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com.johnsnowlabs.nlp.annotators.classifier.dl

MultiClassifierDLApproach

class MultiClassifierDLApproach extends AnnotatorApproach[MultiClassifierDLModel] with ParamsAndFeaturesWritable with ClassifierEncoder

Trains a MultiClassifierDL for Multi-label Text Classification.

MultiClassifierDL uses a Bidirectional GRU with a convolutional model that we have built inside TensorFlow and supports up to 100 classes.

For instantiated/pretrained models, see MultiClassifierDLModel.

The input to MultiClassifierDL are Sentence Embeddings such as the state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings.

In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).

Notes:

Setting a test dataset to monitor model metrics can be done with .setTestDataset. The method expects a path to a parquet file containing a dataframe that has the same required columns as the training dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe. The following example will show how to create the test dataset:

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

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

val preProcessingPipeline = new Pipeline().setStages(Array(documentAssembler, embeddings))

val Array(train, test) = data.randomSplit(Array(0.8, 0.2))
preProcessingPipeline
  .fit(test)
  .transform(test)
  .write
  .mode("overwrite")
  .parquet("test_data")

val multiClassifier = new MultiClassifierDLApproach()
  .setInputCols("sentence_embeddings")
  .setOutputCol("category")
  .setLabelColumn("label")
  .setTestDataset("test_data")

For extended examples of usage, see the Spark NLP Workshop and the MultiClassifierDLTestSpec.

Example

In this example, the training data has the form (Note: labels can be arbitrary)

mr,ref
"name[Alimentum], area[city centre], familyFriendly[no], near[Burger King]",Alimentum is an adult establish found in the city centre area near Burger King.
"name[Alimentum], area[city centre], familyFriendly[yes]",Alimentum is a family-friendly place in the city centre.
...

It needs some pre-processing first, so the labels are of type Array[String]. This can be done like so:

import spark.implicits._
import com.johnsnowlabs.nlp.annotators.classifier.dl.MultiClassifierDLApproach
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
import org.apache.spark.ml.Pipeline
import org.apache.spark.sql.functions.{col, udf}

// Process training data to create text with associated array of labels
def splitAndTrim = udf { labels: String =>
  labels.split(", ").map(x=>x.trim)
}

val smallCorpus = spark.read
  .option("header", true)
  .option("inferSchema", true)
  .option("mode", "DROPMALFORMED")
  .csv("src/test/resources/classifier/e2e.csv")
  .withColumn("labels", splitAndTrim(col("mr")))
  .withColumn("text", col("ref"))
  .drop("mr")

smallCorpus.printSchema()
// root
// |-- ref: string (nullable = true)
// |-- labels: array (nullable = true)
// |    |-- element: string (containsNull = true)

// Then create pipeline for training
val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")
  .setCleanupMode("shrink")

val embeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("embeddings")

val docClassifier = new MultiClassifierDLApproach()
  .setInputCols("embeddings")
  .setOutputCol("category")
  .setLabelColumn("labels")
  .setBatchSize(128)
  .setMaxEpochs(10)
  .setLr(1e-3f)
  .setThreshold(0.5f)
  .setValidationSplit(0.1f)

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

val pipelineModel = pipeline.fit(smallCorpus)
See also

Multi-label classification on Wikipedia

ClassifierDLApproach for single-class classification

SentimentDLApproach for sentiment analysis

Linear Supertypes
ClassifierEncoder, EvaluationDLParams, ParamsAndFeaturesWritable, HasFeatures, AnnotatorApproach[MultiClassifierDLModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[MultiClassifierDLModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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  1. MultiClassifierDLApproach
  2. ClassifierEncoder
  3. EvaluationDLParams
  4. ParamsAndFeaturesWritable
  5. HasFeatures
  6. AnnotatorApproach
  7. CanBeLazy
  8. DefaultParamsWritable
  9. MLWritable
  10. HasOutputAnnotatorType
  11. HasOutputAnnotationCol
  12. HasInputAnnotationCols
  13. Estimator
  14. PipelineStage
  15. Logging
  16. Params
  17. Serializable
  18. Serializable
  19. Identifiable
  20. AnyRef
  21. Any
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Instance Constructors

  1. new MultiClassifierDLApproach()
  2. new MultiClassifierDLApproach(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]): MultiClassifierDLModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val batchSize: IntParam

    Batch size (Default: 64)

    Batch size (Default: 64)

    Definition Classes
    ClassifierEncoder
  12. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  13. def buildDatasetWithLabels(dataset: Dataset[_], inputCols: String): (DataFrame, Array[String])
    Attributes
    protected
    Definition Classes
    MultiClassifierDLApproachClassifierEncoder
  14. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  15. final def clear(param: Param[_]): MultiClassifierDLApproach.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()

