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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.{IntArrayParam, StringArrayParam}
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.{Dataset, SparkSession}
/** ClassifierDL for generic Multi-class Text Classification.
*
* ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text
* classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built
* inside TensorFlow and supports up to 100 classes.
*
* This is the instantiated model of the [[ClassifierDLApproach]]. For training your own model,
* please see the documentation of that class.
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val classifierDL = ClassifierDLModel.pretrained()
* .setInputCols("sentence_embeddings")
* .setOutputCol("classification")
* }}}
* The default model is `"classifierdl_use_trec6"`, if no name is provided. It uses embeddings
* from the [[com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder UniversalSentenceEncoder]]
* and is trained on the
* [[https://deepai.org/dataset/trec-6#:~:text=The%20TREC%20dataset%20is%20dataset,50%20has%20finer%2Dgrained%20labels TREC-6]]
* dataset. For available pretrained models please see the
* [[https://sparknlp.org/models?task=Text+Classification Models Hub]].
*
* For extended examples of usage, see the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb Examples]]
* and the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/ClassifierDLTestSpec.scala ClassifierDLTestSpec]].
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotator.SentenceDetector
* import com.johnsnowlabs.nlp.annotators.classifier.dl.ClassifierDLModel
* import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val sentence = new SentenceDetector()
* .setInputCols("document")
* .setOutputCol("sentence")
*
* val useEmbeddings = UniversalSentenceEncoder.pretrained()
* .setInputCols("document")
* .setOutputCol("sentence_embeddings")
*
* val sarcasmDL = ClassifierDLModel.pretrained("classifierdl_use_sarcasm")
* .setInputCols("sentence_embeddings")
* .setOutputCol("sarcasm")
*
* val pipeline = new Pipeline()
* .setStages(Array(
* documentAssembler,
* sentence,
* useEmbeddings,
* sarcasmDL
* ))
*
* val data = Seq(
* "I'm ready!",
* "If I could put into words how much I love waking up at 6 am on Mondays I would."
* ).toDF("text")
* val result = pipeline.fit(data).transform(data)
*
* result.selectExpr("explode(arrays_zip(sentence, sarcasm)) as out")
* .selectExpr("out.sentence.result as sentence", "out.sarcasm.result as sarcasm")
* .show(false)
* +-------------------------------------------------------------------------------+-------+
* |sentence |sarcasm|
* +-------------------------------------------------------------------------------+-------+
* |I'm ready! |normal |
* |If I could put into words how much I love waking up at 6 am on Mondays I would.|sarcasm|
* +-------------------------------------------------------------------------------+-------+
* }}}
*
* @see
* [[MultiClassifierDLModel]] for multi-class classification
* @see
* [[SentimentDLModel]] for sentiment analysis
* @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 ClassifierDLModel(override val uid: String)
extends AnnotatorModel[ClassifierDLModel]
with HasSimpleAnnotate[ClassifierDLModel]
with WriteTensorflowModel
with HasStorageRef
with ParamsAndFeaturesWritable
with HasEngine {
def this() = this(Identifiable.randomUID("ClassifierDLModel"))
/** Output annotator type : SENTENCE_EMBEDDINGS
*
* @group anno
*/
override val inputAnnotatorTypes: Array[AnnotatorType] = Array(SENTENCE_EMBEDDINGS)
/** Output annotator type : CATEGORY
*
* @group anno
*/
override val outputAnnotatorType: String = CATEGORY
/** 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()")
/** Tensorflow config Protobytes passed to the TF session
*
* @group setParam
*/
def setConfigProtoBytes(bytes: Array[Int]): ClassifierDLModel.this.type =
set(this.configProtoBytes, bytes)
/** Tensorflow config Protobytes passed to the TF session
*
* @group getParam
*/
def getConfigProtoBytes: Option[Array[Byte]] =
get(this.configProtoBytes).map(_.map(_.toByte))
/** Dataset params
*
* @group param
*/
val datasetParams = new StructFeature[ClassifierDatasetEncoderParams](this, "datasetParams")
/** Labels used to train this model
*
* @group param
*/
val classes = new StringArrayParam(this, "classes", "Labels used to train this model")
/** Dataset params
*
* @group setParam
*/
def setDatasetParams(params: ClassifierDatasetEncoderParams): ClassifierDLModel.this.type =
set(this.datasetParams, params)
private var _model: Option[Broadcast[TensorflowClassifier]] = None
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 TensorflowClassifier(tf, encoder, None, Verbose.Silent)))
}
this
}
def getModelIfNotSet: TensorflowClassifier = _model.get.value
/** Labels used to train this model
*
* @group getParam
*/
def getClasses: Array[String] = {
val encoder = new ClassifierDatasetEncoder(datasetParams.get.get)
set(classes, encoder.tags)
encoder.tags
}
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)
else Seq.empty[Annotation]
}
override def onWrite(path: String, spark: SparkSession): Unit = {
super.onWrite(path, spark)
writeTensorflowModel(
path,
spark,
getModelIfNotSet.tensorflow,
"_classifierdl",
ClassifierDLModel.tfFile,
configProtoBytes = getConfigProtoBytes)
}
}
trait ReadablePretrainedClassifierDL
extends ParamsAndFeaturesReadable[ClassifierDLModel]
with HasPretrained[ClassifierDLModel] {
override val defaultModelName: Some[String] = Some("classifierdl_use_trec6")
override def pretrained(name: String, lang: String, remoteLoc: String): ClassifierDLModel = {
ResourceDownloader.downloadModel(ClassifierDLModel, name, Option(lang), remoteLoc)
}
/** Java compliant-overrides */
override def pretrained(): ClassifierDLModel =
pretrained(defaultModelName.get, defaultLang, defaultLoc)
override def pretrained(name: String): ClassifierDLModel =
pretrained(name, defaultLang, defaultLoc)
override def pretrained(name: String, lang: String): ClassifierDLModel =
pretrained(name, lang, defaultLoc)
}
trait ReadClassifierDLTensorflowModel extends ReadTensorflowModel {
this: ParamsAndFeaturesReadable[ClassifierDLModel] =>
override val tfFile: String = "classifierdl_tensorflow"
def readModel(instance: ClassifierDLModel, path: String, spark: SparkSession): Unit = {
val tf = readTensorflowModel(path, spark, "_classifierdl_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 [[ClassifierDLModel]]. Please refer to that class for the
* documentation.
*/
object ClassifierDLModel
extends ReadablePretrainedClassifierDL
with ReadClassifierDLTensorflowModel
