Random Transaction Itemset Instance Generator

Description

Generate exact-count set-valued transactions by choosing a uniform size and a without-replacement subset from distinct supplied item labels; transactions may repeat and imported frequencies count transaction presence.

Synthetic statistical data separates a specified generating model from the values observed in a finite draw. A model parameter, a sample summary, and the result of a fitted analysis answer different questions; retaining them separately makes numerical and statistical checks easier to interpret.

When to use this tool

Use this generator to create reproducible itemset instance cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply items, transactionCount, minimumSize, maximumSize in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read items, transactions, itemCounts. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Draw each transaction's size within the supplied range, shuffle the distinct item bank, and take that many items without repetition inside the transaction. Sort each basket and delegate item frequency counts aligned with the original item order.^[1]

Delegated operations used for this result: frequency. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
items	Items	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 64.
transactionCount	Transaction Count	Required. Safe integer; minimum 1; maximum 512.
minimumSize	Minimum Size	Required. Safe integer; minimum 0; maximum 64.
maximumSize	Maximum Size	Required. Safe integer; minimum 0; maximum 64.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
items	Items	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 64.
transactions	Transactions	Required. Array of Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 0; maximum items 64.; minimum items 1; maximum items 512.
itemCounts	Item Counts	Required. Array of Safe integer; minimum 0; maximum 512.; minimum items 1; maximum items 64.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "items": [
      "bread",
      "milk",
      "eggs",
      "tea"
    ],
    "transactionCount": 4,
    "minimumSize": 1,
    "maximumSize": 3,
    "seed": 42
  },
  "output": {
    "items": [
      "bread",
      "milk",
      "eggs",
      "tea"
    ],
    "transactions": [
      [
        "bread",
        "tea"
      ],
      [
        "tea"
      ],
      [
        "bread",
        "eggs",
        "tea"
      ],
      [
        "bread",
        "eggs",
        "tea"
      ]
    ],
    "itemCounts": [
      3,
      0,
      2,
      4
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Different transactions can repeat. Item counts are transaction occurrence counts because baskets are sets; the tool does not mine rules, enforce support, or simulate a real market.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Transaction Itemset Instance Generator — tool.io
Calculation or validation: list/frequency — tool.io

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