class GenericClassifierModel extends AnnotatorModel[GenericClassifierModel] with GenericClassifierParams with HasStorageRef with ParamsAndFeaturesWritable with WriteTensorflowModel with HasSimpleAnnotate[GenericClassifierModel] with HandleExceptionParams with HasSafeAnnotate[GenericClassifierModel] with CheckLicense

Creates a generic single-label classifier which uses pre-generated Tensorflow graphs. The model operates on FEATURE_VECTOR annotations which can be produced using FeatureAssembler. Requires the FeaturesAssembler to create the input.

See also

GenericClassifierApproach for an example and on how to define your own model

Linear Supertypes
CheckLicense, HasSafeAnnotate[GenericClassifierModel], HandleExceptionParams, HasSimpleAnnotate[GenericClassifierModel], WriteTensorflowModel, HasStorageRef, GenericClassifierParams, AnnotatorModel[GenericClassifierModel], CanBeLazy, RawAnnotator[GenericClassifierModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[GenericClassifierModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
FinanceFewShotClassifierModel, LegalFewShotClassifierModel, FewShotAssertionClassifierModel, FewShotClassifierModel, GenericLogRegClassifierModel, GenericSVMClassifierModel, GenericREModel, PosologyREModel, RelationExtractionModel
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Inherited
  1. GenericClassifierModel
  2. CheckLicense
  3. HasSafeAnnotate
  4. HandleExceptionParams
  5. HasSimpleAnnotate
  6. WriteTensorflowModel
  7. HasStorageRef
  8. GenericClassifierParams
  9. AnnotatorModel
  10. CanBeLazy
  11. RawAnnotator
  12. HasOutputAnnotationCol
  13. HasInputAnnotationCols
  14. HasOutputAnnotatorType
  15. ParamsAndFeaturesWritable
  16. HasFeatures
  17. DefaultParamsWritable
  18. MLWritable
  19. Model
  20. Transformer
  21. PipelineStage
  22. Logging
  23. Params
  24. Serializable
  25. Serializable
  26. Identifiable
  27. AnyRef
  28. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new GenericClassifierModel()
  2. new GenericClassifierModel(uid: String)

    uid

    a unique identifier for the instantiated AnnotatorModel

Type Members

  1. type AnnotationContent = Seq[Row]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

    takes a document and annotations and produces new annotations of this annotator's annotation type

    takes a document and annotations and produces new annotations of this annotator's annotation type

    annotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    GenericClassifierModel → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. val batchSize: IntParam

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  14. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    GenericClassifierModel → AnnotatorModel
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String], metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  17. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  18. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean, metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  19. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean, metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  20. val classes: StringArrayParam
  21. final def clear(param: Param[_]): GenericClassifierModel.this.type
    Definition Classes
    Params
  22. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  23. def copy(extra: ParamMap): GenericClassifierModel
    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  24. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  26. val datasetInfo: Param[String]

    Descriptive information about the dataset being used.

    Descriptive information about the dataset being used.

    Definition Classes
    GenericClassifierParams
  27. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  28. def dfAnnotate: UserDefinedFunction
    Definition Classes
    HasSimpleAnnotate
  29. val doExceptionHandling: BooleanParam

    If true, exceptions are handled.

    If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

    Definition Classes
    HandleExceptionParams
  30. val dropout: FloatParam

    Dropout coefficient.

    Dropout coefficient.

    It is applied to the output of the last hidden layer of the graph. Graphs without hidden layer (GenericLogRegClassifier, FewShotClassifier, GenericSVMClassifier) apply it directly to the input feature vector, so a value greater than 0 drops input features there: set it to 0 for those annotators unless input dropout is what you want.

    Definition Classes
    GenericClassifierParams
  31. val epochsN: IntParam

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  32. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  34. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  35. def explainParams(): String
    Definition Classes
    Params
  36. final val extraInputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  37. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  38. def extraValidateMsg: String
    Attributes
    protected
    Definition Classes
    RawAnnotator
  39. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  40. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  41. val featureScaling: Param[String]

    Feature scaling method.

    Feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

    Definition Classes
    GenericClassifierParams
  42. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  43. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  44. val fixImbalance: BooleanParam

    Fix the imbalance in the training set by replicating examples of under represented categories.

    Fix the imbalance in the training set by replicating examples of under represented categories.

    The class weights are only used by the loss of graphs with a softmax output (the GenericClassifier/RelationExtraction family). Graphs with a sigmoid output (logistic regression, SVM) compute their loss without them, so this parameter has no effect there.

