1112fc6029
### What changes were proposed in this pull request?
Add ```__repr__``` in Python ML Models
### Why are the changes needed?
In Python ML Models, some of them have ```__repr__```, others don't. In the doctest, when calling Model.setXXX, some of the Models print out the xxxModel... correctly, some of them can't because of lacking the ```__repr__``` method. For example:
```
>>> gm = GaussianMixture(k=3, tol=0.0001, seed=10)
>>> model = gm.fit(df)
>>> model.setPredictionCol("newPrediction")
GaussianMixture...
```
After the change, the above code will become the following:
```
>>> gm = GaussianMixture(k=3, tol=0.0001, seed=10)
>>> model = gm.fit(df)
>>> model.setPredictionCol("newPrediction")
GaussianMixtureModel...
```
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
doctest
Closes #26489 from huaxingao/spark-29876.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
451 lines
16 KiB
Python
451 lines
16 KiB
Python
#
|
|
# 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 since
|
|
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.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol
|
|
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
|
|
|
|
def __del__(self):
|
|
if SparkContext._active_spark_context and self._java_obj is not None:
|
|
SparkContext._active_spark_context._gateway.detach(self._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.
|
|
If the param pylist is a 2D array, then a 2D java array will be returned.
|
|
The returned 2D java array is a square, non-jagged 2D array that is big
|
|
enough for all elements. The empty slots in the inner Java arrays will
|
|
be filled with null to make the non-jagged 2D array.
|
|
|
|
: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 = None
|
|
if len(pylist) > 0 and isinstance(pylist[0], list):
|
|
# If pylist is a 2D array, then a 2D java array will be created.
|
|
# The 2D array is a square, non-jagged 2D array that is big enough for all elements.
|
|
inner_array_length = 0
|
|
for i in xrange(len(pylist)):
|
|
inner_array_length = max(inner_array_length, len(pylist[i]))
|
|
java_array = sc._gateway.new_array(java_class, len(pylist), inner_array_length)
|
|
for i in xrange(len(pylist)):
|
|
for j in xrange(len(pylist[i])):
|
|
java_array[i][j] = pylist[i][j]
|
|
else:
|
|
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 _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.
|
|
"""
|
|
pair_defaults = []
|
|
for param in self.params:
|
|
if self.isSet(param):
|
|
pair = self._make_java_param_pair(param, self._paramMap[param])
|
|
self._java_obj.set(pair)
|
|
if self.hasDefault(param):
|
|
pair = self._make_java_param_pair(param, self._defaultParamMap[param])
|
|
pair_defaults.append(pair)
|
|
if len(pair_defaults) > 0:
|
|
sc = SparkContext._active_spark_context
|
|
pair_defaults_seq = sc._jvm.PythonUtils.toSeq(pair_defaults)
|
|
self._java_obj.setDefault(pair_defaults_seq)
|
|
|
|
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
|
|
|
|
def clear(self, param):
|
|
"""
|
|
Clears a param from the param map if it has been explicitly set.
|
|
"""
|
|
super(JavaParams, self).clear(param)
|
|
java_param = self._java_obj.getParam(param.name)
|
|
self._java_obj.clear(java_param)
|
|
|
|
|
|
@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())
|
|
|
|
def __repr__(self):
|
|
return self._call_java("toString")
|
|
|
|
|
|
@inherit_doc
|
|
class _JavaPredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):
|
|
"""
|
|
Params for :py:class:`JavaPredictor` and :py:class:`JavaPredictorModel`.
|
|
|
|
.. versionadded:: 3.0.0
|
|
"""
|
|
pass
|
|
|
|
|
|
@inherit_doc
|
|
class JavaPredictor(JavaEstimator, _JavaPredictorParams):
|
|
"""
|
|
(Private) Java Estimator for prediction tasks (regression and classification).
|
|
"""
|
|
|
|
@since("3.0.0")
|
|
def setLabelCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`labelCol`.
|
|
"""
|
|
return self._set(labelCol=value)
|
|
|
|
@since("3.0.0")
|
|
def setFeaturesCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`featuresCol`.
|
|
"""
|
|
return self._set(featuresCol=value)
|
|
|
|
@since("3.0.0")
|
|
def setPredictionCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`predictionCol`.
|
|
"""
|
|
return self._set(predictionCol=value)
|
|
|
|
|
|
@inherit_doc
|
|
class JavaPredictionModel(JavaModel, _JavaPredictorParams):
|
|
"""
|
|
(Private) Java Model for prediction tasks (regression and classification).
|
|
"""
|
|
|
|
@since("3.0.0")
|
|
def setFeaturesCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`featuresCol`.
|
|
"""
|
|
return self._set(featuresCol=value)
|
|
|
|
@since("3.0.0")
|
|
def setPredictionCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`predictionCol`.
|
|
"""
|
|
return self._set(predictionCol=value)
|
|
|
|
@property
|
|
@since("2.1.0")
|
|
def numFeatures(self):
|
|
"""
|
|
Returns the number of features the model was trained on. If unknown, returns -1
|
|
"""
|
|
return self._call_java("numFeatures")
|
|
|
|
@since("3.0.0")
|
|
def predict(self, value):
|
|
"""
|
|
Predict label for the given features.
|
|
"""
|
|
return self._call_java("predict", value)
|