Source code for synapse.ml.cognitive.anomaly.SimpleDetectMultivariateAnomaly

# 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 SimpleDetectMultivariateAnomaly(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaModel): """ Args: backoffs (list): array of backoffs to use in the handler diagnosticsInfo (object): diagnosticsInfo for training a multivariate anomaly detection model endTime (str): A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time. errorCol (str): column to hold http errors handler (object): Which strategy to use when handling requests initialPollingDelay (int): number of milliseconds to wait before first poll for result inputCols (list): The names of the input columns intermediateSaveDir (str): Blob storage location in HDFS where intermediate data is saved while training. maxPollingRetries (int): number of times to poll modelId (str): Format - uuid. Model identifier. outputCol (str): The name of the output column pollingDelay (int): number of milliseconds to wait between polling startTime (str): A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time. subscriptionKey (object): the API key to use suppressMaxRetriesException (bool): set true to suppress the maxumimum retries exception and report in the error column timestampCol (str): Timestamp column name topContributorCount (int): This is a number that you could specify N from 1 to 30, which will give you the details of top N contributed variables in the anomaly results. For example, if you have 100 variables in the model, but you only care the top five contributed variables in detection results, then you should fill this field with 5. The default number is 10. url (str): Url of the service """ backoffs = Param(Params._dummy(), "backoffs", "array of backoffs to use in the handler", typeConverter=TypeConverters.toListInt) diagnosticsInfo = Param(Params._dummy(), "diagnosticsInfo", "diagnosticsInfo for training a multivariate anomaly detection model") endTime = Param(Params._dummy(), "endTime", "A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.", typeConverter=TypeConverters.toString) errorCol = Param(Params._dummy(), "errorCol", "column to hold http errors", typeConverter=TypeConverters.toString) handler = Param(Params._dummy(), "handler", "Which strategy to use when handling requests") initialPollingDelay = Param(Params._dummy(), "initialPollingDelay", "number of milliseconds to wait before first poll for result", typeConverter=TypeConverters.toInt) inputCols = Param(Params._dummy(), "inputCols", "The names of the input columns", typeConverter=TypeConverters.toListString) intermediateSaveDir = Param(Params._dummy(), "intermediateSaveDir", "Blob storage location in HDFS where intermediate data is saved while training.", typeConverter=TypeConverters.toString) maxPollingRetries = Param(Params._dummy(), "maxPollingRetries", "number of times to poll", typeConverter=TypeConverters.toInt) modelId = Param(Params._dummy(), "modelId", "Format - uuid. Model identifier.", typeConverter=TypeConverters.toString) outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString) pollingDelay = Param(Params._dummy(), "pollingDelay", "number of milliseconds to wait between polling", typeConverter=TypeConverters.toInt) startTime = Param(Params._dummy(), "startTime", "A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.", typeConverter=TypeConverters.toString) subscriptionKey = Param(Params._dummy(), "subscriptionKey", "ServiceParam: the API key to use") suppressMaxRetriesException = Param(Params._dummy(), "suppressMaxRetriesException", "set true to suppress the maxumimum retries exception and report in the error column", typeConverter=TypeConverters.toBoolean) timestampCol = Param(Params._dummy(), "timestampCol", "Timestamp column name", typeConverter=TypeConverters.toString) topContributorCount = Param(Params._dummy(), "topContributorCount", "This is a number that you could specify N from 1 to 30, which will give you the details of top N contributed variables in the anomaly results. For example, if you have 100 variables in the model, but you only care the top five contributed variables in detection results, then you should fill this field with 5. The default number is 10.", typeConverter=TypeConverters.toInt) url = Param(Params._dummy(), "url", "Url of the service", typeConverter=TypeConverters.toString) @keyword_only