class MedicalNerApproach extends AnnotatorApproach[MedicalNerModel] with NerApproach[MedicalNerApproach] with Logging with ParamsAndFeaturesWritable with Licensed

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Licensed, ParamsAndFeaturesWritable, HasFeatures, Logging, NerApproach[MedicalNerApproach], AnnotatorApproach[MedicalNerModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[MedicalNerModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. MedicalNerApproach
  2. Licensed
  3. ParamsAndFeaturesWritable
  4. HasFeatures
  5. Logging
  6. NerApproach
  7. AnnotatorApproach
  8. CanBeLazy
  9. DefaultParamsWritable
  10. MLWritable
  11. HasOutputAnnotatorType
  12. HasOutputAnnotationCol
  13. HasInputAnnotationCols
  14. Estimator
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

Type Members

  1. 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 _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): MedicalNerModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val batchSize: IntParam

    Batch size

  12. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    MedicalNerApproach → AnnotatorApproach
  13. def calculateEmbeddingsDim(sentences: Seq[WordpieceEmbeddingsSentence]): Int
  14. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  15. final def clear(param: Param[_]): MedicalNerApproach.this.type
    Definition Classes
    Params
  16. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  17. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  18. final def copy(extra: ParamMap): Estimator[MedicalNerModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  19. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. val description: String

    Trains Tensorflow based Char-CNN-BLSTM model

    Trains Tensorflow based Char-CNN-BLSTM model

    Definition Classes
    MedicalNerApproach → AnnotatorApproach
  22. val dropout: FloatParam

    "Dropout coefficient

  23. val enableMemoryOptimizer: BooleanParam
  24. val enableOutputLogs: BooleanParam

    Whether to output to annotators log folder

  25. val entities: StringArrayParam
    Definition Classes
    NerApproach
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. val evaluationLogExtended: BooleanParam

    Whether logs for validation to be extended: it displays time and evaluation of each label.

    Whether logs for validation to be extended: it displays time and evaluation of each label. Default is false.

  29. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  30. def explainParams(): String
    Definition Classes
    Params
  31. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  32. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  33. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  34. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  35. final def fit(dataset: Dataset[_]): MedicalNerModel
    Definition Classes
    AnnotatorApproach → Estimator
  36. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[MedicalNerModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  37. def fit(dataset: Dataset[_], paramMap: ParamMap): MedicalNerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  38. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): MedicalNerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  39. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  41. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  42. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  43. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  44. def getBatchSize: Int

    Batch size

  45. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  46. def getConfigProtoBytes: Option[Array[Byte]]

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  47. def getDataSetParams(dsIt: Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]]): (Set[String], Set[Char], Int, Long)
  48. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  49. def getDropout: Float

    Dropout coefficient

  50. def getEnableMemoryOptimizer: Boolean

    Memory Optimizer

  51. def getEnableOutputLogs: Boolean

    Whether to output to annotators log folder

  52. def getIncludeConfidence: Boolean

    whether to include confidence scores in annotation metadata

  53. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  54. def getIteratorFunc(dataset: Dataset[Row]): () ⇒ Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]]
  55. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  56. def getLogName: String
    Definition Classes
    MedicalNerApproach → Logging
  57. def getLr: Float

    Learning Rate

  58. def getMaxEpochs: Int
    Definition Classes
    NerApproach
  59. def getMinEpochs: Int
    Definition Classes
    NerApproach
  60. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  61. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  62. def getOutputLogsPath: String
  63. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  64. def getPo: Float

    Learning rate decay coefficient.

    Learning rate decay coefficient. Real Learning Rage = lr / (1 + po * epoch)

  65. def getRandomSeed: Int
    Definition Classes
    NerApproach
  66. def getUseContrib: Boolean

    Whether to use contrib LSTM Cells.

    Whether to use contrib LSTM Cells. Not compatible with Windows. Might slightly improve accuracy.

  67. 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.

  68. def getVerbose: Int
    Definition Classes
    NerApproach
  69. val graphFolder: Param[String]

    Folder path that contain external graph files

  70. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  71. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  72. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  73. val includeConfidence: BooleanParam

    val includeConfidence = new BooleanParam(this, "includeConfidence", "Whether to include confidence scores in annotation metadata")

  74. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  75. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. val inputAnnotatorTypes: Array[String]

    Input annotator types : DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Input annotator types : DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Definition Classes
    MedicalNerApproach → HasInputAnnotationCols
  77. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  78. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  79. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  80. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  81. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  82. val labelColumn: Param[String]
    Definition Classes
    NerApproach
  83. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  84. def log(value: ⇒ String, minLevel: Level): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  86. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  93. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  94. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  95. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  96. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  97. val logger: Logger
    Attributes
    protected
    Definition Classes
    Logging
  98. val lr: FloatParam

    Learning Rate

  99. val maxEpochs: IntParam
    Definition Classes
    NerApproach
  100. val minEpochs: IntParam
    Definition Classes
    NerApproach
  101. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  102. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  103. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  104. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  105. def onTrained(model: MedicalNerModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  106. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  107. val outputAnnotatorType: String

    Input annotator types : NAMED_ENTITY

    Input annotator types : NAMED_ENTITY

    Definition Classes
    MedicalNerApproach → HasOutputAnnotatorType
  108. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  109. def outputLog(value: ⇒ String, uuid: String, shouldLog: Boolean, outputLogsPath: String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  110. val outputLogsPath: Param[String]
  111. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  112. val po: FloatParam

    Learning rate decay coefficient.

