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| 1 | +/* |
| 2 | + * Licensed to the Apache Software Foundation (ASF) under one or more |
| 3 | + * contributor license agreements. See the NOTICE file distributed with |
| 4 | + * this work for additional information regarding copyright ownership. |
| 5 | + * The ASF licenses this file to You under the Apache License, Version 2.0 |
| 6 | + * (the "License"); you may not use this file except in compliance with |
| 7 | + * the License. You may obtain a copy of the License at |
| 8 | + * |
| 9 | + * http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | + * |
| 11 | + * Unless required by applicable law or agreed to in writing, software |
| 12 | + * distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | + * See the License for the specific language governing permissions and |
| 15 | + * limitations under the License. |
| 16 | + */ |
| 17 | + |
| 18 | +package org.apache.spark.ml.feature |
| 19 | + |
| 20 | +import org.apache.hadoop.fs.Path |
| 21 | + |
| 22 | +import org.apache.spark.annotation.Since |
| 23 | +import org.apache.spark.ml._ |
| 24 | +import org.apache.spark.ml.linalg.{Vector, VectorUDT} |
| 25 | +import org.apache.spark.ml.param._ |
| 26 | +import org.apache.spark.ml.param.shared._ |
| 27 | +import org.apache.spark.ml.util._ |
| 28 | +import org.apache.spark.mllib.feature |
| 29 | +import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors} |
| 30 | +import org.apache.spark.mllib.util.MLUtils |
| 31 | +import org.apache.spark.rdd.RDD |
| 32 | +import org.apache.spark.sql._ |
| 33 | +import org.apache.spark.sql.functions._ |
| 34 | +import org.apache.spark.sql.types.StructType |
| 35 | + |
| 36 | +/** |
| 37 | + * Params for [[IDF]] and [[IDFModel]]. |
| 38 | + */ |
| 39 | +private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol { |
| 40 | + |
| 41 | + /** |
| 42 | + * The minimum number of documents in which a term should appear. |
| 43 | + * Default: 0 |
| 44 | + * @group param |
| 45 | + */ |
| 46 | + final val minDocFreq = new IntParam( |
| 47 | + this, "minDocFreq", "minimum number of documents in which a term should appear for filtering" + |
| 48 | + " (>= 0)", ParamValidators.gtEq(0)) |
| 49 | + |
| 50 | + setDefault(minDocFreq -> 0) |
| 51 | + |
| 52 | + /** @group getParam */ |
| 53 | + def getMinDocFreq: Int = $(minDocFreq) |
| 54 | + |
| 55 | + /** |
| 56 | + * Validate and transform the input schema. |
| 57 | + */ |
| 58 | + protected def validateAndTransformSchema(schema: StructType): StructType = { |
| 59 | + SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT) |
| 60 | + SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT) |
| 61 | + } |
| 62 | +} |
| 63 | + |
| 64 | +/** |
| 65 | + * Compute the Inverse Document Frequency (IDF) given a collection of documents. |
| 66 | + */ |
| 67 | +@Since("1.4.0") |
| 68 | +final class IDF @Since("1.4.0") (@Since("1.4.0") override val uid: String) |
| 69 | + extends Estimator[IDFModel] with IDFBase with DefaultParamsWritable { |
| 70 | + |
| 71 | + @Since("1.4.0") |
| 72 | + def this() = this(Identifiable.randomUID("idf")) |
| 73 | + |
| 74 | + /** @group setParam */ |
| 75 | + @Since("1.4.0") |
| 76 | + def setInputCol(value: String): this.type = set(inputCol, value) |
| 77 | + |
| 78 | + /** @group setParam */ |
| 79 | + @Since("1.4.0") |
| 80 | + def setOutputCol(value: String): this.type = set(outputCol, value) |
| 81 | + |
| 82 | + /** @group setParam */ |
| 83 | + @Since("1.4.0") |
| 84 | + def setMinDocFreq(value: Int): this.type = set(minDocFreq, value) |
| 85 | + |
| 86 | + @Since("2.0.0") |
| 87 | + override def fit(dataset: Dataset[_]): IDFModel = { |
| 88 | + transformSchema(dataset.schema, logging = true) |
| 89 | + val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map { |
| 90 | + case Row(v: Vector) => OldVectors.fromML(v) |
| 91 | + } |
| 92 | + val idf = new feature.IDF($(minDocFreq)).fit(input) |
| 93 | + copyValues(new IDFModel(uid, idf).setParent(this)) |
