# # 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. # import json import os import time import uuid from pyspark import SparkContext, since from pyspark.ml.common import inherit_doc from pyspark.sql import SparkSession from pyspark.util import VersionUtils 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?") [docs]class Identifiable(object): """ Object with a unique ID. """ def __init__(self): #: A unique id for the object. self.uid = self._randomUID() def __repr__(self): return self.uid @classmethod def _randomUID(cls): """ Generate a unique string id for the object. The default implementation concatenates the class name, "_", and 12 random hex chars. """ return str(cls.__name__ + "_" + uuid.uuid4().hex[-12:]) [docs]@inherit_doc class BaseReadWrite(object): """ Base class for MLWriter and MLReader. Stores information about the SparkContext and SparkSession. .. versionadded:: 2.3.0 """ def __init__(self): self._sparkSession = None [docs] def session(self, sparkSession): """ Sets the Spark Session to use for saving/loading. """ self._sparkSession = sparkSession return self @property def sparkSession(self): """ Returns the user-specified Spark Session or the default. """ if self._sparkSession is None: self._sparkSession = SparkSession.builder.getOrCreate() return self._sparkSession @property def sc(self): """ Returns the underlying `SparkContext`. """ return self.sparkSession.sparkContext [docs]@inherit_doc class MLWriter(BaseReadWrite): """ Utility class that can save ML instances. .. versionadded:: 2.0.0 """ def __init__(self): super(MLWriter, self).__init__() self.shouldOverwrite = False self.optionMap = {} def _handleOverwrite(self, path): from pyspark.ml.wrapper import JavaWrapper _java_obj = JavaWrapper._new_java_obj("org.apache.spark.ml.util.FileSystemOverwrite") wrapper = JavaWrapper(_java_obj) wrapper._call_java("handleOverwrite", path, True, self.sparkSession._jsparkSession) [docs] def save(self, path): """Save the ML instance to the input path.""" if self.shouldOverwrite: self._handleOverwrite(path) self.saveImpl(path) [docs] def saveImpl(self, path): """ save() handles overwriting and then calls this method. Subclasses should override this method to implement the actual saving of the instance. """ raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self)) [docs] def overwrite(self): """Overwrites if the output path already exists.""" self.shouldOverwrite = True return self [docs] def option(self, key, value): """ Adds an option to the underlying MLWriter. See the documentation for the specific model's writer for possible options. The option name (key) is case-insensitive. """ self.optionMap[key.lower()] = str(value) return self [docs]@inherit_doc class GeneralMLWriter(MLWriter): """ Utility class that can save ML instances in different formats. .. versionadded:: 2.4.0 """ [docs] def format(self, source): """ Specifies the format of ML export ("pmml", "internal", or the fully qualified class name for export). """ self.source = source return self @inherit_doc class JavaMLWriter(MLWriter): """ (Private) Specialization of :py:class:`MLWriter` for :py:class:`JavaParams` types """ def __init__(self, instance): super(JavaMLWriter, self).__init__() _java_obj = instance._to_java() self._jwrite = _java_obj.write() def save(self, path): """Save the ML instance to the input path.""" if not isinstance(path, str): raise TypeError("path should be a string, got type %s" % type(path)) self._jwrite.save(path) def overwrite(self): """Overwrites if the output path already exists.""" self._jwrite.overwrite() return self def option(self, key, value): self._jwrite.option(key, value) return self def session(self, sparkSession): """Sets the Spark Session to use for saving.""" self._jwrite.session(sparkSession._jsparkSession) return self @inherit_doc class GeneralJavaMLWriter(JavaMLWriter): """ (Private) Specialization of :py:class:`GeneralMLWriter` for :py:class:`JavaParams` types """ def __init__(self, instance): super(GeneralJavaMLWriter, self).