water.bindings.pojos.GLMModelOutputV3 Maven / Gradle / Ivy
/*
* 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 GLMModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Table of Coefficients
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/**
* Table of Random Coefficients for HGLM
*/
@SerializedName("random_coefficients_table")
public TwoDimTableV3 randomCoefficientsTable;
/**
* Table of Coefficients with coefficients denoted with class names for GLM multinonimals only.
*/
@SerializedName("coefficients_table_multinomials_with_class_names")
public TwoDimTableV3 coefficientsTableMultinomialsWithClassNames;
/**
* Standardized Coefficient Magnitudes
*/
@SerializedName("standardized_coefficient_magnitudes")
public TwoDimTableV3 standardizedCoefficientMagnitudes;
/**
* Variable Importances
*/
@SerializedName("variable_importances")
public TwoDimTableV3 variableImportances;
/**
* Lambda minimizing the objective value, only applicable with lambda search or when arrays of alpha and lambdas are
* provided
*/
@SerializedName("lambda_best")
public double lambdaBest;
/**
* Alpha minimizing the objective value, only applicable when arrays of alphas are given
*/
@SerializedName("alpha_best")
public double alphaBest;
/**
* submodel index minimizing the objective value, only applicable for arrays of alphas/lambda
*/
@SerializedName("best_submodel_index")
public int bestSubmodelIndex;
/**
* Lambda best + 1 standard error. Only applicable with lambda search and cross-validation
*/
@SerializedName("lambda_1se")
public double lambda1se;
/**
* Minimum lambda value calculated that may be used for lambda search. Early-stop may happen and the minimum lambda
* value will not be used in this case.
*/
@SerializedName("lambda_min")
public double lambdaMin;
/**
* Starting lambda value used when lambda search is enabled.
*/
@SerializedName("lambda_max")
public double lambdaMax;
/**
* Dispersion parameter, only applicable to Tweedie family (input/output) and fractional Binomial (output only)
*/
public double dispersion;
/**
* Predictor names where variable inflation factors are calculated.
*/
@SerializedName("vif_predictor_names")
public String[] vifPredictorNames;
/**
* GLM model coefficients names.
*/
@SerializedName("coefficient_names")
public String[] coefficientNames;
/**
* predictor variable inflation factors.
*/
@SerializedName("variable_inflation_factors")
public double[] variableInflationFactors;
/**
* Beta (if exists) and linear constraints states
*/
@SerializedName("linear_constraint_states")
public String[] linearConstraintStates;
/**
* Table of beta (if exists) and linear constraints values and status
*/
@SerializedName("linear_constraints_table")
public TwoDimTableV3 linearConstraintsTable;
/**
* Contains the original dataset and the dfbetas calculated for each predictor.
*/
@SerializedName("regression_influence_diagnostics")
public FrameKeyV3 regressionInfluenceDiagnostics;
/**
* True if all constraints conditions are satisfied. Otherwise, false.
*/
@SerializedName("all_constraints_satisfied")
public boolean allConstraintsSatisfied;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map help;
*/
/**
* Public constructor
*/
public GLMModelOutputV3() {
lambdaBest = -1.0;
alphaBest = -1.0;
bestSubmodelIndex = 0;
lambda1se = -1.0;
lambdaMin = -1.0;
lambdaMax = -1.0;
dispersion = 0.0;
allConstraintsSatisfied = false;
modelCategory = ModelCategory.Regression;
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.5;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}