Packages

class SentimentDLApproach extends AnnotatorApproach[SentimentDLModel] with ParamsAndFeaturesWritable with ClassifierEncoder

Trains a SentimentDL, an annotator for multi-class sentiment analysis.

In natural language processing, sentiment analysis is the task of classifying the affective state or subjective view of a text. A common example is if either a product review or tweet can be interpreted positively or negatively.

For the instantiated/pretrained models, see SentimentDLModel.

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 classifier = new SentimentDLApproach()
  .setInputCols("sentence_embeddings")
  .setOutputCol("sentiment")
  .setLabelColumn("label")
  .setTestDataset("test_data")

For extended examples of usage, see the Examples and the SentimentDLTestSpec.

Example

In this example, sentiment.csv is in the form

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

The model can then be trained with

import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.UniversalSentenceEncoder
import com.johnsnowlabs.nlp.annotators.classifier.dl.{SentimentDLApproach, SentimentDLModel}
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 SentimentDLApproach()
  .setInputCols("sentence_embeddings")
  .setOutputCol("sentiment")
  .setLabelColumn("label")
  .setBatchSize(32)
  .setMaxEpochs(1)
  .setLr(5e-3f)
  .setDropout(0.5f)

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

val pipelineModel = pipeline.fit(smallCorpus)
See also

ClassifierDLApproach for general single-class classification

MultiClassifierDLApproach for general multi-class classification

Linear Supertypes
ClassifierEncoder, EvaluationDLParams, ParamsAndFeaturesWritable, HasFeatures, AnnotatorApproach[SentimentDLModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[SentimentDLModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. SentimentDLApproach
  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 SentimentDLApproach()
  2. new SentimentDLApproach(uid: String)

    uid

    required uid for storing annotator to disk

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]): SentimentDLModel
    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
    SentimentDLApproach → AnnotatorApproach
  13. def buildDatasetWithLabels(dataset: Dataset[_], inputCols: String): (DataFrame, Array[String])
    Attributes
    protected
    Definition Classes
    ClassifierEncoder
  14. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  15. final def clear(param: Param[_]): SentimentDLApproach.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[SentimentDLModel]
    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
    Definition Classes
    SentimentDLApproach → AnnotatorApproach
  22. val dropout: FloatParam

    Dropout coefficient (Default: 0.5f)

  23. val enableOutputLogs: BooleanParam

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

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

    Definition Classes
    EvaluationDLParams
  24. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  25. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  26. 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
  27. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  28. def explainParams(): String
    Definition Classes
    Params
  29. def extractInputs(encoder: ClassifierDatasetEncoder, dataframe: DataFrame): (Array[Array[Float]], Array[String])
    Attributes
    protected
    Definition Classes
    ClassifierEncoder
  30. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  31. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  32. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  33. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  34. final def fit(dataset: Dataset[_]): SentimentDLModel
    Definition Classes
    AnnotatorApproach → Estimator
  35. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[SentimentDLModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  36. def fit(dataset: Dataset[_], paramMap: ParamMap): SentimentDLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  37. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SentimentDLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  38. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  41. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  42. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  43. def getBatchSize: Int

    Batch size (Default: 64)

    Batch size (Default: 64)

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

    Tensorflow config Protobytes passed to the TF session

    Tensorflow config Protobytes passed to the TF session

    Definition Classes
    ClassifierEncoder
  46. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  47. def getDropout: Float

  48. def getEnableOutputLogs: Boolean

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

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

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  50. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    ClassifierEncoder
  51. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  52. def getLr: Float

    Learning Rate (Default: 5e-3f)

    Learning Rate (Default: 5e-3f)

    Definition Classes
    ClassifierEncoder
  53. def getMaxEpochs: Int

    Maximum number of epochs to train (Default: 10)

    Maximum number of epochs to train (Default: 10)

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  56. def getOutputLogsPath: String

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

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

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

    Random seed

    Random seed

    Definition Classes
    ClassifierEncoder
  59. def getThreshold: Float

  60. def getThresholdLabel: String

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

    Input Annotator Types: SENTENCE_EMBEDDINGS

    Input Annotator Types: SENTENCE_EMBEDDINGS

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

    Column with label per each document

    Column with label per each document

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

    Learning Rate (Default: 5e-3f)

