# 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.platform import running_on_synapse_internal
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 (str): Metric to evaluate models with
labelCol (str): The name of the label column
scoredLabelsCol (str): Scored labels column name, only required if using SparkML estimators
scoredProbabilitiesCol (str): Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators
scoresCol (str): Scores or raw prediction column name, only required if using SparkML estimators
"""
evaluationMetric = Param(Params._dummy(), "evaluationMetric", "Metric to evaluate models with", typeConverter=TypeConverters.toString)
labelCol = Param(Params._dummy(), "labelCol", "The name of the label column", typeConverter=TypeConverters.toString)
scoredLabelsCol = Param(Params._dummy(), "scoredLabelsCol", "Scored labels column name, only required if using SparkML estimators", typeConverter=TypeConverters.toString)
scoredProbabilitiesCol = Param(Params._dummy(), "scoredProbabilitiesCol", "Scored probabilities, usually calibrated from raw scores, only required if using SparkML estimators", typeConverter=TypeConverters.toString)
scoresCol = Param(Params._dummy(), "scoresCol", "Scores or raw prediction column name, only required if using SparkML estimators", typeConverter=TypeConverters.toString)
@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)