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package ai.catboost.spark.params;
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util.Identifiable
import ai.catboost.spark.params.macros.ParamGetterSetter
import ru.yandex.catboost.spark.catboost4j_spark.core.src.native_impl._ // enums
/** Params for training CatBoost. See documentation on [[https://catboost.ai/docs/]] for details. */
trait TrainingParamsTrait
extends QuantizationParamsTrait with HasLabelCol with HasFeaturesCol with HasWeightCol
{
@ParamGetterSetter
final val sparkPartitionCount: IntParam = new IntParam(
this,
"sparkPartitionCount",
"The number of partitions used during training. Corresponds to the number of active parallel tasks."+
" Set to the number of active executors by default"
)
@ParamGetterSetter
final val trainingDriverListeningPort: IntParam = new IntParam(
this,
"trainingDriverListeningPort",
"Port used for communication on the driver's side during training. Default is 0, that means automatic assignment"
+ ""
)
setDefault(trainingDriverListeningPort, 0)
@ParamGetterSetter
final val workerInitializationTimeout: DurationParam = new DurationParam(
this,
"workerInitializationTimeout",
"Timeout to wait until CatBoost workers on Spark executors are initalized and sent their info to master. "
+ "Depends on dataset size. Default is 10 minutes"
)
setDefault(workerInitializationTimeout, java.time.Duration.ofMinutes(10))
@ParamGetterSetter
final val workerMaxFailures: IntParam = new IntParam(
this,
"workerMaxFailures",
"Number of individual CatBoost workers failures before giving up training. "
+ "Should be greater than or equal to 1. Default is 4"
)
setDefault(workerMaxFailures, 4)
@ParamGetterSetter
final val workerListeningPort: IntParam = new IntParam(
this,
"workerListeningPort",
"Port used for communication on the workers' side during training. Default is 0, that means automatic assignment"
+ ""
)
setDefault(workerListeningPort, 0)
@ParamGetterSetter
final val connectTimeout: DurationParam = new DurationParam(
this,
"connectTimeout",
"Timeout to wait while establishing socket connections between TrainingDriver and workers."
+ "Default is 1 minute"
)
setDefault(connectTimeout, java.time.Duration.ofMinutes(1))
@ParamGetterSetter
final val lossFunction: Param[String] = new Param[String](
this,
"lossFunction",
"The metric to use in training. The specified value also determines the machine learning problem to "
+ "solve. Some metrics support optional parameters (see the Objectives and metrics documentation section "
+ "for details on each metric)."
)
@ParamGetterSetter
final val customMetric: StringArrayParam = new StringArrayParam(
this,
"customMetric",
"Metric values to output during training. These functions are not optimized and are displayed for "
+ " informational purposes only. Some metrics support optional parameters (see the Objectives and "
+ " metrics documentation section for details on each metric)."
)
@ParamGetterSetter
final val evalMetric: Param[String] = new Param[String](
this,
"evalMetric",
"The metric used for overfitting detection (if enabled) and best model selection (if enabled). Some "
+ "metrics support optional parameters (see the Objectives and metrics documentation section for details "
+ "on each metric)."
)
@ParamGetterSetter
final val iterations: IntParam = new IntParam(
this,
"iterations",
"The maximum number of trees that can be built when solving machine learning problems. When using other "
+ "parameters that limit the number of iterations, the final number of trees may be less than the number "
+ "specified in this parameter. "
+ "Default value is 1000."
)
@ParamGetterSetter
final val learningRate: FloatParam = new FloatParam(
this,
"learningRate",
"The learning rate. Used for reducing the gradient step. "
+ "The default value is defined automatically for Logloss, MultiClass & RMSE loss functions depending on "
+ " the number of iterations if none of 'leaf_estimation_iterations', leaf_estimation_method', "
+ "'l2_leaf_reg' is set. In this case, the selected learning rate is printed to stdout and saved in the "
+ "model. In other cases, the default value is 0.03."
