synapse.ml.cognitive.anomaly package

Submodules

synapse.ml.cognitive.anomaly.DetectAnomalies module

class synapse.ml.cognitive.anomaly.DetectAnomalies.DetectAnomalies(java_obj=None, AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, concurrency=1, concurrentTimeout=None, customInterval=None, customIntervalCol=None, errorCol='DetectAnomalies_d144c848a07a_error', granularity=None, granularityCol=None, handler=None, imputeFixedValue=None, imputeFixedValueCol=None, imputeMode=None, imputeModeCol=None, maxAnomalyRatio=None, maxAnomalyRatioCol=None, outputCol='DetectAnomalies_d144c848a07a_output', period=None, periodCol=None, sensitivity=None, sensitivityCol=None, series=None, seriesCol=None, subscriptionKey=None, subscriptionKeyCol=None, timeout=60.0, url=None)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

AADToken¶ (object) – AAD Token used for authentication
CustomAuthHeader¶ (object) – A Custom Value for Authorization Header
concurrency¶ (int) – max number of concurrent calls
concurrentTimeout¶ (float) – max number seconds to wait on futures if concurrency >= 1
customInterval¶ (object) – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.
errorCol¶ (str) – column to hold http errors
granularity¶ (object) – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.
handler¶ (object) – Which strategy to use when handling requests
imputeFixedValue¶ (object) – Optional argument, fixed value to use when imputeMode is set to “fixed”
imputeMode¶ (object) – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill
maxAnomalyRatio¶ (object) – Optional argument, advanced model parameter, max anomaly ratio in a time series.
outputCol¶ (str) – The name of the output column
period¶ (object) – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.
sensitivity¶ (object) – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted
series¶ (object) – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.
subscriptionKey¶ (object) – the API key to use
timeout¶ (float) – number of seconds to wait before closing the connection
url¶ (str) – Url of the service

AADToken = Param(parent='undefined', name='AADToken', doc='ServiceParam: AAD Token used for authentication')

CustomAuthHeader = Param(parent='undefined', name='CustomAuthHeader', doc='ServiceParam: A Custom Value for Authorization Header')

concurrency = Param(parent='undefined', name='concurrency', doc='max number of concurrent calls')

concurrentTimeout = Param(parent='undefined', name='concurrentTimeout', doc='max number seconds to wait on futures if concurrency >= 1')

customInterval = Param(parent='undefined', name='customInterval', doc='ServiceParam: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5. ')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

getAADToken()[source]

Returns: AAD Token used for authentication
Return type: AADToken

getConcurrency()[source]

Returns: max number of concurrent calls
Return type: concurrency

getConcurrentTimeout()[source]

Returns: max number seconds to wait on futures if concurrency >= 1
Return type: concurrentTimeout

getCustomAuthHeader()[source]

Returns: A Custom Value for Authorization Header
Return type: CustomAuthHeader

getCustomInterval()[source]

Returns: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.
Return type: customInterval

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

getGranularity()[source]

Returns: Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.
Return type: granularity

getHandler()[source]

Returns: Which strategy to use when handling requests
Return type: handler

getImputeFixedValue()[source]

Returns: Optional argument, fixed value to use when imputeMode is set to “fixed”
Return type: imputeFixedValue

getImputeMode()[source]

Returns: Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill
Return type: imputeMode

static getJavaPackage()[source]: Returns package name String.

getMaxAnomalyRatio()[source]

Returns: Optional argument, advanced model parameter, max anomaly ratio in a time series.
Return type: maxAnomalyRatio

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getPeriod()[source]

Returns: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.
Return type: period

getSensitivity()[source]

Returns: Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted
Return type: sensitivity

getSeries()[source]

Returns: Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.
Return type: series

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getTimeout()[source]

Returns: number of seconds to wait before closing the connection
Return type: timeout

getUrl()[source]

Returns: Url of the service
Return type: url

granularity = Param(parent='undefined', name='granularity', doc='ServiceParam: Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid. ')

handler = Param(parent='undefined', name='handler', doc='Which strategy to use when handling requests')

imputeFixedValue = Param(parent='undefined', name='imputeFixedValue', doc='ServiceParam: Optional argument, fixed value to use when imputeMode is set to "fixed" ')

imputeMode = Param(parent='undefined', name='imputeMode', doc='ServiceParam: Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill ')

maxAnomalyRatio = Param(parent='undefined', name='maxAnomalyRatio', doc='ServiceParam: Optional argument, advanced model parameter, max anomaly ratio in a time series. ')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

period = Param(parent='undefined', name='period', doc='ServiceParam: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically. ')

classmethod read()[source]: Returns an MLReader instance for this class.

sensitivity = Param(parent='undefined', name='sensitivity', doc='ServiceParam: Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted ')

series = Param(parent='undefined', name='series', doc='ServiceParam: Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned. ')

setAADToken(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setAADTokenCol(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setConcurrency(value)[source]

Parameters: concurrency¶ – max number of concurrent calls

setConcurrentTimeout(value)[source]

Parameters: concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1

setCustomAuthHeader(value)[source]

Parameters: CustomAuthHeader¶ – A Custom Value for Authorization Header

setCustomAuthHeaderCol(value)[source]

Parameters: CustomAuthHeader¶ – A Custom Value for Authorization Header

setCustomInterval(value)[source]

