Update py4j to 0.9.

[kevincox]

Py4J 0.9 has a performance improvement for unescape_new_line which significantly reduces the time required to transfer large strings.

cc @angelini

[JoshRosen]

Jenkins, this is ok to test.

[SparkQA]

Test build #42347 has finished for PR 8722 at commit a6f93e5.

• This patch fails to build.
• This patch merges cleanly.
• This patch adds the following public classes (experimental):
  • class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
  • class MultilayerPerceptronClassificationModel(JavaModel):
  • ("thresholds", "Thresholds in multi-class classification to adjust the probability of " +
  • class HasElasticNetParam(Params):
  • class HasFitIntercept(Params):
  • class HasStandardization(Params):
  • class HasThresholds(Params):
  • thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")
  • self.thresholds = Param(self, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")

[kevincox]

Updated to reference the right package.

[giwa]

Is this duplicated? #8615

[kevincox]

It looks like it is.

[srowen]

Yes, do you mind closing this PR? best to search JIRA and/or PRs first.

[SparkQA]

Test build #42349 has finished for PR 8722 at commit 1e30ef6.

• This patch fails Spark unit tests.
• This patch merges cleanly.
• This patch adds the following public classes (experimental):
  • class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
  • class MultilayerPerceptronClassificationModel(JavaModel):
  • ("thresholds", "Thresholds in multi-class classification to adjust the probability of " +
  • class HasElasticNetParam(Params):
  • class HasFitIntercept(Params):
  • class HasStandardization(Params):
  • class HasThresholds(Params):
  • thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")
  • self.thresholds = Param(self, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")