)
@ParamGetterSetter
final val randomSeed: IntParam = new IntParam(
this,
"randomSeed",
"The random seed used for training. Default value is 0."
)
@ParamGetterSetter
final val l2LeafReg: FloatParam = new FloatParam(
this,
"l2LeafReg",
"Coefficient at the L2 regularization term of the cost function. Any positive value is allowed. "
+ "Default value is 3.0."
)
@ParamGetterSetter
final val bootstrapType: EnumParam[EBootstrapType] = new EnumParam[EBootstrapType](
this,
"bootstrapType",
"Bootstrap type. Defines the method for sampling the weights of objects."
+ "The default value depends on the selected mode and processing unit type: "
+ "QueryCrossEntropy, YetiRankPairwise, PairLogitPairwise: Bernoulli with the subsample parameter set to 0.5."
+ " MultiClass and MultiClassOneVsAll: Bayesian."
+ " Other modes: MVS with the subsample parameter set to 0.8."
)
@ParamGetterSetter
final val baggingTemperature: FloatParam = new FloatParam(
this,
"baggingTemperature",
"This parameter can be used if the selected bootstrap type is Bayesian. "
+ "Possible values are in the range [0, +inf). The higher the value the more aggressive the bagging is."
+ "Default value in 1.0."
)
@ParamGetterSetter
final val subsample: FloatParam = new FloatParam(
this,
"subsample",
"Sample rate for bagging. "
+ "The default value depends on the dataset size and the bootstrap type, see documentation for details."
)
@ParamGetterSetter
final val samplingFrequency: EnumParam[ESamplingFrequency] = new EnumParam[ESamplingFrequency](
this,
"samplingFrequency",
"Frequency to sample weights and objects when building trees. "
+ "Default value is 'PerTreeLevel'"
)
@ParamGetterSetter
final val samplingUnit: EnumParam[ESamplingUnit] = new EnumParam[ESamplingUnit](
this,
"samplingUnit",
"The sampling scheme, see documentation for details. "
+ "Default value is 'Object'"
)
@ParamGetterSetter
final val mvsReg: FloatParam = new FloatParam(
this,
"mvsReg",
"Affects the weight of the denominator and can be used for balancing between the importance and "
+ "Bernoulli sampling (setting it to 0 implies importance sampling and to +Inf - Bernoulli)."
+ "Note: This parameter is supported only for the MVS sampling method."
)
@ParamGetterSetter
final val randomStrength: FloatParam = new FloatParam(
this,
"randomStrength",
"The amount of randomness to use for scoring splits when the tree structure is selected. Use this "
+ "parameter to avoid overfitting the model. See documentation for details. "
+ "Default value is 1.0"
)
@ParamGetterSetter
final val useBestModel: BooleanParam = new BooleanParam(
this,
"useBestModel",
"If this parameter is set, the number of trees that are saved in the resulting model is selected based"
+ " on the optimal value of the evalMetric. This option requires a validation dataset to be provided."
)
@ParamGetterSetter
final val bestModelMinTrees: IntParam = new IntParam(
this,
"bestModelMinTrees",
"The minimal number of trees that the best model should have. If set, the output model contains at least"
+ " the given number of trees even if the optimal value of the evaluation metric on the validation"
+ " dataset is achieved with smaller number of trees."
+ " Should be used with the useBestModel parameter."
+ " No limit by default."
)
@ParamGetterSetter
final val depth: IntParam = new IntParam(
this,
"depth",
"Depth of the trees."
+ "Default value is 6."
)
@ParamGetterSetter
final val oneHotMaxSize: IntParam = new IntParam(
this,
"oneHotMaxSize",
"Use one-hot encoding for all categorical features with a number of different values less than or equal "
+ "to the given parameter value. Ctrs are not calculated for such features."
