REPLACE_OUTLIERS

Updates the data point values that classify as outliers, based on the settings in the parameters.

Parameters

• sourceColumn – Specifies the name of an existing numeric column that might contain outliers.

• outlierStrategy – Specifies the approach to use in detecting outliers. Valid values include the following:

  • Z_SCORE – Identifies a value as an outlier when it deviates from the mean by more than the standard deviation threshold.

  • MODIFIED_Z_SCORE – Identifies a value as an outlier when it deviates from the median by more than the median absolute deviation threshold.

  • IQR – Identifies a values as an outlier when it falls beyond the first and last quartile of column data. The interquartile range (IQR) measures where the middle 50% of the data points are.

• threshold – Specifies the threshold value to use when detecting outliers. The sourceColumn value is identified as an outlier if the score that's calculated with the outlierStrategy exceeds this number. The default is 3.

• replaceType – Specifies the method to use when replacing outliers. Valid values include the following:

  • WINSORIZE_VALUES – Specifies using the minimum and maximum percentile to cap the values.

  • REPLACE_WITH_CUSTOM

  • REPLACE_WITH_EMPTY

  • REPLACE_WITH_NULL

  • REPLACE_WITH_MODE

  • REPLACE_WITH_AVERAGE

  • REPLACE_WITH_MEDIAN

  • REPLACE_WITH_SUM

  • REPLACE_WITH_MAX

• modeType – Indicates the type of modal function to use when replaceType is REPLACE_WITH_MODE. Valid values include the following: MIN, MAX, and AVERAGE.

• minValue – Indicates the minimum percentile value for the outlier range that is to be applied when trimValue is used. Valid range is 0–100.

• maxValue – Indicates the maximum percentile value for the outlier range that is to be applied when trimValue is used. . Valid range is 0–100.

• value – Specifies the value to insert when using REPLACE_WITH_CUSTOM.

• trimValue – Specifies whether to remove all or some of the outliers. This Boolean value is set to TRUE when replaceType is REPLACE_WITH_NULL, REPLACE_WITH_MODE, or WINSORIZE_VALUES. It defaults to FALSE for all others.

  • FALSE – Removes all outliers

  • TRUE –Removes outliers that rank outside of the percentile cap threshold specified in minValue and maxValue.

The following examples display syntax for a single RecipeAction operation. A recipe contains at least one RecipeStep operation, and a recipe step contains at least one recipe action. A recipe action runs the data transform that you specify. A group of recipe actions run in sequential order to create the final dataset.

JSON

The following shows an example RecipeAction to use as member of an example RecipeStep for a DataBrew Recipe, using JSON syntax. For syntax examples showing a list of recipe actions, see Defining a recipe structure.

Example in JSON

{
    "Action": {
        "Operation": "REPLACE_OUTLIERS",
        "Parameters": {
            "maxValue": "95",
            "minValue": "5",
            "modeType": "AVERAGE",
            "outlierStrategy": "Z_SCORE",
            "replaceType": "REPLACE_WITH_MODE",
            "sourceColumn": "name-of-existing-column",
            "threshold": "3",
            "trimValue": "TRUE"
        }
    }
}

For more information on using this recipe action in an API operation, see CreateRecipe or UpdateRecipe. You can use these and other API operations in your own code.

YAML

The following shows an example RecipeAction to use as member of an example RecipeStep for a DataBrew Recipe, using YAML syntax. For syntax examples showing a list of recipe actions, see Defining a recipe structure.

Example in YAML

- Action:
  Operation: REMOVE_OUTLIERS
  Parameters:
    sourceColumn: name-of-existing-column
    outlierStrategy: Z_SCORE
    threshold: '3'
    replaceType: REPLACE_WITH_MODE
    modeType: AVERAGE
    minValue: '5'
    maxValue: '95'
    trimValue: 'TRUE'

For more information on using this recipe action in an API operation, see CreateRecipe or UpdateRecipe. You can use these and other API operations in your own code.