hex.schemas.ModelSelectionV3 Maven / Gradle / Ivy
package hex.schemas;
import hex.glm.GLMModel;
import hex.modelselection.ModelSelection;
import hex.modelselection.ModelSelectionModel;
import water.api.API;
import water.api.EnumValuesProvider;
import water.api.schemas3.KeyV3;
import water.api.schemas3.ModelParametersSchemaV3;
public class ModelSelectionV3 extends ModelBuilderSchema {
public static final class ModelSelectionParametersV3 extends ModelParametersSchemaV3 {
public static final String[] fields = new String[]{
"model_id",
"training_frame",
"validation_frame",
"nfolds",
"seed",
"fold_assignment",
"fold_column",
"response_column",
"ignored_columns",
"ignore_const_cols",
"score_each_iteration",
"score_iteration_interval",
"offset_column",
"weights_column",
"family",
"link",
"tweedie_variance_power",
"tweedie_link_power",
"theta", // equals to 1/r and should be > 0 and <=1, used by negative binomial
"solver",
"alpha",
"lambda",
"lambda_search",
"early_stopping",
"nlambdas",
"standardize",
"missing_values_handling",
"plug_values",
"compute_p_values",
"remove_collinear_columns",
"intercept",
"non_negative",
"max_iterations",
"objective_epsilon",
"beta_epsilon",
"gradient_epsilon",
"startval", // initial starting values for fixed and randomized coefficients, double array
"prior",
"cold_start", // if true, will start GLM model from initial values and conditions
"lambda_min_ratio",
"beta_constraints",
"max_active_predictors",
"obj_reg",
"stopping_rounds",
"stopping_metric",
"stopping_tolerance",
// dead unused args forced here by backwards compatibility, remove in V4
"balance_classes",
"class_sampling_factors",
"max_after_balance_size",
"max_confusion_matrix_size",
"max_runtime_secs",
"custom_metric_func",
"nparallelism",
"max_predictor_number", // denote maximum number of predictors to build models for
"min_predictor_number",
"mode", // naive, maxr, maxrsweep, backward
"build_glm_model",
"p_values_threshold",
"influence",
"multinode_mode"
};
@API(help = "Seed for pseudo random number generator (if applicable)", gridable = true)
public long seed;
// Input fields
@API(help = "Family. For maxr/maxrsweep, only gaussian. For backward, ordinal and multinomial families are not supported",
values = {"AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson",
"gamma", "tweedie", "negativebinomial"}, level = API.Level.critical)
// took tweedie out since it's not reliable
public GLMModel.GLMParameters.Family family;
@API(help = "Tweedie variance power", level = API.Level.critical, gridable = true)
public double tweedie_variance_power;
@API(help = "Tweedie link power", level = API.Level.critical, gridable = true)
public double tweedie_link_power;
@API(help = "Theta", level = API.Level.critical, gridable = true)
public double theta; // used by negtaive binomial distribution family
@API(help = "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.", values = {"AUTO", "IRLSM", "L_BFGS",
"COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"},
level = API.Level.critical)
public GLMModel.GLMParameters.Solver solver;
@API(help = "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.", level = API.Level.critical, gridable = true)
public double[] alpha;
@API(help = "Regularization strength", required = false, level = API.Level.critical, gridable = true)
public double[] lambda;
@API(help = "Use lambda search starting at lambda max, given lambda is then interpreted as lambda min",
level = API.Level.critical)
public boolean lambda_search;
@API(help = "For maxrsweep only. If enabled, will attempt to perform sweeping action using multiple nodes in " +
"the cluster. Defaults to false.",
level = API.Level.critical)
public boolean multinode_mode;
@API(help = "For maxrsweep mode only. If true, will return full blown GLM models with the desired predictor" +
"subsets. If false, only the predictor subsets, predictor coefficients are returned. This is for" +
"speeding up the model selection process. The users can choose to build the GLM models themselves" +
"by using the predictor subsets themselves. Defaults to false.",
level = API.Level.critical)
public boolean build_glm_model;
@API(help="Stop early when there is no more relative improvement on train or validation (if provided)")
public boolean early_stopping;
@API(help = "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.", level = API.Level.critical)
public int nlambdas;
@API(help = "Perform scoring for every score_iteration_interval iterations", level = API.Level.secondary)
public int score_iteration_interval;
@API(help = "Standardize numeric columns to have zero mean and unit variance", level = API.Level.critical)
public boolean standardize;
@API(help = "Only applicable to multiple alpha/lambda values. 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.", level = API.Level.critical)
public boolean cold_start;
@API(help = "Handling of missing values. Either MeanImputation, Skip or PlugValues.",
values = { "MeanImputation", "Skip", "PlugValues" }, level = API.Level.expert,
direction=API.Direction.INOUT, gridable = true)
public GLMModel.GLMParameters.MissingValuesHandling missing_values_handling;
@API(help = "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)",
direction = API.Direction.INPUT)
public KeyV3.FrameKeyV3 plug_values;
@API(help = "Restrict coefficients (not intercept) to be non-negative")
public boolean non_negative;
@API(help = "Maximum number of iterations", level = API.Level.secondary)
public int max_iterations;
@API(help = "Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM" +
" solver ", level = API.Level.expert)
public double beta_epsilon;
@API(help = "Converge if objective value changes less than this."+ " Default (of -1.0) 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.", level = API.Level.expert)
public double objective_epsilon;
@API(help = "Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS" +
" solver. Default (of -1.0) 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.",
level = API.Level.expert)
public double gradient_epsilon;
@API(help="Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs")
public double obj_reg;
@API(help = "Link function.", level = API.Level.secondary, values = {"family_default", "identity", "logit", "log",
"inverse", "tweedie", "ologit"}) //"oprobit", "ologlog": will be supported.
