获取pyspark中分类后的所有评估指标

Question

我已经训练了一个模型，想要计算几个重要的指标，例如accuracy、、 和。precisionrecallf1 score

我遵循的过程是：

from pyspark.ml.classification import LogisticRegression

lr = LogisticRegression(featuresCol='features',labelCol='label')
lrModel = lr.fit(train)
lrPredictions = lrModel.transform(test)

from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.evaluation import BinaryClassificationEvaluator

eval_accuracy = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="accuracy")
eval_precision = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="precision")
eval_recall = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="recall")
eval_f1 = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="f1Measure")

eval_auc = BinaryClassificationEvaluator(labelCol="label", rawPredictionCol="prediction")

accuracy = eval_accuracy.evaluate(lrPredictions)
precision = eval_precision.evaluate(lrPredictions)
recall = eval_recall.evaluate(lrPredictions)
f1score = eval_f1.evaluate(lrPredictions)

auc = eval_accuracy.evaluate(lrPredictions)

然而，它只能计算accuracy和auc，而不能计算其他三个。这里我应该修改什么？

Answer 1

根据文档，对于 F1 度量、精度和召回率， 的相关参数MulticlassClassificationEvaluator应分别为

metricName="f1"
metricName="precisionByLabel"
metricName="recallByLabel"