    Definition Classes
    ClassifierEncoder
  18. final def copy(extra: ParamMap): Estimator[MultiClassifierDLModel]
    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 model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    MultiClassifierDLApproachAnnotatorApproach
  22. val enableOutputLogs: BooleanParam

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

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

    Definition Classes
    EvaluationDLParams
  23. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  25. val evaluationLogExtended: BooleanParam

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

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

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

    Batch size (Default: 64)

    Batch size (Default: 64)

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

    Tensorflow config Protobytes passed to the TF session

    Tensorflow config Protobytes passed to the TF session

    Definition Classes
    ClassifierEncoder
  45. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. def getEnableOutputLogs: Boolean

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

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

    Definition Classes
    EvaluationDLParams
  47. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  48. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    ClassifierEncoder
  49. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  50. def getLr: Float

    Learning Rate (Default: 5e-3f)

    Learning Rate (Default: 5e-3f)

    Definition Classes
    ClassifierEncoder
  51. def getMaxEpochs: Int

    Maximum number of epochs to train (Default: 10)

    Maximum number of epochs to train (Default: 10)

    Definition Classes
    ClassifierEncoder
  52. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  53. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  54. def getOutputLogsPath: String

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

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

    Definition Classes
    EvaluationDLParams
  55. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  56. def getRandomSeed: Int

    Random seed

    Random seed

    Definition Classes
    ClassifierEncoder
  57. def getShufflePerEpoch: Boolean

    Max sequence length to feed into TensorFlow

  58. def getThreshold: Float

    The minimum threshold for each label to be accepted (Default: 0.5f)

  59. def getValidationSplit: Float

    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.

    Definition Classes
    EvaluationDLParams
  60. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  61. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  62. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  63. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  64. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : SENTENCE_EMBEDDINGS

    Input annotator type : SENTENCE_EMBEDDINGS

    Definition Classes
    MultiClassifierDLApproachHasInputAnnotationCols
  66. final val inputCols: StringArrayParam

    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
  67. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  68. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  69. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  70. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  71. val labelColumn: Param[String]

    Column with label per each document

    Column with label per each document

    Definition Classes
    ClassifierEncoder
  72. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  73. def loadSavedModel(): TensorflowWrapper
  74. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  75. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  82. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. val lr: FloatParam

    Learning Rate (Default: 5e-3f)

    Learning Rate (Default: 5e-3f)

    Definition Classes
    ClassifierEncoder
  87. val maxEpochs: IntParam

    Maximum number of epochs to train (Default: 10)

    Maximum number of epochs to train (Default: 10)

    Definition Classes
    ClassifierEncoder
  88. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  89. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  90. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  91. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  92. def onTrained(model: MultiClassifierDLModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  93. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  94. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  95. val outputAnnotatorType: String

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    MultiClassifierDLApproachHasOutputAnnotatorType
  96. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  97. val outputLogsPath: Param[String]

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

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

    Definition Classes
    EvaluationDLParams
  98. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  99. val randomSeed: IntParam

    Random seed for shuffling the dataset

    Random seed for shuffling the dataset

    Definition Classes
    ClassifierEncoder
  100. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  101. def set[T](feature: StructFeature[T], value: T): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def set[T](feature: SetFeature[T], value: Set[T]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. def set[T](feature: ArrayFeature[T], value: Array[T]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  105. final def set(paramPair: ParamPair[_]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  106. final def set(param: String, value: Any): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  107. final def set[T](param: Param[T], value: T): MultiClassifierDLApproach.this.type
    Definition Classes
    Params
  108. def setBatchSize(batch: Int): MultiClassifierDLApproach.this.type

    Batch size (Default: 64)

    Batch size (Default: 64)

    Definition Classes
    ClassifierEncoder
  109. def setConfigProtoBytes(bytes: Array[Int]): MultiClassifierDLApproach.this.type

    Tensorflow config Protobytes passed to the TF session

    Tensorflow config Protobytes passed to the TF session

    Definition Classes
    ClassifierEncoder
  110. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  114. final def setDefault(paramPairs: ParamPair[_]*): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  115. final def setDefault[T](param: Param[T], value: T): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  116. def setEnableOutputLogs(enableOutputLogs: Boolean): MultiClassifierDLApproach.this.type

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

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

    Definition Classes
    EvaluationDLParams
  117. def setEvaluationLogExtended(evaluationLogExtended: Boolean): MultiClassifierDLApproach.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.

