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#
# 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
from pyspark import SparkContext
from pyspark.sql import DataFrame
from pyspark.ml.param import Params
from pyspark.ml.pipeline import Estimator, Transformer
from pyspark.ml.util import inherit_doc
def _jvm():
"""
Returns the JVM view associated with SparkContext. Must be called
after SparkContext is initialized.
"""
jvm = SparkContext._jvm
if jvm:
return jvm
else:
raise AttributeError("Cannot load _jvm from SparkContext. Is SparkContext initialized?")
@inherit_doc
class JavaWrapper(Params):
"""
Utility class to help create wrapper classes from Java/Scala
implementations of pipeline components.
"""
__metaclass__ = ABCMeta
#: Fully-qualified class name of the wrapped Java component.
_java_class = None
def _java_obj(self):
"""
Returns or creates a Java object.
"""
java_obj = _jvm()
for name in self._java_class.split("."):
java_obj = getattr(java_obj, name)
return java_obj()
def _transfer_params_to_java(self, params, java_obj):
"""
Transforms the embedded params and additional params to the
input Java object.
:param params: additional params (overwriting embedded values)
:param java_obj: Java object to receive the params
"""
paramMap = self._merge_params(params)
for param in self.params:
if param in paramMap:
java_obj.set(param.name, paramMap[param])
def _empty_java_param_map(self):
"""
Returns an empty Java ParamMap reference.
"""
return _jvm().org.apache.spark.ml.param.ParamMap()
def _create_java_param_map(self, params, java_obj):
paramMap = self._empty_java_param_map()
for param, value in params.items():
if param.parent is self:
paramMap.put(java_obj.getParam(param.name), value)
return paramMap
@inherit_doc
class JavaEstimator(Estimator, JavaWrapper):
"""
Base class for :py:class:`Estimator`s that wrap Java/Scala
implementations.
"""
__metaclass__ = ABCMeta
def _create_model(self, java_model):
"""
Creates a model from the input Java model reference.
"""
return JavaModel(java_model)
def _fit_java(self, dataset, params={}):
"""
Fits a Java model to the input dataset.
:param dataset: input dataset, which is an instance of
:py:class:`pyspark.sql.SchemaRDD`
:param params: additional params (overwriting embedded values)
:return: fitted Java model
"""
java_obj = self._java_obj()
self._transfer_params_to_java(params, java_obj)
return java_obj.fit(dataset._jdf, self._empty_java_param_map())
def fit(self, dataset, params={}):
java_model = self._fit_java(dataset, params)
return self._create_model(java_model)
@inherit_doc
class JavaTransformer(Transformer, JavaWrapper):
"""
Base class for :py:class:`Transformer`s that wrap Java/Scala
implementations.
"""
__metaclass__ = ABCMeta
def transform(self, dataset, params={}):
java_obj = self._java_obj()
self._transfer_params_to_java({}, java_obj)
java_param_map = self._create_java_param_map(params, java_obj)
return DataFrame(java_obj.transform(dataset._jdf, java_param_map),
dataset.sql_ctx)
@inherit_doc
class JavaModel(JavaTransformer):
"""
Base class for :py:class:`Model`s that wrap Java/Scala
implementations.
"""
__metaclass__ = ABCMeta
def __init__(self, java_model):
super(JavaTransformer, self).__init__()
self._java_model = java_model
def _java_obj(self):
return self._java_model
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