class BM25Approach extends AnnotatorApproach[BM25Model]

Trains a BM25 (Okapi BM25) lexical ranker over a corpus of tokenized documents.

BM25 is a bag-of-words retrieval function that ranks documents against a query based on the query terms appearing in each document. Because the score of a document depends on corpus-level statistics (how many documents contain a term, and the average document length), BM25 has to be implemented as a two-phase Estimator/Model pair:

  • BM25Approach (this class) scans the full corpus once during fit() and learns:
    • the total document count N
    • the document frequency df(t) of every vocabulary term
    • the average document length avgdl
    • the inverse document frequency idf(t) of every term
  • BM25Model reuses those statistics at query time to score every document against a user-provided query, emitting a BM25_RANKINGS annotation with the relevance score.

The IDF uses the non-negative (Lucene / Elasticsearch) variant:

idf(t) = ln(1 + (N - df(t) + 0.5) / (df(t) + 0.5))

and each document is scored as:

score(D, Q) = sum over t in Q of
  idf(t) * (tf(t, D) * (k1 + 1)) / (tf(t, D) + k1 * (1 - b + b * |D| / avgdl))

The input is a column of TOKEN annotations, so BM25 is normally placed after a Tokenizer (optionally followed by a Normalizer and/or StopWordsCleaner). For the produced model and usage examples see BM25Model.

The learned vocabulary (document frequencies and IDF) is collected to the driver during fit(). For corpora with a very large number of distinct terms, raise minDocFreq to prune rare terms and keep the driver-side vocabulary bounded.

Example

import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.{StopWordsCleaner, Tokenizer}
import com.johnsnowlabs.nlp.annotators.similarity.BM25Approach
import org.apache.spark.ml.Pipeline

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

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val stopWords = new StopWordsCleaner()
  .setInputCols("token")
  .setOutputCol("clean_token")
  .setCaseSensitive(false)

val bm25 = new BM25Approach()
  .setInputCols("clean_token")
  .setOutputCol("bm25_rankings")
  .setK1(1.2)
  .setB(0.75)
  .setMinDocFreq(1)
  .setCaseSensitive(false)

val pipeline = new Pipeline().setStages(
  Array(documentAssembler, tokenizer, stopWords, bm25))

val model = pipeline.fit(corpus)
model.stages.last.asInstanceOf[BM25Model].setQuery("vitamin C health benefits fruits")
model.transform(corpus).selectExpr("explode(bm25_rankings) as ranking").show(false)
Linear Supertypes
AnnotatorApproach[BM25Model], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[BM25Model], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. BM25Approach
  2. AnnotatorApproach
  3. CanBeLazy
  4. DefaultParamsWritable
  5. MLWritable
  6. HasOutputAnnotatorType
  7. HasOutputAnnotationCol
  8. HasInputAnnotationCols
  9. Estimator
  10. PipelineStage
  11. Logging
  12. Params
  13. Serializable
  14. Serializable
  15. Identifiable
  16. AnyRef
  17. Any
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Visibility
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Instance Constructors

  1. new BM25Approach()
  2. new BM25Approach(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. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): BM25Model
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. val b: DoubleParam

    Length-normalization parameter b.

    Length-normalization parameter b. 0.0 disables document-length normalization, 1.0 applies it fully. Range [0.0, 1.0] (Default: 0.75).

  8. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  9. val caseSensitive: BooleanParam

    Whether to treat tokens case-sensitively.

    Whether to treat tokens case-sensitively. When false (Default), terms are lowercased before the corpus statistics are computed (and again when a query is scored).

  10. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  11. final def clear(param: Param[_]): BM25Approach.this.type
    Definition Classes
    Params
  12. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  13. final def copy(extra: ParamMap): Estimator[BM25Model]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  14. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  15. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  16. val description: String
    Definition Classes
    BM25Approach → AnnotatorApproach
  17. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  19. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  20. def explainParams(): String
    Definition Classes
    Params
  21. final val extraInputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  22. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  23. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  24. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. final def fit(dataset: Dataset[_]): BM25Model
    Definition Classes
    AnnotatorApproach → Estimator
  26. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[BM25Model]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  27. def fit(dataset: Dataset[_], paramMap: ParamMap): BM25Model
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  28. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): BM25Model
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  29. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  30. def getB: Double

  31. def getCaseSensitive: Boolean

  32. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  33. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  34. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  35. def getK1: Double

  36. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  37. def getMinDocFreq: Int

  38. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  39. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  40. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  41. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  42. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  43. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  44. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  45. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  46. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type: TOKEN

    Input annotator type: TOKEN

    Definition Classes
    BM25Approach → HasInputAnnotationCols
  47. 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
  48. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  49. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  50. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  51. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  52. val k1: DoubleParam

    Term-frequency saturation parameter k1.

    Term-frequency saturation parameter k1. Higher values let the score keep growing with term frequency; lower values saturate faster. Typical range [1.0, 2.0] (Default: 1.2).

  53. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  54. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  55. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  62. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. val minDocFreq: IntParam

    Minimum document frequency for a term to be kept in the vocabulary.

    Minimum document frequency for a term to be kept in the vocabulary. Terms appearing in fewer than minDocFreq documents are dropped from the IDF map (Default: 1).

  67. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  68. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  69. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  70. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  71. def onTrained(model: BM25Model, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  72. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  73. val outputAnnotatorType: AnnotatorType

    Output annotator type: BM25_RANKINGS

    Output annotator type: BM25_RANKINGS

    Definition Classes
    BM25Approach → HasOutputAnnotatorType
  74. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  75. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  76. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  77. final def set(paramPair: ParamPair[_]): BM25Approach.this.type
    Attributes
    protected
    Definition Classes
    Params
  78. final def set(param: String, value: Any): BM25Approach.this.type
    Attributes
    protected
    Definition Classes
    Params
  79. final def set[T](param: Param[T], value: T): BM25Approach.this.type
    Definition Classes
    Params
  80. def setB(value: Double): BM25Approach.this.type

  81. def setCaseSensitive(value: Boolean): BM25Approach.this.type

  82. final def setDefault(paramPairs: ParamPair[_]*): BM25Approach.this.type
    Attributes
    protected
    Definition Classes
    Params
  83. final def setDefault[T](param: Param[T], value: T): BM25Approach.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  84. def setExtraInputCols(value: Array[String]): BM25Approach.this.type
    Definition Classes
    HasInputAnnotationCols
  85. final def setInputCols(value: String*): BM25Approach.this.type
    Definition Classes
    HasInputAnnotationCols
  86. def setInputCols(value: Array[String]): BM25Approach.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  87. def setK1(value: Double): BM25Approach.this.type

  88. def setLazyAnnotator(value: Boolean): BM25Approach.this.type
    Definition Classes
    CanBeLazy
  89. def setMinDocFreq(value: Int): BM25Approach.this.type

  90. final def setOutputCol(value: String): BM25Approach.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  91. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  92. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  93. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): BM25Model
    Definition Classes
    BM25Approach → AnnotatorApproach
  94. 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
  95. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  96. val uid: String
    Definition Classes
    BM25Approach → Identifiable
  97. 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
  98. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  99. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  100. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  101. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from AnnotatorApproach[BM25Model]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[BM25Model]

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

Parameter setters

Parameter getters

Ungrouped