Source code for synapse.ml.automl.TuneHyperparameters

# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.


import sys
if sys.version >= '3':
    basestring = str

from pyspark import SparkContext, SQLContext
from pyspark.sql import DataFrame
from pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from synapse.ml.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.evaluation import JavaEvaluator
from pyspark.ml.common import inherit_doc
from synapse.ml.core.schema.Utils import *
from pyspark.ml.param import TypeConverters
from synapse.ml.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
from synapse.ml.automl.TuneHyperparametersModel import TuneHyperparametersModel

[docs]@inherit_doc class TuneHyperparameters(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator): """ Args: evaluationMetric (object): Metric to evaluate models with models (object): Estimators to run numFolds (int): Number of folds numRuns (int): Termination criteria for randomized search parallelism (int): The number of models to run in parallel paramSpace (object): Parameter space for generating hyperparameters seed (long): Random number generator seed """ evaluationMetric = Param(Params._dummy(), "evaluationMetric", "Metric to evaluate models with") models = Param(Params._dummy(), "models", "Estimators to run") numFolds = Param(Params._dummy(), "numFolds", "Number of folds", typeConverter=TypeConverters.toInt) numRuns = Param(Params._dummy(), "numRuns", "Termination criteria for randomized search", typeConverter=TypeConverters.toInt) parallelism = Param(Params._dummy(), "parallelism", "The number of models to run in parallel", typeConverter=TypeConverters.toInt) paramSpace = Param(Params._dummy(), "paramSpace", "Parameter space for generating hyperparameters") seed = Param(Params._dummy(), "seed", "Random number generator seed") @keyword_only def __init__( self, java_obj=None, evaluationMetric=None, models=None, numFolds=None, numRuns=None, parallelism=None, paramSpace=None, seed=0 ): super(TuneHyperparameters, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.automl.TuneHyperparameters", self.uid) else: self._java_obj = java_obj self._setDefault(seed=0) if hasattr(self, "_input_kwargs"): kwargs = self._input_kwargs else: kwargs = self.__init__._input_kwargs if java_obj is None: for k,v in kwargs.items(): if v is not None: getattr(self, "set" + k[0].upper() + k[1:])(v)
[docs] @keyword_only def setParams( self, evaluationMetric=None, models=None, numFolds=None, numRuns=None, parallelism=None, paramSpace=None, seed=0 ): """ Set the (keyword only) parameters """ if hasattr(self, "_input_kwargs"): kwargs = self._input_kwargs else: kwargs = self.__init__._input_kwargs return self._set(**kwargs)
[docs] @classmethod def read(cls): """ Returns an MLReader instance for this class. """ return JavaMMLReader(cls)
[docs] @staticmethod def getJavaPackage(): """ Returns package name String. """ return "com.microsoft.azure.synapse.ml.automl.TuneHyperparameters"
@staticmethod def _from_java(java_stage): module_name=TuneHyperparameters.__module__ module_name=module_name.rsplit(".", 1)[0] + ".TuneHyperparameters" return from_java(java_stage, module_name)
[docs] def setEvaluationMetric(self, value): """ Args: evaluationMetric: Metric to evaluate models with """ self._set(evaluationMetric=value) return self
[docs] def setModels(self, value): """ Args: models: Estimators to run """ self._set(models=value) return self
[docs] def setNumFolds(self, value): """ Args: numFolds: Number of folds """ self._set(numFolds=value) return self
[docs] def setNumRuns(self, value): """ Args: numRuns: Termination criteria for randomized search """ self._set(numRuns=value) return self
[docs] def setParallelism(self, value): """ Args: parallelism: The number of models to run in parallel """ self._set(parallelism=value) return self
[docs] def setParamSpace(self, value): """ Args: paramSpace: Parameter space for generating hyperparameters """ self._set(paramSpace=value) return self
[docs] def setSeed(self, value): """ Args: seed: Random number generator seed """ self._set(seed=value) return self
[docs] def getEvaluationMetric(self): """ Returns: evaluationMetric: Metric to evaluate models with """ return self.getOrDefault(self.evaluationMetric)
[docs] def getModels(self): """ Returns: models: Estimators to run """ return self.getOrDefault(self.models)
[docs] def getNumFolds(self): """ Returns: numFolds: Number of folds """ return self.getOrDefault(self.numFolds)
[docs] def getNumRuns(self): """ Returns: numRuns: Termination criteria for randomized search """ return self.getOrDefault(self.numRuns)
[docs] def getParallelism(self): """ Returns: parallelism: The number of models to run in parallel """ return self.getOrDefault(self.parallelism)
[docs] def getParamSpace(self): """ Returns: paramSpace: Parameter space for generating hyperparameters """ return self.getOrDefault(self.paramSpace)
[docs] def getSeed(self): """ Returns: seed: Random number generator seed """ return self.getOrDefault(self.seed)
def _create_model(self, java_model): try: model = TuneHyperparametersModel(java_obj=java_model) model._transfer_params_from_java() except TypeError: model = TuneHyperparametersModel._from_java(java_model) return model def _fit(self, dataset): java_model = self._fit_java(dataset) return self._create_model(java_model)