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/*
* 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.annotators.classifier.dl
import com.johnsnowlabs.ml.tensorflow._
import com.johnsnowlabs.nlp.AnnotatorType.{CATEGORY, SENTENCE_EMBEDDINGS}
import com.johnsnowlabs.nlp._
import com.johnsnowlabs.nlp.annotators.ner.Verbose
import com.johnsnowlabs.nlp.pretrained.ResourceDownloader
import com.johnsnowlabs.nlp.serialization.StructFeature
import com.johnsnowlabs.storage.HasStorageRef
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.param.{FloatParam, IntArrayParam, Param, StringArrayParam}
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.{Dataset, SparkSession}
/** 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.
*
* This is the instantiated model of the [[SentimentDLApproach]]. For training your own model,
* please see the documentation of that class.
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val sentiment = SentimentDLModel.pretrained()
* .setInputCols("sentence_embeddings")
* .setOutputCol("sentiment")
* }}}
* The default model is `"sentimentdl_use_imdb"`, if no name is provided. It is english sentiment
* analysis trained on the IMDB dataset. For available pretrained models please see the
* [[https://sparknlp.org/models?task=Sentiment+Analysis Models Hub]].
*
* For extended examples of usage, see the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/SentimentDL_train_multiclass_sentiment_classifier.ipynb Examples]]
* and the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/SentimentDLTestSpec.scala SentimentDLTestSpec]].
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotator.UniversalSentenceEncoder
* import com.johnsnowlabs.nlp.annotators.classifier.dl.SentimentDLModel
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val useEmbeddings = UniversalSentenceEncoder.pretrained()
* .setInputCols("document")
* .setOutputCol("sentence_embeddings")
*
* val sentiment = SentimentDLModel.pretrained("sentimentdl_use_twitter")
* .setInputCols("sentence_embeddings")
* .setThreshold(0.7F)
* .setOutputCol("sentiment")
*
* val pipeline = new Pipeline().setStages(Array(
* documentAssembler,
* useEmbeddings,
* sentiment
* ))
*
* val data = Seq(
* "Wow, the new video is awesome!",
* "bruh what a damn waste of time"
* ).toDF("text")
* val result = pipeline.fit(data).transform(data)
*
* result.select("text", "sentiment.result").show(false)
* +------------------------------+----------+
* |text |result |
* +------------------------------+----------+
* |Wow, the new video is awesome!|[positive]|
* |bruh what a damn waste of time|[negative]|
* +------------------------------+----------+
* }}}
*
* @see
* [[ClassifierDLModel]] for general single-class classification
* @see
* [[MultiClassifierDLModel]] for general multi-class classification
* @param uid
* required uid for storing annotator to disk
* @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 SentimentDLModel(override val uid: String)
extends AnnotatorModel[SentimentDLModel]
with HasSimpleAnnotate[SentimentDLModel]
with WriteTensorflowModel
with HasStorageRef
with ParamsAndFeaturesWritable
with HasEngine {
def this() = this(Identifiable.randomUID("SentimentDLModel"))
/** Input Annotator Types: SENTENCE_EMBEDDINGS
*
* @group anno
*/
override val inputAnnotatorTypes: Array[AnnotatorType] = Array(SENTENCE_EMBEDDINGS)
/** Output Annotator Types: CATEGORY
*
* @group anno
*/
override val outputAnnotatorType: String = CATEGORY
/** The minimum threshold for the final result otherwise it will be either neutral or the value
* set in thresholdLabel (Default: `0.6f`)
*
* @group param
*/
val threshold = new FloatParam(
this,
"threshold",
"The minimum threshold for the final result otherwise it will be either neutral or the value set in thresholdLabel.s")
/** In case the score is less than threshold, what should be the label (Default: `"neutral"`)
*
* @group param
*/
val thresholdLabel = new Param[String](
this,
"thresholdLabel",
"In case the score is less than threshold, what should be the label. Default is neutral.")