)
@ParamGetterSetter
final val hasTime: BooleanParam = new BooleanParam(
this,
"hasTime",
"Use the order of objects in the input data (do not perform random permutations during Choosing the tree "
+ "structure stage)."
)
@ParamGetterSetter
final val rsm: FloatParam = new FloatParam(
this,
"rsm",
"Random subspace method. The percentage of features to use at each split selection, when features are "
+ "selected over again at random. "
+ "The value must be in the range (0;1]. Default value is 1."
)
@ParamGetterSetter
final val foldPermutationBlock: IntParam = new IntParam(
this,
"foldPermutationBlock",
"Objects in the dataset are grouped in blocks before the random permutations. This parameter defines the "
+ "size of the blocks. The smaller is the value, the slower is the training. Large values may result in "
+ "quality degradation. "
+ "Default value is 1."
)
@ParamGetterSetter
final val leafEstimationMethod: EnumParam[ELeavesEstimation] = new EnumParam[ELeavesEstimation](
this,
"leafEstimationMethod",
"The method used to calculate the values in leaves. See documentation for details."
)
@ParamGetterSetter
final val leafEstimationIterations: IntParam = new IntParam(
this,
"leafEstimationIterations",
"CatBoost might calculate leaf values using several gradient or newton steps instead of a single one. "
+ "This parameter regulates how many steps are done in every tree when calculating leaf values."
)
@ParamGetterSetter
final val leafEstimationBacktracking: EnumParam[ELeavesEstimationStepBacktracking]
= new EnumParam[ELeavesEstimationStepBacktracking](
this,
"leafEstimationBacktracking",
"When the value of the leafEstimationIterations parameter is greater than 1, CatBoost makes several "
+ "gradient or newton steps when calculating the resulting leaf values of a tree. "
+ "The behaviour differs depending on the value of this parameter. See documentation for details. "
+ "Default value is 'AnyImprovement'"
)
@ParamGetterSetter
final val foldLenMultiplier: FloatParam = new FloatParam(
this,
"foldLenMultiplier",
"Coefficient for changing the length of folds. The value must be greater than 1. The best validation "
+ "result is achieved with minimum values. "
+ "Default value is 2.0."
)
@ParamGetterSetter
final val approxOnFullHistory: BooleanParam = new BooleanParam(
this,
"approxOnFullHistory",
"Use all the preceding rows in the fold for calculating the approximated values. This mode is slower and "
+ "in rare cases slightly more accurate."
)
@ParamGetterSetter
final val diffusionTemperature: FloatParam = new FloatParam(
this,
"diffusionTemperature",
"The diffusion temperature of the Stochastic Gradient Langevin Boosting mode. "
+ "Only non-negative values are supported. Default value is 10000."
)
@ParamGetterSetter
final val allowConstLabel: BooleanParam = new BooleanParam(
this,
"allowConstLabel",
"Use it to train models with datasets that have equal label values for all objects."
)
@ParamGetterSetter
final val scoreFunction: EnumParam[EScoreFunction] = new EnumParam[EScoreFunction](
this,
"scoreFunction",
"The score type used to select the next split during the tree construction. See documentation for details. "
+ "Default value is 'Cosine'"
)
@ParamGetterSetter
final val featureWeightsMap: OrderedStringMapParam[Double] = new OrderedStringMapParam[Double](
this,
"featureWeightsMap",
"Per-feature multiplication weights used when choosing the best split. Map is 'feature_name' -> weight. "
+ "The score of each candidate is multiplied by the weights of features from the current split."
+ "This parameter is mutually exclusive with featureWeightsList."
)
@ParamGetterSetter
final val featureWeightsList: DoubleArrayParam = new DoubleArrayParam(
this,
"featureWeightsList",
"Per-feature multiplication weights used when choosing the best split. Array indices correspond to "
+ "feature indices. The score of each candidate is multiplied by the weights of features from the current "
+ "split."
+ "This parameter is mutually exclusive with featureWeightsMap."