    Definition Classes
    GenericClassifierParams
  45. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  46. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  47. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  48. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  49. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  50. def getBatchSize: Int

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  51. def getCategories(): Array[String]
  52. def getCategoryName(id: Int): String
  53. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  54. def getDatasetInfo: String

    get descriptive information about the dataset being used

    get descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  55. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  56. def getDropout: Float

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  57. def getEncoder: GenericClassifierDataEncoder
  58. def getFeatureScaling: String

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierParams
  59. def getFixImbalance: Boolean

    Fix imbalance in training set

    Fix imbalance in training set

    Definition Classes
    GenericClassifierParams
  60. def getGraphFolder: String

    Folder with the TensorFlow graphs to choose the model graph from

    Folder with the TensorFlow graphs to choose the model graph from

    Definition Classes
    GenericClassifierParams
  61. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  62. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  63. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  64. def getLearningRate: Float

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  65. def getMaxEpochs: Int

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  66. def getModelFile: String

    Path of the TensorFlow graph to train with, empty when it is resolved automatically

    Path of the TensorFlow graph to train with, empty when it is resolved automatically

    Definition Classes
    GenericClassifierParams
  67. def getMultiClass: Boolean

    Gets the model multi class prediction mode

    Gets the model multi class prediction mode

    Definition Classes
    GenericClassifierParams
  68. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  69. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  70. def getOutputLogsPath: String

    Get output logs path

    Get output logs path

    Definition Classes
    GenericClassifierParams
  71. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  72. def getStorageRef: String
    Definition Classes
    HasStorageRef
  73. def getValidationSplit: Float

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

    Definition Classes
    GenericClassifierParams
  74. val graphFolder: Param[String]

    Folder with TensorFlow graphs to choose the model graph from.

    Folder with TensorFlow graphs to choose the model graph from.

    When it is not set, the graphs embedded in the library are used. It is ignored when modelFile is set explicitly.

    Definition Classes
    GenericClassifierParams
  75. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  76. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  77. def hasParent: Boolean
    Definition Classes
    Model
  78. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  79. val inExceptionMode: Boolean
    Attributes
    protected
    Definition Classes
    HasSafeAnnotate
  80. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  81. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. val inputAnnotatorTypes: Array[AnnotatorType]

    Output annotator type : FEATURE_VECTOR

    Output annotator type : FEATURE_VECTOR

    Definition Classes
    GenericClassifierModel → HasInputAnnotationCols
  83. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  84. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  85. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  86. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  87. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  88. val labelColumn: Param[String]

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  89. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  90. val learningRate: FloatParam

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  91. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  92. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  93. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  94. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  95. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  96. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  97. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  98. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  99. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  100. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  101. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  102. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  103. def model: TensorflowGenericClassifier
  104. val modelFile: Param[String]

    Path of the TensorFlow graph to train with.

    Path of the TensorFlow graph to train with.

    Empty by default: a graph with suitable dimensions is then selected automatically, either from graphFolder or from the graphs embedded in the library. Set it to train with one specific graph.

    Definition Classes
    GenericClassifierParams
  105. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  106. val multiClass: BooleanParam

    If multiClass is set, the model will return all the labels with corresponding scores.

    If multiClass is set, the model will return all the labels with corresponding scores. By default, multiClass is false.

    Definition Classes
    GenericClassifierParams
  107. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  108. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  109. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  110. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    GenericClassifierModel → ParamsAndFeaturesWritable
  111. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  112. val outputAnnotatorType: String

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    GenericClassifierModel → HasOutputAnnotatorType
  113. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  114. val outputLogsPath: Param[String]

    Folder path to save training logs.

    Folder path to save training logs. If no path is specified, the logs won't be stored in disk. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

    Definition Classes
    GenericClassifierParams
  115. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  116. var parent: Estimator[GenericClassifierModel]
    Definition Classes
    Model
  117. def safeAnnotate(annotations: Seq[Annotation]): Seq[Annotation]

    A protected method designed to safely annotate a sequence of Annotation objects by handling exceptions.

    A protected method designed to safely annotate a sequence of Annotation objects by handling exceptions.

    annotations

    A sequence of Annotation.

    returns

    A sequence of Annotation objects after processing, potentially containing error annotations.

    Attributes
    protected
    Definition Classes
    HasSafeAnnotate
  118. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  119. def scaleFeatures(features: Array[Array[Float]]): Array[Array[Float]]
    Attributes
    protected
  120. def set[T](feature: StructFeature[T], value: T): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  121. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  122. def set[T](feature: SetFeature[T], value: Set[T]): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  123. def set[T](feature: ArrayFeature[T], value: Array[T]): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  124. final def set(paramPair: ParamPair[_]): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  125. final def set(param: String, value: Any): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  126. final def set[T](param: Param[T], value: T): GenericClassifierModel.this.type
    Definition Classes
    Params
  127. def setBatchSize(batch: Int): GenericClassifierModel.this.type

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  128. def setCategoryNames(categoryNames: Array[String]): GenericClassifierModel.this.type
  129. def setDatasetInfo(value: String): GenericClassifierModel.this.type

    set descriptive information about the dataset being used

    set descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  130. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  131. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  132. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  133. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  134. final def setDefault(paramPairs: ParamPair[_]*): GenericClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  135. final def setDefault[T](param: Param[T], value: T): GenericClassifierModel.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  136. def setDoExceptionHandling(value: Boolean): GenericClassifierModel.this.type

    If true, exceptions are handled.

