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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 DeepLearningParametersV3 extends ModelParametersSchemaV3 {
/**
* 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;
/**
* Activation function.
*/
public DeepLearningActivation activation;
/**
* Hidden layer sizes (e.g. [100, 100]).
*/
public int[] hidden;
/**
* How many times the dataset should be iterated (streamed), can be fractional.
*/
public double epochs;
/**
* Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available
* data (e.g., replicated training data), -2: automatic.
*/
@SerializedName("train_samples_per_iteration")
public long trainSamplesPerIteration;
/**
* Target ratio of communication overhead to computation. Only for multi-node operation and
* train_samples_per_iteration = -2 (auto-tuning).
*/
@SerializedName("target_ratio_comm_to_comp")
public double targetRatioCommToComp;
/**
* Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded.
*/
public long seed;
/**
* Adaptive learning rate.
*/
@SerializedName("adaptive_rate")
public boolean adaptiveRate;
/**
* Adaptive learning rate time decay factor (similarity to prior updates).
*/
public double rho;
/**
* Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress).
*/
public double epsilon;
/**
* Learning rate (higher => less stable, lower => slower convergence).
*/
public double rate;
/**
* Learning rate annealing: rate / (1 + rate_annealing * samples).
*/
@SerializedName("rate_annealing")
public double rateAnnealing;
/**
* Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1).
*/
@SerializedName("rate_decay")
public double rateDecay;
/**
* Initial momentum at the beginning of training (try 0.5).
*/
@SerializedName("momentum_start")
public double momentumStart;
/**
* Number of training samples for which momentum increases.
*/
@SerializedName("momentum_ramp")
public double momentumRamp;
/**
* Final momentum after the ramp is over (try 0.99).
*/
@SerializedName("momentum_stable")
public double momentumStable;
/**
* Use Nesterov accelerated gradient (recommended).
*/
@SerializedName("nesterov_accelerated_gradient")
public boolean nesterovAcceleratedGradient;
/**
* Input layer dropout ratio (can improve generalization, try 0.1 or 0.2).
*/
@SerializedName("input_dropout_ratio")
public double inputDropoutRatio;
/**
* Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.
*/
@SerializedName("hidden_dropout_ratios")
public double[] hiddenDropoutRatios;
/**
* L1 regularization (can add stability and improve generalization, causes many weights to become 0).
*/
public double l1;
/**
* L2 regularization (can add stability and improve generalization, causes many weights to be small.
*/
public double l2;
/**
* Constraint for squared sum of incoming weights per unit (e.g. for Rectifier).
*/
@SerializedName("max_w2")
public float maxW2;
/**
* Initial weight distribution.
*/
@SerializedName("initial_weight_distribution")
public DeepLearningInitialWeightDistribution initialWeightDistribution;
/**
* Uniform: -value...value, Normal: stddev.
*/
@SerializedName("initial_weight_scale")
public double initialWeightScale;
/**
* A list of H2OFrame ids to initialize the weight matrices of this model with.
*/
@SerializedName("initial_weights")
public FrameKeyV3[] initialWeights;
/**
* A list of H2OFrame ids to initialize the bias vectors of this model with.
*/
@SerializedName("initial_biases")
public FrameKeyV3[] initialBiases;
/**
* Loss function.
*/
public DeepLearningLoss loss;
/**
* Shortest time interval (in seconds) between model scoring.
*/
@SerializedName("score_interval")
public double scoreInterval;
/**
* Number of training set samples for scoring (0 for all).
*/
@SerializedName("score_training_samples")
public long scoreTrainingSamples;
/**
* Number of validation set samples for scoring (0 for all).
*/
@SerializedName("score_validation_samples")
public long scoreValidationSamples;
/**
* Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring).
*/
@SerializedName("score_duty_cycle")
public double scoreDutyCycle;
/**
* Stopping criterion for classification error fraction on training data (-1 to disable).
*/
@SerializedName("classification_stop")
public double classificationStop;
/**
* Stopping criterion for regression error (MSE) on training data (-1 to disable).
*/
@SerializedName("regression_stop")
public double regressionStop;
/**
* Enable quiet mode for less output to standard output.
*/
@SerializedName("quiet_mode")
public boolean quietMode;
/**
* Method used to sample validation dataset for scoring.
*/
@SerializedName("score_validation_sampling")
public DeepLearningClassSamplingMethod scoreValidationSampling;
/**
* If enabled, override the final model with the best model found during training.
*/
@SerializedName("overwrite_with_best_model")
public boolean overwriteWithBestModel;
/**
* Auto-Encoder.
*/
public boolean autoencoder;
/**
* Use all factor levels of categorical variables. Otherwise, the first factor level is omitted (without loss of
* accuracy). Useful for variable importances and auto-enabled for autoencoder.
*/
@SerializedName("use_all_factor_levels")
public boolean useAllFactorLevels;
/**
* If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data.
*/
public boolean standardize;
/**
* Enable diagnostics for hidden layers.
