Spark ML機器學習庫評估指標示例

本文主要對 Spark ML庫下模型評估指標的講解，以下代碼均以Jupyter Notebook進行講解，Spark版本爲2.4.5。模型評估指標位於包org.apache.spark.ml.evaluation下。

模型評估指標是指測試集的評估指標，而不是訓練集的評估指標

1、迴歸評估指標

RegressionEvaluator

Evaluator for regression, which expects two input columns: prediction and label.

評估指標支持以下幾種：

val metricName: Param[String]

• "rmse" (default): root mean squared error
• "mse": mean squared error
• "r2": R2 metric
• "mae": mean absolute error

Examples

# 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 predictions = lrModel.transform(test)

// 計算精度
val evaluator = new RegressionEvaluator()
  .setLabelCol("label")
  .setPredictionCol("prediction")
  .setMetricName("mse")
val accuracy = evaluator.evaluate(predictions)
print(s"Test MSE: ${accuracy}")

輸出：

Train MSE: 101.57870147367461
Train RMSE: 10.078625971513905
Train MAE: 8.108865602095849
Train r2: 0.039467152584195975

Test MSE: 114.28454406581636

2、分類評估指標

2.1 BinaryClassificationEvaluator

Evaluator for binary classification, which expects two input columns: rawPrediction and label. The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).

評估指標支持以下幾種：

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

Examples

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)

// 模型評估
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}")

輸出結果：

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|
+-----+--------------------+--------------------+--------------------+----------+

Test AUC: 1.0

Test weightedPrecision: 1.0

2.2 MulticlassClassificationEvaluator

Evaluator for multiclass classification, which expects two input columns: prediction and label.

注：既然適用於多分類，當然適用於上面的二分類

評估指標支持如下幾種：

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

Examples

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)}")

輸出結果：

+--------------+-----+--------------------+
|predictedLabel|label|            features|
+--------------+-----+--------------------+
|           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...|
+--------------+-----+--------------------+
only showing top 5 rows

Test Error = 0.040000000000000036