Source code for

# 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 import *
from pyspark import keyword_only
from import JavaMLReadable, JavaMLWritable
from import running_on_synapse_internal
from import *
from import JavaTransformer, JavaEstimator, JavaModel
from import JavaEvaluator
from import inherit_doc
from import *
from import TypeConverters
from import generateTypeConverter, complexTypeConverter

[docs]@inherit_doc class SummarizeData(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer): """ Args: basic (bool): Compute basic statistics counts (bool): Compute count statistics errorThreshold (float): Threshold for quantiles - 0 is exact percentiles (bool): Compute percentiles sample (bool): Compute sample statistics """ basic = Param(Params._dummy(), "basic", "Compute basic statistics", typeConverter=TypeConverters.toBoolean) counts = Param(Params._dummy(), "counts", "Compute count statistics", typeConverter=TypeConverters.toBoolean) errorThreshold = Param(Params._dummy(), "errorThreshold", "Threshold for quantiles - 0 is exact", typeConverter=TypeConverters.toFloat) percentiles = Param(Params._dummy(), "percentiles", "Compute percentiles", typeConverter=TypeConverters.toBoolean) sample = Param(Params._dummy(), "sample", "Compute sample statistics", typeConverter=TypeConverters.toBoolean) @keyword_only def __init__( self, java_obj=None, basic=True, counts=True, errorThreshold=0.0, percentiles=True, sample=True ): super(SummarizeData, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("", self.uid) else: self._java_obj = java_obj self._setDefault(basic=True) self._setDefault(counts=True) self._setDefault(errorThreshold=0.0) self._setDefault(percentiles=True) self._setDefault(sample=True) 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, basic=True, counts=True, errorThreshold=0.0, percentiles=True, sample=True ): """ 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 ""
@staticmethod def _from_java(java_stage): module_name=SummarizeData.__module__ module_name=module_name.rsplit(".", 1)[0] + ".SummarizeData" return from_java(java_stage, module_name)
[docs] def setBasic(self, value): """ Args: basic: Compute basic statistics """ self._set(basic=value) return self
[docs] def setCounts(self, value): """ Args: counts: Compute count statistics """ self._set(counts=value) return self
[docs] def setErrorThreshold(self, value): """ Args: errorThreshold: Threshold for quantiles - 0 is exact """ self._set(errorThreshold=value) return self
[docs] def setPercentiles(self, value): """ Args: percentiles: Compute percentiles """ self._set(percentiles=value) return self
[docs] def setSample(self, value): """ Args: sample: Compute sample statistics """ self._set(sample=value) return self
[docs] def getBasic(self): """ Returns: basic: Compute basic statistics """ return self.getOrDefault(self.basic)
[docs] def getCounts(self): """ Returns: counts: Compute count statistics """ return self.getOrDefault(self.counts)
[docs] def getErrorThreshold(self): """ Returns: errorThreshold: Threshold for quantiles - 0 is exact """ return self.getOrDefault(self.errorThreshold)
[docs] def getPercentiles(self): """ Returns: percentiles: Compute percentiles """ return self.getOrDefault(self.percentiles)
[docs] def getSample(self): """ Returns: sample: Compute sample statistics """ return self.getOrDefault(self.sample)