#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from abc import ABCMeta, abstractmethod
import sys
if sys.version >= '3':
xrange = range
from pyspark import SparkContext
from pyspark.sql import DataFrame
from pyspark.ml import Estimator, Transformer, Model
from pyspark.ml.param import Params
from pyspark.ml.util import _jvm
from pyspark.ml.common import inherit_doc, _java2py, _py2java
class JavaWrapper(object):
"""
Wrapper class for a Java companion object
"""
def __init__(self, java_obj=None):
super(JavaWrapper, self).__init__()
self._java_obj = java_obj
@classmethod
def _create_from_java_class(cls, java_class, *args):
"""
Construct this object from given Java classname and arguments
"""
java_obj = JavaWrapper._new_java_obj(java_class, *args)
return cls(java_obj)
def _call_java(self, name, *args):
m = getattr(self._java_obj, name)
sc = SparkContext._active_spark_context
java_args = [_py2java(sc, arg) for arg in args]
return _java2py(sc, m(*java_args))
@staticmethod
def _new_java_obj(java_class, *args):
"""
Returns a new Java object.
"""
sc = SparkContext._active_spark_context
java_obj = _jvm()
for name in java_class.split("."):
java_obj = getattr(java_obj, name)
java_args = [_py2java(sc, arg) for arg in args]
return java_obj(*java_args)
@staticmethod
def _new_java_array(pylist, java_class):
"""
Create a Java array of given java_class type. Useful for
calling a method with a Scala Array from Python with Py4J.
:param pylist:
Python list to convert to a Java Array.
:param java_class:
Java class to specify the type of Array. Should be in the
form of sc._gateway.jvm.* (sc is a valid Spark Context).
:return:
Java Array of converted pylist.
Example primitive Java classes:
- basestring -> sc._gateway.jvm.java.lang.String
- int -> sc._gateway.jvm.java.lang.Integer
- float -> sc._gateway.jvm.java.lang.Double
- bool -> sc._gateway.jvm.java.lang.Boolean
"""
sc = SparkContext._active_spark_context
java_array = sc._gateway.new_array(java_class, len(pylist))
for i in xrange(len(pylist)):
java_array[i] = pylist[i]
return java_array
@inherit_doc
class JavaParams(JavaWrapper, Params):
"""
Utility class to help create wrapper classes from Java/Scala
implementations of pipeline components.
"""
#: The param values in the Java object should be
#: synced with the Python wrapper in fit/transform/evaluate/copy.
__metaclass__ = ABCMeta
def __del__(self):
if SparkContext._active_spark_context:
SparkContext._active_spark_context._gateway.detach(self._java_obj)
def _make_java_param_pair(self, param, value):
"""
Makes a Java param pair.
"""
sc = SparkContext._active_spark_context
param = self._resolveParam(param)
java_param = self._java_obj.getParam(param.name)
java_value = _py2java(sc, value)
return java_param.w(java_value)
def _transfer_params_to_java(self):
"""
Transforms the embedded params to the companion Java object.
"""
paramMap = self.extractParamMap()
for param in self.params:
if param in paramMap:
pair = self._make_java_param_pair(param, paramMap[param])
self._java_obj.set(pair)
def _transfer_param_map_to_java(self, pyParamMap):
"""
Transforms a Python ParamMap into a Java ParamMap.
"""
paramMap = JavaWrapper._new_java_obj("org.apache.spark.ml.param.ParamMap")
for param in self.params:
if param in pyParamMap:
pair = self._make_java_param_pair(param, pyParamMap[param])
paramMap.put([pair])
return paramMap
def _create_params_from_java(self):
"""
SPARK-10931: Temporary fix to create params that are defined in the Java obj but not here
"""
java_params = list(self._java_obj.params())
from pyspark.ml.param import Param
for java_param in java_params:
java_param_name = java_param.name()
if not hasattr(self, java_param_name):
param = Param(self, java_param_name, java_param.doc())
setattr(param, "created_from_java_param", True)
setattr(self, java_param_name, param)
self._params = None # need to reset so self.params will discover new params
def _transfer_params_from_java(self):
"""
Transforms the embedded params from the companion Java object.
"""
sc = SparkContext._active_spark_context
for param in self.params:
if self._java_obj.hasParam(param.name):
java_param = self._java_obj.getParam(param.name)
# SPARK-14931: Only check set params back to avoid default params mismatch.
if self._java_obj.isSet(java_param):
value = _java2py(sc, self._java_obj.getOrDefault(java_param))
self._set(**{param.name: value})
# SPARK-10931: Temporary fix for params that have a default in Java
if self._java_obj.hasDefault(java_param) and not self.isDefined(param):
value = _java2py(sc, self._java_obj.getDefault(java_param)).get()
self._setDefault(**{param.name: value})
def _transfer_param_map_from_java(self, javaParamMap):
"""
Transforms a Java ParamMap into a Python ParamMap.