)
@ParamGetterSetter
final val firstFeatureUsePenaltiesMap: OrderedStringMapParam[Double] = new OrderedStringMapParam[Double](
this,
"firstFeatureUsePenaltiesMap",
"Per-feature penalties for the first occurrence of the feature in the model. The given value is "
+ "subtracted from the score if the current candidate is the first one to include the feature in the "
+ "model. Map is 'feature_name' -> penalty. See documentation for details. "
+ "This parameter is mutually exclusive with firstFeatureUsePenaltiesList."
)
@ParamGetterSetter
final val firstFeatureUsePenaltiesList: DoubleArrayParam = new DoubleArrayParam(
this,
"firstFeatureUsePenaltiesList",
"Per-feature penalties for the first occurrence of the feature in the model. The given value is "
+ "subtracted from the score if the current candidate is the first one to include the feature in the "
+ "model. Array indices correspond to feature indices. See documentation for details. "
+ "This parameter is mutually exclusive with firstFeatureUsePenaltiesMap."
)
@ParamGetterSetter
final val penaltiesCoefficient: FloatParam = new FloatParam(
this,
"penaltiesCoefficient",
"A single-value common coefficient to multiply all penalties. Non-negative values are supported. "
+ "Default value is 1.0."
)
@ParamGetterSetter
final val perObjectFeaturePenaltiesMap: OrderedStringMapParam[Double] = new OrderedStringMapParam[Double](
this,
"perObjectFeaturePenaltiesMap",
"Per-object penalties for the first use of the feature for the object. The given value is multiplied by "
+ "the number of objects that are divided by the current split and use the feature for the first time. "
+ "Map is 'feature_name' -> penalty. See documentation for details. "
+ "This parameter is mutually exclusive with perObjectFeaturePenaltiesList."
)
@ParamGetterSetter
final val perObjectFeaturePenaltiesList: DoubleArrayParam = new DoubleArrayParam(
this,
"perObjectFeaturePenaltiesList",
"Per-object penalties for the first use of the feature for the object. The given value is multiplied by "
+ "the number of objects that are divided by the current split and use the feature for the first time. "
+ "Array indices correspond to feature indices. See documentation for details. "
+ "This parameter is mutually exclusive with perObjectFeaturePenaltiesMap."
)
@ParamGetterSetter
final val modelShrinkRate: FloatParam = new FloatParam(
this,
"modelShrinkRate",
"The constant used to calculate the coefficient for multiplying the model on each iteration. "
+ "See documentation for details."
)
@ParamGetterSetter
final val modelShrinkMode: EnumParam[EModelShrinkMode] = new EnumParam[EModelShrinkMode](
this,
"modelShrinkMode",
"Determines how the actual model shrinkage coefficient is calculated at each iteration. See "
+ "documentation for details. "
+ "Default value is 'Constant'"
)
// Overfitting detection settings
@ParamGetterSetter
final val earlyStoppingRounds: IntParam = new IntParam(
this,
"earlyStoppingRounds",
"Sets the overfitting detector type to Iter and stops the training after the specified number of "
+ "iterations since the iteration with the optimal metric value."
)
@ParamGetterSetter
final val odType: EnumParam[EOverfittingDetectorType] = new EnumParam[EOverfittingDetectorType](
this,
"odType",
"The type of the overfitting detector to use. See documentation for details. "
+ "Default value is 'IncToDec'"
)
@ParamGetterSetter
final val odPval: FloatParam = new FloatParam(
this,
"odPval",
"The threshold for the IncToDec overfitting detector type. The training is stopped when the specified "
+ "value is reached. Requires that a validation dataset was input. See documentation for details."
+ "Turned off by default."
)
@ParamGetterSetter
final val odWait: IntParam = new IntParam(
this,
"odWait",
"The number of iterations to continue the training after the iteration with the optimal metric value. "
+ "See documentation for details. "
+ "Default value is 20."