/** @group setParam */
def setThreshold(threshold: Float): SentimentDLModel.this.type = set(this.threshold, threshold)
/** @group setParam */
def setThresholdLabel(label: String): SentimentDLModel.this.type =
set(this.thresholdLabel, label)
/** @group getParam */
def getThreshold: Float = $(this.threshold)
/** @group getParam */
def getThresholdLabel: String = $(this.thresholdLabel)
/** ConfigProto from tensorflow, serialized into byte array. Get with
* config_proto.SerializeToString()
*
* @group param
*/
val configProtoBytes = new IntArrayParam(
this,
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()")
/** @group setParam */
def setConfigProtoBytes(bytes: Array[Int]): SentimentDLModel.this.type =
set(this.configProtoBytes, bytes)
def getConfigProtoBytes: Option[Array[Byte]] =
get(this.configProtoBytes).map(_.map(_.toByte))
/** Dataset Params
*
* @group param
*/
val datasetParams = new StructFeature[ClassifierDatasetEncoderParams](this, "datasetParams")
/** @group setParam */
def setDatasetParams(params: ClassifierDatasetEncoderParams): SentimentDLModel.this.type =
set(this.datasetParams, params)
/** Labels that the model was trained with
*
* @group param
*/
val classes =
new StringArrayParam(this, "classes", "keep an internal copy of classes for Python")
private var _model: Option[Broadcast[TensorflowSentiment]] = None
/** @group setParam */
def setModelIfNotSet(spark: SparkSession, tf: TensorflowWrapper): this.type = {
if (_model.isEmpty) {
require(datasetParams.isSet, "datasetParams must be set before usage")
val encoder = new ClassifierDatasetEncoder(datasetParams.get.get)
_model = Some(
spark.sparkContext.broadcast(new TensorflowSentiment(tf, encoder, Verbose.Silent)))
}
this
}
/** @group getParam */
def getModelIfNotSet: TensorflowSentiment = _model.get.value
/** get the tags used to trained this NerDLModel
*
* @group getParam
*/
def getClasses: Array[String] = {
val encoder = new ClassifierDatasetEncoder(datasetParams.get.get)
set(classes, encoder.tags)
encoder.tags
}
setDefault(threshold -> 0.6f, thresholdLabel -> "neutral")
override protected def beforeAnnotate(dataset: Dataset[_]): Dataset[_] = {
validateStorageRef(dataset, $(inputCols), AnnotatorType.SENTENCE_EMBEDDINGS)
dataset
}
/** takes a document and annotations and produces new annotations of this annotator's annotation
* type
*
* @param annotations
* Annotations that correspond to inputAnnotationCols generated by previous annotators if any
* @return
* any number of annotations processed for every input annotation. Not necessary one to one
* relationship
*/
override def annotate(annotations: Seq[Annotation]): Seq[Annotation] = {
val sentences = annotations
.filter(_.annotatorType == SENTENCE_EMBEDDINGS)
.groupBy(_.metadata.getOrElse[String]("sentence", "0").toInt)
.toSeq
.sortBy(_._1)
if (sentences.nonEmpty)
getModelIfNotSet.predict(sentences, getConfigProtoBytes, $(threshold), $(thresholdLabel))
else Seq.empty[Annotation]
}
override def onWrite(path: String, spark: SparkSession): Unit = {
super.onWrite(path, spark)
writeTensorflowModel(
path,
spark,
getModelIfNotSet.tensorflow,
"_sentimentdl",
SentimentDLModel.tfFile,
configProtoBytes = getConfigProtoBytes)
}
}
trait ReadablePretrainedSentimentDL
extends ParamsAndFeaturesReadable[SentimentDLModel]
with HasPretrained[SentimentDLModel] {
override val defaultModelName: Some[String] = Some("sentimentdl_use_imdb")
override def pretrained(name: String, lang: String, remoteLoc: String): SentimentDLModel = {
ResourceDownloader.downloadModel(SentimentDLModel, name, Option(lang), remoteLoc)
}
/** Java compliant-overrides */
override def pretrained(): SentimentDLModel =
pretrained(defaultModelName.get, defaultLang, defaultLoc)
override def pretrained(name: String): SentimentDLModel =
pretrained(name, defaultLang, defaultLoc)
override def pretrained(name: String, lang: String): SentimentDLModel =
pretrained(name, lang, defaultLoc)
}
trait ReadSentimentDLTensorflowModel extends ReadTensorflowModel {
this: ParamsAndFeaturesReadable[SentimentDLModel] =>
override val tfFile: String = "sentimentdl_tensorflow"
def readModel(instance: SentimentDLModel, path: String, spark: SparkSession): Unit = {
val tf = readTensorflowModel(path, spark, "_sentimentdl_tf", initAllTables = true)
instance.setModelIfNotSet(spark, tf)
// This allows for Python to access getClasses function
val encoder = new ClassifierDatasetEncoder(instance.datasetParams.get.get)
instance.set(instance.classes, encoder.tags)
}
addReader(readModel)
}
/** This is the companion object of [[SentimentDLModel]]. Please refer to that class for the
* documentation.
*/
object SentimentDLModel extends ReadablePretrainedSentimentDL with ReadSentimentDLTensorflowModel
