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/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class UpliftDRFParametersV3 extends SharedTreeParametersV3 {
/**
* Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for
* classification and p/3 for regression (where p is the # of predictors
*/
public int mtries;
/**
* Row sample rate per tree (from 0.0 to 1.0)
*/
@SerializedName("sample_rate")
public double sampleRate;
/**
* Define the column which will be used for computing uplift gain to select best split for a tree. The column has to
* divide the dataset into treatment (value 1) and control (value 0) groups.
*/
@SerializedName("treatment_column")
public String treatmentColumn;
/**
* Divergence metric used to find best split when building an uplift tree.
*/
@SerializedName("uplift_metric")
public TreeupliftUpliftDRFModelUpliftDRFParametersUpliftMetricType upliftMetric;
/**
* Metric used to calculate Area Under Uplift Curve.
*/
@SerializedName("auuc_type")
public AUUCType auucType;
/**
* Number of bins to calculate Area Under Uplift Curve.
*/
@SerializedName("auuc_nbins")
public int auucNbins;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Balance training data class counts via over/under-sampling (for imbalanced data).
public boolean balanceClasses;
// Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be
// automatically computed to obtain class balance during training. Requires balance_classes.
public float[] classSamplingFactors;
// Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
// balance_classes.
public float maxAfterBalanceSize;
// [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
public int maxConfusionMatrixSize;
// Number of trees.
public int ntrees;
// Maximum tree depth (0 for unlimited).
public int maxDepth;
// Fewest allowed (weighted) observations in a leaf.
public double minRows;
// For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
public int nbins;
// For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease
// by factor of two per level
public int nbinsTopLevel;
// For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher
// values can lead to more overfitting.
public int nbinsCats;
// r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and
// stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds
// this
public double r2Stopping;
// Seed for pseudo random number generator (if applicable)
public long seed;
// Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
public boolean buildTreeOneNode;
// A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree
public double[] sampleRatePerClass;
// Column sample rate per tree (from 0.0 to 1.0)
public double colSampleRatePerTree;
// Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
public double colSampleRateChangePerLevel;
// Score the model after every so many trees. Disabled if set to 0.
public int scoreTreeInterval;
// Minimum relative improvement in squared error reduction for a split to happen
public double minSplitImprovement;
// What type of histogram to use for finding optimal split points
public TreeSharedTreeModelSharedTreeParametersHistogramType histogramType;
// Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can
// provide more accurate estimates of class probabilities.
public boolean calibrateModel;
// Data for model calibration
public FrameKeyV3 calibrationFrame;
// Calibration method to use
public TreeCalibrationHelperCalibrationMethod calibrationMethod;
// Check if response column is constant. If enabled, then an exception is thrown if the response column is a
// constant value.If disabled, then model will train regardless of the response column being a constant value or
// not.
public boolean checkConstantResponse;
// Create checkpoints into defined directory while training process is still running. In case of cluster shutdown,
// this checkpoint can be used to restart training.
public String inTrainingCheckpointsDir;
// Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is
// defined
public int inTrainingCheckpointsTreeInterval;
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the
// dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights
// are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame.
// This is typically the number of times a row is repeated, but non-integer values are supported as well. During
// training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify
// the folds based on the response variable, for classification problems.
public ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the
// stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public UpliftDRFParametersV3() {
mtries = -2;
sampleRate = 0.632;
treatmentColumn = "treatment";
upliftMetric = TreeupliftUpliftDRFModelUpliftDRFParametersUpliftMetricType.AUTO;
auucType = AUUCType.AUTO;
auucNbins = -1;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
ntrees = 50;
maxDepth = 20;
minRows = 1.0;
nbins = 20;
nbinsTopLevel = 1024;
nbinsCats = 1024;
r2Stopping = 1.7976931348623157e+308;
seed = -1L;
buildTreeOneNode = false;
colSampleRatePerTree = 1.0;
colSampleRateChangePerLevel = 1.0;
scoreTreeInterval = 0;
minSplitImprovement = 1e-05;
histogramType = TreeSharedTreeModelSharedTreeParametersHistogramType.AUTO;
calibrateModel = false;
calibrationMethod = TreeCalibrationHelperCalibrationMethod.AUTO;
checkConstantResponse = true;
inTrainingCheckpointsDir = "";
inTrainingCheckpointsTreeInterval = 1;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