"""
sc = SparkContext._active_spark_context
paramMap = dict()
for pair in javaParamMap.toList():
param = pair.param()
if self.hasParam(str(param.name())):
paramMap[self.getParam(param.name())] = _java2py(sc, pair.value())
return paramMap
@staticmethod
def _empty_java_param_map():
"""
Returns an empty Java ParamMap reference.
"""
return _jvm().org.apache.spark.ml.param.ParamMap()
def _to_java(self):
"""
Transfer this instance's Params to the wrapped Java object, and return the Java object.
Used for ML persistence.
Meta-algorithms such as Pipeline should override this method.
:return: Java object equivalent to this instance.
"""
self._transfer_params_to_java()
return self._java_obj
@staticmethod
def _from_java(java_stage):
"""
Given a Java object, create and return a Python wrapper of it.
Used for ML persistence.
Meta-algorithms such as Pipeline should override this method as a classmethod.
"""
def __get_class(clazz):
"""
Loads Python class from its name.
"""
parts = clazz.split('.')
module = ".".join(parts[:-1])
m = __import__(module)
for comp in parts[1:]:
m = getattr(m, comp)
return m
stage_name = java_stage.getClass().getName().replace("org.apache.spark", "pyspark")
# Generate a default new instance from the stage_name class.
py_type = __get_class(stage_name)
if issubclass(py_type, JavaParams):
# Load information from java_stage to the instance.
py_stage = py_type()
py_stage._java_obj = java_stage
# SPARK-10931: Temporary fix so that persisted models would own params from Estimator
if issubclass(py_type, JavaModel):
py_stage._create_params_from_java()
py_stage._resetUid(java_stage.uid())
py_stage._transfer_params_from_java()
elif hasattr(py_type, "_from_java"):
py_stage = py_type._from_java(java_stage)
else:
raise NotImplementedError("This Java stage cannot be loaded into Python currently: %r"
% stage_name)
return py_stage
def copy(self, extra=None):
"""
Creates a copy of this instance with the same uid and some
extra params. This implementation first calls Params.copy and
then make a copy of the companion Java pipeline component with
extra params. So both the Python wrapper and the Java pipeline
component get copied.
:param extra: Extra parameters to copy to the new instance
:return: Copy of this instance
"""
if extra is None:
extra = dict()
that = super(JavaParams, self).copy(extra)
if self._java_obj is not None:
that._java_obj = self._java_obj.copy(self._empty_java_param_map())
that._transfer_params_to_java()
return that
@inherit_doc
class JavaEstimator(JavaParams, Estimator):
"""
Base class for :py:class:`Estimator`s that wrap Java/Scala
implementations.
"""
__metaclass__ = ABCMeta
@abstractmethod
def _create_model(self, java_model):
"""
Creates a model from the input Java model reference.
"""
raise NotImplementedError()
def _fit_java(self, dataset):
"""
Fits a Java model to the input dataset.
:param dataset: input dataset, which is an instance of
:py:class:`pyspark.sql.DataFrame`
:param params: additional params (overwriting embedded values)
:return: fitted Java model
"""
self._transfer_params_to_java()
return self._java_obj.fit(dataset._jdf)
def _fit(self, dataset):
java_model = self._fit_java(dataset)
model = self._create_model(java_model)
return self._copyValues(model)
@inherit_doc
class JavaTransformer(JavaParams, Transformer):
"""
Base class for :py:class:`Transformer`s that wrap Java/Scala
implementations. Subclasses should ensure they have the transformer Java object
available as _java_obj.
"""
__metaclass__ = ABCMeta
def _transform(self, dataset):
self._transfer_params_to_java()
return DataFrame(self._java_obj.transform(dataset._jdf), dataset.sql_ctx)
@inherit_doc
class JavaModel(JavaTransformer, Model):
"""
Base class for :py:class:`Model`s that wrap Java/Scala
implementations. Subclasses should inherit this class before
param mix-ins, because this sets the UID from the Java model.
"""
__metaclass__ = ABCMeta
def __init__(self, java_model=None):
"""
Initialize this instance with a Java model object.
Subclasses should call this constructor, initialize params,
and then call _transfer_params_from_java.
This instance can be instantiated without specifying java_model,
it will be assigned after that, but this scenario only used by
:py:class:`JavaMLReader` to load models. This is a bit of a
hack, but it is easiest since a proper fix would require
MLReader (in pyspark.ml.util) to depend on these wrappers, but
these wrappers depend on pyspark.ml.util (both directly and via
other ML classes).
"""
super(JavaModel, self).__init__(java_model)
if java_model is not None:
# SPARK-10931: This is a temporary fix to allow models to own params
# from estimators. Eventually, these params should be in models through
# using common base classes between estimators and models.
self._create_params_from_java()
self._resetUid(java_model.uid())