)
// Output settings
@ParamGetterSetter
final val loggingLevel: EnumParam[ELoggingLevel] = new EnumParam[ELoggingLevel](
this,
"loggingLevel",
"The logging level to output to stdout. See documentation for details. "
+ "Default value is 'Verbose'"
)
@ParamGetterSetter
final val metricPeriod: IntParam = new IntParam(
this,
"metricPeriod",
"The frequency of iterations to calculate the values of objectives and metrics. The value should be a "
+ " positive integer. The usage of this parameter speeds up the training. "
+ "Default value is 1."
)
@ParamGetterSetter
final val trainDir: Param[String] = new Param[String](
this,
"trainDir",
"The directory for storing the files on Driver node generated during training. "
+ "Default value is 'catboost_info'"
)
@ParamGetterSetter
final val allowWritingFiles: BooleanParam = new BooleanParam(
this,
"allowWritingFiles",
"Allow to write analytical and snapshot files during training. "
+ "Enabled by default."
)
@ParamGetterSetter
final val saveSnapshot: BooleanParam = new BooleanParam(
this,
"saveSnapshot",
"Enable snapshotting for restoring the training progress after an interruption. If enabled, the default "
+ " period for making snapshots is 600 seconds. Use the snapshotInterval parameter to change this period."
)
@ParamGetterSetter
final val snapshotFile: Param[String] = new Param[String](
this,
"snapshotFile",
"The name of the file to save the training progress information in. This file is used for recovering "
+ "training after an interruption."
)
@ParamGetterSetter
final val snapshotInterval: DurationParam = new DurationParam(
this,
"snapshotInterval",
"The interval between saving snapshots. See documentation for details. "
+ "Default value is 600 seconds."
)
}
/** Params for training [[CatBoostClassifier]]. See documentation on [[https://catboost.ai/docs/]]
* for details.
*/
trait ClassifierTrainingParamsTrait extends TrainingParamsTrait {
@ParamGetterSetter
final val classWeightsMap: OrderedStringMapParam[Double] = new OrderedStringMapParam[Double](
this,
"classWeightsMap",
"Map from class name to weight. The values are used as multipliers for the object weights. "
+ " This parameter is mutually exclusive with classWeightsList."
)
@ParamGetterSetter
final val classWeightsList: DoubleArrayParam = new DoubleArrayParam(
this,
"classWeightsList",
"List of weights for each class. The values are used as multipliers for the object weights. "
+ " This parameter is mutually exclusive with classWeightsMap."
)
@ParamGetterSetter
final val classNames: StringArrayParam = new StringArrayParam(
this,
"classNames",
"Allows to redefine the default values (consecutive integers)."
)
@ParamGetterSetter
final val autoClassWeights: EnumParam[EAutoClassWeightsType] = new EnumParam[EAutoClassWeightsType](
this,
"autoClassWeights",
"Automatically calculate class weights based either on the total weight or the total number of objects in"
+ " each class. The values are used as multipliers for the object weights. "
+ "Default value is 'None'"
)
@ParamGetterSetter
final val scalePosWeight: FloatParam = new FloatParam(
this,
"scalePosWeight",
"The weight for class 1 in binary classification. The value is used as a multiplier for the weights of "
+ "objects from class 1. "
+ "Default value is 1 (both classes have equal weight)."
)
@ParamGetterSetter
final val classesCount: IntParam = new IntParam(
this,
"classesCount",
"The upper limit for the numeric class label. Defines the number of classes for multiclassification. "
+ "See documentation for details."
)
// Target quantization settings
@ParamGetterSetter
final val targetBorder: FloatParam = new FloatParam(
this,
"targetBorder",
"If set, defines the border for converting target values to 0 and 1 classes."
)
}
/** Params for training [[CatBoostRegressor]]. See documentation at [[https://catboost.ai/docs/]]
* for details.
*/
trait RegressorTrainingParamsTrait extends TrainingParamsTrait {
}