Parameters: customInterval¶ – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.

setCustomIntervalCol(value)[source]

Parameters: customInterval¶ – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.

setCustomServiceName(value)[source]

setDefaultInternalEndpoint(value)[source]

setEndpoint(value)[source]

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setGranularity(value)[source]

Parameters: granularity¶ – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.

setGranularityCol(value)[source]

Parameters: granularity¶ – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.

setHandler(value)[source]

Parameters: handler¶ – Which strategy to use when handling requests

setImputeFixedValue(value)[source]

Parameters: imputeFixedValue¶ – Optional argument, fixed value to use when imputeMode is set to “fixed”

setImputeFixedValueCol(value)[source]

Parameters: imputeFixedValue¶ – Optional argument, fixed value to use when imputeMode is set to “fixed”

setImputeMode(value)[source]

Parameters: imputeMode¶ – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill

setImputeModeCol(value)[source]

Parameters: imputeMode¶ – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill

setLinkedService(value)[source]

setLocation(value)[source]

setMaxAnomalyRatio(value)[source]

Parameters: maxAnomalyRatio¶ – Optional argument, advanced model parameter, max anomaly ratio in a time series.

setMaxAnomalyRatioCol(value)[source]

Parameters: maxAnomalyRatio¶ – Optional argument, advanced model parameter, max anomaly ratio in a time series.

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, concurrency=1, concurrentTimeout=None, customInterval=None, customIntervalCol=None, errorCol='DetectAnomalies_d144c848a07a_error', granularity=None, granularityCol=None, handler=None, imputeFixedValue=None, imputeFixedValueCol=None, imputeMode=None, imputeModeCol=None, maxAnomalyRatio=None, maxAnomalyRatioCol=None, outputCol='DetectAnomalies_d144c848a07a_output', period=None, periodCol=None, sensitivity=None, sensitivityCol=None, series=None, seriesCol=None, subscriptionKey=None, subscriptionKeyCol=None, timeout=60.0, url=None)[source]: Set the (keyword only) parameters

setPeriod(value)[source]

Parameters: period¶ – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.

setPeriodCol(value)[source]

Parameters: period¶ – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.

setSensitivity(value)[source]

Parameters: sensitivity¶ – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted

setSensitivityCol(value)[source]

Parameters: sensitivity¶ – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted

setSeries(value)[source]

Parameters: series¶ – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.

setSeriesCol(value)[source]

Parameters: series¶ – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setTimeout(value)[source]

Parameters: timeout¶ – number of seconds to wait before closing the connection

setUrl(value)[source]

Parameters: url¶ – Url of the service

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

timeout = Param(parent='undefined', name='timeout', doc='number of seconds to wait before closing the connection')

url = Param(parent='undefined', name='url', doc='Url of the service')

synapse.ml.cognitive.anomaly.DetectLastAnomaly module

class synapse.ml.cognitive.anomaly.DetectLastAnomaly.DetectLastAnomaly(java_obj=None, AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, concurrency=1, concurrentTimeout=None, customInterval=None, customIntervalCol=None, errorCol='DetectLastAnomaly_0ebb01be18a2_error', granularity=None, granularityCol=None, handler=None, imputeFixedValue=None, imputeFixedValueCol=None, imputeMode=None, imputeModeCol=None, maxAnomalyRatio=None, maxAnomalyRatioCol=None, outputCol='DetectLastAnomaly_0ebb01be18a2_output', period=None, periodCol=None, sensitivity=None, sensitivityCol=None, series=None, seriesCol=None, subscriptionKey=None, subscriptionKeyCol=None, timeout=60.0, url=None)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

AADToken¶ (object) – AAD Token used for authentication
CustomAuthHeader¶ (object) – A Custom Value for Authorization Header
concurrency¶ (int) – max number of concurrent calls
concurrentTimeout¶ (float) – max number seconds to wait on futures if concurrency >= 1
customInterval¶ (object) – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.
errorCol¶ (str) – column to hold http errors
granularity¶ (object) – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.
handler¶ (object) – Which strategy to use when handling requests
imputeFixedValue¶ (object) – Optional argument, fixed value to use when imputeMode is set to “fixed”
imputeMode¶ (object) – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill
maxAnomalyRatio¶ (object) – Optional argument, advanced model parameter, max anomaly ratio in a time series.
outputCol¶ (str) – The name of the output column
period¶ (object) – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.
sensitivity¶ (object) – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted
series¶ (object) – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.
subscriptionKey¶ (object) – the API key to use
timeout¶ (float) – number of seconds to wait before closing the connection
url¶ (str) – Url of the service

AADToken = Param(parent='undefined', name='AADToken', doc='ServiceParam: AAD Token used for authentication')

CustomAuthHeader = Param(parent='undefined', name='CustomAuthHeader', doc='ServiceParam: A Custom Value for Authorization Header')

concurrency = Param(parent='undefined', name='concurrency', doc='max number of concurrent calls')

concurrentTimeout = Param(parent='undefined', name='concurrentTimeout', doc='max number seconds to wait on futures if concurrency >= 1')

customInterval = Param(parent='undefined', name='customInterval', doc='ServiceParam: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5. ')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

getAADToken()[source]

Returns: AAD Token used for authentication
Return type: AADToken

getConcurrency()[source]

