fMeasure()缺少一个必需的位置参数：'label‘-print_performance_metrics(预测)

Question

from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.mllib.evaluation import BinaryClassificationMetrics, MulticlassMetrics

def print_performance_metrics(predictions):
  # Evaluate model
  evaluator = BinaryClassificationEvaluator(rawPredictionCol="rawPrediction")
  auc = evaluator.evaluate(predictions, {evaluator.metricName: "areaUnderROC"})
  aupr = evaluator.evaluate(predictions, {evaluator.metricName: "areaUnderPR"})
  print("auc = {}".format(auc))
  print("aupr = {}".format(aupr))

  # get rdd of predictions and labels for mllib eval metrics
  predictionAndLabels = predictions.select("prediction","label").rdd

  # Instantiate metrics objects
  binary_metrics = BinaryClassificationMetrics(predictionAndLabels)
  multi_metrics = MulticlassMetrics(predictionAndLabels)

  # Area under precision-recall curve
  print("Area under PR = {}".format(binary_metrics.areaUnderPR))
  # Area under ROC curve
  print("Area under ROC = {}".format(binary_metrics.areaUnderROC))
  # Accuracy
  print("Accuracy = {}".format(multi_metrics.accuracy))
  # Confusion Matrix
  print(multi_metrics.confusionMatrix())
  

  ### Question F1, Precision Score ###
  
  # F1
  print("F1 = {}".format(multi_metrics.fMeasure()))
  # Precision
 # print("Precision = {}".format(multi_metrics.precision()))
  # Recall
 # print("Recall = {}".format(multi_metrics.recall()))
  # FPR
 # print("FPR = {}".format(multi_metrics.falsePositiveRate(0.0)))
  # TPR
 # print("TPR = {}".for

 mat(multi_metrics.truePositiveRate(0.0)))

我正在为模型分数做上述功能。它给了我auc和准确性评分，但它不适用于多类度量。我无法获得它给出的f1和精确分数。

fMeasure()缺少一个必需的位置参数：'label‘

Answer 1

如果你看一下F1评分公式，你会发现它只对一个类的真/假标记感兴趣。因此，F1评分是以标签为中心的。它考虑了某一类模型的真假标号，对一种模型的准确性进行了评估。MultiClassMetrics的正式文档下的“示例”部分清楚地表明，您需要在现有类中选择一个标签来调用fMeasure，当您意识到它是以标签为中心的度量时，这是合理的，正如我所解释的。