public GLMModel.GLMParameters.Link link;
@API(help = "double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.",
gridable=true)
public double[] startval;
@API(help = "if true, will return likelihood function value for HGLM.") // not gridable
public boolean calc_like;
@API(level = API.Level.critical, direction = API.Direction.INOUT,
valuesProvider = ModelSelectionModeProvider.class,
help = "Mode: Used to choose model selection algorithms to use. Options include "
+ "'allsubsets' for all subsets, "
+ "'maxr' that uses sequential replacement and GLM to build all models, slow but works with cross-validation, validation frames for more robust results, "
+ "'maxrsweep' that uses sequential replacement and sweeping action, much faster than 'maxr', "
+ "'backward' for backward selection."
)
public ModelSelectionModel.ModelSelectionParameters.Mode mode;
@API(help="Include constant term in the model", level = API.Level.expert)
public boolean intercept;
@API(help = "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.", level = API.Level.expert)
public double prior;
@API(help = "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.",
level = API.Level.expert)
public double lambda_min_ratio;
@API(help = "Beta constraints", direction = API.Direction.INPUT /* Not required, to allow initial params validation: , required=true */)
public KeyV3.FrameKeyV3 beta_constraints;
@API(help="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.",
direction = API.Direction.INPUT, level = API.Level.expert)
public int max_active_predictors = -1;
// dead unused args, formely inherited from supervised model schema
/**
* For imbalanced data, balance training data class counts via
* over/under-sampling. This can result in improved predictive accuracy.
*/
@API(help = "Balance training data class counts via over/under-sampling (for imbalanced data).",
level = API.Level.secondary, direction = API.Direction.INOUT)
public boolean balance_classes;
/**
* Desired over/under-sampling ratios per class (lexicographic order).
* Only when balance_classes is enabled.
* If not specified, they will be automatically computed to obtain class balance during training.
*/
@API(help = "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.", level = API.Level.expert, direction = API.Direction.INOUT)
public float[] class_sampling_factors;
/**
* When classes are balanced, limit the resulting dataset size to the
* specified multiple of the original dataset size.
*/
@API(help = "Maximum relative size of the training data after balancing class counts (can be less than 1.0)." +
" Requires balance_classes.", /* dmin=1e-3, */ level = API.Level.expert,
direction = API.Direction.INOUT)
public float max_after_balance_size;
/** For classification models, the maximum size (in terms of classes) of
* the confusion matrix for it to be printed. This option is meant to
* avoid printing extremely large confusion matrices. */
@API(help = "[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs",
level = API.Level.secondary, direction = API.Direction.INOUT)
public int max_confusion_matrix_size;
@API(help="Request p-values computation, p-values work only with IRLSM solver and no regularization",
level = API.Level.secondary, direction = API.Direction.INPUT)
public boolean compute_p_values; // _remove_collinear_columns
@API(help="In case of linearly dependent columns, remove some of the dependent columns",
level = API.Level.secondary, direction = API.Direction.INPUT)
public boolean remove_collinear_columns; // _remove_collinear_columns
@API(help = "Maximum number of predictors to be considered when building GLM models. Defaults to 1.",
level = API.Level.secondary, direction = API.Direction.INPUT)
public int max_predictor_number;
@API(help = "For mode = 'backward' only. Minimum number of predictors to be considered when building GLM " +
"models starting with all predictors to be included. Defaults to 1.",
level = API.Level.secondary, direction = API.Direction.INPUT)
public int min_predictor_number;
@API(help = "number of models to build in parallel. Defaults to 0.0 which is adaptive to the system capability",
level = API.Level.secondary, gridable = true)
public int nparallelism;
@API(help = "For mode='backward' only. If specified, will stop the model building process when all coefficients" +
"p-values drop below this threshold ", level = API.Level.expert)
public double p_values_threshold;
@API(help = "If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in " +
"the dataset.", values = { "dfbetas" }, level = API.Level.expert, gridable = false)
public GLMModel.GLMParameters.Influence influence;
}
public static final class ModelSelectionModeProvider extends EnumValuesProvider {
public ModelSelectionModeProvider() { super(ModelSelectionModel.ModelSelectionParameters.Mode.class); }
}
}
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