Returns: max number of concurrent calls
Return type: concurrency

getConcurrentTimeout()[source]

Returns: max number seconds to wait on futures if concurrency >= 1
Return type: concurrentTimeout

getCustomAuthHeader()[source]

Returns: A Custom Value for Authorization Header
Return type: CustomAuthHeader

getCustomInterval()[source]

Returns: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.
Return type: customInterval

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

getGranularity()[source]

Returns: Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.
Return type: granularity

getHandler()[source]

Returns: Which strategy to use when handling requests
Return type: handler

getImputeFixedValue()[source]

Returns: Optional argument, fixed value to use when imputeMode is set to “fixed”
Return type: imputeFixedValue

getImputeMode()[source]

Returns: Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill
Return type: imputeMode

static getJavaPackage()[source]: Returns package name String.

getMaxAnomalyRatio()[source]

Returns: Optional argument, advanced model parameter, max anomaly ratio in a time series.
Return type: maxAnomalyRatio

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getPeriod()[source]

Returns: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.
Return type: period

getSensitivity()[source]

Returns: Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted
Return type: sensitivity

getSeries()[source]

Returns: Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.
Return type: series

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getTimeout()[source]

Returns: number of seconds to wait before closing the connection
Return type: timeout

getUrl()[source]

Returns: Url of the service
Return type: url

granularity = Param(parent='undefined', name='granularity', doc='ServiceParam: Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid. ')

handler = Param(parent='undefined', name='handler', doc='Which strategy to use when handling requests')

imputeFixedValue = Param(parent='undefined', name='imputeFixedValue', doc='ServiceParam: Optional argument, fixed value to use when imputeMode is set to "fixed" ')

imputeMode = Param(parent='undefined', name='imputeMode', doc='ServiceParam: Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill ')

maxAnomalyRatio = Param(parent='undefined', name='maxAnomalyRatio', doc='ServiceParam: Optional argument, advanced model parameter, max anomaly ratio in a time series. ')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

period = Param(parent='undefined', name='period', doc='ServiceParam: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically. ')

classmethod read()[source]: Returns an MLReader instance for this class.

sensitivity = Param(parent='undefined', name='sensitivity', doc='ServiceParam: Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted ')

series = Param(parent='undefined', name='series', doc='ServiceParam: Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned. ')

setAADToken(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setAADTokenCol(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setConcurrency(value)[source]

Parameters: concurrency¶ – max number of concurrent calls

setConcurrentTimeout(value)[source]

Parameters: concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1

setCustomAuthHeader(value)[source]

Parameters: CustomAuthHeader¶ – A Custom Value for Authorization Header

setCustomAuthHeaderCol(value)[source]

Parameters: CustomAuthHeader¶ – A Custom Value for Authorization Header

setCustomInterval(value)[source]

Parameters: customInterval¶ – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.

setCustomIntervalCol(value)[source]

Parameters: customInterval¶ – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.

setCustomServiceName(value)[source]

setDefaultInternalEndpoint(value)[source]

setEndpoint(value)[source]

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setGranularity(value)[source]

Parameters: granularity¶ – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.

setGranularityCol(value)[source]

Parameters: granularity¶ – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.

setHandler(value)[source]

Parameters: handler¶ – Which strategy to use when handling requests

setImputeFixedValue(value)[source]

Parameters: imputeFixedValue¶ – Optional argument, fixed value to use when imputeMode is set to “fixed”

setImputeFixedValueCol(value)[source]

Parameters: imputeFixedValue¶ – Optional argument, fixed value to use when imputeMode is set to “fixed”

setImputeMode(value)[source]

Parameters: imputeMode¶ – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill

setImputeModeCol(value)[source]

Parameters: imputeMode¶ – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill

setLinkedService(value)[source]

setLocation(value)[source]

setMaxAnomalyRatio(value)[source]

Parameters: maxAnomalyRatio¶ – Optional argument, advanced model parameter, max anomaly ratio in a time series.

setMaxAnomalyRatioCol(value)[source]

Parameters: maxAnomalyRatio¶ – Optional argument, advanced model parameter, max anomaly ratio in a time series.

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, concurrency=1, concurrentTimeout=None, customInterval=None, customIntervalCol=None, errorCol='DetectLastAnomaly_0ebb01be18a2_error', granularity=None, granularityCol=None, handler=None, imputeFixedValue=None, imputeFixedValueCol=None, imputeMode=None, imputeModeCol=None, maxAnomalyRatio=None, maxAnomalyRatioCol=None, outputCol='DetectLastAnomaly_0ebb01be18a2_output', period=None, periodCol=None, sensitivity=None, sensitivityCol=None, series=None, seriesCol=None, subscriptionKey=None, subscriptionKeyCol=None, timeout=60.0, url=None)[source]: Set the (keyword only) parameters

setPeriod(value)[source]

Parameters: period¶ – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.

setPeriodCol(value)[source]

Parameters: period¶ – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.

setSensitivity(value)[source]

Parameters: sensitivity¶ – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted

setSensitivityCol(value)[source]

Parameters: sensitivity¶ – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted

setSeries(value)[source]

Parameters: series¶ – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.

setSeriesCol(value)[source]

Parameters: series¶ – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setTimeout(value)[source]

