Source code for synapse.ml.train.TrainedClassifierModel

# 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


[docs]@inherit_doc class TrainedClassifierModel(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaModel): """ Args: featuresCol (object): The name of the features column labelCol (object): The name of the label column levels (object): the levels model (object): model produced by training """ featuresCol = Param(Params._dummy(), "featuresCol", "The name of the features column") labelCol = Param(Params._dummy(), "labelCol", "The name of the label column") levels = Param(Params._dummy(), "levels", "the levels") model = Param(Params._dummy(), "model", "model produced by training") @keyword_only def __init__( self, java_obj=None, featuresCol=None, labelCol=None, levels=None, model=None ): super(TrainedClassifierModel, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.train.TrainedClassifierModel", self.uid) else: self._java_obj = java_obj 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, featuresCol=None, labelCol=None, levels=None, model=None ): """ 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.train.TrainedClassifierModel"
@staticmethod def _from_java(java_stage): module_name=TrainedClassifierModel.__module__ module_name=module_name.rsplit(".", 1)[0] + ".TrainedClassifierModel" return from_java(java_stage, module_name)
[docs] def setFeaturesCol(self, value): """ Args: featuresCol: The name of the features column """ self._set(featuresCol=value) return self
[docs] def setLabelCol(self, value): """ Args: labelCol: The name of the label column """ self._set(labelCol=value) return self
[docs] def setLevels(self, value): """ Args: levels: the levels """ self._set(levels=value) return self
[docs] def setModel(self, value): """ Args: model: model produced by training """ self._set(model=value) return self
[docs] def getFeaturesCol(self): """ Returns: featuresCol: The name of the features column """ return self.getOrDefault(self.featuresCol)
[docs] def getLabelCol(self): """ Returns: labelCol: The name of the label column """ return self.getOrDefault(self.labelCol)
[docs] def getLevels(self): """ Returns: levels: the levels """ return self.getOrDefault(self.levels)
[docs] def getModel(self): """ Returns: model: model produced by training """ return JavaParams._from_java(self._java_obj.getModel())