Examples of cascading.pattern.model.tree.TreeSpec

cascading.pattern.model.tree.TreeSpec
Class TreeSpec is used to define a decision tree model. It simply holds a {@link Tree} instancepopulated with {@link cascading.pattern.model.tree.predicate.Predicate} instances.

    {
    ModelSchema modelSchema = createModelSchema( model );


    Tree tree = createTree( model, modelSchema );


    TreeSpec treeSpec = new TreeSpec( modelSchema, tree );


    return create( tail, modelSchema, new TreeFunction( treeSpec ) );
    }

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        throw new PatternException( "segment predicates currently not supported, got: " + segment.getPredicate() );


      Model segmentModel = segment.getModel();


      if( segmentModel instanceof TreeModel )
        models.add( new TreeSpec( modelSchema, createTree( (TreeModel) segmentModel, modelSchema ) ) );
      else
        throw new PatternException( "ensemble model currently not supported, got: " + segmentModel );
      }


    EnsembleSpec<TreeSpec> miningSpec = new EnsembleSpec<TreeSpec>( modelSchema, models );

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    EnsembleSpec<TreeSpec> ensembleSpec = new EnsembleSpec<TreeSpec>( modelSchema );


    ensembleSpec.setSelectionStrategy( new MajorityVote() );


    {
    TreeSpec treeSpec = new TreeSpec( modelSchema );


    Tree tree = new Tree( "1" );


    tree.addPredicate( "1", "2", new LessOrEqualThanPredicate( "var0", 0.5d ) );
    tree.addPredicate( "2", "4", new LessOrEqualThanPredicate( "var2", 0.5d ), "1" );
    tree.addPredicate( "2", "5", new GreaterThanPredicate( "var2", 0.5d ), "0" );
    tree.addPredicate( "1", "3", new GreaterThanPredicate( "var0", 0.5d ) );
    tree.addPredicate( "3", "6", new LessOrEqualThanPredicate( "var1", 0.5d ), "0" );
    tree.addPredicate( "3", "7", new GreaterThanPredicate( "var1", 0.5d ), "1" );


    treeSpec.setTree( tree );


    ensembleSpec.addModelSpec( treeSpec );
    }


    {
    TreeSpec treeSpec = new TreeSpec( modelSchema );


    Tree tree = new Tree( "1" );


    tree.addPredicate( "1", "2", new LessOrEqualThanPredicate( "var1", 0.5d ), "1" );
    tree.addPredicate( "1", "3", new GreaterThanPredicate( "var1", 0.5d ), "0" );


    treeSpec.setTree( tree );


    ensembleSpec.addModelSpec( treeSpec );
    }


    {
    TreeSpec treeSpec = new TreeSpec( modelSchema );


    Tree tree = new Tree( "1" );


    tree.addPredicate( "1", "2", new LessOrEqualThanPredicate( "var0", 0.5d ), "1" );
    tree.addPredicate( "1", "3", new GreaterThanPredicate( "var0", 0.5d ), "0" );


    treeSpec.setTree( tree );


    ensembleSpec.addModelSpec( treeSpec );
    }


    String inputData = "randomforest.tsv";

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    EnsembleSpec<TreeSpec> ensembleSpec = new EnsembleSpec<TreeSpec>( modelSchema );


    ensembleSpec.setSelectionStrategy( new Average() );


    {
    TreeSpec treeSpec = new TreeSpec( modelSchema );


    Tree tree = new Tree( "1" );


    tree.addPredicate( "1", "2", new LessOrEqualThanPredicate( "var0", 0.5d ) );
    tree.addPredicate( "2", "4", new LessOrEqualThanPredicate( "var2", 0.5d ), 1 );
    tree.addPredicate( "2", "5", new GreaterThanPredicate( "var2", 0.5d ), 0 );
    tree.addPredicate( "1", "3", new GreaterThanPredicate( "var0", 0.5d ) );
    tree.addPredicate( "3", "6", new LessOrEqualThanPredicate( "var1", 0.5d ), 0 );
    tree.addPredicate( "3", "7", new GreaterThanPredicate( "var1", 0.5d ), 1 );


    treeSpec.setTree( tree );


    ensembleSpec.addModelSpec( treeSpec );
    }


    {
    TreeSpec treeSpec = new TreeSpec( modelSchema );


    Tree tree = new Tree( "1" );


    tree.addPredicate( "1", "2", new LessOrEqualThanPredicate( "var1", 0.5d ), 1 );
    tree.addPredicate( "1", "3", new GreaterThanPredicate( "var1", 0.5d ), 0 );


    treeSpec.setTree( tree );


    ensembleSpec.addModelSpec( treeSpec );
    }


    {
    TreeSpec treeSpec = new TreeSpec( modelSchema );


    Tree tree = new Tree( "1" );


    tree.addPredicate( "1", "2", new LessOrEqualThanPredicate( "var0", 0.5d ), 1 );
    tree.addPredicate( "1", "3", new GreaterThanPredicate( "var0", 0.5d ), 0 );


    treeSpec.setTree( tree );


    ensembleSpec.addModelSpec( treeSpec );
    }


    String inputData = "randomforest-predict.tsv";

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      .append( new Fields( "var1", double.class ) )
      .append( new Fields( "var2", double.class ) );


    ModelSchema modelSchema = new ModelSchema( expectedFields, predictedFields );


    TreeSpec treeSpec = new TreeSpec( modelSchema );


    Tree tree = new Tree( "1" );


    tree.addPredicate( "1", "2", new LessOrEqualThanPredicate( "var0", 0.5d ) );
    tree.addPredicate( "2", "4", new LessOrEqualThanPredicate( "var2", 0.5d ), "1" );
    tree.addPredicate( "2", "5", new GreaterThanPredicate( "var2", 0.5d ), "0" );
    tree.addPredicate( "1", "3", new GreaterThanPredicate( "var0", 0.5d ) );
    tree.addPredicate( "3", "6", new LessOrEqualThanPredicate( "var1", 0.5d ), "0" );
    tree.addPredicate( "3", "7", new GreaterThanPredicate( "var1", 0.5d ), "1" );


    treeSpec.setTree( tree );


    TreeFunction treeFunction = new TreeFunction( treeSpec );


    TupleEntry tupleArguments = new TupleEntry( expectedFields, new Tuple( 0d, 1d, 0d ) );

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Related Classes of cascading.pattern.model.tree.TreeSpec

cascading.pattern.ensemble.SimpleEnsemblePlatformTest

cascading.pattern.model.ModelTest

cascading.pattern.pmml.PMMLPlanner

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