    Learning rate decay coefficient. Real Learning Rage = lr / (1 + po * epoch)

  113. val randomSeed: IntParam
    Definition Classes
    NerApproach
  114. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  115. def set[T](feature: StructFeature[T], value: T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  116. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  117. def set[T](feature: SetFeature[T], value: Set[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  118. def set[T](feature: ArrayFeature[T], value: Array[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  119. final def set(paramPair: ParamPair[_]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  120. final def set(param: String, value: Any): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  121. final def set[T](param: Param[T], value: T): MedicalNerApproach.this.type
    Definition Classes
    Params
  122. def setBatchSize(batch: Int): MedicalNerApproach.this.type

    Batch size

  123. def setConfigProtoBytes(bytes: Array[Int]): MedicalNerApproach.this.type

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  124. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  125. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  126. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  127. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  128. final def setDefault(paramPairs: ParamPair[_]*): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  129. final def setDefault[T](param: Param[T], value: T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  130. def setDropout(dropout: Float): MedicalNerApproach.this.type

    Dropout coefficient

  131. def setEnableMemoryOptimizer(value: Boolean): MedicalNerApproach.this.type
  132. def setEnableOutputLogs(enableOutputLogs: Boolean): MedicalNerApproach.this.type

    Whether to output to annotators log folder

  133. def setEntities(tags: Array[String]): MedicalNerApproach
    Definition Classes
    NerApproach
  134. def setEvaluationLogExtended(evaluationLogExtended: Boolean): MedicalNerApproach.this.type

    Whether logs for validation to be extended: it displays time and evaluation of each label.

    Whether logs for validation to be extended: it displays time and evaluation of each label. Default is false.

  135. def setGraphFolder(path: String): MedicalNerApproach.this.type

    Folder path that contain external graph files

  136. def setIncludeConfidence(value: Boolean): MedicalNerApproach.this.type

    Whether to include confidence scores in annotation metadata

  137. final def setInputCols(value: String*): MedicalNerApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  138. final def setInputCols(value: Array[String]): MedicalNerApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  139. def setLabelColumn(column: String): MedicalNerApproach
    Definition Classes
    NerApproach
  140. def setLazyAnnotator(value: Boolean): MedicalNerApproach.this.type
    Definition Classes
    CanBeLazy
  141. def setLr(lr: Float): MedicalNerApproach.this.type

    Learning Rate

  142. def setMaxEpochs(epochs: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  143. def setMinEpochs(epochs: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  144. final def setOutputCol(value: String): MedicalNerApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  145. def setOutputLogsPath(path: String): MedicalNerApproach.this.type
  146. def setPo(po: Float): MedicalNerApproach.this.type

    Learning rate decay coefficient.

    Learning rate decay coefficient. Real Learning Rage = lr / (1 + po * epoch)

  147. def setRandomSeed(seed: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  148. def setTestDataset(er: ExternalResource): MedicalNerApproach.this.type

    Path to test dataset.

    Path to test dataset. If set used to calculate statistic on it during training.

  149. def setTestDataset(path: String, readAs: Format = ReadAs.SPARK, options: Map[String, String] = Map("format" -> "parquet")): MedicalNerApproach.this.type

    Path to test dataset.

    Path to test dataset. If set used to calculate statistic on it during training.

  150. def setUseContrib(value: Boolean): MedicalNerApproach.this.type

    Whether to use contrib LSTM Cells.

    Whether to use contrib LSTM Cells. Not compatible with Windows. Might slightly improve accuracy.

  151. def setValidationSplit(validationSplit: Float): MedicalNerApproach.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.

  152. def setVerbose(verbose: Level): MedicalNerApproach
    Definition Classes
    NerApproach
  153. def setVerbose(verbose: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  154. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  155. val testDataset: ExternalResourceParam

    val testDataset = new ExternalResourceParam(this, "testDataset", "Path to test dataset.

    val testDataset = new ExternalResourceParam(this, "testDataset", "Path to test dataset. If set used to calculate statistic on it during training.")

  156. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  157. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): MedicalNerModel
    Definition Classes
    MedicalNerApproach → AnnotatorApproach
  158. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  159. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  160. val uid: String
    Definition Classes
    MedicalNerApproach → Identifiable
  161. val useContrib: BooleanParam

    whether to use contrib LSTM Cells.

    whether to use contrib LSTM Cells. Not compatible with Windows. Might slightly improve accuracy.

  162. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  163. val validationSplit: FloatParam

    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.

  164. val verbose: IntParam
    Definition Classes
    NerApproach
  165. val verboseLevel: Level
    Definition Classes
    MedicalNerApproach → Logging
  166. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  167. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  168. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  169. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from Licensed

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from Logging

Inherited from NerApproach[MedicalNerApproach]

Inherited from AnnotatorApproach[MedicalNerModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[MedicalNerModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

anno

getParam

param

setParam

Ungrouped