def __init__( self, java_obj=None, backoffs=[100,500,1000], diagnosticsInfo=None, endTime=None, errorCol="SimpleDetectMultivariateAnomaly_e2de67cdadb3_error", handler=None, initialPollingDelay=300, inputCols=None, intermediateSaveDir=None, maxPollingRetries=1000, modelId=None, outputCol="SimpleDetectMultivariateAnomaly_e2de67cdadb3_output", pollingDelay=300, startTime=None, subscriptionKey=None, subscriptionKeyCol=None, suppressMaxRetriesException=False, timestampCol="timestamp", topContributorCount=10, url=None ): super(SimpleDetectMultivariateAnomaly, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.cognitive.anomaly.SimpleDetectMultivariateAnomaly", self.uid) else: self._java_obj = java_obj self._setDefault(backoffs=[100,500,1000]) self._setDefault(errorCol="SimpleDetectMultivariateAnomaly_e2de67cdadb3_error") self._setDefault(initialPollingDelay=300) self._setDefault(maxPollingRetries=1000) self._setDefault(outputCol="SimpleDetectMultivariateAnomaly_e2de67cdadb3_output") self._setDefault(pollingDelay=300) self._setDefault(suppressMaxRetriesException=False) self._setDefault(timestampCol="timestamp") self._setDefault(topContributorCount=10) 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, backoffs=[100,500,1000], diagnosticsInfo=None, endTime=None, errorCol="SimpleDetectMultivariateAnomaly_e2de67cdadb3_error", handler=None, initialPollingDelay=300, inputCols=None, intermediateSaveDir=None, maxPollingRetries=1000, modelId=None, outputCol="SimpleDetectMultivariateAnomaly_e2de67cdadb3_output", pollingDelay=300, startTime=None, subscriptionKey=None, subscriptionKeyCol=None, suppressMaxRetriesException=False, timestampCol="timestamp", topContributorCount=10, url=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.cognitive.anomaly.SimpleDetectMultivariateAnomaly"
@staticmethod def _from_java(java_stage): module_name=SimpleDetectMultivariateAnomaly.__module__ module_name=module_name.rsplit(".", 1)[0] + ".SimpleDetectMultivariateAnomaly" return from_java(java_stage, module_name)
[docs] def setBackoffs(self, value): """ Args: backoffs: array of backoffs to use in the handler """ self._set(backoffs=value) return self
[docs] def setDiagnosticsInfo(self, value): """ Args: diagnosticsInfo: diagnosticsInfo for training a multivariate anomaly detection model """ self._set(diagnosticsInfo=value) return self
[docs] def setEndTime(self, value): """ Args: endTime: A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time. """ self._set(endTime=value) return self
[docs] def setErrorCol(self, value): """ Args: errorCol: column to hold http errors """ self._set(errorCol=value) return self
[docs] def setHandler(self, value): """ Args: handler: Which strategy to use when handling requests """ self._set(handler=value) return self
[docs] def setInitialPollingDelay(self, value): """ Args: initialPollingDelay: number of milliseconds to wait before first poll for result """ self._set(initialPollingDelay=value) return self
[docs] def setInputCols(self, value): """ Args: inputCols: The names of the input columns """ self._set(inputCols=value) return self
[docs] def setIntermediateSaveDir(self, value): """ Args: intermediateSaveDir: Blob storage location in HDFS where intermediate data is saved while training. """ self._set(intermediateSaveDir=value) return self
[docs] def setMaxPollingRetries(self, value): """ Args: maxPollingRetries: number of times to poll """ self._set(maxPollingRetries=value) return self
[docs] def setModelId(self, value): """ Args: modelId: Format - uuid. Model identifier. """ self._set(modelId=value) return self
[docs] def setOutputCol(self, value): """ Args: outputCol: The name of the output column """ self._set(outputCol=value) return self
[docs] def setPollingDelay(self, value): """ Args: pollingDelay: number of milliseconds to wait between polling """ self._set(pollingDelay=value) return self
[docs] def setStartTime(self, value): """ Args: startTime: A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time. """ self._set(startTime=value) return self
[docs] def setSubscriptionKey(self, value): """ Args: subscriptionKey: the API key to use """ if isinstance(value, list): value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value) self._java_obj = self._java_obj.setSubscriptionKey(value) return self
