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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 GAMParametersV3 extends ModelParametersSchemaV3 {
/**
* Seed for pseudo random number generator (if applicable)
*/
public long seed;
/**
* Family. Use binomial for classification with logistic regression, others are for regression problems.
*/
public GLMFamily family;
/**
* Tweedie variance power
*/
@SerializedName("tweedie_variance_power")
public double tweedieVariancePower;
/**
* Tweedie link power
*/
@SerializedName("tweedie_link_power")
public double tweedieLinkPower;
/**
* Theta
*/
public double theta;
/**
* AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small
* number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.
*/
public GLMSolver solver;
/**
* Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents
* Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing
* between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise.
*/
public double[] alpha;
/**
* Regularization strength
*/
public double[] lambda;
/**
* double array to initialize coefficients for GAM.
*/
public double[] startval;
/**
* Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
*/
@SerializedName("lambda_search")
public boolean lambdaSearch;
/**
* Stop early when there is no more relative improvement on train or validation (if provided)
*/
@SerializedName("early_stopping")
public boolean earlyStopping;
/**
* Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True,
* the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100.
*/
public int nlambdas;
/**
* Standardize numeric columns to have zero mean and unit variance
*/
public boolean standardize;
/**
* Handling of missing values. Either MeanImputation, Skip or PlugValues.
*/
@SerializedName("missing_values_handling")
public GLMMissingValuesHandling missingValuesHandling;
/**
* Plug Values (a single row frame containing values that will be used to impute missing values of the
* training/validation frame, use with conjunction missing_values_handling = PlugValues)
*/
@SerializedName("plug_values")
public FrameKeyV3 plugValues;
/**
* Restrict coefficients (not intercept) to be non-negative
*/
@SerializedName("non_negative")
public boolean nonNegative;
/**
* Maximum number of iterations
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver
*/
@SerializedName("beta_epsilon")
public double betaEpsilon;
/**
* Converge if objective value changes less than this. Default indicates: If lambda_search is set to True the value
* of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value
* of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is
* set to .0001.
*/
@SerializedName("objective_epsilon")
public double objectiveEpsilon;
/**
* Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default
* indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is
* equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values
* above are 1E-8 and 1E-6 respectively.
*/
@SerializedName("gradient_epsilon")
public double gradientEpsilon;
/**
* Likelihood divider in objective value computation, default is 1/nobs
*/
@SerializedName("obj_reg")
public double objReg;
/**
* Link function.
*/
public GLMLink link;
/**
* Include constant term in the model
*/
public boolean intercept;
/**
* Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of
* response does not reflect reality.
*/
public double prior;
/**
* Only applicable to multiple alpha/lambda values when calling GLM from GAM. If false, build the next model for
* next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model
* from scratch.
*/
@SerializedName("cold_start")
public boolean coldStart;
/**
* Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all
* coefficients to zero). Default indicates: if the number of observations is greater than the number of variables,
* then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then
* lambda_min_ratio is set to 0.01.
*/
@SerializedName("lambda_min_ratio")
public double lambdaMinRatio;
/**
* Beta constraints
*/
@SerializedName("beta_constraints")
public FrameKeyV3 betaConstraints;
/**
* Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model
* building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors
* is set to 5000 otherwise it is set to 100000000.
*/
@SerializedName("max_active_predictors")
public int maxActivePredictors;
/**
* A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
*/
public String[] interactions;
/**
* A list of pairwise (first order) column interactions.
*/
@SerializedName("interaction_pairs")
public StringPairV3[] interactionPairs;
/**
* Balance training data class counts via over/under-sampling (for imbalanced data).
*/
@SerializedName("balance_classes")
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.
*/
@SerializedName("class_sampling_factors")
public float[] classSamplingFactors;
/**
* Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
* balance_classes.
