Packages

class AssertionDLApproach extends AnnotatorApproach[AssertionDLModel] with Logging with Licensed

Contains all the methods for training an AssertionDLModel. For pretrained models please use AssertionDLModel and see the Models Hub for available models.

Example

First, pipeline stages for pre-processing the dataset (containing columns for text and label) are defined.

val document = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")
val chunk = new Doc2Chunk()
  .setInputCols("document")
  .setOutputCol("chunk")
val token = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")
val embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols("document", "token")
  .setOutputCol("embeddings")

Define AssertionDLApproach with parameters and start training

val assertionStatus = new AssertionDLApproach()
  .setLabelCol("label")
  .setInputCols("document", "chunk", "embeddings")
  .setOutputCol("assertion")
  .setBatchSize(128)
  .setDropout(0.012f)
  .setLearningRate(0.015f)
  .setEpochs(1)
  .setStartCol("start")
  .setEndCol("end")
  .setMaxSentLen(250)

val trainingPipeline = new Pipeline().setStages(Array(
  document,
  chunk,
  token,
  embeddings,
  assertionStatus
))

val assertionModel = trainingPipeline.fit(data)
val assertionResults = assertionModel.transform(data).cache()
See also

AssertionDLModel for using pretrained models

AssertionLogRegModel for non deep learning based extraction

Linear Supertypes
Licensed, Logging, AnnotatorApproach[AssertionDLModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[AssertionDLModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. AssertionDLApproach
  2. Licensed
  3. Logging
  4. AnnotatorApproach
  5. CanBeLazy
  6. DefaultParamsWritable
  7. MLWritable
  8. HasOutputAnnotatorType
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

    uid

    a unique identifier for the instantiated AnnotatorModel

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. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): AssertionDLModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. val batchSize: IntParam

    Size for each batch in the optimization process (Default: 64)

  8. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  9. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  10. val chunkCol: Param[String]

    Column with extracted NER chunks

  11. final def clear(param: Param[_]): AssertionDLApproach.this.type
    Definition Classes
    Params
  12. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  13. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

  14. final def copy(extra: ParamMap): Estimator[AssertionDLModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  15. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  16. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  17. val description: String
    Definition Classes
    AssertionDLApproach → AnnotatorApproach
  18. val dropout: FloatParam

    Dropout at the output of each layer (Default: 0.05f)

  19. val enableOutputLogs: BooleanParam

    Whether to output to annotators log folder (Default: false)

  20. val endCol: Param[String]

    Column with token number for last target token

  21. val epochs: IntParam

    Number of epochs for the optimization process (Default: 5)

  22. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  23. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  24. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  25. def explainParams(): String
    Definition Classes
    Params
  26. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  27. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  28. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  29. final def fit(dataset: Dataset[_]): AssertionDLModel
    Definition Classes
    AnnotatorApproach → Estimator
  30. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[AssertionDLModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  31. def fit(dataset: Dataset[_], paramMap: ParamMap): AssertionDLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  32. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): AssertionDLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  33. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  34. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  35. def getConfigProtoBytes: Option[Array[Byte]]

    ConfigProto from tensorflow, serialized into byte array.

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

  36. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  37. def getEnableOutputLogs: Boolean

    Whether to output to annotators log folder

  38. def getIncludeConfidence: Boolean

    whether to include confidence scores in annotation metadata

  39. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  40. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  41. def getLogName: String
    Definition Classes
    Logging
  42. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  43. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  44. def getOutputLogsPath: String

    Folder path to save training logs

  45. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  46. val graphFolder: Param[String]

    Folder path that contain external graph files

  47. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  48. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  49. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  50. val includeConfidence: BooleanParam

    Whether to include confidence scores in annotation metadata

  51. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  52. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. val inputAnnotatorTypes: Array[String]

    Input Annotator Types: DOCUMENT, CHUNK, WORD_EMBEDDINGS

    Input Annotator Types: DOCUMENT, CHUNK, WORD_EMBEDDINGS

    Definition Classes
    AssertionDLApproach → HasInputAnnotationCols
  54. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  55. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  56. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  57. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  58. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  59. val labelCol: Param[String]

    Column with one label per document.

    Column with one label per document. Example of possible values: “present”, “absent”, “hypothetical”, “conditional”, “associated_with_other_person”, etc.

