--- title: DecisionTree description: "DecisionTree分为ML Classification API和ML Regression API两大类模型接口。" url: https://www.hikunpeng.com/document/detail/zh/kunpengboostkithistory/2003/bds/kunpengbdssparkml_20_0028.html sourcePath: /source/zh/kunpengboostkithistory/2003/bds/kunpengbdssparkml_20_0028.html indexId: 7db9663bb72023176262885e8db1146e6db3b1aaba240c05c4a00535f42cb24073 --- # DecisionTree DecisionTree分为ML Classification API和ML Regression API两大类模型接口。 | 模型接口类别 | 函数接口 | | --- | --- | | ML Classification API | def fit(dataset: Dataset[\_]): DecisionTreeClassificationModel | | ML Classification API | def fit(dataset: Dataset[\_], paramMaps: Array[ParamMap]): Seq[DecisionTreeClassificationModel] | | ML Classification API | def fit(dataset: Dataset[\_], paramMap: ParamMap): DecisionTreeClassificationModel | | ML Classification API | def fit(dataset: Dataset[\_], firstParamPair: ParamPair[\_], otherParamPairs: ParamPair[\_]\*): DecisionTreeClassificationModel | | ML Regression API | def fit(dataset: Dataset[\_]): DecisionTreeRegressionModel | | ML Regression API | def fit(dataset: Dataset[\_], paramMaps: Array[ParamMap]): Seq[DecisionTreeRegressionModel] | | ML Regression API | def fit(dataset: Dataset[\_], paramMap: ParamMap): DecisionTreeRegressionModel | | ML Regression API | def fit(dataset: Dataset[\_], firstParamPair: ParamPair[\_], otherParamPairs: ParamPair[\_]\*): DecisionTreeRegressionModel | #### ML Classification API - 功能描述 传入Dataset格式的样本数据，调用训练接口，输出决策树分类模型。 - 输入输出 1. 包名：package org.apache.spark.ml.classification 2. 类名：DecisionTreeClassifier 3. 方法名：fit 4. 输入：Dataset[_]，训练样本数据，必须字段如下。| Param name | Type(s) | Default | Description | | --- | --- | --- | --- | | labelCol | Double | "label" | Label to predict | | featuresCol | Vector | "features" | 特征标签 | 5. 算法参数| 算法参数 | | --- | | def setCheckpointInterval(value: Int): DecisionTreeClassifier.this.type Specifies how often to checkpoint the cached node IDs. def setFeaturesCol(value: String): DecisionTreeClassifier def setImpurity(value: String): DecisionTreeClassifier.this.type def setLabelCol(value: String): DecisionTreeClassifier def setMaxBins(value: Int):DecisionTreeClassifier.this.type def setMaxDepth(value: Int): DecisionTreeClassifier.this.type def setMinInfoGain(value: Double): DecisionTreeClassifier.this.type def setMinInstancesPerNode(value: Int):DecisionTreeClassifier.this.type def setPredictionCol(value: String): DecisionTreeClassifier def setProbabilityCol(value: String): DecisionTreeClassifier def setRawPredictionCol(value: String): DecisionTreeClassifier def setSeed(value: Long): DecisionTreeClassifier.this.type def setThresholds(value: Array[Double]): DecisionTreeClassifier | 6. 新增算法参数。| 参数名称 | 参数含义 | 取值类型 | | --- | --- | --- | | numTrainingDataCopi es | 训练数据的副本数量 | Int，缺省值为1，必须大于等于1 | | broadcastVariables | 是否广播具有较大存储空间的变量 | Boolean，缺省值为false | | numPartsPerTrainingD ataCopy | 单个训练数据副本的分区数 | Int，必须大于等于0，缺省值为0，0\->不重新分区 | | binnedFeaturesDataTy pe | 训练样本数据中特征的存储格式 | String，取值范围： array或者fasthashmap，缺省值为array | | copyStrategy | 副本分配策略的选择 | String，取值范围： normal或plus，缺省值为normal | | numFeaturesOptFindS plits | 启动高维特征切分点搜索优化的维度阈值 | Int，缺省值为8196 | 参数及fit代码接口示例： import org.apache.spark.ml.param.{ParamMap, ParamPair} val dt= new DecisionTreeClassifier()//定义 //定义def fit(dataset: Dataset[_], paramMap: ParamMap) 接口参数 val paramMap = ParamMap(dt.maxDepth -> maxDepth).put(dt.maxBins, maxBins) // 定义def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): 接口参数 val paramMaps = new Array[ParamMap](2) for (i <- 0 to paramMaps.size) { paramMaps(i) = ParamMap(dt.maxDepth -> maxDepth) .put(dt.maxBins, maxBins) }//对paramMaps进行赋值 // 定义def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) 接口参数 val firstParamPair= ParamPair(dt.maxDepth, maxDepth1) val otherParamPairs_1st= ParamPair(dt.maxDepth, maxDepth2) val otherParamPairs_2nd= ParamPair(dt.maxBins, maxBins) // 调用各个fit接口 model = dt.fit(trainingData) model = dt.fit(trainingData, paramMap) models = dt.fit(trainingData, paramMaps) model = dt.fit(trainingData, firstParamPair, otherParamPairs_1st, otherParamPairs_2nd) 7. 