Module association_rules 

Apriori-style frequent itemset mining + association-rule generation over per-class callSequences blobs.

Each class node carries a callSequences JSON blob with shape

{"sequences": [{"method": str, "calls": [str], …}, …]}

Every sequence with len(calls) >= 2 becomes one transaction (the frozenset of its calls). We then:

1. Mine frequent itemsets by Apriori (bottom-up by k), capped at k = 5 to match the Python reference.
2. Generate rules A → C for every non-empty proper subset A of each frequent itemset, scoring with confidence = sup(A∪C)/sup(A) and lift = confidence / (sup(C)/n).
3. Classify each rule as invariant (conf ≥ 0.99), strong (≥ 0.85), or moderate (≥ 0.5).

Mirrors analysis/association_rules.py in the visiting tool.

See DESIGN.md (Phase 3) at the workspace root.

Structs

AssociationAnalysis

Whole-graph association-rule readout.

AssociationRule

An association rule antecedent → consequent with support, confidence, and lift.

CallSequence

One call-sequence record parsed from a class’s callSequences blob.

FrequentItemset

A frequent itemset and its support count across the transaction list.

Functions

classify_rule

Classify by confidence threshold — invariant (≥0.99), strong (≥0.85), moderate otherwise.

compute_association_analysis

Walk every node’s callSequences blob and run end-to-end mining.

generate_rules

Generate association rules from frequent itemsets.

mine_frequent_itemsets

Mine frequent itemsets by Apriori. Caps k at 5 (matches Python ref) to keep candidate generation tractable on large unique-item alphabets.

parse_call_sequences

Parse the {"sequences": [...]} blob attached to a single class.