Clust](machine_learning/k_means_clust.py)
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* [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
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349
machine_learning/frequent_pattern_growth.py
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349
machine_learning/frequent_pattern_growth.py
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@ -0,0 +1,349 @@
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"""
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The Frequent Pattern Growth algorithm (FP-Growth) is a widely used data mining
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technique for discovering frequent itemsets in large transaction databases.
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It overcomes some of the limitations of traditional methods such as Apriori by
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efficiently constructing the FP-Tree
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WIKI: https://athena.ecs.csus.edu/~mei/associationcw/FpGrowth.html
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Examples: https://www.javatpoint.com/fp-growth-algorithm-in-data-mining
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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@dataclass
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class TreeNode:
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"""
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A node in a Frequent Pattern tree.
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Args:
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name: The name of this node.
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num_occur: The number of occurrences of the node.
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parent_node: The parent node.
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Example:
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>>> parent = TreeNode("Parent", 1, None)
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>>> child = TreeNode("Child", 2, parent)
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>>> child.name
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'Child'
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>>> child.count
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2
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"""
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name: str
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count: int
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parent: TreeNode | None = None
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children: dict[str, TreeNode] = field(default_factory=dict)
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node_link: TreeNode | None = None
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def __repr__(self) -> str:
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return f"TreeNode({self.name!r}, {self.count!r}, {self.parent!r})"
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def inc(self, num_occur: int) -> None:
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self.count += num_occur
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def disp(self, ind: int = 1) -> None:
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print(f"{' ' * ind} {self.name} {self.count}")
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for child in self.children.values():
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child.disp(ind + 1)
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def create_tree(data_set: list, min_sup: int = 1) -> tuple[TreeNode, dict]:
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"""
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Create Frequent Pattern tree
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Args:
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data_set: A list of transactions, where each transaction is a list of items.
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min_sup: The minimum support threshold.
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Items with support less than this will be pruned. Default is 1.
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Returns:
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The root of the FP-Tree.
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header_table: The header table dictionary with item information.
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Example:
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>>> data_set = [
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... ['A', 'B', 'C'],
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... ['A', 'C'],
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... ['A', 'B', 'E'],
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... ['A', 'B', 'C', 'E'],
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... ['B', 'E']
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... ]
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>>> min_sup = 2
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>>> fp_tree, header_table = create_tree(data_set, min_sup)
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>>> fp_tree
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TreeNode('Null Set', 1, None)
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>>> len(header_table)
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4
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>>> header_table["A"]
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[[4, None], TreeNode('A', 4, TreeNode('Null Set', 1, None))]
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>>> header_table["E"][1] # doctest: +NORMALIZE_WHITESPACE
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TreeNode('E', 1, TreeNode('B', 3, TreeNode('A', 4, TreeNode('Null Set', 1, None))))
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>>> sorted(header_table)
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['A', 'B', 'C', 'E']
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>>> fp_tree.name
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'Null Set'
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>>> sorted(fp_tree.children)
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['A', 'B']
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>>> fp_tree.children['A'].name
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'A'
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>>> sorted(fp_tree.children['A'].children)
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['B', 'C']
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"""
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header_table: dict = {}
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for trans in data_set:
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for item in trans:
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header_table[item] = header_table.get(item, [0, None])
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header_table[item][0] += 1
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for k in list(header_table):
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if header_table[k][0] < min_sup:
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del header_table[k]
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if not (freq_item_set := set(header_table)):
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return TreeNode("Null Set", 1, None), {}
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for k in header_table:
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header_table[k] = [header_table[k], None]
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fp_tree = TreeNode("Null Set", 1, None) # Parent is None for the root node
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for tran_set in data_set:
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local_d = {
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item: header_table[item][0] for item in tran_set if item in freq_item_set
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}
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if local_d:
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sorted_items = sorted(
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local_d.items(), key=lambda item_info: item_info[1], reverse=True
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)
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ordered_items = [item[0] for item in sorted_items]
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update_tree(ordered_items, fp_tree, header_table, 1)
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return fp_tree, header_table
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def update_tree(items: list, in_tree: TreeNode, header_table: dict, count: int) -> None:
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"""
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Update the FP-Tree with a transaction.
