package hex.schemas;
import hex.deeplearning.DeepLearningModel.DeepLearningParameters;
import hex.glm.GLM;
import hex.glm.GLMModel.GLMParameters;
import hex.glm.GLMModel.GLMParameters.Solver;
import water.api.API;
import water.api.API.Direction;
import water.api.API.Level;
import water.api.schemas3.KeyV3.FrameKeyV3;
import water.api.schemas3.ModelParametersSchemaV3;
/**
* Created by tomasnykodym on 8/29/14.
*/
public class GLMV3 extends ModelBuilderSchema<GLM,GLMV3,GLMV3.GLMParametersV3> {
public static final class GLMParametersV3 extends ModelParametersSchemaV3<GLMParameters, GLMParametersV3> {
static public String[] fields = new String[]{
"model_id",
"training_frame",
"validation_frame",
"nfolds",
"seed",
"keep_cross_validation_predictions",
"keep_cross_validation_fold_assignment",
"fold_assignment",
"fold_column",
"response_column",
"ignored_columns",
"ignore_const_cols",
"score_each_iteration",
"offset_column",
"weights_column",
"family",
"tweedie_variance_power",
"tweedie_link_power",
"solver",
"alpha",
"lambda",
"lambda_search",
"early_stopping",
"nlambdas",
"standardize",
"missing_values_handling",
"compute_p_values",
"remove_collinear_columns",
"intercept",
"non_negative",
"max_iterations",
"objective_epsilon",
"beta_epsilon",
"gradient_epsilon",
"link",
"prior",
"lambda_min_ratio",
"beta_constraints",
"max_active_predictors",
"interactions",
// 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_hit_ratio_k",
"max_runtime_secs"
};
@API(help = "Seed for pseudo random number generator (if applicable)", gridable = true)
public long seed;
// Input fields
@API(help = "Family. Use binomial for classification with logistic regression, others are for regression problems.", values = {"gaussian", "binomial","quasibinomial","multinomial", "poisson", "gamma", "tweedie"}, level = Level.critical)
// took tweedie out since it's not reliable
public GLMParameters.Family family;
@API(help = "Tweedie variance power", level = Level.critical, gridable = true)
public double tweedie_variance_power;
@API(help = "Tweedie link power", level = Level.critical, gridable = true)
public double tweedie_link_power;
@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. Coordinate descent is experimental (beta).", values = {"AUTO", "IRLSM", "L_BFGS","COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT"}, level = Level.critical)
public Solver solver;
@API(help = "distribution of regularization between L1 and L2. Default value of alpha is 0 when SOLVER = 'L-BFGS', 0.5 otherwise", level = Level.critical, gridable = true)
public double[] alpha;
@API(help = "regularization strength", required = false, level = 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 = Level.critical)
public boolean lambda_search;
@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 = Level.critical)
public int nlambdas;
@API(help = "Standardize numeric columns to have zero mean and unit variance", level = Level.critical)
public boolean standardize;
@API(help = "Handling of missing values. Either MeanImputation or Skip.", values = { "MeanImputation", "Skip" }, level = API.Level.expert, direction=API.Direction.INOUT, gridable = true)
public DeepLearningParameters.MissingValuesHandling missing_values_handling;
@API(help = "Restrict coefficients (not intercept) to be non-negative")
public boolean non_negative;
@API(help = "Maximum number of iterations", level = 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 = Level.expert)
public double beta_epsilon;
@API(help = "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.", level = 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 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 = Level.expert)
public double gradient_epsilon;
@API(help="likelihood divider in objective value computation, default is 1/nobs")
public double obj_reg;
@API(help = "", level = Level.secondary, values = {"family_default", "identity", "logit", "log", "inverse", "tweedie"})
public GLMParameters.Link link;
@API(help="include constant term in the model", level = Level.expert)
public boolean intercept;
// @API(help = "Tweedie variance power", level = Level.secondary)
// public double tweedie_variance_power;
//
// @API(help = "Tweedie link power", level = Level.secondary)
// public double tweedie_link_power;
@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 = Level.expert)
public double prior;
@API(help = "Min lambda used in lambda search, specified as a ratio of lambda_max." +
" 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 = 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 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 7000 otherwise it is set to 100000000.", direction = Direction.INPUT, level = Level.expert)
public int max_active_predictors = -1;
@API(help="A list of predictor column indices to interact. All pairwise combinations will be computed for the list.", direction=Direction.INPUT, level=Level.expert)
public String[] interactions;
// 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;
/**
* The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
*/
@API(help = "Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)", level = API.Level.secondary, direction=API.Direction.INOUT)
public int max_hit_ratio_k;
@API(help="request p-values computation, p-values work only with IRLSM solver and no regularization", level = Level.secondary, direction = 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 = Level.secondary, direction = Direction.INPUT)
public boolean remove_collinear_columns; // _remove_collinear_columns
/////////////////////
}
}