随机森林（Random Forest）算法原理及Spark MLlib调用实例（Scala/Java/python）

随机森林分类器：

算法简介：

        随机森林是决策树的集成算法。随机森林包含多个决策树来降低过拟合的风险。随机森林同样具有易解释性、可处理类别特征、易扩展到多分类问题、不需特征缩放等性质。

       随机森林分别训练一系列的决策树，所以训练过程是并行的。因算法中加入随机过程，所以每个决策树又有少量区别。通过合并每个树的预测结果来减少预测的方差，提高在测试集上的性能表现。

       随机性体现：

1.每次迭代时，对原始数据进行二次抽样来获得不同的训练数据。

2.对于每个树节点，考虑不同的随机特征子集来进行分裂。

        除此之外，决策时的训练过程和单独决策树训练过程相同。

        对新实例进行预测时，随机森林需要整合其各个决策树的预测结果。回归和分类问题的整合的方式略有不同。分类问题采取投票制，每个决策树投票给一个类别，获得最多投票的类别为最终结果。回归问题每个树得到的预测结果为实数，最终的预测结果为各个树预测结果的平均值。

        spark.ml支持二分类、多分类以及回归的随机森林算法，适用于连续特征以及类别特征。

参数：

checkpointInterval:

类型：整数型。

含义：设置检查点间隔（>=1），或不设置检查点（-1）。

featureSubsetStrategy:

类型：字符串型。

含义：每次分裂候选特征数量。

featuresCol:

类型：字符串型。

含义：特征列名。

impurity:

类型：字符串型。

含义：计算信息增益的准则（不区分大小写）。

labelCol:

类型：字符串型。

含义：标签列名。

maxBins:

类型：整数型。

含义：连续特征离散化的最大数量，以及选择每个节点分裂特征的方式。

maxDepth:

类型：整数型。

含义：树的最大深度（>=0）。

minInfoGain:

类型：双精度型。

含义：分裂节点时所需最小信息增益。

minInstancesPerNode:

类型：整数型。

含义：分裂后自节点最少包含的实例数量。

numTrees:

类型：整数型。

含义：训练的树的数量。

predictionCol:

类型：字符串型。

含义：预测结果列名。

probabilityCol:

类型：字符串型。

含义：类别条件概率预测结果列名。

rawPredictionCol:

类型：字符串型。

含义：原始预测。

seed:

类型：长整型。

含义：随机种子。

subsamplingRate:

类型：双精度型。

含义：学习一棵决策树使用的训练数据比例，范围[0,1]。

thresholds:

类型：双精度数组型。

含义：多分类预测的阀值，以调整预测结果在各个类别的概率。

示例：

        下面的例子导入LibSVM格式数据，并将之划分为训练数据和测试数据。使用第一部分数据进行训练，剩下数据来测试。训练之前我们使用了两种数据预处理方法来对特征进行转换，并且添加了元数据到DataFrame。

Scala:

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}

// Load and parse the data file, converting it to a DataFrame.
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.
// Set maxCategories so features with > 4 distinct values are treated 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 RandomForest model.
val rf = new RandomForestClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures")
.setNumTrees(10)

// Convert indexed labels back to original labels.
val labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("predictedLabel")
.setLabels(labelIndexer.labels)

// Chain indexers and forest in a Pipeline.
val pipeline = new Pipeline()
.setStages(Array(labelIndexer, featureIndexer, rf, 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("Test Error = " + (1.0 - accuracy))

val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
println("Learned classification forest model:\n" + rfModel.toDebugString)

Java:

import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.classification.RandomForestClassificationModel;
import org.apache.spark.ml.classification.RandomForestClassifier;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.ml.feature.*;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;

// Load and parse the data file, converting it to a DataFrame.
Dataset<Row> 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.
StringIndexerModel labelIndexer = new StringIndexer()
.setInputCol("label")
.setOutputCol("indexedLabel")
.fit(data);
// Automatically identify categorical features, and index them.
// Set maxCategories so features with > 4 distinct values are treated as continuous.
VectorIndexerModel featureIndexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexedFeatures")
.setMaxCategories(4)
.fit(data);

// Split the data into training and test sets (30% held out for testing)
Dataset<Row>[] splits = data.randomSplit(new double[] {0.7, 0.3});
Dataset<Row> trainingData = splits[0];
Dataset<Row> testData = splits[1];

// Train a RandomForest model.
RandomForestClassifier rf = new RandomForestClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures");

// Convert indexed labels back to original labels.
IndexToString labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("predictedLabel")
.setLabels(labelIndexer.labels());

// Chain indexers and forest in a Pipeline
Pipeline pipeline = new Pipeline()
.setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter});

// Train model. This also runs the indexers.
PipelineModel model = pipeline.fit(trainingData);

// Make predictions.
Dataset<Row> predictions = model.transform(testData);

// Select example rows to display.
predictions.select("predictedLabel", "label", "features").show(5);

// Select (prediction, true label) and compute test error
MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("indexedLabel")
.setPredictionCol("prediction")
.setMetricName("accuracy");
double accuracy = evaluator.evaluate(predictions);
System.out.println("Test Error = " + (1.0 - accuracy));

RandomForestClassificationModel rfModel = (RandomForestClassificationModel)(model.stages()[2]);
System.out.println("Learned classification forest model:\n" + rfModel.toDebugString());

Python：

from pyspark.ml import Pipeline
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer
from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# Load and parse the data file, converting it to a DataFrame.
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.
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
# Automatically identify categorical features, and index them.
# Set maxCategories so features with > 4 distinct values are treated as continuous.
featureIndexer =\
VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)

# Split the data into training and test sets (30% held out for testing)
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a RandomForest model.
rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", numTrees=10)

# Chain indexers and forest in a Pipeline
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf])

# Train model.  This also runs the indexers.
model = pipeline.fit(trainingData)

# Make predictions.
predictions = model.transform(testData)

# Select example rows to display.
predictions.select("prediction", "indexedLabel", "features").show(5)

# Select (prediction, true label) and compute test error
evaluator = MulticlassClassificationEvaluator(
labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))

rfModel = model.stages[2]
print(rfModel)  # summary only