  60. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  61. val learningRate: FloatParam

    Learning rate for the optimization process (Default: 0.0012f)

  62. def log(value: ⇒ String, minLevel: Level): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  64. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  71. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. val logger: Logger
    Attributes
    protected
    Definition Classes
    Logging
  76. val maxSentLen: IntParam

    Max possible length of a sentence, must match graph model (Default: 250)

  77. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  78. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  79. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  80. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  81. def onTrained(model: AssertionDLModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  82. val outputAnnotatorType: AnnotatorType

    Output annotator type: ASSERTION

    Output annotator type: ASSERTION

    Definition Classes
    AssertionDLApproach → HasOutputAnnotatorType
  83. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  84. def outputLog(value: ⇒ String, uuid: String, shouldLog: Boolean, outputLogsPath: String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. val outputLogsPath: Param[String]

    Folder path to save training logs

  86. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  87. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  88. final def set(paramPair: ParamPair[_]): AssertionDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  89. final def set(param: String, value: Any): AssertionDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  90. final def set[T](param: Param[T], value: T): AssertionDLApproach.this.type
    Definition Classes
    Params
  91. def setBatchSize(size: Int): AssertionDLApproach.this.type

    Size for each batch in the optimization process

  92. def setChunkCol(c: String): AssertionDLApproach.this.type

    Column with extracted NER chunks

  93. def setConfigProtoBytes(bytes: Array[Int]): AssertionDLApproach.this.type

    ConfigProto from tensorflow, serialized into byte array.

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

  94. final def setDefault(paramPairs: ParamPair[_]*): AssertionDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  95. final def setDefault[T](param: Param[T], value: T): AssertionDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  96. def setDropout(factor: Float): AssertionDLApproach.this.type

    Dropout at the output of each layer

  97. def setEnableOutputLogs(v: Boolean): AssertionDLApproach.this.type

    Whether to output to annotators log folder

  98. def setEndCol(e: String): AssertionDLApproach.this.type

    Column with token number for last target token

  99. def setEpochs(number: Int): AssertionDLApproach.this.type

    Number of epochs for the optimization process

  100. def setGraphFolder(path: String): AssertionDLApproach.this.type

    Folder path that contain external graph files

  101. def setIncludeConfidence(value: Boolean): AssertionDLApproach.this.type

    Whether to include confidence scores in annotation metadata

  102. final def setInputCols(value: String*): AssertionDLApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  103. final def setInputCols(value: Array[String]): AssertionDLApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  104. def setLabelCol(label: String): AssertionDLApproach.this.type

    Column with one label per document

  105. def setLazyAnnotator(value: Boolean): AssertionDLApproach.this.type
    Definition Classes
    CanBeLazy
  106. def setLearningRate(rate: Float): AssertionDLApproach.this.type

    Learning rate for the optimization process

  107. def setMaxSentLen(len: Int): AssertionDLApproach.this.type

    Max possible length of a sentence, must match graph model

  108. final def setOutputCol(value: String): AssertionDLApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  109. def setOutputLogsPath(v: String): AssertionDLApproach.this.type

    Folder path to save training logs

  110. def setStartCol(s: String): AssertionDLApproach.this.type

    Column with token number for first target token

  111. def setValidationSplit(validationSplit: Float): AssertionDLApproach.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.

  112. def setVerbose(verbose: Level): AssertionDLApproach.this.type

    Level of verbosity during training

  113. val startCol: Param[String]

    Column with token number for first target token

  114. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  115. val testDataset: ExternalResourceParam

    Path to test dataset.

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

  116. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  117. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): AssertionDLModel

    Trains the dataset with recursive pipeline and uses methods trainWithChunk() and trainwithStartEnd() The choice of training happens based on the startCol value of the DL Approach

    Trains the dataset with recursive pipeline and uses methods trainWithChunk() and trainwithStartEnd() The choice of training happens based on the startCol value of the DL Approach

    dataset

    a collection of inputs to train

    recursivePipeline

    an instance of PipelineModel

    returns

    an instance of trained AssertionDLModel

    Definition Classes
    AssertionDLApproach → AnnotatorApproach
  118. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  119. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  120. val uid: String
    Definition Classes
    AssertionDLApproach → Identifiable
  121. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  122. 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.

  123. val verbose: IntParam

    Level of verbosity during training

  124. val verboseLevel: Level
    Definition Classes
    AssertionDLApproach → Logging
  125. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  126. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  127. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  128. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from Licensed

Inherited from Logging

Inherited from AnnotatorApproach[AssertionDLModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[AssertionDLModel]

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