class_docstr: Model fitted by FPGrowth.
  
  .. versionadded:: 2.2.0
class_docstr_tok: ["Model","fitted","by","FPGrowth",".","..","versionadded",":",":","2.2.0"]
docstr: DataFrame with four columns:
  * `antecedent`  - Array of the same type as the input column.
  * `consequent`  - Array of the same type as the input column.
  * `confidence`  - Confidence for the rule (`DoubleType`).
  * `lift`        - Lift for the rule (`DoubleType`).
docstr_tok: ["DataFrame","with","four","columns",":","*","`","antecedent","`","-","Array","of","the","same","type","as","the","input","column",".","*","`","consequent","`","-","Array","of","the","same","type","as","the","input","column",".","*","`","confidence","`","-","Confidence","for","the","rule","(","`","DoubleType","`",")",".","*","`","lift","`","-","Lift","for","the","rule","(","`","DoubleType","`",")","."]
code: def associationRules(self) -> DataFrame:
      """
      DataFrame with four columns:
      * `antecedent`  - Array of the same type as the input column.
      * `consequent`  - Array of the same type as the input column.
      * `confidence`  - Confidence for the rule (`DoubleType`).
      * `lift`        - Lift for the rule (`DoubleType`).
      """
      return self._call_java("associationRules")
  
imports: import sys
  from typing import Any, Dict, Optional, TYPE_CHECKING
  from pyspark import keyword_only, since
  from pyspark.sql import DataFrame
  from pyspark.ml.util import JavaMLWritable, JavaMLReadable
  from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams
  from pyspark.ml.param.shared import HasPredictionCol, Param, TypeConverters, Params
label_desc: Use image node_id 5 for calling the FPGrowthModel obj's underlying member method code with example usage: obj.associationRules() and returns: self