Add: FP Growth Algorithm

DIRECTORY.md

@@ -541,6 +541,7 @@
   * [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
   * Forecasting
     * [Run](machine_learning/forecasting/run.py)
+  * [Frequent Pattern Growth Algorithm](machine_learning/frequent_pattern_growth.py)
   * [Gradient Descent](machine_learning/gradient_descent.py)
   * [K Means Clust](machine_learning/k_means_clust.py)
   * [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)

machine_learning/frequent_pattern_growth.py

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