com.johnsnowlabs.nlp.embeddings.Doc2VecApproach.scala Maven / Gradle / Ivy
/*
* Copyright 2017-2022 John Snow Labs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.johnsnowlabs.nlp.embeddings
import com.johnsnowlabs.nlp.AnnotatorType.{SENTENCE_EMBEDDINGS, TOKEN}
import com.johnsnowlabs.nlp.{AnnotatorApproach, HasEnableCachingProperties}
import com.johnsnowlabs.storage.HasStorageRef
import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.param.{DoubleParam, IntParam}
import org.apache.spark.ml.util.{DefaultParamsReadable, Identifiable}
import org.apache.spark.mllib.feature.Word2Vec
import org.apache.spark.sql.{Dataset, SparkSession}
/** Trains a Word2Vec model that creates vector representations of words in a text corpus.
*
* The algorithm first constructs a vocabulary from the corpus and then learns vector
* representation of words in the vocabulary. The vector representation can be used as features
* in natural language processing and machine learning algorithms.
*
* We use Word2Vec implemented in Spark ML. It uses skip-gram model in our implementation and a
* hierarchical softmax method to train the model. The variable names in the implementation match
* the original C implementation.
*
* For instantiated/pretrained models, see [[Doc2VecModel]].
*
* '''Sources''' :
*
* For the original C implementation, see https://code.google.com/p/word2vec/
*
* For the research paper, see
* [[https://arxiv.org/abs/1301.3781 Efficient Estimation of Word Representations in Vector Space]]
* and
* [[https://arxiv.org/pdf/1310.4546v1.pdf Distributed Representations of Words and Phrases and their Compositionality]].
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.annotator.{Tokenizer, Doc2VecApproach}
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val tokenizer = new Tokenizer()
* .setInputCols(Array("document"))
* .setOutputCol("token")
*
* val embeddings = new Doc2VecApproach()
* .setInputCols("token")
* .setOutputCol("embeddings")
*
* val pipeline = new Pipeline()
* .setStages(Array(
* documentAssembler,
* tokenizer,
* embeddings
* ))
*
* val path = "src/test/resources/spell/sherlockholmes.txt"
* val dataset = spark.sparkContext.textFile(path)
* .toDF("text")
* val pipelineModel = pipeline.fit(dataset)
* }}}
*
* @groupname anno Annotator types
* @groupdesc anno
* Required input and expected output annotator types
* @groupname Ungrouped Members
* @groupname param Parameters
* @groupname setParam Parameter setters
* @groupname getParam Parameter getters
* @groupname Ungrouped Members
* @groupprio param 1
* @groupprio anno 2
* @groupprio Ungrouped 3
* @groupprio setParam 4
* @groupprio getParam 5
* @groupdesc param
* A list of (hyper-)parameter keys this annotator can take. Users can set and get the
* parameter values through setters and getters, respectively.
*/
class Doc2VecApproach(override val uid: String)
extends AnnotatorApproach[Doc2VecModel]
with HasStorageRef
with HasEnableCachingProperties {
def this() = this(Identifiable.randomUID("Doc2VecApproach"))
override val description =
"Distributed Representations of Words and Phrases and their Compositionality"
/** Input Annotator Types: TOKEN
*
* @group anno
*/
override val inputAnnotatorTypes: Array[AnnotatorType] = Array(TOKEN)
/** Output Annotator Types: SENTENCE_EMBEDDINGS
*
* @group anno
*/
override val outputAnnotatorType: String = SENTENCE_EMBEDDINGS
/** The dimension of the code that you want to transform from words (Default: `100`).
*
* @group param
*/
val vectorSize =
new IntParam(this, "vectorSize", "the dimension of codes after transforming from words (> 0)")
/** @group setParam */
def setVectorSize(value: Int): this.type = {
require(value > 0, s"vector size must be positive but got $value")
set(vectorSize, value)
this
}
/** @group getParam */
def getVectorSize: Int = $(vectorSize)
/** The window size (context words from [-window, window]) (Default: `5`)
*
* @group param
*/
val windowSize = new IntParam(
this,
"windowSize",
"the window size (context words from [-window, window]) (> 0)")
/** @group setParam */
def setWindowSize(value: Int): this.type = {
require(value > 0, s"Window of words must be positive but got $value")
set(windowSize, value)
this
}
/** @group getParam */
def getWindowSize: Int = $(windowSize)
/** Number of partitions for sentences of words (Default: `1`).
