diff options
| -rw-r--r-- | python/pyspark/ml/pipeline.py | 53 |
1 files changed, 7 insertions, 46 deletions
diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py index 6f599b5159..e2651aebdf 100644 --- a/python/pyspark/ml/pipeline.py +++ b/python/pyspark/ml/pipeline.py @@ -30,24 +30,6 @@ from pyspark.mllib.common import inherit_doc @inherit_doc -class PipelineMLWriter(JavaMLWriter): - """ - Private Pipeline utility class that can save ML instances through their Scala implementation. - - We can currently use JavaMLWriter, rather than MLWriter, since Pipeline implements _to_java. - """ - - -@inherit_doc -class PipelineMLReader(JavaMLReader): - """ - Private utility class that can load Pipeline instances through their Scala implementation. - - We can currently use JavaMLReader, rather than MLReader, since Pipeline implements _from_java. - """ - - -@inherit_doc class Pipeline(Estimator, MLReadable, MLWritable): """ A simple pipeline, which acts as an estimator. A Pipeline consists @@ -154,8 +136,8 @@ class Pipeline(Estimator, MLReadable, MLWritable): @since("2.0.0") def write(self): - """Returns an JavaMLWriter instance for this ML instance.""" - return PipelineMLWriter(self) + """Returns an MLWriter instance for this ML instance.""" + return JavaMLWriter(self) @since("2.0.0") def save(self, path): @@ -166,7 +148,7 @@ class Pipeline(Estimator, MLReadable, MLWritable): @since("2.0.0") def read(cls): """Returns an MLReader instance for this class.""" - return PipelineMLReader(cls) + return JavaMLReader(cls) @classmethod def _from_java(cls, java_stage): @@ -202,27 +184,6 @@ class Pipeline(Estimator, MLReadable, MLWritable): @inherit_doc -class PipelineModelMLWriter(JavaMLWriter): - """ - Private PipelineModel utility class that can save ML instances through their Scala - implementation. - - We can (currently) use JavaMLWriter, rather than MLWriter, since PipelineModel implements - _to_java. - """ - - -@inherit_doc -class PipelineModelMLReader(JavaMLReader): - """ - Private utility class that can load PipelineModel instances through their Scala implementation. - - We can currently use JavaMLReader, rather than MLReader, since PipelineModel implements - _from_java. - """ - - -@inherit_doc class PipelineModel(Model, MLReadable, MLWritable): """ Represents a compiled pipeline with transformers and fitted models. @@ -254,8 +215,8 @@ class PipelineModel(Model, MLReadable, MLWritable): @since("2.0.0") def write(self): - """Returns an JavaMLWriter instance for this ML instance.""" - return PipelineModelMLWriter(self) + """Returns an MLWriter instance for this ML instance.""" + return JavaMLWriter(self) @since("2.0.0") def save(self, path): @@ -265,8 +226,8 @@ class PipelineModel(Model, MLReadable, MLWritable): @classmethod @since("2.0.0") def read(cls): - """Returns an JavaMLReader instance for this class.""" - return PipelineModelMLReader(cls) + """Returns an MLReader instance for this class.""" + return JavaMLReader(cls) @classmethod def _from_java(cls, java_stage): |