Class/Object

com.johnsnowlabs.nlp.annotators.classifier.dl

ClassifierDLApproach

Related Docs: object ClassifierDLApproach | package dl

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class ClassifierDLApproach extends AnnotatorApproach[ClassifierDLModel] with ParamsAndFeaturesWritable

Trains a ClassifierDL for generic Multi-class Text Classification.

ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 100 classes.

For instantiated/pretrained models, see ClassifierDLModel.

Notes:

For extended examples of usage, see the Spark NLP Workshop [1] [2] and the ClassifierDLTestSpec.

Example

In this example, the training data "sentiment.csv" has the form of

text,label
This movie is the best movie I have wached ever! In my opinion this movie can win an award.,0
This was a terrible movie! The acting was bad really bad!,1
...

Then traning can be done like so:

import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
import com.johnsnowlabs.nlp.annotators.classifier.dl.ClassifierDLApproach
import org.apache.spark.ml.Pipeline

val smallCorpus = spark.read.option("header","true").csv("src/test/resources/classifier/sentiment.csv")

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val useEmbeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("sentence_embeddings")

val docClassifier = new ClassifierDLApproach()
  .setInputCols("sentence_embeddings")
  .setOutputCol("category")
  .setLabelColumn("label")
  .setBatchSize(64)
  .setMaxEpochs(20)
  .setLr(5e-3f)
  .setDropout(0.5f)

val pipeline = new Pipeline()
  .setStages(
    Array(
      documentAssembler,
      useEmbeddings,
      docClassifier
    )
  )

val pipelineModel = pipeline.fit(smallCorpus)
See also

SentimentDLApproach for sentiment analysis

MultiClassifierDLApproach for multi-class classification

Linear Supertypes
ParamsAndFeaturesWritable, HasFeatures, AnnotatorApproach[ClassifierDLModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[ClassifierDLModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ClassifierDLApproach
  2. ParamsAndFeaturesWritable
  3. HasFeatures
  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 ClassifierDLApproach()

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  2. new ClassifierDLApproach(uid: String)

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Type Members

  1. type AnnotatorType = String

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    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params

  4. def $$[T](feature: StructFeature[T]): T

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    Attributes
    protected
    Definition Classes
    HasFeatures

  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]

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    Attributes
    protected
    Definition Classes
    HasFeatures

  6. def $$[T](feature: SetFeature[T]): Set[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures

  7. def $$[T](feature: ArrayFeature[T]): Array[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures

  8. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  9. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): ClassifierDLModel

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    Attributes
    protected
    Definition Classes
    AnnotatorApproach

  10. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  11. val batchSize: IntParam

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    Batch size (Default: 64)

  12. def beforeTraining(spark: SparkSession): Unit

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    Definition Classes
    ClassifierDLApproach → AnnotatorApproach

  13. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols

  14. final def clear(param: Param[_]): ClassifierDLApproach.this.type

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    Definition Classes
    Params

  15. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  16. val configProtoBytes: IntArrayParam

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    ConfigProto from tensorflow, serialized into byte array.

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

  17. final def copy(extra: ParamMap): Estimator[ClassifierDLModel]

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    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params

  18. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  19. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  20. val description: String

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    Trains TensorFlow model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    ClassifierDLApproach → AnnotatorApproach

  21. val dropout: FloatParam

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    Dropout coefficient (Default: 0.5f)

  22. val enableOutputLogs: BooleanParam

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

  23. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  24. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  25. def explainParam(param: Param[_]): String

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    Definition Classes
    Params

  26. def explainParams(): String

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    Definition Classes
    Params

  27. final def extractParamMap(): ParamMap

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    Definition Classes
    Params

  28. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params

  29. val features: ArrayBuffer[Feature[_, _, _]]

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    Definition Classes
    HasFeatures

  30. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  31. final def fit(dataset: Dataset[_]): ClassifierDLModel

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    Definition Classes
    AnnotatorApproach → Estimator

  32. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[ClassifierDLModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  33. def fit(dataset: Dataset[_], paramMap: ParamMap): ClassifierDLModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  34. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): ClassifierDLModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()

  35. def get[T](feature: StructFeature[T]): Option[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures

  36. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]

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    Attributes
    protected
    Definition Classes
    HasFeatures

  37. def get[T](feature: SetFeature[T]): Option[Set[T]]

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    Attributes
    protected
    Definition Classes
    HasFeatures

  38. def get[T](feature: ArrayFeature[T]): Option[Array[T]]

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    Attributes
    protected
    Definition Classes
    HasFeatures

  39. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  40. def getBatchSize: Int

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    Batch size (Default: 64)

  41. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  42. def getConfigProtoBytes: Option[Array[Byte]]

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    Tensorflow config Protobytes passed to the TF session

  43. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  44. def getDropout: Float

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    Dropout coefficient (Default: 0.5f)

  45. def getEnableOutputLogs: Boolean

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

  46. def getInputCols: Array[String]

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    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols

  47. def getLabelColumn: String

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    Column with label per each document

  48. def getLazyAnnotator: Boolean

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    Definition Classes
    CanBeLazy

  49. def getLr: Float

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    Learning Rate (Default: 5e-3f)

  50. def getMaxEpochs: Int

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    Maximum number of epochs to train (Default: 10)

  51. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params

  52. final def getOutputCol: String

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    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol

  53. def getOutputLogsPath: String

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    Folder path to save training logs (Default: "")

  54. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params

  55. def getRandomSeed: Int

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    Random seed

  56. def getValidationSplit: Float

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    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).

