Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/4151#discussion_r23655941
--- Diff: python/pyspark/ml/__init__.py ---
@@ -0,0 +1,324 @@
+#
+# 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, abstractproperty
+
+from pyspark import SparkContext
+from pyspark.sql import SchemaRDD, inherit_doc # TODO: move inherit_doc
to Spark Core
+from pyspark.ml.param import Param, Params
+from pyspark.ml.util import Identifiable
+
+__all__ = ["Pipeline", "Transformer", "Estimator", "param", "feature",
"classification"]
+
+
+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 PipelineStage(Params):
+ """
+ A stage in a pipeline, either an :py:class:`Estimator` or a
+ :py:class:`Transformer`.
+ """
+
+ __metaclass__ = ABCMeta
+
+ def __init__(self):
+ super(PipelineStage, self).__init__()
+
+
+@inherit_doc
+class Estimator(PipelineStage):
+ """
+ Abstract class for estimators that fit models to data.
+ """
+
+ __metaclass__ = ABCMeta
+
+ def __init__(self):
+ super(Estimator, self).__init__()
+
+ @abstractmethod
+ def fit(self, dataset, params={}):
+ """
+ Fits a model to the input dataset with optional parameters.
+
+ :param dataset: input dataset, which is an instance of
+ :py:class:`pyspark.sql.SchemaRDD`
+ :param params: an optional param map that overwrites embedded
+ params
+ :returns: fitted model
+ """
+ raise NotImplementedError()
+
+
+@inherit_doc
+class Transformer(PipelineStage):
+ """
+ Abstract class for transformers that transform one dataset into
+ another.
+ """
+
+ __metaclass__ = ABCMeta
+
+ def __init__(self):
+ super(Transformer, self).__init__()
+
+ @abstractmethod
+ def transform(self, dataset, params={}):
+ """
+ Transforms the input dataset with optional parameters.
+
+ :param dataset: input dataset, which is an instance of
+ :py:class:`pyspark.sql.SchemaRDD`
+ :param params: an optional param map that overwrites embedded
+ params
+ :returns: transformed dataset
+ """
+ raise NotImplementedError()
+
+
+@inherit_doc
+class Model(Transformer):
+ """
+ Abstract class for models fitted by :py:class:`Estimator`s.
+ """
+
+ ___metaclass__ = ABCMeta
+
+ def __init__(self):
+ super(Model, self).__init__()
+
+
+@inherit_doc
+class Pipeline(Estimator):
+ """
+ A simple pipeline, which acts as an estimator. A Pipeline consists
+ of a sequence of stages, each of which is either an
+ :py:class:`Estimator` or a :py:class:`Transformer`. When
+ :py:meth:`Pipeline.fit` is called, the stages are executed in
+ order. If a stage is an :py:class:`Estimator`, its
+ :py:meth:`Estimator.fit` method will be called on the input
+ dataset to fit a model. Then the model, which is a transformer,
+ will be used to transform the dataset as the input to the next
+ stage. If a stage is a :py:class:`Transformer`, its
+ :py:meth:`Transformer.transform` method will be called to produce
+ the dataset for the next stage. The fitted model from a
+ :py:class:`Pipeline` is an :py:class:`PipelineModel`, which
+ consists of fitted models and transformers, corresponding to the
+ pipeline stages. If there are no stages, the pipeline acts as an
+ identity transformer.
+ """
+
+ def __init__(self):
+ super(Pipeline, self).__init__()
+ #: Param for pipeline stages.
+ self.stages = Param(self, "stages", "pipeline stages")
+
+ def setStages(self, value):
+ """
+ Set pipeline stages.
+ :param value: a list of transformers or estimators
+ :return: the pipeline instance
+ """
+ self.paramMap[self.stages] = value
+ return self
+
+ def getStages(self):
+ """
+ Get pipeline stages.
+ """
+ if self.stages in self.paramMap:
+ return self.paramMap[self.stages]
+
+ def fit(self, dataset, params={}):
+ paramMap = self._merge_params(params)
+ stages = paramMap[self.stages]
+ for stage in stages:
+ if not (isinstance(stage, Estimator) or isinstance(stage,
Transformer)):
+ raise ValueError(
+ "Cannot recognize a pipeline stage of type %s." %
type(stage).__name__)
+ indexOfLastEstimator = -1
--- End diff --
Would you like to add some comments about why do this? (avoid the
unnecessary transforms)
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