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[SPARK-29489][ML][PySpark] ml.evaluation support log-loss #26135

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[SPARK-29489][ML][PySpark] ml.evaluation support log-loss #26135

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b97e8e8
create pr
zhengruifeng Oct 15, 2019
dadf716
update doc
zhengruifeng Oct 16, 2019
90c8ef2
fix style
zhengruifeng Oct 16, 2019
2b94170
fix pytest
zhengruifeng Oct 16, 2019
a981f7b
logloss -> logLoss
zhengruifeng Oct 16, 2019
38b901c
address some commments
zhengruifeng Oct 17, 2019
f46046c
address some commments
zhengruifeng Oct 18, 2019
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address some commments
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@zhengruifeng
zhengruifeng committed Oct 18, 2019
commit f46046cdf8370a77d77f5afbaab6d4230fa91ffb
2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
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Expand Up @@ -29,7 +29,7 @@ import org.apache.spark.sql.types.DoubleType

/**
* Evaluator for multiclass classification, which expects input columns: prediction, label,
* weight(optional) and probability(only for logLoss).
* weight (optional) and probability (only for logLoss).
*/
@Since("1.5.0")
class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") override val uid: String)
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4 changes: 2 additions & 2 deletions mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
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Expand Up @@ -36,8 +36,8 @@ class MulticlassMetrics @Since("1.1.0") (predictionAndLabels: RDD[_ <: Product])

/**
* An auxiliary constructor taking a DataFrame.
* @param predictionAndLabels a DataFrame with columns: prediction, label, weight(optional)
* and probability(only for logLoss)
* @param predictionAndLabels a DataFrame with columns: prediction, label, weight (optional)
* and probability (only for logLoss)
*/
private[mllib] def this(predictionAndLabels: DataFrame) =
this(predictionAndLabels.rdd.map { r =>
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@zhengruifeng zhengruifeng Oct 16, 2019

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matching will not work in pyspark, so I have to use r.get instead.
MultilabelMetrics also deals with dataframe in this way.

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2 changes: 1 addition & 1 deletion python/pyspark/ml/evaluation.py
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Expand Up @@ -317,7 +317,7 @@ class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictio
HasProbabilityCol, JavaMLReadable, JavaMLWritable):
"""
Evaluator for Multiclass Classification, which expects input
columns: prediction, label, weight(optional) and probabilityCol(only for logLoss).
columns: prediction, label, weight (optional) and probabilityCol (only for logLoss).

>>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]
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