Parameters: timeout¶ – number of seconds to wait before closing the connection

setUrl(value)[source]

Parameters: url¶ – Url of the service

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

timeout = Param(parent='undefined', name='timeout', doc='number of seconds to wait before closing the connection')

url = Param(parent='undefined', name='url', doc='Url of the service')

synapse.ml.cognitive.anomaly.DetectLastMultivariateAnomaly module

class synapse.ml.cognitive.anomaly.DetectLastMultivariateAnomaly.DetectLastMultivariateAnomaly(java_obj=None, AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, batchSize=300, concurrency=1, concurrentTimeout=None, diagnosticsInfo=None, errorCol='DetectLastMultivariateAnomaly_7223138c4ee0_error', handler=None, inputVariablesCols=None, modelId=None, outputCol='DetectLastMultivariateAnomaly_7223138c4ee0_output', subscriptionKey=None, subscriptionKeyCol=None, timeout=60.0, timestampCol='timestamp', topContributorCount=10, url=None)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

AADToken¶ (object) – AAD Token used for authentication
CustomAuthHeader¶ (object) – A Custom Value for Authorization Header
batchSize¶ (int) – The max size of the buffer
concurrency¶ (int) – max number of concurrent calls
concurrentTimeout¶ (float) – max number seconds to wait on futures if concurrency >= 1
diagnosticsInfo¶ (object) – diagnosticsInfo for training a multivariate anomaly detection model
errorCol¶ (str) – column to hold http errors
handler¶ (object) – Which strategy to use when handling requests
inputVariablesCols¶ (list) – The names of the input variables columns
modelId¶ (str) – Format - uuid. Model identifier.
outputCol¶ (str) – The name of the output column
subscriptionKey¶ (object) – the API key to use
timeout¶ (float) – number of seconds to wait before closing the connection
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

AADToken = Param(parent='undefined', name='AADToken', doc='ServiceParam: AAD Token used for authentication')

CustomAuthHeader = Param(parent='undefined', name='CustomAuthHeader', doc='ServiceParam: A Custom Value for Authorization Header')

batchSize = Param(parent='undefined', name='batchSize', doc='The max size of the buffer')

concurrency = Param(parent='undefined', name='concurrency', doc='max number of concurrent calls')

concurrentTimeout = Param(parent='undefined', name='concurrentTimeout', doc='max number seconds to wait on futures if concurrency >= 1')

diagnosticsInfo = Param(parent='undefined', name='diagnosticsInfo', doc='diagnosticsInfo for training a multivariate anomaly detection model')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

getAADToken()[source]

Returns: AAD Token used for authentication
Return type: AADToken

getBatchSize()[source]

Returns: The max size of the buffer
Return type: batchSize

getConcurrency()[source]

Returns: max number of concurrent calls
Return type: concurrency

getConcurrentTimeout()[source]

Returns: max number seconds to wait on futures if concurrency >= 1
Return type: concurrentTimeout

getCustomAuthHeader()[source]

Returns: A Custom Value for Authorization Header
Return type: CustomAuthHeader

getDiagnosticsInfo()[source]

Returns: diagnosticsInfo for training a multivariate anomaly detection model
Return type: diagnosticsInfo

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

getHandler()[source]

Returns: Which strategy to use when handling requests
Return type: handler

getInputVariablesCols()[source]

Returns: The names of the input variables columns
Return type: inputVariablesCols

static getJavaPackage()[source]: Returns package name String.

getModelId()[source]

Returns: Format - uuid. Model identifier.
Return type: modelId

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getTimeout()[source]

Returns: number of seconds to wait before closing the connection
Return type: timeout

getTimestampCol()[source]

Returns: Timestamp column name
Return type: timestampCol

getTopContributorCount()[source]

Returns: 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 type: topContributorCount

getUrl()[source]

Returns: Url of the service
Return type: url

handler = Param(parent='undefined', name='handler', doc='Which strategy to use when handling requests')

inputVariablesCols = Param(parent='undefined', name='inputVariablesCols', doc='The names of the input variables columns')

modelId = Param(parent='undefined', name='modelId', doc='Format - uuid. Model identifier.')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

classmethod read()[source]: Returns an MLReader instance for this class.

setAADToken(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setAADTokenCol(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setBatchSize(value)[source]

Parameters: batchSize¶ – The max size of the buffer

setConcurrency(value)[source]

Parameters: concurrency¶ – max number of concurrent calls

setConcurrentTimeout(value)[source]

Parameters: concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1

setCustomAuthHeader(value)[source]

Parameters: CustomAuthHeader¶ – A Custom Value for Authorization Header

setCustomAuthHeaderCol(value)[source]

Parameters: CustomAuthHeader¶ – A Custom Value for Authorization Header

setCustomServiceName(value)[source]

setDefaultInternalEndpoint(value)[source]

setDiagnosticsInfo(value)[source]

Parameters: diagnosticsInfo¶ – diagnosticsInfo for training a multivariate anomaly detection model

setEndpoint(value)[source]

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setHandler(value)[source]

Parameters: handler¶ – Which strategy to use when handling requests

setInputVariablesCols(value)[source]

Parameters: inputVariablesCols¶ – The names of the input variables columns

setLocation(value)[source]

setModelId(value)[source]

Parameters: modelId¶ – Format - uuid. Model identifier.

