在pyspark中获取分类后的所有评估指标

Question

我训练了一个模型，想计算几个重要指标，例如 accuracy、precision、recall 和 f1 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)

但是，它只能计算accuracy和auc，而不能计算其他三个。我应该在这里修改什么？

Answer 1

根据docs，对于F1 measure，precision，recall，MulticlassClassificationEvaluator的相关参数应该分别是

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