__init__(instance) def format(self, source): """ Specifies the format of ML export ("pmml", "internal", or the fully qualified class name for export). """ self._jwrite.format(source) return self [docs]@inherit_doc class MLWritable(object): """ Mixin for ML instances that provide :py:class:`MLWriter`. .. versionadded:: 2.0.0 """ [docs] def write(self): """Returns an MLWriter instance for this ML instance.""" raise NotImplementedError("MLWritable is not yet implemented for type: %r" % type(self)) [docs] def save(self, path): """Save this ML instance to the given path, a shortcut of 'write().save(path)'.""" self.write().save(path) @inherit_doc class JavaMLWritable(MLWritable): """ (Private) Mixin for ML instances that provide :py:class:`JavaMLWriter`. """ def write(self): """Returns an MLWriter instance for this ML instance.""" return JavaMLWriter(self) @inherit_doc class GeneralJavaMLWritable(JavaMLWritable): """ (Private) Mixin for ML instances that provide :py:class:`GeneralJavaMLWriter`. """ def write(self): """Returns an GeneralMLWriter instance for this ML instance.""" return GeneralJavaMLWriter(self) [docs]@inherit_doc class MLReader(BaseReadWrite): """ Utility class that can load ML instances. .. versionadded:: 2.0.0 """ def __init__(self): super(MLReader, self).__init__() [docs] def load(self, path): """Load the ML instance from the input path.""" raise NotImplementedError("MLReader is not yet implemented for type: %s" % type(self)) @inherit_doc class JavaMLReader(MLReader): """ (Private) Specialization of :py:class:`MLReader` for :py:class:`JavaParams` types """ def __init__(self, clazz): super(JavaMLReader, self).__init__() self._clazz = clazz self._jread = self._load_java_obj(clazz).read() def load(self, path): """Load the ML instance from the input path.""" if not isinstance(path, str): raise TypeError("path should be a string, got type %s" % type(path)) java_obj = self._jread.load(path) if not hasattr(self._clazz, "_from_java"): raise NotImplementedError("This Java ML type cannot be loaded into Python currently: %r" % self._clazz) return self._clazz._from_java(java_obj) def session(self, sparkSession): """Sets the Spark Session to use for loading.""" self._jread.session(sparkSession._jsparkSession) return self @classmethod def _java_loader_class(cls, clazz): """ Returns the full class name of the Java ML instance. The default implementation replaces "pyspark" by "org.apache.spark" in the Python full class name. """ java_package = clazz.__module__.replace("pyspark", "org.apache.spark") if clazz.__name__ in ("Pipeline", "PipelineModel"): # Remove the last package name "pipeline" for Pipeline and PipelineModel. java_package = ".".join(java_package.split(".")[0:-1]) return java_package + "." + clazz.__name__ @classmethod def _load_java_obj(cls, clazz): """Load the peer Java object of the ML instance.""" java_class = cls._java_loader_class(clazz) java_obj = _jvm() for name in java_class.split("."): java_obj = getattr(java_obj, name) return java_obj [docs]@inherit_doc class MLReadable(object): """ Mixin for instances that provide :py:class:`MLReader`. .. versionadded:: 2.0.0 """ [docs] @classmethod def read(cls): """Returns an MLReader instance for this class.""" raise NotImplementedError("MLReadable.read() not implemented for type: %r" % cls) [docs] @classmethod def load(cls, path): """Reads an ML instance from the input path, a shortcut of `read().load(path)`.""" return cls.read().load(path) @inherit_doc class JavaMLReadable(MLReadable): """ (Private) Mixin for instances that provide JavaMLReader. """ @classmethod def read(cls): """Returns an MLReader instance for this class.""" return JavaMLReader(cls) [docs]@inherit_doc class DefaultParamsWritable(MLWritable): """ Helper trait for making simple :py:class:`Params` types writable. If a :py:class:`Params` class stores all data as :py:class:`Param` values, then extending this trait will provide a default implementation of writing saved instances of the class. This only handles simple :py:class:`Param` types; e.g., it will not handle :py:class:`Dataset`. See :py:class:`DefaultParamsReadable`, the counterpart to this trait. .. versionadded:: 2.3.0 """ [docs] def write(self): """Returns a DefaultParamsWriter instance for this class.""" from pyspark.ml.param import Params if isinstance(self, Params): return DefaultParamsWriter(self) else: raise TypeError("Cannot use DefautParamsWritable with type %s because it does not " + " extend Params.", type(self)) [docs]@inherit_doc class DefaultParamsWriter(MLWriter): """ Specialization of :py:class:`MLWriter` for :py:class:`Params` types Class for writing Estimators and Transformers whose parameters are JSON-serializable. .. versionadded:: 2.3.0 """ def __init__(self, instance): super(DefaultParamsWriter, self).