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, batchSize=300, concurrency=1, concurrentTimeout=None, diagnosticsInfo=None, errorCol='DetectLastMultivariateAnomaly_7223138c4ee0_error', handler=None, inputVariablesCols=None, modelId=None, outputCol='DetectLastMultivariateAnomaly_7223138c4ee0_output', subscriptionKey=None, subscriptionKeyCol=None, timeout=60.0, timestampCol='timestamp', topContributorCount=10, url=None)[source]: Set the (keyword only) parameters

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setTimeout(value)[source]

Parameters: timeout¶ – number of seconds to wait before closing the connection

setTimestampCol(value)[source]

Parameters: timestampCol¶ – Timestamp column name

setTopContributorCount(value)[source]

Parameters: 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.

setUrl(value)[source]

Parameters: url¶ – Url of the service

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

timeout = Param(parent='undefined', name='timeout', doc='number of seconds to wait before closing the connection')

timestampCol = Param(parent='undefined', name='timestampCol', doc='Timestamp column name')

topContributorCount = Param(parent='undefined', name='topContributorCount', doc='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 = Param(parent='undefined', name='url', doc='Url of the service')

synapse.ml.cognitive.anomaly.SimpleDetectAnomalies module

class synapse.ml.cognitive.anomaly.SimpleDetectAnomalies.SimpleDetectAnomalies(java_obj=None, AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, concurrency=1, concurrentTimeout=None, customInterval=None, customIntervalCol=None, errorCol='SimpleDetectAnomalies_680906fc7074_error', granularity=None, granularityCol=None, groupbyCol=None, handler=None, imputeFixedValue=None, imputeFixedValueCol=None, imputeMode=None, imputeModeCol=None, maxAnomalyRatio=None, maxAnomalyRatioCol=None, outputCol='SimpleDetectAnomalies_680906fc7074_output', period=None, periodCol=None, sensitivity=None, sensitivityCol=None, series=None, seriesCol=None, subscriptionKey=None, subscriptionKeyCol=None, timeout=60.0, timestampCol='timestamp', url=None, valueCol='value')[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

AADToken¶ (object) – AAD Token used for authentication
CustomAuthHeader¶ (object) – A Custom Value for Authorization Header
concurrency¶ (int) – max number of concurrent calls
concurrentTimeout¶ (float) – max number seconds to wait on futures if concurrency >= 1
customInterval¶ (object) – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.
errorCol¶ (str) – column to hold http errors
granularity¶ (object) – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.
groupbyCol¶ (str) – column that groups the series
handler¶ (object) – Which strategy to use when handling requests
imputeFixedValue¶ (object) – Optional argument, fixed value to use when imputeMode is set to “fixed”
imputeMode¶ (object) – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill
maxAnomalyRatio¶ (object) – Optional argument, advanced model parameter, max anomaly ratio in a time series.
outputCol¶ (str) – The name of the output column
period¶ (object) – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.
sensitivity¶ (object) – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted
series¶ (object) – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.
subscriptionKey¶ (object) – the API key to use
timeout¶ (float) – number of seconds to wait before closing the connection
timestampCol¶ (str) – column representing the time of the series
url¶ (str) – Url of the service
valueCol¶ (str) – column representing the value of the series

AADToken = Param(parent='undefined', name='AADToken', doc='ServiceParam: AAD Token used for authentication')

CustomAuthHeader = Param(parent='undefined', name='CustomAuthHeader', doc='ServiceParam: A Custom Value for Authorization Header')

concurrency = Param(parent='undefined', name='concurrency', doc='max number of concurrent calls')

concurrentTimeout = Param(parent='undefined', name='concurrentTimeout', doc='max number seconds to wait on futures if concurrency >= 1')

customInterval = Param(parent='undefined', name='customInterval', doc='ServiceParam: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5. ')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

getAADToken()[source]

Returns: AAD Token used for authentication
Return type: AADToken

getConcurrency()[source]

Returns: max number of concurrent calls
Return type: concurrency

getConcurrentTimeout()[source]

Returns: max number seconds to wait on futures if concurrency >= 1
Return type: concurrentTimeout

getCustomAuthHeader()[source]

Returns: A Custom Value for Authorization Header
Return type: CustomAuthHeader

getCustomInterval()[source]

Returns: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.
Return type: customInterval

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

getGranularity()[source]

Returns: Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.
Return type: granularity

getGroupbyCol()[source]

Returns: column that groups the series
Return type: groupbyCol

getHandler()[source]

Returns: Which strategy to use when handling requests
Return type: handler

getImputeFixedValue()[source]

Returns: Optional argument, fixed value to use when imputeMode is set to “fixed”
Return type: imputeFixedValue

getImputeMode()[source]

Returns: Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill
Return type: imputeMode

static getJavaPackage()[source]: Returns package name String.

getMaxAnomalyRatio()[source]

Returns: Optional argument, advanced model parameter, max anomaly ratio in a time series.
Return type: maxAnomalyRatio

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getPeriod()[source]

Returns: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.
Return type: period

getSensitivity()[source]

Returns: Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted
Return type: sensitivity

getSeries()[source]

Returns: Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.
Return type: series

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getTimeout()[source]

Returns: number of seconds to wait before closing the connection
Return type: timeout

getTimestampCol()[source]

