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updata ml algorithm lib
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ml-accelerator/src/main/scala/org/apache/spark/ml/feature/IDF.scala
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You 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. | ||
| */ | ||
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| package org.apache.spark.ml.feature | ||
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| import org.apache.hadoop.fs.Path | ||
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| import org.apache.spark.annotation.Since | ||
| import org.apache.spark.ml._ | ||
| import org.apache.spark.ml.linalg.{Vector, VectorUDT} | ||
| import org.apache.spark.ml.param._ | ||
| import org.apache.spark.ml.param.shared._ | ||
| import org.apache.spark.ml.util._ | ||
| import org.apache.spark.mllib.feature | ||
| import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors} | ||
| import org.apache.spark.mllib.util.MLUtils | ||
| import org.apache.spark.rdd.RDD | ||
| import org.apache.spark.sql._ | ||
| import org.apache.spark.sql.functions._ | ||
| import org.apache.spark.sql.types.StructType | ||
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| /** | ||
| * Params for [[IDF]] and [[IDFModel]]. | ||
| */ | ||
| private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol { | ||
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| /** | ||
| * The minimum number of documents in which a term should appear. | ||
| * Default: 0 | ||
| * @group param | ||
| */ | ||
| final val minDocFreq = new IntParam( | ||
| this, "minDocFreq", "minimum number of documents in which a term should appear for filtering" + | ||
| " (>= 0)", ParamValidators.gtEq(0)) | ||
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| setDefault(minDocFreq -> 0) | ||
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| /** @group getParam */ | ||
| def getMinDocFreq: Int = $(minDocFreq) | ||
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| /** | ||
| * Validate and transform the input schema. | ||
| */ | ||
| protected def validateAndTransformSchema(schema: StructType): StructType = { | ||
| SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT) | ||
| SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT) | ||
| } | ||
| } | ||
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| /** | ||
| * Compute the Inverse Document Frequency (IDF) given a collection of documents. | ||
| */ | ||
| @Since("1.4.0") | ||
| final class IDF @Since("1.4.0") (@Since("1.4.0") override val uid: String) | ||
| extends Estimator[IDFModel] with IDFBase with DefaultParamsWritable { | ||
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| @Since("1.4.0") | ||
| def this() = this(Identifiable.randomUID("idf")) | ||
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| /** @group setParam */ | ||
| @Since("1.4.0") | ||
| def setInputCol(value: String): this.type = set(inputCol, value) | ||
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| /** @group setParam */ | ||
| @Since("1.4.0") | ||
| def setOutputCol(value: String): this.type = set(outputCol, value) | ||
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| /** @group setParam */ | ||
| @Since("1.4.0") | ||
| def setMinDocFreq(value: Int): this.type = set(minDocFreq, value) | ||
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| @Since("2.0.0") | ||
| override def fit(dataset: Dataset[_]): IDFModel = { | ||
| transformSchema(dataset.schema, logging = true) | ||
| val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map { | ||
| case Row(v: Vector) => OldVectors.fromML(v) | ||
| } | ||
| val idf = new feature.IDF($(minDocFreq)).fit(input) | ||
| copyValues(new IDFModel(uid, idf).setParent(this)) | ||
| } | ||
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| @Since("1.4.0") | ||
| override def transformSchema(schema: StructType): StructType = { | ||
| validateAndTransformSchema(schema) | ||
| } | ||
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| @Since("1.4.1") | ||
| override def copy(extra: ParamMap): IDF = defaultCopy(extra) | ||
| } | ||
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| @Since("1.6.0") | ||
| object IDF extends DefaultParamsReadable[IDF] { | ||
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| @Since("1.6.0") | ||
| override def load(path: String): IDF = super.load(path) | ||
| } | ||
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| /** | ||
| * Model fitted by [[IDF]]. | ||
| */ | ||
| @Since("1.4.0") | ||
| class IDFModel private[ml] ( | ||
| @Since("1.4.0") override val uid: String, | ||
| idfModel: feature.IDFModel) | ||
| extends Model[IDFModel] with IDFBase with MLWritable { | ||
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| import IDFModel._ | ||
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| /** @group setParam */ | ||
| @Since("1.4.0") | ||
| def setInputCol(value: String): this.type = set(inputCol, value) | ||
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| /** @group setParam */ | ||
| @Since("1.4.0") | ||
| def setOutputCol(value: String): this.type = set(outputCol, value) | ||
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| @Since("2.0.0") | ||
| override def transform(dataset: Dataset[_]): DataFrame = { | ||
| transformSchema(dataset.schema, logging = true) | ||
| // TODO: Make the idfModel.transform natively in ml framework to avoid extra conversion. | ||
| val idf = udf { vec: Vector => idfModel.transform(OldVectors.fromML(vec)).asML } | ||
| dataset.withColumn($(outputCol), idf(col($(inputCol)))) | ||
| } | ||
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| @Since("1.4.0") | ||
| override def transformSchema(schema: StructType): StructType = { | ||
| validateAndTransformSchema(schema) | ||
| } | ||
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| @Since("1.4.1") | ||
| override def copy(extra: ParamMap): IDFModel = { | ||
| val copied = new IDFModel(uid, idfModel) | ||
| copyValues(copied, extra).setParent(parent) | ||
| } | ||
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| /** Returns the IDF vector. */ | ||
| @Since("2.0.0") | ||
| def idf: Vector = idfModel.idf.asML | ||
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| @Since("1.6.0") | ||
| override def write: MLWriter = new IDFModelWriter(this) | ||
| } | ||
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| @Since("1.6.0") | ||
| object IDFModel extends MLReadable[IDFModel] { | ||
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| private[IDFModel] class IDFModelWriter(instance: IDFModel) extends MLWriter { | ||
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| private case class Data(idf: Vector) | ||
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| override protected def saveImpl(path: String): Unit = { | ||
| DefaultParamsWriter.saveMetadata(instance, path, sc) | ||
| val data = Data(instance.idf) | ||
| val dataPath = new Path(path, "data").toString | ||
| sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath) | ||
| } | ||
| } | ||
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| private class IDFModelReader extends MLReader[IDFModel] { | ||
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| private val className = classOf[IDFModel].getName | ||
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| override def load(path: String): IDFModel = { | ||
| val metadata = DefaultParamsReader.loadMetadata(path, sc, className) | ||
| val dataPath = new Path(path, "data").toString | ||
| val data = sparkSession.read.parquet(dataPath) | ||
| val Row(idf: Vector) = MLUtils.convertVectorColumnsToML(data, "idf") | ||
| .select("idf") | ||
| .head() | ||
| val model = new IDFModel(metadata.uid, new feature.IDFModel(OldVectors.fromML(idf))) | ||
| DefaultParamsReader.getAndSetParams(model, metadata) | ||
| model | ||
| } | ||
| } | ||
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| @Since("1.6.0") | ||
| override def read: MLReader[IDFModel] = new IDFModelReader | ||
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| @Since("1.6.0") | ||
| override def load(path: String): IDFModel = super.load(path) | ||
| } |
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