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Posted to commits@spark.apache.org by me...@apache.org on 2015/05/11 18:14:23 UTC
spark git commit: [SPARK-6092] [MLLIB] Add RankingMetrics in
PySpark/MLlib
Repository: spark
Updated Branches:
refs/heads/master d70a07689 -> 042dda3c5
[SPARK-6092] [MLLIB] Add RankingMetrics in PySpark/MLlib
Author: Yanbo Liang <yb...@gmail.com>
Closes #6044 from yanboliang/spark-6092 and squashes the following commits:
726a9b1 [Yanbo Liang] add newRankingMetrics
33f649c [Yanbo Liang] Add RankingMetrics in PySpark/MLlib
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/042dda3c
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/042dda3c
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/042dda3c
Branch: refs/heads/master
Commit: 042dda3c5c25b5ecb6ae4fd37c85b211b01c187b
Parents: d70a076
Author: Yanbo Liang <yb...@gmail.com>
Authored: Mon May 11 09:14:20 2015 -0700
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Mon May 11 09:14:20 2015 -0700
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.../spark/mllib/api/python/PythonMLLibAPI.scala | 10 +++
python/pyspark/mllib/evaluation.py | 78 +++++++++++++++++++-
2 files changed, 86 insertions(+), 2 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/042dda3c/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
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diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 8c30ad4..f4c4775 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -32,6 +32,7 @@ import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
import org.apache.spark.api.python.SerDeUtil
import org.apache.spark.mllib.classification._
import org.apache.spark.mllib.clustering._
+import org.apache.spark.mllib.evaluation.RankingMetrics
import org.apache.spark.mllib.feature._
import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
import org.apache.spark.mllib.linalg._
@@ -50,6 +51,7 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTree
import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomForest}
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.DataFrame
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.Utils
@@ -923,6 +925,14 @@ private[python] class PythonMLLibAPI extends Serializable {
RG.gammaVectorRDD(jsc.sc, shape, scale, numRows, numCols, parts, s)
}
+ /**
+ * Java stub for the constructor of Python mllib RankingMetrics
+ */
+ def newRankingMetrics(predictionAndLabels: DataFrame): RankingMetrics[Any] = {
+ new RankingMetrics(predictionAndLabels.map(
+ r => (r.getSeq(0).toArray[Any], r.getSeq(1).toArray[Any])))
+ }
+
}
http://git-wip-us.apache.org/repos/asf/spark/blob/042dda3c/python/pyspark/mllib/evaluation.py
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diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py
index 3691459..4c777f2 100644
--- a/python/pyspark/mllib/evaluation.py
+++ b/python/pyspark/mllib/evaluation.py
@@ -15,9 +15,12 @@
# limitations under the License.
#
-from pyspark.mllib.common import JavaModelWrapper
+from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
from pyspark.sql import SQLContext
-from pyspark.sql.types import StructField, StructType, DoubleType
+from pyspark.sql.types import StructField, StructType, DoubleType, IntegerType, ArrayType
+
+__all__ = ['BinaryClassificationMetrics', 'RegressionMetrics',
+ 'MulticlassMetrics', 'RankingMetrics']
class BinaryClassificationMetrics(JavaModelWrapper):
@@ -270,6 +273,77 @@ class MulticlassMetrics(JavaModelWrapper):
return self.call("weightedFMeasure", beta)
+class RankingMetrics(JavaModelWrapper):
+ """
+ Evaluator for ranking algorithms.
+
+ >>> predictionAndLabels = sc.parallelize([
+ ... ([1, 6, 2, 7, 8, 3, 9, 10, 4, 5], [1, 2, 3, 4, 5]),
+ ... ([4, 1, 5, 6, 2, 7, 3, 8, 9, 10], [1, 2, 3]),
+ ... ([1, 2, 3, 4, 5], [])])
+ >>> metrics = RankingMetrics(predictionAndLabels)
+ >>> metrics.precisionAt(1)
+ 0.33...
+ >>> metrics.precisionAt(5)
+ 0.26...
+ >>> metrics.precisionAt(15)
+ 0.17...
+ >>> metrics.meanAveragePrecision
+ 0.35...
+ >>> metrics.ndcgAt(3)
+ 0.33...
+ >>> metrics.ndcgAt(10)
+ 0.48...
+
+ """
+
+ def __init__(self, predictionAndLabels):
+ """
+ :param predictionAndLabels: an RDD of (predicted ranking, ground truth set) pairs.
+ """
+ sc = predictionAndLabels.ctx
+ sql_ctx = SQLContext(sc)
+ df = sql_ctx.createDataFrame(predictionAndLabels,
+ schema=sql_ctx._inferSchema(predictionAndLabels))
+ java_model = callMLlibFunc("newRankingMetrics", df._jdf)
+ super(RankingMetrics, self).__init__(java_model)
+
+ def precisionAt(self, k):
+ """
+ Compute the average precision of all the queries, truncated at ranking position k.
+
+ If for a query, the ranking algorithm returns n (n < k) results, the precision value
+ will be computed as #(relevant items retrieved) / k. This formula also applies when
+ the size of the ground truth set is less than k.
+
+ If a query has an empty ground truth set, zero will be used as precision together
+ with a log warning.
+ """
+ return self.call("precisionAt", int(k))
+
+ @property
+ def meanAveragePrecision(self):
+ """
+ Returns the mean average precision (MAP) of all the queries.
+ If a query has an empty ground truth set, the average precision will be zero and
+ a log warining is generated.
+ """
+ return self.call("meanAveragePrecision")
+
+ def ndcgAt(self, k):
+ """
+ Compute the average NDCG value of all the queries, truncated at ranking position k.
+ The discounted cumulative gain at position k is computed as:
+ sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1),
+ and the NDCG is obtained by dividing the DCG value on the ground truth set.
+ In the current implementation, the relevance value is binary.
+
+ If a query has an empty ground truth set, zero will be used as ndcg together with
+ a log warning.
+ """
+ return self.call("ndcgAt", int(k))
+
+
def _test():
import doctest
from pyspark import SparkContext
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