diff options
| author | Weichen Xu <weichen.xu@databricks.com> | 2017-08-28 13:31:01 -0700 |
|---|---|---|
| committer | Joseph K. Bradley <joseph@databricks.com> | 2017-08-28 13:31:01 -0700 |
| commit | c7270a46fc340db62c87ddfc6568603d0b832845 (patch) | |
| tree | 1129f071f96b57fd81580a7affaa12e2f4f33ed1 /project | |
| parent | 73e64f7d50ba7a8469bd76f97e0a22fad41c2caa (diff) | |
[SPARK-17139][ML] Add model summary for MultinomialLogisticRegression
## What changes were proposed in this pull request?
Add 4 traits, using the following hierarchy:
LogisticRegressionSummary
LogisticRegressionTrainingSummary: LogisticRegressionSummary
BinaryLogisticRegressionSummary: LogisticRegressionSummary
BinaryLogisticRegressionTrainingSummary: LogisticRegressionTrainingSummary, BinaryLogisticRegressionSummary
and the public method such as `def summary` only return trait type listed above.
and then implement 4 concrete classes:
LogisticRegressionSummaryImpl (multiclass case)
LogisticRegressionTrainingSummaryImpl (multiclass case)
BinaryLogisticRegressionSummaryImpl (binary case).
BinaryLogisticRegressionTrainingSummaryImpl (binary case).
## How was this patch tested?
Existing tests & added tests.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes #15435 from WeichenXu123/mlor_summary.
Diffstat (limited to 'project')
| -rw-r--r-- | project/MimaExcludes.scala | 21 |
1 files changed, 20 insertions, 1 deletions
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 9bda917377..eecda26abb 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -44,7 +44,26 @@ object MimaExcludes { ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.status.api.v1.ShuffleReadMetricDistributions.this"), // [SPARK-21276] Update lz4-java to the latest (v1.4.0) - ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.io.LZ4BlockInputStream") + ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.io.LZ4BlockInputStream"), + + // [SPARK-17139] Add model summary for MultinomialLogisticRegression + ProblemFilters.exclude[IncompatibleTemplateDefProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary"), + ProblemFilters.exclude[IncompatibleTemplateDefProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.predictionCol"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.labels"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.truePositiveRateByLabel"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.falsePositiveRateByLabel"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.precisionByLabel"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.recallByLabel"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.fMeasureByLabel"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.accuracy"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedTruePositiveRate"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedFalsePositiveRate"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedRecall"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedPrecision"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedFMeasure"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.org$apache$spark$ml$classification$LogisticRegressionSummary$$multiclassMetrics"), + ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.org$apache$spark$ml$classification$LogisticRegressionSummary$_setter_$org$apache$spark$ml$classification$LogisticRegressionSummary$$multiclassMetrics_=") ) // Exclude rules for 2.2.x |