QFARM

QFARM is a research tool for range-based rule mining, combining DFS rule-tree exploration with multi-objective genetic algorithms (Jenetics-based). It discovers high-quality rules of the form:

$$(attr1\ ∈\ [a,b])\ \land\ (attr2\ ∈\ [c,d])\ \land ...\ \implies\ (rhs\ ∈\ [lo,hi])$$

The system evaluates rules using multiple fitness metrics, evolves Pareto fronts, and outputs DOT graphs and JSON logs describing the search.

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How to Run

1. Build the shadow JAR

From the project root:

./gradlew shadowJar

The runnable JAR is generated at:

build/libs/qfarm-<version>.jar

Example:

build/libs/qfarm-0.1.build.jar

Rebuild the JAR whenever you modify the source code.

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2. Running the Application

Basic syntax:

java -jar qfarm.jar \
  --data <path/to.csv> \
  --rhs <column_name> \
  [--rhs-range <lo,hi> | --rhs-range-percentile <pLo,pHi>] \
  [optional hyperparameters...]

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Required arguments

--data
Path to CSV dataset.
NOTE: the full dataset is always loaded; no row or column limits are applied.

--rhs
Column name of the right-hand-side attribute.

Exactly one of:
--rhs-range
--rhs-range-percentile

Examples:

java -jar qfarm.jar \
  --data data.csv \
  --rhs y \
  --rhs-range-percentile 90,100

java -jar qfarm.jar \
  --data data.csv \
  --rhs y \
  --rhs-range 4.0, MAX

NOTE (zsh): Only quote bracket or no bracket expressions:

--rhs-range-percentile 90,100
--rhs-range-percentile "[90,100]"

Both ranges accept these formats: lo,hi lo..hi MIN,MAX MIN,6.0 4.0,MAX

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Optional Hyperparameters

All hyperparameters can be overridden through CLI flags. Anything not provided falls back to defaults in HyperParameters.

Rule constraints

--min-support (default: 100)
--max-support (default: 5000)
--max-depth (default: 2)
--max-children (default: 1)
--max-first-children (default: 1)

Evolution parameters

--evo-first-pop (default: 100)
--evo-first-gen (default: 100)
--evo-next-pop (default: 500)
--evo-next-gen (default: 200)
--evo-range-pop (default: 200)
--evo-range-gen (default: 500)

Mutation parameters

--prob-mutation (default: 1.0)
--std-mutation (default: 0.15)

Thresholds

--improvement-threshold (default: 10.0)

Dataset & run metadata

--excl-cols (default: [])
Comma-separated list of column names to exclude from the dataset before rule mining.

Example:
    --excl-cols ID,Timestamp

--name (default: auto-generated)
Optional run name / experiment label. Used for logging, plots, output directories, and DOT URLs.

Example:
    --name experiment_1

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Full Example

java -jar qfarm.jar \
  --data data/friedman1.csv \
  --rhs y \
  --rhs-range-percentile 80,100 \
  --name experiment_1 \
  --excl-cols id,timestamp \
  --min-support 1 \
  --max-support 500 \
  --max-depth 5 \
  --max-children 5 \
  --max-first-children 5 \
  --evo-cheap-pop 50 \
  --evo-cheap-gen 50 \
  --evo-full-pop 100 \
  --evo-full-gen 100 \
  --prob-mutation 0.8 \
  --std-mutation 0.2 \
  --alpha-threshold 0.01

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Output Files

After execution, QFARM produces:

tree.dot
GraphViz DOT file describing the discovered rule tree (with color-coded cumulative front improvement).

tree.svg
The rendered tree.

step_log.json
NDJSON step-by-step evolution log.

stdout
Contains printed rules, evolutionary progress, and runtime information.

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Developer Mode

To run without rebuilding the JAR each time, add this to build.gradle.kts:

application {
    mainClass = "MainKt"
}

Run with:

./gradlew run --args="--data data.csv --rhs LDL --rhs-range 4.0..7.0"

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Repository Structure (high-level)

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Contributing

1. Fork this repository

2. Create a feature branch:

   git checkout -b feature/my-feature
   

3. Commit your work

4. Push your branch:

   git push origin feature/my-feature
   

5. Open a pull request

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License

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