# 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.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from mmlspark.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.common import inherit_doc
from mmlspark.core.schema.Utils import *
[docs]@inherit_doc
class ComputeModelStatistics(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
evaluationMetric (str): Metric to evaluate models with (default: all)
labelCol (str): The name of the label column
scoredLabelsCol (str): Scored labels column name, only required if using SparkML estimators
scoresCol (str): Scores or raw prediction column name, only required if using SparkML estimators
"""
@keyword_only
def __init__(self, evaluationMetric="all", labelCol=None, scoredLabelsCol=None, scoresCol=None):
super(ComputeModelStatistics, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.train.ComputeModelStatistics")
self.evaluationMetric = Param(self, "evaluationMetric", "evaluationMetric: Metric to evaluate models with (default: all)")
self._setDefault(evaluationMetric="all")
self.labelCol = Param(self, "labelCol", "labelCol: The name of the label column")
self.scoredLabelsCol = Param(self, "scoredLabelsCol", "scoredLabelsCol: Scored labels column name, only required if using SparkML estimators")
self.scoresCol = Param(self, "scoresCol", "scoresCol: Scores or raw prediction column name, only required if using SparkML estimators")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, evaluationMetric="all", labelCol=None, scoredLabelsCol=None, scoresCol=None):
"""
Set the (keyword only) parameters
Args:
evaluationMetric (str): Metric to evaluate models with (default: all)
labelCol (str): The name of the label column
scoredLabelsCol (str): Scored labels column name, only required if using SparkML estimators
scoresCol (str): Scores or raw prediction column name, only required if using SparkML estimators
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def setEvaluationMetric(self, value):
"""
Args:
evaluationMetric (str): Metric to evaluate models with (default: all)
"""
self._set(evaluationMetric=value)
return self
[docs] def getEvaluationMetric(self):
"""
Returns:
str: Metric to evaluate models with (default: all)
"""
return self.getOrDefault(self.evaluationMetric)
[docs] def setLabelCol(self, value):
"""
Args:
labelCol (str): The name of the label column
"""
self._set(labelCol=value)
return self
[docs] def getLabelCol(self):
"""
Returns:
str: The name of the label column
"""
return self.getOrDefault(self.labelCol)
[docs] def setScoredLabelsCol(self, value):
"""
Args:
scoredLabelsCol (str): Scored labels column name, only required if using SparkML estimators
"""
self._set(scoredLabelsCol=value)
return self
[docs] def getScoredLabelsCol(self):
"""
Returns:
str: Scored labels column name, only required if using SparkML estimators
"""
return self.getOrDefault(self.scoredLabelsCol)
[docs] def setScoresCol(self, value):
"""
Args:
scoresCol (str): Scores or raw prediction column name, only required if using SparkML estimators
"""
self._set(scoresCol=value)
return self
[docs] def getScoresCol(self):
"""
Returns:
str: Scores or raw prediction column name, only required if using SparkML estimators
"""
return self.getOrDefault(self.scoresCol)
[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.ml.spark.train.ComputeModelStatistics"
@staticmethod
def _from_java(java_stage):
module_name=ComputeModelStatistics.__module__
module_name=module_name.rsplit(".", 1)[0] + ".ComputeModelStatistics"
return from_java(java_stage, module_name)