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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.annotators.ner.Verbose
import com.johnsnowlabs.nlp.util.io.ResourceHelper
import com.johnsnowlabs.nlp.{AnnotatorApproach, AnnotatorType, ParamsAndFeaturesWritable}
import com.johnsnowlabs.storage.HasStorageRef
import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.param._
import org.apache.spark.ml.util.{DefaultParamsReadable, Identifiable}
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.types._
import scala.util.Random
/** Trains a 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.
*
* For instantiated/pretrained models, see [[ClassifierDLModel]].
*
* '''Notes''':
* - This annotator accepts a label column of a single item in either type of String, Int,
* Float, or Double.
* - [[com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder UniversalSentenceEncoder]],
* [[com.johnsnowlabs.nlp.embeddings.BertSentenceEmbeddings BertSentenceEmbeddings]], or
* [[com.johnsnowlabs.nlp.embeddings.SentenceEmbeddings SentenceEmbeddings]] can be used for
* the `inputCol`.
*
* 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 ClassifierDLApproach()
* .setInputCols("sentence_embeddings")
* .setOutputCol("category")
* .setLabelColumn("label")
* .setTestDataset("test_data")
* }}}
*
* For extended examples of usage, see the Examples
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/scala/training/Train%20Multi-Class%20Text%20Classification%20on%20News%20Articles.scala [1]]]
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb [2]]]
* and the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/ClassifierDLTestSpec.scala ClassifierDLTestSpec]].
*
* ==Example==
* In this example, the training data `"sentiment.csv"` has the form of
* {{{
* text,label
* This movie is the best movie I have wached ever! In my opinion this movie can win an award.,0
* This was a terrible movie! The acting was bad really bad!,1
* ...
* }}}
* Then traning can be done like so:
* {{{
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
* import com.johnsnowlabs.nlp.annotators.classifier.dl.ClassifierDLApproach
* 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 ClassifierDLApproach()
* .setInputCols("sentence_embeddings")
* .setOutputCol("category")
* .setLabelColumn("label")
* .setBatchSize(64)
* .setMaxEpochs(20)
* .setLr(5e-3f)
* .setDropout(0.5f)
*
* val pipeline = new Pipeline()
* .setStages(
* Array(
* documentAssembler,
* useEmbeddings,
* docClassifier
* )
* )
*
* val pipelineModel = pipeline.fit(smallCorpus)
* }}}
*
* @see
* [[MultiClassifierDLApproach]] for multi-class classification
* @see
* [[SentimentDLApproach]] 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 ClassifierDLApproach(override val uid: String)
extends AnnotatorApproach[ClassifierDLModel]
with ParamsAndFeaturesWritable
with ClassifierEncoder {
def this() = this(Identifiable.randomUID("ClassifierDL"))
/** Trains TensorFlow model for multi-class text classification */
override val description = "Trains TensorFlow model for multi-class text classification"
/** Input 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
/** Dropout coefficient (Default: `0.5f`)
*
* @group param
*/
val dropout = new FloatParam(this, "dropout", "Dropout coefficient")
/** Dropout coefficient (Default: `0.5f`)
*
* @group setParam
*/
def setDropout(dropout: Float): ClassifierDLApproach.this.type = set(this.dropout, dropout)
/** Dropout coefficient (Default: `0.5f`)
*
* @group getParam
*/
def getDropout: Float = $(this.dropout)
setDefault(maxEpochs -> 10, lr -> 5e-3f, dropout -> 0.5f, batchSize -> 64)
override def train(
dataset: Dataset[_],
recursivePipeline: Option[PipelineModel]): ClassifierDLModel = {
val labelColType = dataset.schema($(labelColumn)).dataType
require(
labelColType == StringType | labelColType == IntegerType | labelColType == DoubleType | labelColType == FloatType | labelColType == LongType,
s"The label column $labelColumn type is $labelColType and it's not compatible. " +
s"Compatible types are StringType, IntegerType, DoubleType, LongType, or FloatType. ")
val (trainDataset, trainLabels) = buildDatasetWithLabels(dataset, getInputCols(0))
val settings = ClassifierDatasetEncoderParams(tags = trainLabels)
val encoder = new ClassifierDatasetEncoder(settings)
val trainInputs = extractInputs(encoder, trainDataset)
var testEncoder: Option[ClassifierDatasetEncoder] = None
val testInputs =
if (!isDefined(testDataset)) None
else {
val testDataFrame = ResourceHelper.readSparkDataFrame($(testDataset))
val (test, testLabels) = buildDatasetWithLabels(testDataFrame, getInputCols(0))
val settings = ClassifierDatasetEncoderParams(tags = testLabels)
testEncoder = Some(new ClassifierDatasetEncoder(settings))
Option(extractInputs(testEncoder.get, test))
}
val tfWrapper: TensorflowWrapper = loadSavedModel()
val classifier =
try {
val model = new TensorflowClassifier(
tensorflow = tfWrapper,
encoder,
testEncoder,
Verbose($(verbose)))
if (isDefined(randomSeed)) {
Random.setSeed($(randomSeed))
}
model.train(
trainInputs,
testInputs,
trainLabels.length,
lr = $(lr),
batchSize = $(batchSize),
dropout = $(dropout),
endEpoch = $(maxEpochs),
configProtoBytes = getConfigProtoBytes,
validationSplit = $(validationSplit),
evaluationLogExtended = $(evaluationLogExtended),
enableOutputLogs = $(enableOutputLogs),
outputLogsPath = $(outputLogsPath),
uuid = this.uid)
model
} catch {
case e: Exception =>
throw e
}
val newWrapper = new TensorflowWrapper(
TensorflowWrapper.extractVariablesSavedModel(
tfWrapper.getTFSession(configProtoBytes = getConfigProtoBytes)),
tfWrapper.graph)
val embeddingsRef = HasStorageRef.getStorageRefFromInput(
dataset,
$(inputCols),
AnnotatorType.SENTENCE_EMBEDDINGS)
val model = new ClassifierDLModel()
.setDatasetParams(classifier.encoder.params)
.setModelIfNotSet(dataset.sparkSession, newWrapper)
.setStorageRef(embeddingsRef)
if (get(configProtoBytes).isDefined)
model.setConfigProtoBytes($(configProtoBytes))
model
}
def loadSavedModel(): TensorflowWrapper = {
val wrapper =
TensorflowWrapper
.readZippedSavedModel("/classifier-dl", tags = Array("serve"), initAllTables = true)
wrapper.variables = Variables(Array.empty[Array[Byte]], Array.empty[Byte])
wrapper
}
}
/** This is the companion object of [[ClassifierDLApproach]]. Please refer to that class for the
* documentation.
*/
object ClassifierDLApproach extends DefaultParamsReadable[ClassifierDLApproach]