__init__() self.instance = instance [docs] def saveImpl(self, path): DefaultParamsWriter.saveMetadata(self.instance, path, self.sc) [docs] @staticmethod def extractJsonParams(instance, skipParams): paramMap = instance.extractParamMap() jsonParams = {param.name: value for param, value in paramMap.items() if param.name not in skipParams} return jsonParams [docs] @staticmethod def saveMetadata(instance, path, sc, extraMetadata=None, paramMap=None): """ Saves metadata + Params to: path + "/metadata" - class - timestamp - sparkVersion - uid - paramMap - defaultParamMap (since 2.4.0) - (optionally, extra metadata) Parameters ---------- extraMetadata : dict, optional Extra metadata to be saved at same level as uid, paramMap, etc. paramMap : dict, optional If given, this is saved in the "paramMap" field. """ metadataPath = os.path.join(path, "metadata") metadataJson = DefaultParamsWriter._get_metadata_to_save(instance, sc, extraMetadata, paramMap) sc.parallelize([metadataJson], 1).saveAsTextFile(metadataPath) @staticmethod def _get_metadata_to_save(instance, sc, extraMetadata=None, paramMap=None): """ Helper for :py:meth:`DefaultParamsWriter.saveMetadata` which extracts the JSON to save. This is useful for ensemble models which need to save metadata for many sub-models. Notes ----- See :py:meth:`DefaultParamsWriter.saveMetadata` for details on what this includes. """ uid = instance.uid cls = instance.__module__ + '.' + instance.__class__.__name__ # User-supplied param values params = instance._paramMap jsonParams = {} if paramMap is not None: jsonParams = paramMap else: for p in params: jsonParams[p.name] = params[p] # Default param values jsonDefaultParams = {} for p in instance._defaultParamMap: jsonDefaultParams[p.name] = instance._defaultParamMap[p] basicMetadata = {"class": cls, "timestamp": int(round(time.time() * 1000)), "sparkVersion": sc.version, "uid": uid, "paramMap": jsonParams, "defaultParamMap": jsonDefaultParams} if extraMetadata is not None: basicMetadata.update(extraMetadata) return json.dumps(basicMetadata, separators=[',', ':']) [docs]@inherit_doc class DefaultParamsReadable(MLReadable): """ Helper trait for making simple :py:class:`Params` types readable. If a :py:class:`Params` class stores all data as :py:class:`Param` values, then extending this trait will provide a default implementation of reading saved instances of the class. This only handles simple :py:class:`Param` types; e.g., it will not handle :py:class:`Dataset`. See :py:class:`DefaultParamsWritable`, the counterpart to this trait. .. versionadded:: 2.3.0 """ [docs] @classmethod def read(cls): """Returns a DefaultParamsReader instance for this class.""" return DefaultParamsReader(cls) [docs]@inherit_doc class DefaultParamsReader(MLReader): """ Specialization of :py:class:`MLReader` for :py:class:`Params` types Default :py:class:`MLReader` implementation for transformers and estimators that contain basic (json-serializable) params and no data. This will not handle more complex params or types with data (e.g., models with coefficients). .. versionadded:: 2.3.0 """ def __init__(self, cls): super(DefaultParamsReader, self).__init__() self.cls = cls @staticmethod 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 [docs] def load(self, path): metadata = DefaultParamsReader.loadMetadata(path, self.sc) py_type = DefaultParamsReader.__get_class(metadata['class']) instance = py_type() instance._resetUid(metadata['uid']) DefaultParamsReader.getAndSetParams(instance, metadata) return instance [docs] @staticmethod def loadMetadata(path, sc, expectedClassName=""): """ Load metadata saved using :py:meth:`DefaultParamsWriter.saveMetadata` Parameters ---------- path : str sc : :py:class:`pyspark.SparkContext` expectedClassName : str, optional If non empty, this is checked against the loaded metadata. """ metadataPath = os.path.join(path, "metadata") metadataStr = sc.textFile(metadataPath, 1).first() loadedVals = DefaultParamsReader._parseMetaData(metadataStr, expectedClassName) return loadedVals @staticmethod def _parseMetaData(metadataStr, expectedClassName=""): """ Parse metadata JSON string produced by :py:meth`DefaultParamsWriter._get_metadata_to_save`. This