[docs] def setSubscriptionKeyCol(self, value): """ Args: subscriptionKey: the API key to use """ self._java_obj = self._java_obj.setSubscriptionKeyCol(value) return self
[docs] def setSuppressMaxRetriesException(self, value): """ Args: suppressMaxRetriesException: set true to suppress the maxumimum retries exception and report in the error column """ self._set(suppressMaxRetriesException=value) return self
[docs] def setTimestampCol(self, value): """ Args: timestampCol: Timestamp column name """ self._set(timestampCol=value) return self
[docs] def setTopContributorCount(self, value): """ Args: topContributorCount: This is a number that you could specify N from 1 to 30, which will give you the details of top N contributed variables in the anomaly results. For example, if you have 100 variables in the model, but you only care the top five contributed variables in detection results, then you should fill this field with 5. The default number is 10. """ self._set(topContributorCount=value) return self
[docs] def setUrl(self, value): """ Args: url: Url of the service """ self._set(url=value) return self
[docs] def getBackoffs(self): """ Returns: backoffs: array of backoffs to use in the handler """ return self.getOrDefault(self.backoffs)
[docs] def getDiagnosticsInfo(self): """ Returns: diagnosticsInfo: diagnosticsInfo for training a multivariate anomaly detection model """ return self.getOrDefault(self.diagnosticsInfo)
[docs] def getEndTime(self): """ Returns: endTime: A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time. """ return self.getOrDefault(self.endTime)
[docs] def getErrorCol(self): """ Returns: errorCol: column to hold http errors """ return self.getOrDefault(self.errorCol)
[docs] def getHandler(self): """ Returns: handler: Which strategy to use when handling requests """ return self.getOrDefault(self.handler)
[docs] def getInitialPollingDelay(self): """ Returns: initialPollingDelay: number of milliseconds to wait before first poll for result """ return self.getOrDefault(self.initialPollingDelay)
[docs] def getInputCols(self): """ Returns: inputCols: The names of the input columns """ return self.getOrDefault(self.inputCols)
[docs] def getIntermediateSaveDir(self): """ Returns: intermediateSaveDir: Blob storage location in HDFS where intermediate data is saved while training. """ return self.getOrDefault(self.intermediateSaveDir)
[docs] def getMaxPollingRetries(self): """ Returns: maxPollingRetries: number of times to poll """ return self.getOrDefault(self.maxPollingRetries)
[docs] def getModelId(self): """ Returns: modelId: Format - uuid. Model identifier. """ return self.getOrDefault(self.modelId)
[docs] def getOutputCol(self): """ Returns: outputCol: The name of the output column """ return self.getOrDefault(self.outputCol)
[docs] def getPollingDelay(self): """ Returns: pollingDelay: number of milliseconds to wait between polling """ return self.getOrDefault(self.pollingDelay)
[docs] def getStartTime(self): """ Returns: startTime: A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time. """ return self.getOrDefault(self.startTime)
[docs] def getSubscriptionKey(self): """ Returns: subscriptionKey: the API key to use """ return self._java_obj.getSubscriptionKey()
[docs] def getSuppressMaxRetriesException(self): """ Returns: suppressMaxRetriesException: set true to suppress the maxumimum retries exception and report in the error column """ return self.getOrDefault(self.suppressMaxRetriesException)
[docs] def getTimestampCol(self): """ Returns: timestampCol: Timestamp column name """ return self.getOrDefault(self.timestampCol)
[docs] def getTopContributorCount(self): """ Returns: topContributorCount: This is a number that you could specify N from 1 to 30, which will give you the details of top N contributed variables in the anomaly results. For example, if you have 100 variables in the model, but you only care the top five contributed variables in detection results, then you should fill this field with 5. The default number is 10. """ return self.getOrDefault(self.topContributorCount)
[docs] def getUrl(self): """ Returns: url: Url of the service """ return self.getOrDefault(self.url)
[docs] def setLocation(self, value): self._java_obj = self._java_obj.setLocation(value) return self
[docs] def cleanUpIntermediateData(self): self._java_obj.cleanUpIntermediateData() return