b8733e0ad9
## What changes were proposed in this pull request? Remove ML methods we deprecated in 2.1. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #17867 from yanboliang/spark-20606.
250 lines
7.4 KiB
Python
250 lines
7.4 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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import uuid
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import warnings
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if sys.version > '3':
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basestring = str
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unicode = str
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from pyspark import SparkContext, since
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from pyspark.ml.common import inherit_doc
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def _jvm():
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"""
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Returns the JVM view associated with SparkContext. Must be called
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after SparkContext is initialized.
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"""
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jvm = SparkContext._jvm
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if jvm:
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return jvm
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else:
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raise AttributeError("Cannot load _jvm from SparkContext. Is SparkContext initialized?")
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class Identifiable(object):
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"""
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Object with a unique ID.
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"""
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def __init__(self):
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#: A unique id for the object.
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self.uid = self._randomUID()
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def __repr__(self):
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return self.uid
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@classmethod
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def _randomUID(cls):
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"""
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Generate a unique unicode id for the object. The default implementation
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concatenates the class name, "_", and 12 random hex chars.
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"""
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return unicode(cls.__name__ + "_" + uuid.uuid4().hex[12:])
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@inherit_doc
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class MLWriter(object):
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"""
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Utility class that can save ML instances.
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.. versionadded:: 2.0.0
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"""
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def save(self, path):
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"""Save the ML instance to the input path."""
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raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
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def overwrite(self):
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"""Overwrites if the output path already exists."""
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raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
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def session(self, sparkSession):
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"""Sets the Spark Session to use for saving."""
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raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
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@inherit_doc
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class JavaMLWriter(MLWriter):
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"""
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(Private) Specialization of :py:class:`MLWriter` for :py:class:`JavaParams` types
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"""
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def __init__(self, instance):
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super(JavaMLWriter, self).__init__()
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_java_obj = instance._to_java()
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self._jwrite = _java_obj.write()
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def save(self, path):
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"""Save the ML instance to the input path."""
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if not isinstance(path, basestring):
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raise TypeError("path should be a basestring, got type %s" % type(path))
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self._jwrite.save(path)
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def overwrite(self):
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"""Overwrites if the output path already exists."""
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self._jwrite.overwrite()
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return self
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def session(self, sparkSession):
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"""Sets the Spark Session to use for saving."""
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self._jwrite.session(sparkSession._jsparkSession)
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return self
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@inherit_doc
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class MLWritable(object):
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"""
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Mixin for ML instances that provide :py:class:`MLWriter`.
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.. versionadded:: 2.0.0
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"""
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def write(self):
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"""Returns an MLWriter instance for this ML instance."""
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raise NotImplementedError("MLWritable is not yet implemented for type: %r" % type(self))
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def save(self, path):
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"""Save this ML instance to the given path, a shortcut of `write().save(path)`."""
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self.write().save(path)
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@inherit_doc
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class JavaMLWritable(MLWritable):
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"""
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(Private) Mixin for ML instances that provide :py:class:`JavaMLWriter`.
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"""
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def write(self):
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"""Returns an MLWriter instance for this ML instance."""
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return JavaMLWriter(self)
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@inherit_doc
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class MLReader(object):
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"""
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Utility class that can load ML instances.
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.. versionadded:: 2.0.0
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"""
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def load(self, path):
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"""Load the ML instance from the input path."""
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raise NotImplementedError("MLReader is not yet implemented for type: %s" % type(self))
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def session(self, sparkSession):
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"""Sets the Spark Session to use for loading."""
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raise NotImplementedError("MLReader is not yet implemented for type: %s" % type(self))
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@inherit_doc
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class JavaMLReader(MLReader):
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"""
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(Private) Specialization of :py:class:`MLReader` for :py:class:`JavaParams` types
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"""
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def __init__(self, clazz):
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self._clazz = clazz
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self._jread = self._load_java_obj(clazz).read()
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def load(self, path):
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"""Load the ML instance from the input path."""
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if not isinstance(path, basestring):
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raise TypeError("path should be a basestring, got type %s" % type(path))
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java_obj = self._jread.load(path)
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if not hasattr(self._clazz, "_from_java"):
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raise NotImplementedError("This Java ML type cannot be loaded into Python currently: %r"
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% self._clazz)
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return self._clazz._from_java(java_obj)
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def session(self, sparkSession):
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"""Sets the Spark Session to use for loading."""
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self._jread.session(sparkSession._jsparkSession)
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return self
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@classmethod
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def _java_loader_class(cls, clazz):
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"""
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Returns the full class name of the Java ML instance. The default
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implementation replaces "pyspark" by "org.apache.spark" in
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the Python full class name.
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"""
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java_package = clazz.__module__.replace("pyspark", "org.apache.spark")
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if clazz.__name__ in ("Pipeline", "PipelineModel"):
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# Remove the last package name "pipeline" for Pipeline and PipelineModel.
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java_package = ".".join(java_package.split(".")[0:-1])
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return java_package + "." + clazz.__name__
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@classmethod
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def _load_java_obj(cls, clazz):
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"""Load the peer Java object of the ML instance."""
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java_class = cls._java_loader_class(clazz)
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java_obj = _jvm()
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for name in java_class.split("."):
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java_obj = getattr(java_obj, name)
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return java_obj
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@inherit_doc
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class MLReadable(object):
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"""
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Mixin for instances that provide :py:class:`MLReader`.
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.. versionadded:: 2.0.0
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"""
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@classmethod
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def read(cls):
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"""Returns an MLReader instance for this class."""
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raise NotImplementedError("MLReadable.read() not implemented for type: %r" % cls)
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@classmethod
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def load(cls, path):
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"""Reads an ML instance from the input path, a shortcut of `read().load(path)`."""
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return cls.read().load(path)
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@inherit_doc
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class JavaMLReadable(MLReadable):
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"""
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(Private) Mixin for instances that provide JavaMLReader.
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"""
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@classmethod
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def read(cls):
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"""Returns an MLReader instance for this class."""
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return JavaMLReader(cls)
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@inherit_doc
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class JavaPredictionModel():
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"""
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(Private) Java Model for prediction tasks (regression and classification).
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To be mixed in with class:`pyspark.ml.JavaModel`
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"""
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@property
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@since("2.1.0")
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def numFeatures(self):
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"""
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Returns the number of features the model was trained on. If unknown, returns -1
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"""
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return self._call_java("numFeatures")
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