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Args:
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items: List of items in the transaction.
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in_tree: The current node in the FP-Tree.
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header_table: The header table dictionary with item information.
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count: The count of the transaction.
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Example:
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>>> data_set = [
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... ['A', 'B', 'C'],
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... ['A', 'C'],
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... ['A', 'B', 'E'],
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... ['A', 'B', 'C', 'E'],
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... ['B', 'E']
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... ]
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>>> min_sup = 2
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>>> fp_tree, header_table = create_tree(data_set, min_sup)
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>>> fp_tree
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TreeNode('Null Set', 1, None)
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>>> transaction = ['A', 'B', 'E']
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>>> update_tree(transaction, fp_tree, header_table, 1)
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>>> fp_tree
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TreeNode('Null Set', 1, None)
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>>> fp_tree.children['A'].children['B'].children['E'].children
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{}
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>>> fp_tree.children['A'].children['B'].children['E'].count
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2
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>>> header_table['E'][1].name
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'E'
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"""
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if items[0] in in_tree.children:
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in_tree.children[items[0]].inc(count)
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else:
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in_tree.children[items[0]] = TreeNode(items[0], count, in_tree)
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if header_table[items[0]][1] is None:
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header_table[items[0]][1] = in_tree.children[items[0]]
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else:
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update_header(header_table[items[0]][1], in_tree.children[items[0]])
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if len(items) > 1:
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update_tree(items[1:], in_tree.children[items[0]], header_table, count)
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def update_header(node_to_test: TreeNode, target_node: TreeNode) -> TreeNode:
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"""
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Update the header table with a node link.
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Args:
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node_to_test: The node to be updated in the header table.
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target_node: The node to link to.
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Example:
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>>> data_set = [
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... ['A', 'B', 'C'],
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... ['A', 'C'],
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... ['A', 'B', 'E'],
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... ['A', 'B', 'C', 'E'],
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... ['B', 'E']
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... ]
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>>> min_sup = 2
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>>> fp_tree, header_table = create_tree(data_set, min_sup)
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>>> fp_tree
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TreeNode('Null Set', 1, None)
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>>> node1 = TreeNode("A", 3, None)
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>>> node2 = TreeNode("B", 4, None)
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>>> node1
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TreeNode('A', 3, None)
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>>> node1 = update_header(node1, node2)
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>>> node1
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TreeNode('A', 3, None)
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>>> node1.node_link
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TreeNode('B', 4, None)
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>>> node2.node_link is None
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True
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"""
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while node_to_test.node_link is not None:
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node_to_test = node_to_test.node_link
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if node_to_test.node_link is None:
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node_to_test.node_link = target_node
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# Return the updated node
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return node_to_test
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def ascend_tree(leaf_node: TreeNode, prefix_path: list[str]) -> None:
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"""
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Ascend the FP-Tree from a leaf node to its root, adding item names to the prefix
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path.
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Args:
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leaf_node: The leaf node to start ascending from.
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prefix_path: A list to store the item as they are ascended.
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Example:
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>>> data_set = [
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... ['A', 'B', 'C'],
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... ['A', 'C'],
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... ['A', 'B', 'E'],
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... ['A', 'B', 'C', 'E'],
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... ['B', 'E']
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... ]
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>>> min_sup = 2
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>>> fp_tree, header_table = create_tree(data_set, min_sup)
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>>> path = []
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>>> ascend_tree(fp_tree.children['A'], path)
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>>> path # ascending from a leaf node 'A'
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['A']
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"""
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if leaf_node.parent is not None:
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prefix_path.append(leaf_node.name)
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ascend_tree(leaf_node.parent, prefix_path)
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def find_prefix_path(base_pat: frozenset, tree_node: TreeNode | None) -> dict:
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"""
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Find the conditional pattern base for a given base pattern.