Returns: column representing the time of the series
Return type: timestampCol

getUrl()[source]

Returns: Url of the service
Return type: url

getValueCol()[source]

Returns: column representing the value of the series
Return type: valueCol

granularity = Param(parent='undefined', name='granularity', doc='ServiceParam: Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid. ')

groupbyCol = Param(parent='undefined', name='groupbyCol', doc='column that groups the series')

handler = Param(parent='undefined', name='handler', doc='Which strategy to use when handling requests')

imputeFixedValue = Param(parent='undefined', name='imputeFixedValue', doc='ServiceParam: Optional argument, fixed value to use when imputeMode is set to "fixed" ')

imputeMode = Param(parent='undefined', name='imputeMode', doc='ServiceParam: Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill ')

maxAnomalyRatio = Param(parent='undefined', name='maxAnomalyRatio', doc='ServiceParam: Optional argument, advanced model parameter, max anomaly ratio in a time series. ')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

period = Param(parent='undefined', name='period', doc='ServiceParam: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically. ')

classmethod read()[source]: Returns an MLReader instance for this class.

sensitivity = Param(parent='undefined', name='sensitivity', doc='ServiceParam: Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted ')

series = Param(parent='undefined', name='series', doc='ServiceParam: Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned. ')

setAADToken(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setAADTokenCol(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setConcurrency(value)[source]

Parameters: concurrency¶ – max number of concurrent calls

setConcurrentTimeout(value)[source]

Parameters: concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1

setCustomAuthHeader(value)[source]

Parameters: CustomAuthHeader¶ – A Custom Value for Authorization Header

setCustomAuthHeaderCol(value)[source]

Parameters: CustomAuthHeader¶ – A Custom Value for Authorization Header

setCustomInterval(value)[source]

Parameters: customInterval¶ – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.

setCustomIntervalCol(value)[source]

Parameters: customInterval¶ – Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as granularity=minutely, customInterval=5.

setCustomServiceName(value)[source]

setDefaultInternalEndpoint(value)[source]

setEndpoint(value)[source]

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setGranularity(value)[source]

Parameters: granularity¶ – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.

setGranularityCol(value)[source]

Parameters: granularity¶ – Can only be one of yearly, monthly, weekly, daily, hourly or minutely. Granularity is used for verify whether input series is valid.

setGroupbyCol(value)[source]

Parameters: groupbyCol¶ – column that groups the series

setHandler(value)[source]

Parameters: handler¶ – Which strategy to use when handling requests

setImputeFixedValue(value)[source]

Parameters: imputeFixedValue¶ – Optional argument, fixed value to use when imputeMode is set to “fixed”

setImputeFixedValueCol(value)[source]

Parameters: imputeFixedValue¶ – Optional argument, fixed value to use when imputeMode is set to “fixed”

setImputeMode(value)[source]

Parameters: imputeMode¶ – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill

setImputeModeCol(value)[source]

Parameters: imputeMode¶ – Optional argument, impute mode of a time series. Possible values: auto, previous, linear, fixed, zero, notFill

setLinkedService(value)[source]

setLocation(value)[source]

setMaxAnomalyRatio(value)[source]

Parameters: maxAnomalyRatio¶ – Optional argument, advanced model parameter, max anomaly ratio in a time series.

setMaxAnomalyRatioCol(value)[source]

Parameters: maxAnomalyRatio¶ – Optional argument, advanced model parameter, max anomaly ratio in a time series.

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, concurrency=1, concurrentTimeout=None, customInterval=None, customIntervalCol=None, errorCol='SimpleDetectAnomalies_680906fc7074_error', granularity=None, granularityCol=None, groupbyCol=None, handler=None, imputeFixedValue=None, imputeFixedValueCol=None, imputeMode=None, imputeModeCol=None, maxAnomalyRatio=None, maxAnomalyRatioCol=None, outputCol='SimpleDetectAnomalies_680906fc7074_output', period=None, periodCol=None, sensitivity=None, sensitivityCol=None, series=None, seriesCol=None, subscriptionKey=None, subscriptionKeyCol=None, timeout=60.0, timestampCol='timestamp', url=None, valueCol='value')[source]: Set the (keyword only) parameters

setPeriod(value)[source]

Parameters: period¶ – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.

setPeriodCol(value)[source]

Parameters: period¶ – Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically.

setSensitivity(value)[source]

Parameters: sensitivity¶ – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted

setSensitivityCol(value)[source]

Parameters: sensitivity¶ – Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted

setSeries(value)[source]

Parameters: series¶ – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.

setSeriesCol(value)[source]

Parameters: series¶ – Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned.