| 94 | + } |
| 95 | + |
| 96 | + @Since("1.4.0") |
| 97 | + override def transformSchema(schema: StructType): StructType = { |
| 98 | + validateAndTransformSchema(schema) |
| 99 | + } |
| 100 | + |
| 101 | + @Since("1.4.1") |
| 102 | + override def copy(extra: ParamMap): IDF = defaultCopy(extra) |
| 103 | +} |
| 104 | + |
| 105 | +@Since("1.6.0") |
| 106 | +object IDF extends DefaultParamsReadable[IDF] { |
| 107 | + |
| 108 | + @Since("1.6.0") |
| 109 | + override def load(path: String): IDF = super.load(path) |
| 110 | +} |
| 111 | + |
| 112 | +/** |
| 113 | + * Model fitted by [[IDF]]. |
| 114 | + */ |
| 115 | +@Since("1.4.0") |
| 116 | +class IDFModel private[ml] ( |
| 117 | + @Since("1.4.0") override val uid: String, |
| 118 | + idfModel: feature.IDFModel) |
| 119 | + extends Model[IDFModel] with IDFBase with MLWritable { |
| 120 | + |
| 121 | + import IDFModel._ |
| 122 | + |
| 123 | + /** @group setParam */ |
| 124 | + @Since("1.4.0") |
| 125 | + def setInputCol(value: String): this.type = set(inputCol, value) |
| 126 | + |
| 127 | + /** @group setParam */ |
| 128 | + @Since("1.4.0") |
| 129 | + def setOutputCol(value: String): this.type = set(outputCol, value) |
| 130 | + |
| 131 | + @Since("2.0.0") |
| 132 | + override def transform(dataset: Dataset[_]): DataFrame = { |
| 133 | + transformSchema(dataset.schema, logging = true) |
| 134 | + // TODO: Make the idfModel.transform natively in ml framework to avoid extra conversion. |
| 135 | + val idf = udf { vec: Vector => idfModel.transform(OldVectors.fromML(vec)).asML } |
| 136 | + dataset.withColumn($(outputCol), idf(col($(inputCol)))) |
| 137 | + } |
| 138 | + |
| 139 | + @Since("1.4.0") |
| 140 | + override def transformSchema(schema: StructType): StructType = { |
| 141 | + validateAndTransformSchema(schema) |
| 142 | + } |
| 143 | + |
| 144 | + @Since("1.4.1") |
| 145 | + override def copy(extra: ParamMap): IDFModel = { |
| 146 | + val copied = new IDFModel(uid, idfModel) |
| 147 | + copyValues(copied, extra).setParent(parent) |
| 148 | + } |
| 149 | + |
| 150 | + /** Returns the IDF vector. */ |
| 151 | + @Since("2.0.0") |
| 152 | + def idf: Vector = idfModel.idf.asML |
| 153 | + |
| 154 | + @Since("1.6.0") |
| 155 | + override def write: MLWriter = new IDFModelWriter(this) |
| 156 | +} |
| 157 | + |
| 158 | +@Since("1.6.0") |
| 159 | +object IDFModel extends MLReadable[IDFModel] { |
| 160 | + |
| 161 | + private[IDFModel] class IDFModelWriter(instance: IDFModel) extends MLWriter { |
| 162 | + |
| 163 | + private case class Data(idf: Vector) |
| 164 | + |
| 165 | + override protected def saveImpl(path: String): Unit = { |
| 166 | + DefaultParamsWriter.saveMetadata(instance, path, sc) |
| 167 | + val data = Data(instance.idf) |
| 168 | + val dataPath = new Path(path, "data").toString |
| 169 | + sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath) |
| 170 | + } |
| 171 | + } |
| 172 | + |
| 173 | + private class IDFModelReader extends MLReader[IDFModel] { |
| 174 | + |
| 175 | + private val className = classOf[IDFModel].getName |
| 176 | + |
| 177 | + override def load(path: String): IDFModel = { |
| 178 | + val metadata = DefaultParamsReader.loadMetadata(path, sc, className) |
| 179 | + val dataPath = new Path(path, "data").toString |
| 180 | + val data = sparkSession.read.parquet(dataPath) |
| 181 | + val Row(idf: Vector) = MLUtils.convertVectorColumnsToML(data, "idf") |
| 182 | + .select("idf") |
| 183 | + .head() |
| 184 | + val model = new IDFModel(metadata.uid, new feature.IDFModel(OldVectors.fromML(idf))) |
| 185 | + DefaultParamsReader.getAndSetParams(model, metadata) |
| 186 | + model |
| 187 | + } |
| 188 | + } |
| 189 | + |
| 190 | + @Since("1.6.0") |
| 191 | + override def read: MLReader[IDFModel] = new IDFModelReader |
| 192 | + |
| 193 | + @Since("1.6.0") |
| 194 | + override def load(path: String): IDFModel = super.load(path) |
| 195 | +} |
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