输出：DecisionTreeClassificationModel，决策树分类模型，模型预测时的输出字段如下。| Param name | Type(s) | Default | Description | | --- | --- | --- | --- | | predictionCol | Double | "prediction" | predictionCol | | rawPredictionCo l | Vector | "rawPrediction" | Vector of length \# classes, with the counts of training instance labels at the tree node which makes the prediction | | probabilityCol | Vector | "probability" | Vector of length \# classes equal to rawPrediction normalized to a multinomial distribution | - 使用样例 ``` import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.DecisionTreeClassificationModel import org.apache.spark.ml.classification.DecisionTreeClassifier import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator import org.apache.spark.ml.feature. { IndexToString , StringIndexer , VectorIndexer } // Load the data stored in LIBSVM format as a DataFrame. val val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") // Index labels, adding metadata to the label column. // Fit on whole dataset to include all labels in index. val labelIndexer = new StringIndexer () .setInputCol("label") .setOutputCol("indexedLabel") .fit(data) // Automatically identify categorical features, and index them. val featureIndexer = new VectorIndexer () .setInputCol("features") .setOutputCol("indexedFeatures") .setMaxCategories(4) // features with > 4 distinct values are treated as continuous. .fit(data) // Split the data into training and test sets (30% held out for testing). val Array (trainingData, testData) = data.randomSplit( Array (0.7, 0.3)) // Train a DecisionTree model. val dt = new DecisionTreeClassifier () .setLabelCol("indexedLabel") .setFeaturesCol("indexedFeatures") // Convert indexed labels back to original labels. val labelConverter = new IndexToString () .setInputCol("prediction") .setOutputCol("predictedLabel") .setLabels(labelIndexer.labels) // Chain indexers and tree in a Pipeline. val pipeline = new Pipeline () .setStages( Array (labelIndexer, featureIndexer, dt, labelConverter)) // Train model. This also runs the indexers. val model = pipeline.fit(trainingData) // Make predictions. val predictions = model.transform(testData) // Select example rows to display. predictions.select("predictedLabel", "label", "features").show(5) // Select (prediction, true label) and compute test error. val evaluator = new MulticlassClassificationEvaluator () .setLabelCol("indexedLabel") .setPredictionCol("prediction") .setMetricName("accuracy") val accuracy = evaluator.evaluate(predictions) println(s"Test Error = ${(1.0 - accuracy)}") val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel] println(s"Learned classification tree model:\n ${treeModel.toDebugString}") ``` - 结果样例 ``` +--------------+-----+--------------------+ |predictedLabel|label| features| +--------------+-----+--------------------+ | 1.0| 1.0|(47236,[270,439,5...| | 1.0| 1.0|(47236,[3023,6093...| | -1.0| -1.0|(47236,[270,391,4...| | -1.0| -1.0|(47236,[3718,3723...| | 1.0| 1.0|(47236,[729,760,1...| +--------------+-----+--------------------+ only showing top 5 rows Test Error = 0.06476632743800015 ``` #### ML Regression API - 功能描述 传入Dataset格式的样本数据，调用训练接口，输出决策树分类模型。 - 输入输出 1. 包名：package org.apache.spark.ml.regression 2. 类名：DecisionTreeClassifier 3. 方法名：fit 4. 输入：Dataset[_]，训练样本数据，必须字段如下。| Param name | Type(s) | Default | Description | | --- | --- | --- | --- | | labelCol | Double | "label" | Label to predict | | featuresCol | Vector | "features" | 特征标签 | 5. 算法参数| 算法参数 | | --- | | def setCheckpointInterval(value: Int): DecisionTreeRegressor.this.type Specifies how often to checkpoint the cached node IDs. def setFeaturesCol(value: String): DecisionTreeRegressor def setImpurity(value: String): DecisionTreeRegressor.this.type def setLabelCol(value: String): DecisionTreeRegressor def setMaxBins(value: Int): DecisionTreeRegressor.this.type def setMaxDepth(value: Int): DecisionTreeRegressor.this.type def setMinInfoGain(value: Double): DecisionTreeRegressor.this.type def setMinInstancesPerNode(value: Int): DecisionTreeRegressor.this.type def setPredictionCol(value: String): DecisionTreeRegressor def setSeed(value: Long): DecisionTreeRegressor.this.type def setVarianceCol(value: String): DecisionTreeRegressor.this.type | 6. 