hex.schemas.IsolationForestV3 Maven / Gradle / Ivy

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package hex.schemas;

import hex.tree.isofor.IsolationForest;
import hex.tree.isofor.IsolationForestModel;
import water.api.API;
import water.api.schemas3.FrameV3;

public class IsolationForestV3 extends SharedTreeV3 {

    public static final class IsolationForestParametersV3 extends SharedTreeV3.SharedTreeParametersV3 {
        static public String[] fields = new String[]{
                "model_id",
                "training_frame",
                "score_each_iteration",
                "score_tree_interval",
                "ignored_columns",
                "ignore_const_cols",
                "ntrees",
                "max_depth",
                "min_rows",
                "max_runtime_secs",
                "seed",
                "build_tree_one_node",
                "mtries",
                "sample_size",
                "sample_rate",
                "col_sample_rate_change_per_level",
                "col_sample_rate_per_tree",
                "categorical_encoding",
                "stopping_rounds",
                "stopping_metric",
                "stopping_tolerance",
                "export_checkpoints_dir",
                "contamination",
                "validation_frame",
                "validation_response_column"
        };

        // Input fields
        @API(help = "Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored.", gridable = true)
        public long sample_size;

        @API(help = "Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead.", gridable = true)
        public double sample_rate;

        @API(help = "Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3.", gridable = true)
        public int mtries;

        @API(help = "Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined).")
        public double contamination;

        @API(level = API.Level.secondary, direction = API.Direction.INOUT,
                is_member_of_frames = {"validation_frame"},
                is_mutually_exclusive_with = {"ignored_columns"},
                help = "(experimental) Name of the response column in the validation frame. " +
                        "Response column should be binary and indicate not anomaly/anomaly.")
        public FrameV3.ColSpecifierV3 validation_response_column;
        
        @Override
        public IsolationForestParametersV3 fillFromImpl(IsolationForestModel.IsolationForestParameters impl) {
            IsolationForestParametersV3 pv3 = super.fillFromImpl(impl);
            if (impl._response_column != null) {
                pv3.validation_response_column = new FrameV3.ColSpecifierV3(impl._response_column);
            }
            return pv3;
        }

        @Override
        public IsolationForestModel.IsolationForestParameters fillImpl(IsolationForestModel.IsolationForestParameters impl) {
            IsolationForestModel.IsolationForestParameters p = super.fillImpl(impl);
            if (validation_response_column != null) {
                p._response_column = validation_response_column.column_name;
            }
            return p;
        }

    }
}