Spark ML机器学习库评估指标示例

本文主要针对火花ML库下模型评估指标的讲解，以下代码均以Jupyter笔记本进行讲解，火花版本为2.4.5。模型评估指标位于包org.apache.spark.ml.evaluation下。

模型评估指标是指测试集的评估指标，而不是训练集的评估指标

1，回归评估指标

回归评估器

回归评估器，需要两个输入列：预测和标签。

评估指标支持以下几种：

val ** metricName **：Param [String]

• “RMSE”（最大值）：根均方误差- “MSE”：均方误差- “R2”：R2变量- “前”：平均绝对误差

范例

# import dependencies
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.evaluation.RegressionEvaluator

// Load training data
val data = spark.read.format("libsvm")
  .load("/data1/software/spark/data/mllib/sample_linear_regression_data.txt")

val lr = new LinearRegression()
  .setMaxIter(10)
  .setRegParam(0.3)
  .setElasticNetParam(0.8)

// Fit the model
val lrModel = lr.fit(training)

// Summarize the model over the training set and print out some metrics
val trainingSummary = lrModel.summary
println(s"Train MSE: ${trainingSummary.meanSquaredError}"）
 println （ s “ Train RMSE：$ {trainingSummary.rootMeanSquaredError}” ）
 println （ s “ Train MAE：$ {trainingSummary.meanAbsoluteError}” ）
 println （ s “ Train r2：$ {trainingSummary.r2}” ）val预测= lrModel 。转换（测试）//计算精度val evaluator = new RegressionEvaluator （）。setLabelCol （“ label” ）。setPredictionCol （“预测”




 
  
  ）。setMetricName （“ mse” ）val精度=求值器。评估（预测）印刷（小号“测试MSE：$ {}精度” ） ```
  




输出：

火车MSE：101.57870147367461
火车RMSE：10.078625971513905
火车MAE：8.108865602095849
列R2：0.039467152584195975975测试MSE：114.28454406581636`''

2，分类评估指标

2.1 BinaryClassificationEvaluator

二进制分类的评估程序，需要两个输入列：rawPrediction和label。rawPrediction列的类型可以是double（二进制0/1预测，或标签1的概率），也可以是vector（原始预测，分数或标签概率的length-2矢量）类型。

评估指标支持以下几种：

val metricName: Param[String]
param for metric name in evaluation (supports "areaUnderROC" (default), "areaUnderPR")

例子

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

// Load training data
val data = spark.read.format("libsvm").load("/data1/software/spark/data/mllib/sample_libsvm_data.txt")

val Array(train, test) = data.randomSplit(Array(0.8, 0.2))

val lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.3)
  .setElasticNetParam(0.8)

// Fit the model
val lrModel = lr.fit(train)

// Summarize the model over the training set and print out some metrics
val trainSummary = lrModel.summary
println(s"Train accuracy: ${trainSummary.accuracy}")
println(s"Train weightedPrecision: ${trainSummary.weightedPrecision}")
println(s"Train weightedRecall: ${trainSummary.weightedRecall}")
println(s"Train weightedFMeasure: ${trainSummary.weightedFMeasure}")

val predictions = lrModel.transform(test)
predictions.show(5)

// 模型评估
val evaluator = new BinaryClassificationEvaluator()
  .setLabelCol("label")
  .setRawPredictionCol("rawPrediction")
  .setMetricName("areaUnderROC")
val auc = evaluator.evaluate(predictions)
print(s"Test AUC: ${auc}")

val mulEvaluator = new MulticlassClassificationEvaluator()
  .setLabelCol("label")
  .setPredictionCol("prediction")
  .setMetricName("weightedPrecision")
val precision = evaluator.evaluate(predictions)
print(s"Test weightedPrecision: ${precision}")

输出结果：

Train accuracy: 0.9873417721518988
Train weightedPrecision: 0.9876110961486668
Train weightedRecall: 0.9873417721518987
Train weightedFMeasure: 0.9873124561568825

+-----+--------------------+--------------------+--------------------+----------+
|label|            features|       rawPrediction|         probability|prediction|
+-----+--------------------+--------------------+--------------------+----------+
|  0.0|(692,[122,123,148...|[0.29746771419036...|[0.57382336211209...|       0.0|
|  0.0|(692,[125,126,127...|[0.42262389447949...|[0.60411095396791...|       0.0|
|  0.0|(692,[126,127,128...|[0.74220898710237...|[0.67747871191347...|       0.0|
| 0.0 |（692，[126,127,128 ... | [0.77729372618481 ... | [0.68509655708828 ... | 0.0 
|| 0.0 |（692，[127,128,129 ... | [0.70928896866149 ... | [0.67024402884354 ... | 0.0 | 
+ ----- + -------------------- + -------------------- + -------------------- + ---------- + 测试AUC：1.0 测试加权精度：1.0```






### 2.2 MulticlassClassificationEvaluator

> 用于多类分类的评估程序，需要两个输入列：预测和标签。

> 注：既然适用于多分类，当然适用于上面的二分类

评估指标支持如下几种：

```scala
val metricName: Param[String]
param for metric name in evaluation (supports "f1" (default), "weightedPrecision", "weightedRecall", "accuracy")

例子

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 data = spark.read.format("libsvm").load("/data1/software/spark/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)}")

输出结果：

`

• -------------- + ----- + -------------------- + | ForecastLabel |标签| 功能| + -------------- + ----- + -------------------- + | 0.0 | 0.0 |（692，[95,96,97,12…| | | 0.0 | 0.0 |（692，[122,123,124…| | | 0.0 | 0.0 |（692，[122,123,148…| | | 0.0 | 0.0 | （692，[126,127,128…| | | 0.0 | 0.0 |（692，[126,127,128…| | + -------------- + ----- + ----- --------------- +只表示顶部5行测试错误= 0.040000000000000036` ！