新增算法参数| 参数名称 | 参数含义 | 取值类型 | | --- | --- | --- | | numTrainingDataCopi es | 训练数据的副本数量 | Int，缺省值为1，必须大于等于1 | | broadcastVariables | 是否广播具有较大存储空间的变量 | Boolean，缺省值为false | | numPartsPerTrainingD ataCopy | 单个训练数据副本的分区数 | Int，必须大于等于0，缺省值为0，0\->不重新分区 | | binnedFeaturesDataTy pe | 训练样本数据中特征的存储格式 | String，取值范围： array或者fasthashmap，缺省值为array | | copyStrategy | 副本分配策略的选择 | String，取值范围： normal或plus，缺省值为normal | | numFeaturesOptFindS plits | 启动高维特征切分点搜索优化的维度阈值 | Int，缺省值为8196 | 参数及fit代码接口示例： import org.apache.spark.ml.param.{ParamMap, ParamPair} val rf= new DecisionTreeClassifier()//定义 //定义def fit(dataset: Dataset[_], paramMap: ParamMap) 接口参数 val paramMap = ParamMap(dt.maxDepth -> maxDepth).put(dt.maxBins, maxBins) // 定义def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): 接口参数 val paramMaps = new Array[ParamMap](2) for (i <- 0 to paramMaps.size) { paramMaps(i) = ParamMap(dt.maxDepth -> maxDepth) .put(dt.maxBins, maxBins) }//对paramMaps进行赋值 // 定义def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) 接口参数 val firstParamPair= ParamPair(dt.maxDepth, maxDepth1) val otherParamPairs_1st= ParamPair(dt.maxDepth, maxDepth2) val otherParamPairs_2nd= ParamPair(dt.maxBins, maxBins) // 调用各个fit接口 model = dt.fit(trainingData) model = dt.fit(trainingData, paramMap) models = dt.fit(trainingData, paramMaps) model = dt.fit(trainingData, firstParamPair, otherParamPairs_1st, otherParamPairs_2nd) 7. 输出：DecisionTreeRegressionModel，决策树回归模型，模型预测时的输出字段如下。| Param name | Type(s) | Default | Description | | --- | --- | --- | --- | | predictionCol | Double | "prediction" | Predicted label | | varianceCol | Double | \- | The biased sample variance of prediction | - 使用样例 ``` import org.apache.spark.ml.Pipeline import org.apache.spark.ml.evaluation.RegressionEvaluator import org.apache.spark.ml.feature.VectorIndexer import org.apache.spark.ml.regression.DecisionTreeRegressionModel import org.apache.spark.ml.regression.DecisionTreeRegressor // Load the data stored in LIBSVM format as a DataFrame. val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") // Automatically identify categorical features, and index them. // Here, we treat features with > 4 distinct values as continuous. val featureIndexer = new VectorIndexer () .setInputCol("features") .setOutputCol("indexedFeatures") .setMaxCategories(4) .fit(data) // Split the data into training and test sets (30% held out for testing). val Array (trainingData, testData) = data.randomSplit( Array (0.7, 0.3)) // Train a DecisionTree model. val dt = new DecisionTreeRegressor () .setLabelCol("label") .setFeaturesCol("indexedFeatures") // Chain indexer and tree in a Pipeline. val pipeline = new Pipeline () .setStages( Array (featureIndexer, dt)) // Train model. This also runs the indexer. val model = pipeline.fit(trainingData) // Make predictions. val predictions = model.transform(testData) // Select example rows to display. predictions.select("prediction", "label", "features").show(5) // Select (prediction, true label) and compute test error. val evaluator = new RegressionEvaluator () .setLabelCol("label") .setPredictionCol("prediction") .setMetricName("rmse") val rmse = evaluator.evaluate(predictions) println(s"Root Mean Squared Error (RMSE) on test data = $rmse") val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel] println(s"Learned regression tree model:\n ${treeModel.toDebugString}") ``` - 结果样例 ``` +----------+-----+--------------------+ |prediction|label| features| +----------+-----+--------------------+ | 0.51| 0.3|(1000,[0,1,2,3,4,...| +----------+-----+--------------------+ Root Mean Squared Error (RMSE) on test data = 0.21000000000000002 ```