Github user sethah commented on a diff in the pull request: https://github.com/apache/spark/pull/13796#discussion_r75351025 --- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/MultinomialLogisticRegression.scala --- @@ -0,0 +1,611 @@ +/* + * 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. + */ + +package org.apache.spark.ml.classification + +import scala.collection.mutable + +import breeze.linalg.{DenseVector => BDV} +import breeze.optimize.{CachedDiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN} +import org.apache.hadoop.fs.Path + +import org.apache.spark.SparkException +import org.apache.spark.annotation.{Experimental, Since} +import org.apache.spark.internal.Logging +import org.apache.spark.ml.feature.Instance +import org.apache.spark.ml.linalg._ +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util._ +import org.apache.spark.mllib.linalg.VectorImplicits._ +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{Dataset, Row} +import org.apache.spark.sql.functions.{col, lit} +import org.apache.spark.sql.types.DoubleType +import org.apache.spark.storage.StorageLevel + +/** + * Params for multinomial logistic (softmax) regression. + */ +private[classification] trait MultinomialLogisticRegressionParams + extends ProbabilisticClassifierParams with HasRegParam with HasElasticNetParam with HasMaxIter + with HasFitIntercept with HasTol with HasStandardization with HasWeightCol { + + /** + * Set thresholds in multiclass (or binary) classification to adjust the probability of + * predicting each class. Array must have length equal to the number of classes, with values >= 0. + * The class with largest value p/t is predicted, where p is the original probability of that + * class and t is the class' threshold. + * + * @group setParam + */ + def setThresholds(value: Array[Double]): this.type = { + set(thresholds, value) + } + + /** + * Get thresholds for binary or multiclass classification. + * + * @group getParam + */ + override def getThresholds: Array[Double] = { + $(thresholds) + } +} + +/** + * :: Experimental :: + * Multinomial Logistic (softmax) regression. + */ +@Since("2.1.0") +@Experimental +class MultinomialLogisticRegression @Since("2.1.0") ( + @Since("2.1.0") override val uid: String) + extends ProbabilisticClassifier[Vector, + MultinomialLogisticRegression, MultinomialLogisticRegressionModel] + with MultinomialLogisticRegressionParams with DefaultParamsWritable with Logging { + + @Since("2.1.0") + def this() = this(Identifiable.randomUID("mlogreg")) + + /** + * Set the regularization parameter. + * Default is 0.0. + * + * @group setParam + */ + @Since("2.1.0") + def setRegParam(value: Double): this.type = set(regParam, value) + setDefault(regParam -> 0.0) + + /** + * Set the ElasticNet mixing parameter. + * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. + * For 0 < alpha < 1, the penalty is a combination of L1 and L2. + * Default is 0.0 which is an L2 penalty. + * + * @group setParam + */ + @Since("2.1.0") + def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value) + setDefault(elasticNetParam -> 0.0) + + /** + * Set the maximum number of iterations. + * Default is 100. + * + * @group setParam + */ + @Since("2.1.0") + def setMaxIter(value: Int): this.type = set(maxIter, value) + setDefault(maxIter -> 100) + + /** + * Set the convergence tolerance of iterations. + * Smaller value will lead to higher accuracy with the cost of more iterations. + * Default is 1E-6. + * + * @group setParam + */ + @Since("2.1.0") + def setTol(value: Double): this.type = set(tol, value) + setDefault(tol -> 1E-6) + + /** + * Whether to fit an intercept term. + * Default is true. + * + * @group setParam + */ + @Since("2.1.0") + def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value) + setDefault(fitIntercept -> true) + + /** + * Whether to standardize the training features before fitting the model. + * The coefficients of models will be always returned on the original scale, + * so it will be transparent for users. Note that with/without standardization, + * the models should always converge to the same solution when no regularization + * is applied. In R's GLMNET package, the default behavior is true as well. + * Default is true. + * + * @group setParam + */ + @Since("2.1.0") + def setStandardization(value: Boolean): this.type = set(standardization, value) + setDefault(standardization -> true) + + /** + * Sets the value of param [[weightCol]]. + * If this is not set or empty, we treat all instance weights as 1.0. + * Default is not set, so all instances have weight one. + * + * @group setParam + */ + @Since("2.1.0") + def setWeightCol(value: String): this.type = set(weightCol, value) + + @Since("2.1.0") + override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value) + + override protected[spark] def train(dataset: Dataset[_]): MultinomialLogisticRegressionModel = { + val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol)) + val instances: RDD[Instance] = + dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map { + case Row(label: Double, weight: Double, features: Vector) => + Instance(label, weight, features) + } + + val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE + if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) + + val instr = Instrumentation.create(this, instances) + instr.logParams(regParam, elasticNetParam, standardization, thresholds, + maxIter, tol, fitIntercept) + + val (summarizer, labelSummarizer) = { + val seqOp = (c: (MultivariateOnlineSummarizer, MultiClassSummarizer), + instance: Instance) => + (c._1.add(instance.features, instance.weight), c._2.add(instance.label, instance.weight)) + + val combOp = (c1: (MultivariateOnlineSummarizer, MultiClassSummarizer), + c2: (MultivariateOnlineSummarizer, MultiClassSummarizer)) => + (c1._1.merge(c2._1), c1._2.merge(c2._2)) + + instances.treeAggregate( + new MultivariateOnlineSummarizer, new MultiClassSummarizer)(seqOp, combOp) + } + + val histogram = labelSummarizer.histogram + val numInvalid = labelSummarizer.countInvalid + val numFeatures = summarizer.mean.size + val numFeaturesPlusIntercept = if (getFitIntercept) numFeatures + 1 else numFeatures + + val numClasses = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { + case Some(n: Int) => + require(n >= histogram.length, s"Specified number of classes $n was " + + s"less than the number of unique labels ${histogram.length}") + n + case None => histogram.length + } + + instr.logNumClasses(numClasses) + instr.logNumFeatures(numFeatures) + + val (coefficients, intercepts, objectiveHistory) = { + if (numInvalid != 0) { + val msg = s"Classification labels should be in {0 to ${numClasses - 1} " + + s"Found $numInvalid invalid labels." + logError(msg) + throw new SparkException(msg) + } + + val labelIsConstant = histogram.count(_ != 0) == 1 --- End diff -- done. You can check LOR now to see if it's what you had in mind. I consolidated one of the if statements.
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