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setTimeout(value)[source]

Parameters: timeout¶ – number of seconds to wait before closing the connection

setTimestampCol(value)[source]

Parameters: timestampCol¶ – column representing the time of the series

setUrl(value)[source]

Parameters: url¶ – Url of the service

setValueCol(value)[source]

Parameters: valueCol¶ – column representing the value of the series

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

timeout = Param(parent='undefined', name='timeout', doc='number of seconds to wait before closing the connection')

timestampCol = Param(parent='undefined', name='timestampCol', doc='column representing the time of the series')

url = Param(parent='undefined', name='url', doc='Url of the service')

valueCol = Param(parent='undefined', name='valueCol', doc='column representing the value of the series')

synapse.ml.cognitive.anomaly.SimpleDetectMultivariateAnomaly module

class synapse.ml.cognitive.anomaly.SimpleDetectMultivariateAnomaly.SimpleDetectMultivariateAnomaly(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)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaModel

Parameters

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(parent='undefined', name='backoffs', doc='array of backoffs to use in the handler')

cleanUpIntermediateData()[source]

diagnosticsInfo = Param(parent='undefined', name='diagnosticsInfo', doc='diagnosticsInfo for training a multivariate anomaly detection model')

endTime = Param(parent='undefined', name='endTime', doc='A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

getBackoffs()[source]

Returns: array of backoffs to use in the handler
Return type: backoffs

getDiagnosticsInfo()[source]

Returns: diagnosticsInfo for training a multivariate anomaly detection model
Return type: diagnosticsInfo

getEndTime()[source]

Returns: A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.
Return type: endTime

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

getHandler()[source]

Returns: Which strategy to use when handling requests
Return type: handler

getInitialPollingDelay()[source]

Returns: number of milliseconds to wait before first poll for result
Return type: initialPollingDelay

getInputCols()[source]

Returns: The names of the input columns
Return type: inputCols

getIntermediateSaveDir()[source]

Returns: Blob storage location in HDFS where intermediate data is saved while training.
Return type: intermediateSaveDir

static getJavaPackage()[source]: Returns package name String.

getMaxPollingRetries()[source]

Returns: number of times to poll
Return type: maxPollingRetries

getModelId()[source]

Returns: Format - uuid. Model identifier.
Return type: modelId

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getPollingDelay()[source]

Returns: number of milliseconds to wait between polling
Return type: pollingDelay

getStartTime()[source]

Returns: A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.
Return type: startTime

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getSuppressMaxRetriesException()[source]

Returns: set true to suppress the maxumimum retries exception and report in the error column
Return type: suppressMaxRetriesException

getTimestampCol()[source]

Returns: Timestamp column name
Return type: timestampCol

getTopContributorCount()[source]

Returns: 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 type: topContributorCount

getUrl()[source]

Returns: Url of the service
Return type: url

handler = Param(parent='undefined', name='handler', doc='Which strategy to use when handling requests')

initialPollingDelay = Param(parent='undefined', name='initialPollingDelay', doc='number of milliseconds to wait before first poll for result')

inputCols = Param(parent='undefined', name='inputCols', doc='The names of the input columns')

intermediateSaveDir = Param(parent='undefined', name='intermediateSaveDir', doc='Blob storage location in HDFS where intermediate data is saved while training.')

maxPollingRetries = Param(parent='undefined', name='maxPollingRetries', doc='number of times to poll')

modelId = Param(parent='undefined', name='modelId', doc='Format - uuid. Model identifier.')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

pollingDelay = Param(parent='undefined', name='pollingDelay', doc='number of milliseconds to wait between polling')

classmethod read()[source]: Returns an MLReader instance for this class.

setBackoffs(value)[source]

Parameters: backoffs¶ – array of backoffs to use in the handler

setDiagnosticsInfo(value)[source]

Parameters: diagnosticsInfo¶ – diagnosticsInfo for training a multivariate anomaly detection model

setEndTime(value)[source]

Parameters: endTime¶ – A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setHandler(value)[source]

Parameters: handler¶ – Which strategy to use when handling requests

setInitialPollingDelay(value)[source]

Parameters: initialPollingDelay¶ – number of milliseconds to wait before first poll for result

setInputCols(value)[source]

Parameters: inputCols¶ – The names of the input columns

setIntermediateSaveDir(value)[source]

Parameters: intermediateSaveDir¶ – Blob storage location in HDFS where intermediate data is saved while training.

setLocation(value)[source]

setMaxPollingRetries(value)[source]

Parameters: maxPollingRetries¶ – number of times to poll

setModelId(value)[source]

Parameters: modelId¶ – Format - uuid. Model identifier.

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(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)[source]: Set the (keyword only) parameters

setPollingDelay(value)[source]

Parameters: pollingDelay¶ – number of milliseconds to wait between polling

setStartTime(value)[source]

Parameters: startTime¶ – A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSuppressMaxRetriesException(value)[source]

Parameters: suppressMaxRetriesException¶ – set true to suppress the maxumimum retries exception and report in the error column

setTimestampCol(value)[source]

Parameters: timestampCol¶ – Timestamp column name

setTopContributorCount(value)[source]

Parameters: 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.

setUrl(value)[source]

Parameters: url¶ – Url of the service

startTime = Param(parent='undefined', name='startTime', doc='A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.')