*/
@SerializedName("max_after_balance_size")
public float maxAfterBalanceSize;
/**
* [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
*/
@SerializedName("max_confusion_matrix_size")
public int maxConfusionMatrixSize;
/**
* Request p-values computation, p-values work only with IRLSM solver and no regularization
*/
@SerializedName("compute_p_values")
public boolean computePValues;
/**
* In case of linearly dependent columns, remove some of the dependent columns
*/
@SerializedName("remove_collinear_columns")
public boolean removeCollinearColumns;
/**
* If set to true, will return knot locations as double[][] array for gam column names found knots_for_gam. Default
* to false.
*/
@SerializedName("store_knot_locations")
public boolean storeKnotLocations;
/**
* Number of knots for gam predictors. If specified, must specify one for each gam predictor. For monotone
* I-splines, mininum = 2, for cs spline, minimum = 3. For thin plate, minimum is size of polynomial basis + 2.
*/
@SerializedName("num_knots")
public int[] numKnots;
/**
* Order of I-splines or NBSplineTypeI M-splines used for gam predictors. If specified, must be the same size as
* gam_columns. For I-splines, the spline_orders will be the same as the polynomials used to generate the splines.
* For M-splines, the polynomials used to generate the splines will be spline_order-1. Values for bs=0 or 1 will be
* ignored.
*/
@SerializedName("spline_orders")
public int[] splineOrders;
/**
* Valid for I-spline (bs=2) only. True if the I-splines are monotonically increasing (and monotonically non-
* decreasing) and False if the I-splines are monotonically decreasing (and monotonically non-increasing). If
* specified, must be the same size as gam_columns. Values for other spline types will be ignored. Default to
* true.
*/
@SerializedName("splines_non_negative")
public boolean[] splinesNonNegative;
/**
* Arrays of predictor column names for gam for smoothers using single or multiple predictors like
* {{'c1'},{'c2','c3'},{'c4'},...}
*/
@SerializedName("gam_columns")
public String[][] gamColumns;
/**
* Smoothing parameter for gam predictors. If specified, must be of the same length as gam_columns
*/
public double[] scale;
/**
* Basis function type for each gam predictors, 0 for cr, 1 for thin plate regression with knots, 2 for monotone
* I-splines, 3 for NBSplineTypeI M-splines (refer to doc here: https://github.com/h2oai/h2o-3/issues/6926). If
* specified, must be the same size as gam_columns
*/
public int[] bs;
/**
* Save keys of model matrix
*/
@SerializedName("keep_gam_cols")
public boolean keepGamCols;
/**
* standardize tp (thin plate) predictor columns
*/
@SerializedName("standardize_tp_gam_cols")
public boolean standardizeTpGamCols;
/**
* Scale penalty matrix for tp (thin plate) smoothers as in R
*/
@SerializedName("scale_tp_penalty_mat")
public boolean scaleTpPenaltyMat;
/**
* Array storing frame keys of knots. One for each gam column set specified in gam_columns
*/
@SerializedName("knot_ids")
public String[] knotIds;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// 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 GAMParametersV3() {
seed = -1L;
family = GLMFamily.AUTO;
tweedieVariancePower = 0.0;
tweedieLinkPower = 0.0;
theta = 0.0;
solver = GLMSolver.AUTO;
lambdaSearch = false;
earlyStopping = true;
nlambdas = -1;
standardize = false;
missingValuesHandling = GLMMissingValuesHandling.MeanImputation;
nonNegative = false;
maxIterations = -1;
betaEpsilon = 0.0001;
objectiveEpsilon = -1.0;
gradientEpsilon = -1.0;
objReg = -1.0;
link = GLMLink.family_default;
intercept = true;
prior = -1.0;
coldStart = false;
lambdaMinRatio = -1.0;
maxActivePredictors = -1;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
computePValues = false;
removeCollinearColumns = false;
storeKnotLocations = false;
keepGamCols = false;
standardizeTpGamCols = false;
scaleTpPenaltyMat = false;
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);
}
}