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

  57. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params

  58. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params

  59. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  60. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  61. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  62. val inputAnnotatorTypes: Array[AnnotatorType]

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    Input annotator type : SENTENCE_EMBEDDINGS

    Input annotator type : SENTENCE_EMBEDDINGS

    Definition Classes
    ClassifierDLApproach → HasInputAnnotationCols

  63. final val inputCols: StringArrayParam

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    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols

  64. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params

  65. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  66. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params

  67. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  68. val labelColumn: Param[String]

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    Column with label per each document

  69. val lazyAnnotator: BooleanParam

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    Definition Classes
    CanBeLazy

  70. def loadSavedModel(): TensorflowWrapper

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  71. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging

  72. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  73. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  74. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  75. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  76. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  77. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  78. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging

  79. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  80. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  81. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  82. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  83. val lr: FloatParam

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    Learning Rate (Default: 5e-3f)

  84. val maxEpochs: IntParam

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    Maximum number of epochs to train (Default: 10)

  85. def msgHelper(schema: StructType): String

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols

  86. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  87. final def notify(): Unit

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    Definition Classes
    AnyRef

  88. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  89. def onTrained(model: ClassifierDLModel, spark: SparkSession): Unit

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    Definition Classes
    AnnotatorApproach

  90. def onWrite(path: String, spark: SparkSession): Unit

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    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable

  91. val outputAnnotatorType: String

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    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    ClassifierDLApproach → HasOutputAnnotatorType

  92. final val outputCol: Param[String]

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    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol

  93. val outputLogsPath: Param[String]

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    Folder path to save training logs (Default: "")

  94. lazy val params: Array[Param[_]]

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    Definition Classes
    Params

  95. val randomSeed: IntParam

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    Random seed for shuffling the dataset

  96. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )

  97. def set[T](feature: StructFeature[T], value: T): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures

  98. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures

  99. def set[T](feature: SetFeature[T], value: Set[T]): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures

  100. def set[T](feature: ArrayFeature[T], value: Array[T]): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures

  101. final def set(paramPair: ParamPair[_]): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    Params

  102. final def set(param: String, value: Any): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    Params

  103. final def set[T](param: Param[T], value: T): ClassifierDLApproach.this.type

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    Definition Classes
    Params

  104. def setBatchSize(batch: Int): ClassifierDLApproach.this.type

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    Batch size (Default: 64)

  105. def setConfigProtoBytes(bytes: Array[Int]): ClassifierDLApproach.this.type

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    Tensorflow config Protobytes passed to the TF session

  106. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures

  107. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures

  108. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures

  109. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures

  110. final def setDefault(paramPairs: ParamPair[_]*): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    Params

  111. final def setDefault[T](param: Param[T], value: T): ClassifierDLApproach.this.type

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    Attributes
    protected
    Definition Classes
    Params

  112. def setDropout(dropout: Float): ClassifierDLApproach.this.type

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    Dropout coefficient (Default: 0.5f)

  113. def setEnableOutputLogs(enableOutputLogs: Boolean): ClassifierDLApproach.this.type

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

  114. final def setInputCols(value: String*): ClassifierDLApproach.this.type

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    Definition Classes
    HasInputAnnotationCols

  115. final def setInputCols(value: Array[String]): ClassifierDLApproach.this.type

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    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols

  116. def setLabelColumn(column: String): ClassifierDLApproach.this.type

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    Column with label per each document

  117. def setLazyAnnotator(value: Boolean): ClassifierDLApproach.this.type

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    Definition Classes
    CanBeLazy

  118. def setLr(lr: Float): ClassifierDLApproach.this.type

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    Learning Rate (Default: 5e-3f)

  119. def setMaxEpochs(epochs: Int): ClassifierDLApproach.this.type

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    Maximum number of epochs to train (Default: 10)

  120. final def setOutputCol(value: String): ClassifierDLApproach.this.type

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    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol

  121. def setOutputLogsPath(path: String): ClassifierDLApproach.this.type

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    Folder path to save training logs (Default: "")

  122. def setRandomSeed(seed: Int): ClassifierDLApproach.this.type

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    Random seed

  123. def setValidationSplit(validationSplit: Float): ClassifierDLApproach.this.type

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    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).

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

  124. def setVerbose(verbose: Level): ClassifierDLApproach.this.type

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    Level of verbosity during training (Default: Verbose.Silent.id)

  125. def setVerbose(verbose: Int): ClassifierDLApproach.this.type

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    Level of verbosity during training (Default: Verbose.Silent.id)

  126. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef

  127. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any

  128. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): ClassifierDLModel

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    Definition Classes
    ClassifierDLApproach → AnnotatorApproach

  129. final def transformSchema(schema: StructType): StructType

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    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    AnnotatorApproach → PipelineStage

  130. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()

  131. val uid: String

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    Definition Classes
    ClassifierDLApproach → Identifiable

  132. def validate(schema: StructType): Boolean

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    takes a Dataset and checks to see if all the required annotation types are present.

    takes a Dataset and checks to see if all the required annotation types are present.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    AnnotatorApproach

  133. val validationSplit: FloatParam

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    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).

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

  134. val verbose: IntParam

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    Level of verbosity during training (Default: Verbose.Silent.id)

  135. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  136. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  137. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  138. def write: MLWriter

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    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from AnnotatorApproach[ClassifierDLModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[ClassifierDLModel]

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

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters