<commit_before>package hex.schemas;

import hex.deeplearning.DeepLearning;
import hex.deeplearning.DeepLearningModel.DeepLearningParameters;
import water.Key;
import water.api.API;
import water.api.ModelParametersSchema;
import water.fvec.Frame;
import water.util.PojoUtils;

import java.util.Random;

public class DeepLearningV2 extends ModelBuilderSchema<DeepLearning,DeepLearningV2,DeepLearningV2.DeepLearningParametersV2> {

  public static final class DeepLearningParametersV2 extends ModelParametersSchema<DeepLearningParameters, DeepLearningParametersV2> {
    public String[] fields() { return new String[]{
        "destination_key",
        "training_frame",
        "validation_frame",
        "classification",
        "response_column",
        "ignored_columns",
        "n_folds",
        "keep_cross_validation_splits",
        "checkpoint",
        "override_with_best_model",
        "autoencoder",
        "use_all_factor_levels",
        "activation",
        "hidden",
        "epochs",
        "train_samples_per_iteration",
        "seed",
        "adaptive_rate",
        "rho",
        "epsilon",
        "rate",
        "rate_annealing",
        "rate_decay",
        "momentum_start",
        "momentum_ramp",
        "momentum_stable",
        "nesterov_accelerated_gradient",
        "input_dropout_ratio",
        "hidden_dropout_ratios",
        "l1",
        "l2",
        "max_w2",
        "initial_weight_distribution",
        "initial_weight_scale",
        "loss",
        "score_interval",
        "score_training_samples",
        "score_validation_samples",
        "score_duty_cycle",
        "classification_stop",
        "regression_stop",
        "quiet_mode",
        "max_confusion_matrix_size",
        "max_hit_ratio_k",
        "balance_classes",
        "max_after_balance_size",
        "score_validation_sampling",
        "diagnostics",
        "variable_importances",
        "fast_mode",
        "ignore_const_cols",
        "force_load_balance",
        "replicate_training_data",
        "single_node_mode",
        "shuffle_training_data",
        "missing_values_handling",
        "sparse",
        "col_major",
        "average_activation",
        "sparsity_beta",
      };
    }

    // FIXME
    @API(help="Classification v. regression")
    public boolean classification;
    @API(help="List of ignored columns")
    public int[] ignored_cols;
    @API(help="Number of folds for n-fold cross-validation (0 to n)")
    public int n_folds;
    @API(help="Keep cross-validation Frames")
    public boolean keep_cross_validation_splits = false;

    /**
     * A model key associated with a previously trained Deep Learning
     * model. This option allows users to build a new model as a
     * continuation of a previously generated model (e.g., by a grid search).
     */
    @API(help = "Model checkpoint to resume training with")
    public Key checkpoint;

    /**
     * If enabled, store the best model under the destination key of this model at the end of training.
     * Only applicable if training is not cancelled.
     */
    @API(help = "If enabled, override the final model with the best model found during training")<commit_msg>Unbreak DeepLearning paramerers schema, part 2.<commit_after>package hex.schemas;

import hex.deeplearning.DeepLearning;
import hex.deeplearning.DeepLearningModel.DeepLearningParameters;
import water.Key;
import water.api.API;
import water.api.ModelParametersSchema;
import water.fvec.Frame;
import water.util.PojoUtils;

import java.util.Random;

public class DeepLearningV2 extends ModelBuilderSchema<DeepLearning,DeepLearningV2,DeepLearningV2.DeepLearningParametersV2> {

  public static final class DeepLearningParametersV2 extends ModelParametersSchema<DeepLearningParameters, DeepLearningParametersV2> {
    public String[] fields() { return new String[]{
        "destination_key",
        "training_frame",
        "validation_frame",
        "response_column",
        "ignored_columns",
        "classification",
        "n_folds",
        "keep_cross_validation_splits",
        "checkpoint",
        "override_with_best_model",
        "expert_mode",
        "autoencoder",
        "use_all_factor_levels",
        "activation",
        "hidden",
        "epochs",
        "train_samples_per_iteration",
        "target_ratio_comm_to_comp",
        "seed",
        "adaptive_rate",
        "rho",
        "epsilon",
        "rate",
        "rate_annealing",
        "rate_decay",
        "momentum_start",
        "momentum_ramp",
        "momentum_stable",
        "nesterov_accelerated_gradient",
        "input_dropout_ratio",
        "hidden_dropout_ratios",
        "l1",
        "l2",
        "max_w2",
        "initial_weight_distribution",
        "initial_weight_scale",
        "loss",
        "score_interval",
        "score_training_samples",
        "score_validation_samples",
        "score_duty_cycle",
        "classification_stop",
        "regression_stop",
        "quiet_mode",
        "max_confusion_matrix_size",
        "max_hit_ratio_k",
        "balance_classes",
        "class_sampling_factors",
        "max_after_balance_size",
        "score_validation_sampling",
        "diagnostics",
        "variable_importances",
        "fast_mode",
        "ignore_const_cols",
        "force_load_balance",
        "replicate_training_data",
        "single_node_mode",
        "shuffle_training_data",
        "missing_values_handling",
        "sparse",
        "col_major",
        "average_activation",
        "sparsity_beta",
      };
    }

    // FIXME
    @API(help="Classification v. regression")
    public boolean classification;
    @API(help="Number of folds for n-fold cross-validation (0 to n)")
    public int n_folds;
    @API(help="Keep cross-validation Frames")
    public boolean keep_cross_validation_splits = false;

    /**
     * A model key associated with a previously trained Deep Learning
     * model. This option allows users to build a new model as a
     * continuation of a previously generated model (e.g., by a grid search).
     */
    @API(help = "Model checkpoint to resume training with")
    public Key checkpoint;

    /**
     * If enabled, store the best model under the destination key of this model at the end of training.
     * Only applicable if training is not cancelled.
     */
    @API(help = "If enabled, override the final model with the best model found during training")
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