Package org.apache.spark.ml.fpm

Class FPGrowthModel

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Transformer
org.apache.spark.ml.Model<FPGrowthModel>
org.apache.spark.ml.fpm.FPGrowthModel
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, FPGrowthParams, Params, HasPredictionCol, Identifiable, MLWritable, scala.Serializable
public class FPGrowthModel extends Model<FPGrowthModel> implements FPGrowthParams, MLWritable
Model fitted by FPGrowth.

param: freqItemsets frequent itemsets in the format of DataFrame("items"[Array], "freq"[Long])

See Also:

  • Method Details

    • read

      public static MLReader<FPGrowthModel> read()

    • load

      public static FPGrowthModel load(String path)

    • itemsCol

      public Param<String> itemsCol()
      Description copied from interface: FPGrowthParams
      Items column name. Default: "items"
      Specified by:
      itemsCol in interface FPGrowthParams
      Returns:
      (undocumented)

    • minSupport

      public DoubleParam minSupport()
      Description copied from interface: FPGrowthParams
      Minimal support level of the frequent pattern. [0.0, 1.0]. Any pattern that appears more than (minSupport * size-of-the-dataset) times will be output in the frequent itemsets. Default: 0.3
      Specified by:
      minSupport in interface FPGrowthParams
      Returns:
      (undocumented)

    • numPartitions

      public IntParam numPartitions()
      Description copied from interface: FPGrowthParams
      Number of partitions (at least 1) used by parallel FP-growth. By default the param is not set, and partition number of the input dataset is used.
      Specified by:
      numPartitions in interface FPGrowthParams
      Returns:
      (undocumented)

    • minConfidence

      public DoubleParam minConfidence()
      Description copied from interface: FPGrowthParams
      Minimal confidence for generating Association Rule. minConfidence will not affect the mining for frequent itemsets, but will affect the association rules generation. Default: 0.8
      Specified by:
      minConfidence in interface FPGrowthParams
      Returns:
      (undocumented)

    • predictionCol

      public final Param<String> predictionCol()
      Description copied from interface: HasPredictionCol
      Param for prediction column name.
      Specified by:
      predictionCol in interface HasPredictionCol
      Returns:
      (undocumented)

    • uid

      public String uid()
      Description copied from interface: Identifiable
      An immutable unique ID for the object and its derivatives.
      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • freqItemsets

      public Dataset<Row> freqItemsets()

    • setMinConfidence

      public FPGrowthModel setMinConfidence(double value)

    • setItemsCol

      public FPGrowthModel setItemsCol(String value)

    • setPredictionCol

      public FPGrowthModel setPredictionCol(String value)

    • associationRules

      public Dataset<Row> associationRules()
      Get association rules fitted using the minConfidence. Returns a dataframe with five fields, "antecedent", "consequent", "confidence", "lift" and "support", where "antecedent" and "consequent" are Array[T], whereas "confidence", "lift" and "support" are Double.
      Returns:
      (undocumented)

    • transform

      public Dataset<Row> transform(Dataset<?> dataset)
      The transform method first generates the association rules according to the frequent itemsets. Then for each transaction in itemsCol, the transform method will compare its items against the antecedents of each association rule. If the record contains all the antecedents of a specific association rule, the rule will be considered as applicable and its consequents will be added to the prediction result. The transform method will summarize the consequents from all the applicable rules as prediction. The prediction column has the same data type as the input column(Array[T]) and will not contain existing items in the input column. The null values in the itemsCol columns are treated as empty sets. WARNING: internally it collects association rules to the driver and uses broadcast for efficiency. This may bring pressure to driver memory for large set of association rules.
      Specified by:
      transform in class Transformer
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      public StructType transformSchema(StructType schema)
      Description copied from class: PipelineStage
      Check transform validity and derive the output schema from the input schema.

      We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

      Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

      Specified by:
      transformSchema in class PipelineStage
      Parameters:
      schema - (undocumented)
      Returns:
      (undocumented)

    • copy

      public FPGrowthModel copy(ParamMap extra)
      Description copied from interface: Params
      Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
      Specified by:
      copy in interface Params
      Specified by:
      copy in class Model<FPGrowthModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • write

      public MLWriter write()
      Description copied from interface: MLWritable
      Returns an MLWriter instance for this ML instance.
      Specified by:
      write in interface MLWritable
      Returns:
      (undocumented)

    • toString

      public String toString()
      Specified by:
      toString in interface Identifiable
      Overrides:
      toString in class Object