*
* @group param
*/
val numPartitions =
new IntParam(this, "numPartitions", "number of partitions for sentences of words (> 0)")
/** @group setParam */
def setNumPartitions(value: Int): this.type = {
require(value > 0, s"Number of partitions must be positive but got $value")
set(numPartitions, value)
this
}
/** @group getParam */
def getNumPartitions: Int = $(numPartitions)
/** The minimum number of times a token must appear to be included in the word2vec model's
* vocabulary. Default: 5
*
* @group param
*/
val minCount = new IntParam(
this,
"minCount",
"the minimum number of times a token must " +
"appear to be included in the word2vec model's vocabulary (>= 0)")
/** @group setParam */
def setMinCount(value: Int): this.type = {
require(value > 0, s"Minimum number of times must be nonnegative but got $value")
set(minCount, value)
this
}
/** @group getParam */
def getMinCount: Int = $(minCount)
/** Sets the maximum length (in words) of each sentence in the input data (Default: `1000`). Any
* sentence longer than this threshold will be divided into chunks of up to `maxSentenceLength`
* size.
*
* @group param
*/
val maxSentenceLength = new IntParam(
this,
"maxSentenceLength",
"Maximum length " +
"(in words) of each sentence in the input data. Any sentence longer than this threshold will " +
"be divided into chunks up to the size (> 0)")
/** @group setParam */
def setMaxSentenceLength(value: Int): this.type = {
require(value > 0, s"Maximum length of sentences must be positive but got $value")
set(maxSentenceLength, value)
this
}
/** @group getParam */
def getMaxSentenceLength: Int = $(maxSentenceLength)
/** Param for Step size to be used for each iteration of optimization (> 0) (Default:
* `0.025`).
*
* @group param
*/
val stepSize: DoubleParam = new DoubleParam(
this,
"stepSize",
"Step size (learning rate) to be used for each iteration of optimization (> 0)")
/** @group setParam */
def setStepSize(value: Double): this.type = {
require(value > 0, s"Initial step size must be positive but got $value")
set(stepSize, value)
this
}
/** @group getParam */
def getStepSize: Double = $(stepSize)
/** Param for maximum number of iterations (>= 0) (Default: `1`)
*
* @group param
*/
val maxIter: IntParam = new IntParam(this, "maxIter", "maximum number of iterations (>= 0)")
/** @group setParam */
def setMaxIter(value: Int): this.type = {
require(value > 0, s"Number of iterations must be positive but got $value")
set(maxIter, value)
this
}
/** @group getParam */
def getMaxIter: Int = $(maxIter)
/** Random seed for shuffling the dataset (Default: `44`)
*
* @group param
*/
val seed = new IntParam(this, "seed", "Random seed")
/** @group setParam */
def setSeed(value: Int): Doc2VecApproach.this.type = {
require(value > 0, s"random seed must be positive but got $value")
set(seed, value)
this
}
/** @group getParam */
def getSeed: Int = $(seed)
setDefault(
vectorSize -> 100,
windowSize -> 5,
numPartitions -> 1,
minCount -> 1,
maxSentenceLength -> 1000,
stepSize -> 0.025,
maxIter -> 1,
seed -> 44,
enableCaching -> false)
override def beforeTraining(spark: SparkSession): Unit = {}
override def train(
dataset: Dataset[_],
recursivePipeline: Option[PipelineModel]): Doc2VecModel = {
val tokenResult: String = ".result"
val inputColumns = getInputCols(0) + tokenResult
val word2Vec = new Word2Vec()
.setLearningRate($(stepSize))
.setMinCount($(minCount))
.setNumIterations($(maxIter))
.setNumPartitions($(numPartitions))
.setVectorSize($(vectorSize))
.setWindowSize($(windowSize))
.setMaxSentenceLength($(maxSentenceLength))
.setSeed($(seed))
val input = dataset.select(dataset.col(inputColumns)).rdd.map(r => r.getSeq[String](0))
if (getEnableCaching)
input.cache()
val model = word2Vec.fit(input)
if (getEnableCaching)
input.unpersist()
new Doc2VecModel()
.setWordVectors(model.getVectors)
.setVectorSize($(vectorSize))
.setStorageRef($(storageRef))
.setDimension($(vectorSize))
}
}
/** This is the companion object of [[Doc2VecApproach]]. Please refer to that class for the
* documentation.
*/
object Doc2VecApproach extends DefaultParamsReadable[Doc2VecApproach]
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