    Learning Rate (Default: 5e-3f)

    Definition Classes
    ClassifierEncoder
  89. val maxEpochs: IntParam

    Maximum number of epochs to train (Default: 10)

    Maximum number of epochs to train (Default: 10)

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

    Output Annotator Types: CATEGORY

    Output Annotator Types: CATEGORY

    Definition Classes
    SentimentDLApproach → HasOutputAnnotatorType
  98. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  99. val outputLogsPath: Param[String]

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

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

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

    Random seed for shuffling the dataset

    Random seed for shuffling the dataset

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

    Batch size (Default: 64)

    Batch size (Default: 64)

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

    Tensorflow config Protobytes passed to the TF session

    Tensorflow config Protobytes passed to the TF session

    Definition Classes
    ClassifierEncoder
  112. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  114. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  115. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  116. final def setDefault(paramPairs: ParamPair[_]*): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  117. final def setDefault[T](param: Param[T], value: T): SentimentDLApproach.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  118. def setDropout(dropout: Float): SentimentDLApproach.this.type

  119. def setEnableOutputLogs(enableOutputLogs: Boolean): SentimentDLApproach.this.type

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

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

    Definition Classes
    EvaluationDLParams
  120. def setEvaluationLogExtended(evaluationLogExtended: Boolean): SentimentDLApproach.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
  121. final def setInputCols(value: String*): SentimentDLApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  122. def setInputCols(value: Array[String]): SentimentDLApproach.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  123. def setLabelColumn(column: String): SentimentDLApproach.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    ClassifierEncoder
  124. def setLazyAnnotator(value: Boolean): SentimentDLApproach.this.type
    Definition Classes
    CanBeLazy
  125. def setLr(lr: Float): SentimentDLApproach.this.type

    Learning Rate (Default: 5e-3f)

    Learning Rate (Default: 5e-3f)

    Definition Classes
    ClassifierEncoder
  126. def setMaxEpochs(epochs: Int): SentimentDLApproach.this.type

    Maximum number of epochs to train (Default: 10)

    Maximum number of epochs to train (Default: 10)

    Definition Classes
    ClassifierEncoder
  127. final def setOutputCol(value: String): SentimentDLApproach.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  128. def setOutputLogsPath(path: String): SentimentDLApproach.this.type

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

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

    Definition Classes
    EvaluationDLParams
  129. def setRandomSeed(seed: Int): SentimentDLApproach.this.type

    Random seed

    Random seed

    Definition Classes
    ClassifierEncoder
  130. def setTestDataset(er: ExternalResource): SentimentDLApproach.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
  131. def setTestDataset(path: String, readAs: Format = ReadAs.SPARK, options: Map[String, String] = Map("format" -> "parquet")): SentimentDLApproach.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
  132. def setThreshold(threshold: Float): SentimentDLApproach.this.type

  133. def setThresholdLabel(label: String): SentimentDLApproach.this.type

  134. def setValidationSplit(validationSplit: Float): SentimentDLApproach.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
  135. def setVerbose(verbose: Level): SentimentDLApproach.this.type

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

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

    Definition Classes
    EvaluationDLParams
  136. def setVerbose(verbose: Int): SentimentDLApproach.this.type

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

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

    Definition Classes
    EvaluationDLParams
  137. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  138. 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
  139. val threshold: FloatParam

    The minimum threshold for the final result otherwise it will be either neutral or the value set in thresholdLabel (Default: 0.6f)

  140. val thresholdLabel: Param[String]

    In case the score is less than threshold, what should be the label (Default: "neutral")

  141. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  142. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): SentimentDLModel
    Definition Classes
    SentimentDLApproach → AnnotatorApproach
  143. 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
  144. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  145. val uid: String
    Definition Classes
    SentimentDLApproach → Identifiable
  146. 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
  147. 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
  148. val verbose: IntParam

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

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

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

Inherited from ClassifierEncoder

Inherited from EvaluationDLParams

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from AnnotatorApproach[SentimentDLModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[SentimentDLModel]

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