From 98a46f9dffec294386f6c39acafa7f11adb87a8f Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 20 May 2015 07:55:51 -0700
Subject: [PATCH] [SPARK-6094] [MLLIB] Add MultilabelMetrics in PySpark/MLlib
Add MultilabelMetrics in PySpark/MLlib
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #6276 from yanboliang/spark-6094 and squashes the following commits:
b8e3343 [Yanbo Liang] Add MultilabelMetrics in PySpark/MLlib
---
.../mllib/evaluation/MultilabelMetrics.scala | 8 ++
python/pyspark/mllib/evaluation.py | 117 ++++++++++++++++++
2 files changed, 125 insertions(+)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
index a8378a76d2..bf6eb1d5bd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
@@ -19,6 +19,7 @@ package org.apache.spark.mllib.evaluation
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
+import org.apache.spark.sql.DataFrame
/**
* Evaluator for multilabel classification.
@@ -27,6 +28,13 @@ import org.apache.spark.SparkContext._
*/
class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
+ /**
+ * An auxiliary constructor taking a DataFrame.
+ * @param predictionAndLabels a DataFrame with two double array columns: prediction and label
+ */
+ private[mllib] def this(predictionAndLabels: DataFrame) =
+ this(predictionAndLabels.map(r => (r.getSeq[Double](0).toArray, r.getSeq[Double](1).toArray)))
+
private lazy val numDocs: Long = predictionAndLabels.count()
private lazy val numLabels: Long = predictionAndLabels.flatMap { case (_, labels) =>
diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py
index a5e5ddc8fe..aab5e5f4b7 100644
--- a/python/pyspark/mllib/evaluation.py
+++ b/python/pyspark/mllib/evaluation.py
@@ -343,6 +343,123 @@ class RankingMetrics(JavaModelWrapper):
return self.call("ndcgAt", int(k))
+class MultilabelMetrics(JavaModelWrapper):
+ """
+ Evaluator for multilabel classification.
+
+ >>> predictionAndLabels = sc.parallelize([([0.0, 1.0], [0.0, 2.0]), ([0.0, 2.0], [0.0, 1.0]),
+ ... ([], [0.0]), ([2.0], [2.0]), ([2.0, 0.0], [2.0, 0.0]),
+ ... ([0.0, 1.0, 2.0], [0.0, 1.0]), ([1.0], [1.0, 2.0])])
+ >>> metrics = MultilabelMetrics(predictionAndLabels)
+ >>> metrics.precision(0.0)
+ 1.0
+ >>> metrics.recall(1.0)
+ 0.66...
+ >>> metrics.f1Measure(2.0)
+ 0.5
+ >>> metrics.precision()
+ 0.66...
+ >>> metrics.recall()
+ 0.64...
+ >>> metrics.f1Measure()
+ 0.63...
+ >>> metrics.microPrecision
+ 0.72...
+ >>> metrics.microRecall
+ 0.66...
+ >>> metrics.microF1Measure
+ 0.69...
+ >>> metrics.hammingLoss
+ 0.33...
+ >>> metrics.subsetAccuracy
+ 0.28...
+ >>> metrics.accuracy
+ 0.54...
+ """
+
+ def __init__(self, predictionAndLabels):
+ sc = predictionAndLabels.ctx
+ sql_ctx = SQLContext(sc)
+ df = sql_ctx.createDataFrame(predictionAndLabels,
+ schema=sql_ctx._inferSchema(predictionAndLabels))
+ java_class = sc._jvm.org.apache.spark.mllib.evaluation.MultilabelMetrics
+ java_model = java_class(df._jdf)
+ super(MultilabelMetrics, self).__init__(java_model)
+
+ def precision(self, label=None):
+ """
+ Returns precision or precision for a given label (category) if specified.
+ """
+ if label is None:
+ return self.call("precision")
+ else:
+ return self.call("precision", float(label))
+
+ def recall(self, label=None):
+ """
+ Returns recall or recall for a given label (category) if specified.
+ """
+ if label is None:
+ return self.call("recall")
+ else:
+ return self.call("recall", float(label))
+
+ def f1Measure(self, label=None):
+ """
+ Returns f1Measure or f1Measure for a given label (category) if specified.
+ """
+ if label is None:
+ return self.call("f1Measure")
+ else:
+ return self.call("f1Measure", float(label))
+
+ @property
+ def microPrecision(self):
+ """
+ Returns micro-averaged label-based precision.
+ (equals to micro-averaged document-based precision)
+ """
+ return self.call("microPrecision")
+
+ @property
+ def microRecall(self):
+ """
+ Returns micro-averaged label-based recall.
+ (equals to micro-averaged document-based recall)
+ """
+ return self.call("microRecall")
+
+ @property
+ def microF1Measure(self):
+ """
+ Returns micro-averaged label-based f1-measure.
+ (equals to micro-averaged document-based f1-measure)
+ """
+ return self.call("microF1Measure")
+
+ @property
+ def hammingLoss(self):
+ """
+ Returns Hamming-loss.
+ """
+ return self.call("hammingLoss")
+
+ @property
+ def subsetAccuracy(self):
+ """
+ Returns subset accuracy.
+ (for equal sets of labels)
+ """
+ return self.call("subsetAccuracy")
+
+ @property
+ def accuracy(self):
+ """
+ Returns accuracy.
+ """
+ return self.call("accuracy")
+
+
def _test():
import doctest
from pyspark import SparkContext
--
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