Platform For AI:Anomaly Detection

Anomalous features in data can be detected by using the Box Plot or Attribute Value Frequency (AVF) method.

• Box Plot is used to detect data with continuous features. The detection is performed based on the maximum and minimum values of the box plot chart.
• AVF is used to detect data with enumeration features. The detection is performed based on the frequency and threshold of enumeration features.

You can configure the parameters of the Anomaly Detection component on the pipeline page of Machine Learning Designer of Machine Learning Platform for AI (PAI). Machine Learning Designer is formerly known as Machine Learning Studio. The following table describes the parameters.

Tab	Parameter	Description
Fields Setting	Feature Columns	The fields to be analyzed.
	Anomaly Detection Method	The method used to detect anomalous data. Box Plot is used to detect data with continuous features. AVF is used to detect data with enumeration features.

Configure the component parameters by using PAI commands. You can use the SQL Script component to call PAI commands. For more information, see SQL Script.

PAI -name fe_detect_runner -project algo_public
     -DselectedCols="emp_var_rate,cons_price_rate,cons_conf_idx,euribor3m,nr_employed" \
     -Dlifecycle="28"
     -DdetectStrategy="boxPlot"
     -DmodelTable="pai_temp_2458_23565_2"
     -DinputTable="pai_bank_data"
     -DoutputTable="pai_temp_2458_23565_1";

Parameter	Description	Required
inputTable	The name of the input table.	Yes
inputTablePartitions	The partitions in the input table. By default, all partitions are selected. Specify a single partition in the format of partition_name=value. Specify multiple partitions in the format of name1=value1,name2=value2. Note Separate multiple partitions with commas (,). Specify multi-level partitions in the format of name1=value1/name2=value2.	No
selectedCols	The input features. The data types of the features are not limited.	Yes
detectStrategy	The detection method. Box Plot and AVF are supported. Box Plot is used to detect data with continuous features. AVF is used to detect data with enumeration features.	Yes
outputTable	The output table that contains data with anomalous features.	Yes
modelTable	The anomaly detection model.	Yes
lifecycle	The lifecycle of the output table. Default value: 7.	No
coreNum	The number of cores. This parameter must be used with the memSizePerCore parameter. Note The value of this parameter must be a positive integer. Valid values: 1 to 9999.	No
memSizePerCore	The memory size of each core. Unit: MB. Valid values: [2048,64 × 1024].	No