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

suppressMaxRetriesException = Param(parent='undefined', name='suppressMaxRetriesException', doc='set true to suppress the maxumimum retries exception and report in the error column')

timestampCol = Param(parent='undefined', name='timestampCol', doc='Timestamp column name')

topContributorCount = Param(parent='undefined', name='topContributorCount', doc='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 = Param(parent='undefined', name='url', doc='Url of the service')

synapse.ml.cognitive.anomaly.SimpleFitMultivariateAnomaly module

class synapse.ml.cognitive.anomaly.SimpleFitMultivariateAnomaly.SimpleFitMultivariateAnomaly(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)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaEstimator

Parameters

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(parent='undefined', name='alignMode', doc='An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer}')

backoffs = Param(parent='undefined', name='backoffs', doc='array of backoffs to use in the handler')

cleanUpIntermediateData()[source]

displayName = Param(parent='undefined', name='displayName', doc='optional field, name of the model')

endTime = Param(parent='undefined', name='endTime', doc='A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

fillNAMethod = Param(parent='undefined', name='fillNAMethod', doc='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}')

getAlignMode()[source]

Returns: An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer}
Return type: alignMode

getBackoffs()[source]

Returns: array of backoffs to use in the handler
Return type: backoffs

getDisplayName()[source]

Returns: optional field, name of the model
Return type: displayName

getEndTime()[source]

Returns: A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.
Return type: endTime

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

getFillNAMethod()[source]

Returns: 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 type: fillNAMethod

getInitialPollingDelay()[source]

Returns: number of milliseconds to wait before first poll for result
Return type: initialPollingDelay

getInputCols()[source]

Returns: The names of the input columns
Return type: inputCols

getIntermediateSaveDir()[source]

Returns: Blob storage location in HDFS where intermediate data is saved while training.
Return type: intermediateSaveDir

static getJavaPackage()[source]: Returns package name String.

getMaxPollingRetries()[source]

Returns: number of times to poll
Return type: maxPollingRetries

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getPaddingValue()[source]

Returns: optional field, is only useful if FillNAMethod is set to Fixed.
Return type: paddingValue

getPollingDelay()[source]

Returns: number of milliseconds to wait between polling
Return type: pollingDelay

getSlidingWindow()[source]

Returns: An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point.
Return type: slidingWindow

getStartTime()[source]

Returns: A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.
Return type: startTime

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getSuppressMaxRetriesException()[source]

Returns: set true to suppress the maxumimum retries exception and report in the error column
Return type: suppressMaxRetriesException

getTimestampCol()[source]

Returns: Timestamp column name
Return type: timestampCol

getUrl()[source]

Returns: Url of the service
Return type: url

initialPollingDelay = Param(parent='undefined', name='initialPollingDelay', doc='number of milliseconds to wait before first poll for result')

inputCols = Param(parent='undefined', name='inputCols', doc='The names of the input columns')

intermediateSaveDir = Param(parent='undefined', name='intermediateSaveDir', doc='Blob storage location in HDFS where intermediate data is saved while training.')

maxPollingRetries = Param(parent='undefined', name='maxPollingRetries', doc='number of times to poll')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

paddingValue = Param(parent='undefined', name='paddingValue', doc='optional field, is only useful if FillNAMethod is set to Fixed.')

pollingDelay = Param(parent='undefined', name='pollingDelay', doc='number of milliseconds to wait between polling')

classmethod read()[source]: Returns an MLReader instance for this class.

setAlignMode(value)[source]

Parameters: alignMode¶ – An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer}

setBackoffs(value)[source]

Parameters: backoffs¶ – array of backoffs to use in the handler

setDisplayName(value)[source]

Parameters: displayName¶ – optional field, name of the model

setEndTime(value)[source]

Parameters: endTime¶ – A required field, end time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setFillNAMethod(value)[source]

Parameters: 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}

setInitialPollingDelay(value)[source]

Parameters: initialPollingDelay¶ – number of milliseconds to wait before first poll for result

setInputCols(value)[source]

Parameters: inputCols¶ – The names of the input columns

setIntermediateSaveDir(value)[source]

Parameters: intermediateSaveDir¶ – Blob storage location in HDFS where intermediate data is saved while training.

setLocation(value)[source]

setMaxPollingRetries(value)[source]

Parameters: maxPollingRetries¶ – number of times to poll

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setPaddingValue(value)[source]

Parameters: paddingValue¶ – optional field, is only useful if FillNAMethod is set to Fixed.

setParams(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)[source]: Set the (keyword only) parameters

setPollingDelay(value)[source]

Parameters: pollingDelay¶ – number of milliseconds to wait between polling

setSlidingWindow(value)[source]

Parameters: slidingWindow¶ – An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point.

setStartTime(value)[source]

Parameters: startTime¶ – A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSuppressMaxRetriesException(value)[source]

Parameters: suppressMaxRetriesException¶ – set true to suppress the maxumimum retries exception and report in the error column

setTimestampCol(value)[source]

Parameters: timestampCol¶ – Timestamp column name

setUrl(value)[source]

Parameters: url¶ – Url of the service

slidingWindow = Param(parent='undefined', name='slidingWindow', doc='An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point.')

startTime = Param(parent='undefined', name='startTime', doc='A required field, start time of data to be used for detection/generating multivariate anomaly detection model, should be date-time.')

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

suppressMaxRetriesException = Param(parent='undefined', name='suppressMaxRetriesException', doc='set true to suppress the maxumimum retries exception and report in the error column')

timestampCol = Param(parent='undefined', name='timestampCol', doc='Timestamp column name')

url = Param(parent='undefined', name='url', doc='Url of the service')

Module contents

SynapseML is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK), LightGBM and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of datasources.

SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. In this vein, SynapseML provides easy to use SparkML transformers for a wide variety of Microsoft Cognitive Services. For production grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.

SynapseML requires Scala 2.12, Spark 3.0+, and Python 3.6+.