本文整理汇总了Python中pyspark.ml.Pipeline.write方法的典型用法代码示例。如果您正苦于以下问题：Python Pipeline.write方法的具体用法？Python Pipeline.write怎么用？Python Pipeline.write使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.ml.Pipeline的用法示例。
在下文中一共展示了Pipeline.write方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: H2OXGBoost
# 需要导入模块: from pyspark.ml import Pipeline [as 别名]
# 或者: from pyspark.ml.Pipeline import write [as 别名]
predictionCol="label")
elif algo == "xgboost":
## Create H2OXGBoost model
algoStage = H2OXGBoost(convertUnknownCategoricalLevelsToNa=True,
featuresCols=[idf.getOutputCol()],
predictionCol="label")
## Remove all helper columns
colPruner = ColumnPruner(columns=[idf.getOutputCol(), hashingTF.getOutputCol(), stopWordsRemover.getOutputCol(), tokenizer.getOutputCol()])
## Create the pipeline by defining all the stages
pipeline = Pipeline(stages=[tokenizer, stopWordsRemover, hashingTF, idf, algoStage, colPruner])
## Test exporting and importing the pipeline. On Systems where HDFS & Hadoop is not available, this call store the pipeline
## to local file in the current directory. In case HDFS & Hadoop is available, this call stores the pipeline to HDFS home
## directory for the current user. Absolute paths can be used as wells. The same holds for the model import/export bellow.
pipeline.write().overwrite().save("examples/build/pipeline")
loaded_pipeline = Pipeline.load("examples/build/pipeline")
## Train the pipeline model
data = load()
model = loaded_pipeline.fit(data)
model.write().overwrite().save("examples/build/model")
loaded_model = PipelineModel.load("examples/build/model")
##
## Make predictions on unlabeled data
## Spam detector