PySpark:获取ROC曲线中每个点的阈值(cuttoff)

probability 来自的响应中的字段 model.transform(test)

或者,您可以使用 BinaryClassificationMetrics

不幸的是,PySpark版本似乎没有实现Scala版本所实现的大多数方法,因此需要用Python包装类来实现。

例如:

from pyspark.mllib.evaluation import BinaryClassificationMetrics

# Scala version implements .roc() and .pr()
# Python: https://spark.apache.org/docs/latest/api/python/_modules/pyspark/mllib/common.html
# Scala: https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.html
class CurveMetrics(BinaryClassificationMetrics):
    def __init__(self, *args):
        super(CurveMetrics, self).__init__(*args)

    def _to_list(self, rdd):
        points = []
        # Note this collect could be inefficient for large datasets 
        # considering there may be one probability per datapoint (at most)
        # The Scala version takes a numBins parameter, 
        # but it doesn't seem possible to pass this from Python to Java
        for row in rdd.collect():
            # Results are returned as type scala.Tuple2, 
            # which doesn't appear to have a py4j mapping
            points += [(float(row._1()), float(row._2()))]
        return points

    def get_curve(self, method):
        rdd = getattr(self._java_model, method)().toJavaRDD()
        return self._to_list(rdd)

用法:

import matplotlib.pyplot as plt

preds = predictions.select('label','probability').rdd.map(lambda row: (float(row['probability'][1]), float(row['label'])))

# Returns as a list (false positive rate, true positive rate)
points = CurveMetrics(preds).get_curve('roc')

plt.figure()
x_val = [x[0] for x in points]
y_val = [x[1] for x in points]
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.plot(x_val, y_val)

结果:

下面是一个例子,如果你没有嫁给ROC,F1分数曲线的阈值:

一种方法是使用 sklearn.metrics.roc_curve .

首先使用您的模型进行预测:

from pyspark.ml.classification import LogisticRegression

lr = LogisticRegression(labelCol="label", featuresCol="features")
model = lr.fit(trainingData)
predictions = model.transform(testData)

¹ :

preds = predictions.select('label','probability')\
    .rdd.map(lambda row: (float(row['probability'][1]), float(row['label'])))\
    .collect()

现在转换 preds 与…共事 roc_curve

from sklearn.metrics import roc_curve

y_score, y_true = zip(*preds)
fpr, tpr, thresholds = roc_curve(y_true, y_score, pos_label = 1)

笔记

1. 我不是百分之百确定概率向量总是有序的,这样正标签就会在索引处 1 . 然而,在二进制分类问题中,如果AUC小于0.5,您将立即知道。如果是那样的话,那就吃吧 1-p