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

# 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
from synapse.ml.cognitive.anomaly.SimpleDetectMultivariateAnomaly import SimpleDetectMultivariateAnomaly

[docs]@inherit_doc class SimpleFitMultivariateAnomaly(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator): """ Args: alignMode (str): An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer} backoffs (list): array of backoffs to use in the handler displayName (str): optional field, name of the 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 fillNAMethod (str): An optional field, indicates how missed values will be filled with. Can not be set to NotFill, when alignMode is Outer.{Previous, Subsequent, Linear, Zero, Fixed} 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 outputCol (str): The name of the output column paddingValue (int): optional field, is only useful if FillNAMethod is set to Fixed. pollingDelay (int): number of milliseconds to wait between polling slidingWindow (int): An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point. 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 url (str): Url of the service """ alignMode = Param(Params._dummy(), "alignMode", "An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer}", typeConverter=TypeConverters.toString) backoffs = Param(Params._dummy(), "backoffs", "array of backoffs to use in the handler", typeConverter=TypeConverters.toListInt) displayName = Param(Params._dummy(), "displayName", "optional field, name of the model", typeConverter=TypeConverters.toString) 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) fillNAMethod = Param(Params._dummy(), "fillNAMethod", "An optional field, indicates how missed values will be filled with. Can not be set to NotFill, when alignMode is Outer.{Previous, Subsequent, Linear, Zero, Fixed}", typeConverter=TypeConverters.toString) 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) outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString) paddingValue = Param(Params._dummy(), "paddingValue", "optional field, is only useful if FillNAMethod is set to Fixed.", typeConverter=TypeConverters.toInt) pollingDelay = Param(Params._dummy(), "pollingDelay", "number of milliseconds to wait between polling", typeConverter=TypeConverters.toInt) slidingWindow = Param(Params._dummy(), "slidingWindow", "An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point.", 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) url = Param(Params._dummy(), "url", "Url of the service", typeConverter=TypeConverters.toString) @keyword_only def __init__( self, java_obj=None, alignMode="Outer", backoffs=[100,500,1000], displayName=None, endTime=None, errorCol="SimpleFitMultivariateAnomaly_7d257d9bbdf3_error", fillNAMethod="Linear", initialPollingDelay=300, inputCols=None, intermediateSaveDir=None, maxPollingRetries=1000, outputCol="SimpleFitMultivariateAnomaly_7d257d9bbdf3_output", paddingValue=None, pollingDelay=300, slidingWindow=300, startTime=None, subscriptionKey=None, subscriptionKeyCol=None, suppressMaxRetriesException=False, timestampCol="timestamp", url=None ): super(SimpleFitMultivariateAnomaly, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.cognitive.anomaly.SimpleFitMultivariateAnomaly", self.uid) else: self._java_obj = java_obj self._setDefault(alignMode="Outer") self._setDefault(backoffs=[100,500,1000]) self._setDefault(errorCol="SimpleFitMultivariateAnomaly_7d257d9bbdf3_error") self._setDefault(fillNAMethod="Linear") self._setDefault(initialPollingDelay=300) self._setDefault(maxPollingRetries=1000) self._setDefault(outputCol="SimpleFitMultivariateAnomaly_7d257d9bbdf3_output") self._setDefault(pollingDelay=300) self._setDefault(slidingWindow=300) self._setDefault(suppressMaxRetriesException=False) self._setDefault(timestampCol="timestamp") 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, alignMode="Outer", backoffs=[100,500,1000], displayName=None, endTime=None, errorCol="SimpleFitMultivariateAnomaly_7d257d9bbdf3_error", fillNAMethod="Linear", initialPollingDelay=300, inputCols=None, intermediateSaveDir=None, maxPollingRetries=1000, outputCol="SimpleFitMultivariateAnomaly_7d257d9bbdf3_output", paddingValue=None, pollingDelay=300, slidingWindow=300, startTime=None, subscriptionKey=None, subscriptionKeyCol=None, suppressMaxRetriesException=False, timestampCol="timestamp", 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.SimpleFitMultivariateAnomaly"
@staticmethod def _from_java(java_stage): module_name=SimpleFitMultivariateAnomaly.__module__ module_name=module_name.rsplit(".", 1)[0] + ".SimpleFitMultivariateAnomaly" return from_java(java_stage, module_name)
[docs] def setAlignMode(self, value): """ Args: alignMode: An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer} """ self._set(alignMode=value) return self
[docs] def setBackoffs(self, value): """ Args: backoffs: array of backoffs to use in the handler """ self._set(backoffs=value) return self
[docs] def setDisplayName(self, value): """ Args: displayName: optional field, name of the model """ self._set(displayName=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 setFillNAMethod(self, value): """ Args: fillNAMethod: An optional field, indicates how missed values will be filled with. Can not be set to NotFill, when alignMode is Outer.{Previous, Subsequent, Linear, Zero, Fixed} """ self._set(fillNAMethod=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 setOutputCol(self, value): """ Args: outputCol: The name of the output column """ self._set(outputCol=value) return self
[docs] def setPaddingValue(self, value): """ Args: paddingValue: optional field, is only useful if FillNAMethod is set to Fixed. """ self._set(paddingValue=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 setSlidingWindow(self, value): """ Args: slidingWindow: An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point. """ self._set(slidingWindow=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 setUrl(self, value): """ Args: url: Url of the service """ self._set(url=value) return self
[docs] def getAlignMode(self): """ Returns: alignMode: An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer} """ return self.getOrDefault(self.alignMode)
[docs] def getBackoffs(self): """ Returns: backoffs: array of backoffs to use in the handler """ return self.getOrDefault(self.backoffs)
[docs] def getDisplayName(self): """ Returns: displayName: optional field, name of the model """ return self.getOrDefault(self.displayName)
[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 getFillNAMethod(self): """ Returns: fillNAMethod: An optional field, indicates how missed values will be filled with. Can not be set to NotFill, when alignMode is Outer.{Previous, Subsequent, Linear, Zero, Fixed} """ return self.getOrDefault(self.fillNAMethod)
[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 getOutputCol(self): """ Returns: outputCol: The name of the output column """ return self.getOrDefault(self.outputCol)
[docs] def getPaddingValue(self): """ Returns: paddingValue: optional field, is only useful if FillNAMethod is set to Fixed. """ return self.getOrDefault(self.paddingValue)
[docs] def getPollingDelay(self): """ Returns: pollingDelay: number of milliseconds to wait between polling """ return self.getOrDefault(self.pollingDelay)
[docs] def getSlidingWindow(self): """ Returns: slidingWindow: An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point. """ return self.getOrDefault(self.slidingWindow)
[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 getUrl(self): """ Returns: url: Url of the service """ return self.getOrDefault(self.url)
def _create_model(self, java_model): try: model = SimpleDetectMultivariateAnomaly(java_obj=java_model) model._transfer_params_from_java() except TypeError: model = SimpleDetectMultivariateAnomaly._from_java(java_model) return model def _fit(self, dataset): java_model = self._fit_java(dataset) return self._create_model(java_model)
[docs] def setLocation(self, value): self._java_obj = self._java_obj.setLocation(value) return self
[docs] def cleanUpIntermediateData(self): self._java_obj.cleanUpIntermediateData() return