    Definition Classes
    EvaluationDLParams
  118. final def setInputCols(value: String*): MultiClassifierDLApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  119. def setInputCols(value: Array[String]): MultiClassifierDLApproach.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  120. def setLabelColumn(column: String): MultiClassifierDLApproach.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    ClassifierEncoder
  121. def setLazyAnnotator(value: Boolean): MultiClassifierDLApproach.this.type
    Definition Classes
    CanBeLazy
  122. def setLr(lr: Float): MultiClassifierDLApproach.this.type

    Learning Rate (Default: 5e-3f)

    Learning Rate (Default: 5e-3f)

    Definition Classes
    ClassifierEncoder
  123. def setMaxEpochs(epochs: Int): MultiClassifierDLApproach.this.type

    Maximum number of epochs to train (Default: 10)

    Maximum number of epochs to train (Default: 10)

    Definition Classes
    ClassifierEncoder
  124. final def setOutputCol(value: String): MultiClassifierDLApproach.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  125. def setOutputLogsPath(path: String): MultiClassifierDLApproach.this.type

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

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

    Definition Classes
    EvaluationDLParams
  126. def setRandomSeed(seed: Int): MultiClassifierDLApproach.this.type

    Random seed

    Random seed

    Definition Classes
    ClassifierEncoder
  127. def setShufflePerEpoch(value: Boolean): MultiClassifierDLApproach.this.type

    shufflePerEpoch

  128. def setTestDataset(er: ExternalResource): MultiClassifierDLApproach.this.type

    ExternalResource to a parquet file of a test dataset.

    ExternalResource to a parquet file of a test dataset. If set, it is used to calculate statistics on it during training.

    When using an ExternalResource, only parquet files are accepted for this function.

    The parquet file must be a dataframe that has the same columns as the model that is being trained. For example, if the model needs as input DOCUMENT, TOKEN, WORD_EMBEDDINGS (Features) and NAMED_ENTITY (label) then these columns also need to be present while saving the dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe.

    An example on how to create such a parquet file could be:

    // assuming preProcessingPipeline
    val Array(train, test) = data.randomSplit(Array(0.8, 0.2))
    
    preProcessingPipeline
      .fit(test)
      .transform(test)
      .write
      .mode("overwrite")
      .parquet("test_data")
    
    annotator.setTestDataset("test_data")
    Definition Classes
    EvaluationDLParams
  129. def setTestDataset(path: String, readAs: Format = ReadAs.SPARK, options: Map[String, String] = Map("format" -> "parquet")): MultiClassifierDLApproach.this.type

    Path to a parquet file of a test dataset.

    Path to a parquet file of a test dataset. If set, it is used to calculate statistics on it during training.

    The parquet file must be a dataframe that has the same columns as the model that is being trained. For example, if the model needs as input DOCUMENT, TOKEN, WORD_EMBEDDINGS (Features) and NAMED_ENTITY (label) then these columns also need to be present while saving the dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe.

    An example on how to create such a parquet file could be:

    // assuming preProcessingPipeline
    val Array(train, test) = data.randomSplit(Array(0.8, 0.2))
    
    preProcessingPipeline
      .fit(test)
      .transform(test)
      .write
      .mode("overwrite")
      .parquet("test_data")
    
    annotator.setTestDataset("test_data")
    Definition Classes
    EvaluationDLParams
  130. def setThreshold(threshold: Float): MultiClassifierDLApproach.this.type

    The minimum threshold for each label to be accepted (Default: 0.5f)

  131. def setValidationSplit(validationSplit: Float): MultiClassifierDLApproach.this.type

    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.

    Definition Classes
    EvaluationDLParams
  132. def setVerbose(verbose: Level): MultiClassifierDLApproach.this.type

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

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

    Definition Classes
    EvaluationDLParams
  133. def setVerbose(verbose: Int): MultiClassifierDLApproach.this.type

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

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

    Definition Classes
    EvaluationDLParams
  134. val shufflePerEpoch: BooleanParam

    Whether to shuffle the training data on each Epoch (Default: false)

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

    Path to a parquet file of a test dataset.

    Path to a parquet file of a test dataset. If set, it is used to calculate statistics on it during training.

    Definition Classes
    EvaluationDLParams
  137. val threshold: FloatParam

    The minimum threshold for each label to be accepted (Default: 0.5f)

  138. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  139. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): MultiClassifierDLModel
  140. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

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

    Definition Classes
    AnnotatorApproach → PipelineStage
  141. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  142. val uid: String
    Definition Classes
    MultiClassifierDLApproach → Identifiable
  143. def validate(schema: StructType): Boolean

    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
  144. val validationSplit: FloatParam

    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.

    Definition Classes
    EvaluationDLParams
  145. val verbose: IntParam

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

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

    Definition Classes
    EvaluationDLParams
  146. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  147. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  148. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  149. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from ClassifierEncoder

Inherited from EvaluationDLParams

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[MultiClassifierDLModel]

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