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Args:
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base_pat: The base pattern for which to find the conditional pattern base.
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tree_node: The node in the FP-Tree.
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Example:
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>>> data_set = [
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... ['A', 'B', 'C'],
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... ['A', 'C'],
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... ['A', 'B', 'E'],
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... ['A', 'B', 'C', 'E'],
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... ['B', 'E']
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... ]
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>>> min_sup = 2
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>>> fp_tree, header_table = create_tree(data_set, min_sup)
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>>> fp_tree
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TreeNode('Null Set', 1, None)
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>>> len(header_table)
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4
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>>> base_pattern = frozenset(['A'])
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>>> sorted(find_prefix_path(base_pattern, fp_tree.children['A']))
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[]
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"""
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cond_pats: dict = {}
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while tree_node is not None:
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prefix_path: list = []
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ascend_tree(tree_node, prefix_path)
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if len(prefix_path) > 1:
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cond_pats[frozenset(prefix_path[1:])] = tree_node.count
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tree_node = tree_node.node_link
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return cond_pats
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def mine_tree(
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in_tree: TreeNode,
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header_table: dict,
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min_sup: int,
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pre_fix: set,
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freq_item_list: list,
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) -> None:
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"""
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Mine the FP-Tree recursively to discover frequent itemsets.
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Args:
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in_tree: The FP-Tree to mine.
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header_table: The header table dictionary with item information.
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min_sup: The minimum support threshold.
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pre_fix: A set of items as a prefix for the itemsets being mined.
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freq_item_list: A list to store the frequent itemsets.
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Example:
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>>> data_set = [
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... ['A', 'B', 'C'],
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... ['A', 'C'],
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... ['A', 'B', 'E'],
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... ['A', 'B', 'C', 'E'],
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... ['B', 'E']
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... ]
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>>> min_sup = 2
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>>> fp_tree, header_table = create_tree(data_set, min_sup)
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>>> fp_tree
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TreeNode('Null Set', 1, None)
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>>> frequent_itemsets = []
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>>> mine_tree(fp_tree, header_table, min_sup, set([]), frequent_itemsets)
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>>> expe_itm = [{'C'}, {'C', 'A'}, {'E'}, {'A', 'E'}, {'E', 'B'}, {'A'}, {'B'}]
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>>> all(expected in frequent_itemsets for expected in expe_itm)
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True
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"""
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sorted_items = sorted(header_table.items(), key=lambda item_info: item_info[1][0])
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big_l = [item[0] for item in sorted_items]
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for base_pat in big_l:
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new_freq_set = pre_fix.copy()
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new_freq_set.add(base_pat)
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freq_item_list.append(new_freq_set)
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cond_patt_bases = find_prefix_path(base_pat, header_table[base_pat][1])
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my_cond_tree, my_head = create_tree(list(cond_patt_bases), min_sup)
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if my_head is not None:
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# Pass header_table[base_pat][1] as node_to_test to update_header
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header_table[base_pat][1] = update_header(
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header_table[base_pat][1], my_cond_tree
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)
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mine_tree(my_cond_tree, my_head, min_sup, new_freq_set, freq_item_list)
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if __name__ == "__main__":
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from doctest import testmod
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testmod()
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data_set: list[frozenset] = [
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frozenset(["bread", "milk", "cheese"]),
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frozenset(["bread", "milk"]),
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frozenset(["bread", "diapers"]),
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frozenset(["bread", "milk", "diapers"]),
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frozenset(["milk", "diapers"]),
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frozenset(["milk", "cheese"]),
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frozenset(["diapers", "cheese"]),
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frozenset(["bread", "milk", "cheese", "diapers"]),
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]
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print(f"{len(data_set) = }")
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fp_tree, header_table = create_tree(data_set, min_sup=3)
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print(f"{fp_tree = }")
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print(f"{len(header_table) = }")
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freq_items: list = []
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mine_tree(fp_tree, header_table, 3, set(), freq_items)
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print(f"{freq_items = }")
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