huaxingao commented on a change in pull request #28710: URL: https://github.com/apache/spark/pull/28710#discussion_r434853509
########## File path: mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala ########## @@ -0,0 +1,271 @@ +/* + * 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 org.apache.spark.annotation.Since +import org.apache.spark.ml.functions.checkNonNegativeWeight +import org.apache.spark.ml.linalg.Vector +import org.apache.spark.mllib.evaluation.{BinaryClassificationMetrics, MulticlassMetrics} +import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.sql.functions.{col, lit} +import org.apache.spark.sql.types.DoubleType + + +/** + * Abstraction for multiclass classification results for a given model. + */ +private[classification] trait ClassificationSummary extends Serializable { + + /** + * Dataframe output by the model's `transform` method. + */ + @Since("3.1.0") + def predictions: DataFrame + + /** Field in "predictions" which gives the prediction of each class. */ + @Since("3.1.0") + def predictionCol: String + + /** Field in "predictions" which gives the true label of each instance (if available). */ + @Since("3.1.0") + def labelCol: String + + /** Field in "predictions" which gives the features of each instance as a vector. */ + @Since("3.1.0") + def featuresCol: String + + /** Field in "predictions" which gives the weight of each instance as a vector. */ + @Since("3.1.0") + def weightCol: String + + @transient private val multiclassMetrics = { + if (predictions.schema.fieldNames.contains(weightCol)) { + new MulticlassMetrics( + predictions.select( + col(predictionCol), + col(labelCol).cast(DoubleType), + checkNonNegativeWeight(col(weightCol).cast(DoubleType))).rdd.map { + case Row(prediction: Double, label: Double, weight: Double) => (prediction, label, weight) + }) + } else { + new MulticlassMetrics( + predictions.select( + col(predictionCol), + col(labelCol).cast(DoubleType), + lit(1.0)).rdd.map { + case Row(prediction: Double, label: Double, weight: Double) => (prediction, label, weight) + }) + } + } + + /** + * Returns the sequence of labels in ascending order. This order matches the order used + * in metrics which are specified as arrays over labels, e.g., truePositiveRateByLabel. + * + * Note: In most cases, it will be values {0.0, 1.0, ..., numClasses-1}, However, if the + * training set is missing a label, then all of the arrays over labels + * (e.g., from truePositiveRateByLabel) will be of length numClasses-1 instead of the + * expected numClasses. + */ + @Since("3.1.0") + def labels: Array[Double] = multiclassMetrics.labels + + /** Returns true positive rate for each label (category). */ + @Since("3.1.0") + def truePositiveRateByLabel: Array[Double] = recallByLabel + + /** Returns false positive rate for each label (category). */ + @Since("3.1.0") + def falsePositiveRateByLabel: Array[Double] = { + multiclassMetrics.labels.map(label => multiclassMetrics.falsePositiveRate(label)) + } + + /** Returns precision for each label (category). */ + @Since("3.1.0") + def precisionByLabel: Array[Double] = { + multiclassMetrics.labels.map(label => multiclassMetrics.precision(label)) + } + + /** Returns recall for each label (category). */ + @Since("3.1.0") + def recallByLabel: Array[Double] = { + multiclassMetrics.labels.map(label => multiclassMetrics.recall(label)) + } + + /** Returns f-measure for each label (category). */ + @Since("3.1.0") + def fMeasureByLabel(beta: Double): Array[Double] = { + multiclassMetrics.labels.map(label => multiclassMetrics.fMeasure(label, beta)) + } + + /** Returns f1-measure for each label (category). */ + @Since("3.1.0") + def fMeasureByLabel: Array[Double] = fMeasureByLabel(1.0) + + /** + * Returns accuracy. + * (equals to the total number of correctly classified instances + * out of the total number of instances.) + */ + @Since("3.1.0") + def accuracy: Double = multiclassMetrics.accuracy + + /** + * Returns weighted true positive rate. + * (equals to precision, recall and f-measure) + */ + @Since("3.1.0") + def weightedTruePositiveRate: Double = weightedRecall + + /** Returns weighted false positive rate. */ + @Since("3.1.0") + def weightedFalsePositiveRate: Double = multiclassMetrics.weightedFalsePositiveRate + + /** + * Returns weighted averaged recall. + * (equals to precision, recall and f-measure) + */ + @Since("3.1.0") + def weightedRecall: Double = multiclassMetrics.weightedRecall + + /** Returns weighted averaged precision. */ + @Since("3.1.0") + def weightedPrecision: Double = multiclassMetrics.weightedPrecision + + /** Returns weighted averaged f-measure. */ + @Since("3.1.0") + def weightedFMeasure(beta: Double): Double = multiclassMetrics.weightedFMeasure(beta) + + /** Returns weighted averaged f1-measure. */ + @Since("3.1.0") + def weightedFMeasure: Double = multiclassMetrics.weightedFMeasure(1.0) + + /** + * Convenient method for casting to binary classification summary. + * This method will throw an Exception if the summary is not a binary summary. + */ + @Since("3.1.0") + def asBinary: BinaryClassificationSummary = this match { Review comment: I think this is a convenient method @WeichenXu123 added for LogisticRegressionSummary per Seth's request. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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