Source code for synapse.ml.train.ComputePerInstanceStatistics

# 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 ComputePerInstanceStatistics(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer): """ Args: evaluationMetric (object): Metric to evaluate models with labelCol (object): The name of the label column scoredLabelsCol (object): Scored labels column name, only required if using SparkML estimators scoredProbabilitiesCol (object): Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators scoresCol (object): Scores or raw prediction column name, only required if using SparkML estimators """ evaluationMetric = Param(Params._dummy(), "evaluationMetric", "Metric to evaluate models with") labelCol = Param(Params._dummy(), "labelCol", "The name of the label column") scoredLabelsCol = Param(Params._dummy(), "scoredLabelsCol", "Scored labels column name, only required if using SparkML estimators") scoredProbabilitiesCol = Param(Params._dummy(), "scoredProbabilitiesCol", "Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators") scoresCol = Param(Params._dummy(), "scoresCol", "Scores or raw prediction column name, only required if using SparkML estimators") @keyword_only def __init__( self, java_obj=None, evaluationMetric="all", labelCol=None, scoredLabelsCol=None, scoredProbabilitiesCol=None, scoresCol=None ): super(ComputePerInstanceStatistics, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.train.ComputePerInstanceStatistics", self.uid) else: self._java_obj = java_obj self._setDefault(evaluationMetric="all") 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="all", labelCol=None, scoredLabelsCol=None, scoredProbabilitiesCol=None, scoresCol=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.ComputePerInstanceStatistics"
@staticmethod def _from_java(java_stage): module_name=ComputePerInstanceStatistics.__module__ module_name=module_name.rsplit(".", 1)[0] + ".ComputePerInstanceStatistics" 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 setLabelCol(self, value): """ Args: labelCol: The name of the label column """ self._set(labelCol=value) return self
[docs] def setScoredLabelsCol(self, value): """ Args: scoredLabelsCol: Scored labels column name, only required if using SparkML estimators """ self._set(scoredLabelsCol=value) return self
[docs] def setScoredProbabilitiesCol(self, value): """ Args: scoredProbabilitiesCol: Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators """ self._set(scoredProbabilitiesCol=value) return self
[docs] def setScoresCol(self, value): """ Args: scoresCol: Scores or raw prediction column name, only required if using SparkML estimators """ self._set(scoresCol=value) return self
[docs] def getEvaluationMetric(self): """ Returns: evaluationMetric: Metric to evaluate models with """ return self.getOrDefault(self.evaluationMetric)
[docs] def getLabelCol(self): """ Returns: labelCol: The name of the label column """ return self.getOrDefault(self.labelCol)
[docs] def getScoredLabelsCol(self): """ Returns: scoredLabelsCol: Scored labels column name, only required if using SparkML estimators """ return self.getOrDefault(self.scoredLabelsCol)
[docs] def getScoredProbabilitiesCol(self): """ Returns: scoredProbabilitiesCol: Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators """ return self.getOrDefault(self.scoredProbabilitiesCol)
[docs] def getScoresCol(self): """ Returns: scoresCol: Scores or raw prediction column name, only required if using SparkML estimators """ return self.getOrDefault(self.scoresCol)