is a helper function for :py:meth:`DefaultParamsReader.loadMetadata`. Parameters ---------- metadataStr : str JSON string of metadata expectedClassName : str, optional If non empty, this is checked against the loaded metadata. """ metadata = json.loads(metadataStr) className = metadata['class'] if len(expectedClassName) > 0: assert className == expectedClassName, "Error loading metadata: Expected " + \ "class name {} but found class name {}".format(expectedClassName, className) return metadata [docs] @staticmethod def getAndSetParams(instance, metadata, skipParams=None): """ Extract Params from metadata, and set them in the instance. """ # Set user-supplied param values for paramName in metadata['paramMap']: param = instance.getParam(paramName) if skipParams is None or paramName not in skipParams: paramValue = metadata['paramMap'][paramName] instance.set(param, paramValue) # Set default param values majorAndMinorVersions = VersionUtils.majorMinorVersion(metadata['sparkVersion']) major = majorAndMinorVersions[0] minor = majorAndMinorVersions[1] # For metadata file prior to Spark 2.4, there is no default section. if major > 2 or (major == 2 and minor >= 4): assert 'defaultParamMap' in metadata, "Error loading metadata: Expected " + \ "`defaultParamMap` section not found" for paramName in metadata['defaultParamMap']: paramValue = metadata['defaultParamMap'][paramName] instance._setDefault(**{paramName: paramValue}) [docs] @staticmethod def isPythonParamsInstance(metadata): return metadata['class'].startswith('pyspark.ml.') [docs] @staticmethod def loadParamsInstance(path, sc): """ Load a :py:class:`Params` instance from the given path, and return it. This assumes the instance inherits from :py:class:`MLReadable`. """ metadata = DefaultParamsReader.loadMetadata(path, sc) if DefaultParamsReader.isPythonParamsInstance(metadata): pythonClassName = metadata['class'] else: pythonClassName = metadata['class'].replace("org.apache.spark", "pyspark") py_type = DefaultParamsReader.__get_class(pythonClassName) instance = py_type.load(path) return instance [docs]@inherit_doc class HasTrainingSummary(object): """ Base class for models that provides Training summary. .. versionadded:: 3.0.0 """ @property @since("2.1.0") def hasSummary(self): """ Indicates whether a training summary exists for this model instance. """ return self._call_java("hasSummary") @property @since("2.1.0") def summary(self): """ Gets summary of the model trained on the training set. An exception is thrown if no summary exists. """ return (self._call_java("summary")) class MetaAlgorithmReadWrite: @staticmethod def isMetaEstimator(pyInstance): from pyspark.ml import Estimator, Pipeline from pyspark.ml.tuning import _ValidatorParams from pyspark.ml.classification import OneVsRest return isinstance(pyInstance, Pipeline) or isinstance(pyInstance, OneVsRest) or \ (isinstance(pyInstance, Estimator) and isinstance(pyInstance, _ValidatorParams)) @staticmethod def getAllNestedStages(pyInstance): from pyspark.ml import Pipeline, PipelineModel from pyspark.ml.tuning import _ValidatorParams from pyspark.ml.classification import OneVsRest, OneVsRestModel # TODO: We need to handle `RFormulaModel.pipelineModel` here after Pyspark RFormulaModel # support pipelineModel property. if isinstance(pyInstance, Pipeline): pySubStages = pyInstance.getStages() elif isinstance(pyInstance, PipelineModel): pySubStages = pyInstance.stages elif isinstance(pyInstance, _ValidatorParams): raise ValueError('PySpark does not support nested validator.') elif isinstance(pyInstance, OneVsRest): pySubStages = [pyInstance.getClassifier()] elif isinstance(pyInstance, OneVsRestModel): pySubStages = [pyInstance.getClassifier()] + pyInstance.models else: pySubStages = [] nestedStages = [] for pySubStage in pySubStages: nestedStages.extend(MetaAlgorithmReadWrite.getAllNestedStages(pySubStage)) return [pyInstance] + nestedStages @staticmethod def getUidMap(instance): nestedStages = MetaAlgorithmReadWrite.getAllNestedStages(instance) uidMap = {stage.uid: stage for stage in nestedStages} if len(nestedStages) != len(uidMap): raise RuntimeError(f'{instance.__class__.__module__}.{instance.__class__.__name__}' f'.load found a compound estimator with stages with duplicate ' f'UIDs. List of UIDs: {list(uidMap.keys())}.') return uidMap