*/
public boolean diagnostics;
/**
* Compute variable importances for input features (Gedeon method) - can be slow for large networks.
*/
@SerializedName("variable_importances")
public boolean variableImportances;
/**
* Enable fast mode (minor approximation in back-propagation).
*/
@SerializedName("fast_mode")
public boolean fastMode;
/**
* Force extra load balancing to increase training speed for small datasets (to keep all cores busy).
*/
@SerializedName("force_load_balance")
public boolean forceLoadBalance;
/**
* Replicate the entire training dataset onto every node for faster training on small datasets.
*/
@SerializedName("replicate_training_data")
public boolean replicateTrainingData;
/**
* Run on a single node for fine-tuning of model parameters.
*/
@SerializedName("single_node_mode")
public boolean singleNodeMode;
/**
* Enable shuffling of training data (recommended if training data is replicated and train_samples_per_iteration is
* close to #nodes x #rows, of if using balance_classes).
*/
@SerializedName("shuffle_training_data")
public boolean shuffleTrainingData;
/**
* Handling of missing values. Either MeanImputation or Skip.
*/
@SerializedName("missing_values_handling")
public DeepLearningMissingValuesHandling missingValuesHandling;
/**
* Sparse data handling (more efficient for data with lots of 0 values).
*/
public boolean sparse;
/**
* #DEPRECATED Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow
* down backpropagation.
*/
@SerializedName("col_major")
public boolean colMajor;
/**
* Average activation for sparse auto-encoder. #Experimental
*/
@SerializedName("average_activation")
public double averageActivation;
/**
* Sparsity regularization. #Experimental
*/
@SerializedName("sparsity_beta")
public double sparsityBeta;
/**
* Max. number of categorical features, enforced via hashing. #Experimental
*/
@SerializedName("max_categorical_features")
public int maxCategoricalFeatures;
/**
* Force reproducibility on small data (will be slow - only uses 1 thread).
*/
public boolean reproducible;
/**
* Whether to export Neural Network weights and biases to H2O Frames.
*/
@SerializedName("export_weights_and_biases")
public boolean exportWeightsAndBiases;
/**
* Mini-batch size (smaller leads to better fit, larger can speed up and generalize better).
*/
@SerializedName("mini_batch_size")
public int miniBatchSize;
/**
* Elastic averaging between compute nodes can improve distributed model convergence. #Experimental
*/
@SerializedName("elastic_averaging")
public boolean elasticAveraging;
/**
* Elastic averaging moving rate (only if elastic averaging is enabled).
*/
@SerializedName("elastic_averaging_moving_rate")
public double elasticAveragingMovingRate;
/**
* Elastic averaging regularization strength (only if elastic averaging is enabled).
*/
@SerializedName("elastic_averaging_regularization")
public double elasticAveragingRegularization;
/**
* Pretrained autoencoder model to initialize this model with.
*/
@SerializedName("pretrained_autoencoder")
public ModelKeyV3 pretrainedAutoencoder;
/*------------------------------------------------------------------------------------------------------------------
// 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 DeepLearningParametersV3() {
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
activation = DeepLearningActivation.Rectifier;
hidden = new int[]{200, 200};
epochs = 10.0;
trainSamplesPerIteration = -2L;
targetRatioCommToComp = 0.05;
seed = -1L;
adaptiveRate = true;
rho = 0.99;
epsilon = 1e-08;
rate = 0.005;
rateAnnealing = 1e-06;
rateDecay = 1.0;
momentumStart = 0.0;
momentumRamp = 1000000.0;
momentumStable = 0.0;
nesterovAcceleratedGradient = true;
inputDropoutRatio = 0.0;
l1 = 0.0;
l2 = 0.0;
maxW2 = 3.4028235e+38f;
initialWeightDistribution = DeepLearningInitialWeightDistribution.UniformAdaptive;
initialWeightScale = 1.0;
loss = DeepLearningLoss.Automatic;
scoreInterval = 5.0;
scoreTrainingSamples = 10000L;
scoreValidationSamples = 0L;
scoreDutyCycle = 0.1;
classificationStop = 0.0;
regressionStop = 1e-06;
quietMode = false;
scoreValidationSampling = DeepLearningClassSamplingMethod.Uniform;
overwriteWithBestModel = true;
autoencoder = false;
useAllFactorLevels = true;
standardize = true;
diagnostics = true;
variableImportances = true;
fastMode = true;
forceLoadBalance = true;
replicateTrainingData = true;
singleNodeMode = false;
shuffleTrainingData = false;
missingValuesHandling = DeepLearningMissingValuesHandling.MeanImputation;
sparse = false;
colMajor = false;
averageActivation = 0.0;
sparsityBeta = 0.0;
maxCategoricalFeatures = 2147483647;
reproducible = false;
exportWeightsAndBiases = false;
miniBatchSize = 1;
elasticAveraging = false;
elasticAveragingMovingRate = 0.9;
elasticAveragingRegularization = 0.001;
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 = 5;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.0;
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);
}
}