    If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

    Definition Classes
    HandleExceptionParams
  137. def setDropout(dropout: Float): GenericClassifierModel.this.type

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  138. def setEncoder(encoder: GenericClassifierDataEncoder): GenericClassifierModel.this.type
  139. def setEpochsNumber(epochs: Int): GenericClassifierModel.this.type

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  140. def setExtraInputCols(value: Array[String]): GenericClassifierModel.this.type
    Definition Classes
    HasInputAnnotationCols
  141. def setFeatureScaling(featureScaling: String): GenericClassifierModel.this.type

    Set the feature scaling method.

    Set the feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

    Definition Classes
    GenericClassifierParams
  142. def setFixImbalance(fix: Boolean): GenericClassifierModel.this.type

    Fix imbalance of training set

    Fix imbalance of training set

    Definition Classes
    GenericClassifierParams
  143. def setGraphFolder(path: String): GenericClassifierModel.this.type

    Set the folder with the TensorFlow graphs to choose the model graph from.

    Set the folder with the TensorFlow graphs to choose the model graph from. Ignored when modelFile is set.

    Definition Classes
    GenericClassifierParams
  144. final def setInputCols(value: String*): GenericClassifierModel.this.type
    Definition Classes
    HasInputAnnotationCols
  145. def setInputCols(value: Array[String]): GenericClassifierModel.this.type
    Definition Classes
    HasInputAnnotationCols
  146. def setLabelColumn(column: String): GenericClassifierModel.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  147. def setLazyAnnotator(value: Boolean): GenericClassifierModel.this.type
    Definition Classes
    CanBeLazy
  148. def setModelFile(modelFile: String): GenericClassifierModel.this.type

    Set the model file name

    Set the model file name

    Definition Classes
    GenericClassifierParams
  149. def setMultiClass(value: Boolean): GenericClassifierModel.this.type

    Sets the model in multi class prediction mode

    Sets the model in multi class prediction mode

    Definition Classes
    GenericClassifierParams
  150. final def setOutputCol(value: String): GenericClassifierModel.this.type
    Definition Classes
    HasOutputAnnotationCol
  151. def setOutputLogsPath(outputLogsPath: String): GenericClassifierModel.this.type

    Set the output log path

    Set the output log path

    Definition Classes
    GenericClassifierParams
  152. def setParent(parent: Estimator[GenericClassifierModel]): GenericClassifierModel
    Definition Classes
    Model
  153. def setStorageRef(value: String): GenericClassifierModel.this.type
    Definition Classes
    HasStorageRef
  154. def setTensorflowModel(spark: SparkSession, tf: TensorflowWrapper): GenericClassifierModel.this.type
  155. def setValidationSplit(validationSplit: Float): GenericClassifierModel.this.type

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

    Definition Classes
    GenericClassifierParams
  156. def setlearningRate(lr: Float): GenericClassifierModel.this.type

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  157. val storageRef: Param[String]
    Definition Classes
    HasStorageRef
  158. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  159. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  160. def trainedLabelIds: Option[Array[Int]]

    Output units of the graph which correspond to a class learned during training.

    Output units of the graph which correspond to a class learned during training. It is None for models without category names, in which case every output unit is considered valid.

    Attributes
    protected
  161. final def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    AnnotatorModel → Transformer
  162. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  163. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  164. final def transformSchema(schema: StructType): StructType
    Definition Classes
    RawAnnotator → PipelineStage
  165. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  166. val uid: String
    Definition Classes
    GenericClassifierModel → Identifiable
  167. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  168. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  169. val validationSplit: FloatParam

    The proportion of training dataset to be used as validation set.

    The proportion of training dataset to be used as validation set.

    The model will be validated against this dataset on each Epoch and will not be used for training. The value should be between 0.0 and 1.0.

    Definition Classes
    GenericClassifierParams
  170. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  171. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  172. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  173. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  174. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  175. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
    Definition Classes
    WriteTensorflowModel
  176. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
    Definition Classes
    WriteTensorflowModel
  177. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]], savedSignatures: Option[Map[String, String]]): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from CheckLicense

Inherited from HasSafeAnnotate[GenericClassifierModel]

Inherited from HandleExceptionParams

Inherited from HasSimpleAnnotate[GenericClassifierModel]

Inherited from WriteTensorflowModel

Inherited from HasStorageRef

Inherited from GenericClassifierParams

Inherited from AnnotatorModel[GenericClassifierModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[GenericClassifierModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[GenericClassifierModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters