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Rename by Replacing (AI Studio Core)

Synopsis

This operator can be used to rename a set of attributes by replacing parts of the attribute names by a specified replacement.

Description

The Rename by Replacing operator replaces parts of the attribute names by the specified replacement. This operator is used mostly for removing unwanted parts of attribute names like whitespaces, parentheses, or other unwanted characters. The replace what parameter defines that part of the attribute name that should be replaced. It can be defined as a regular expression which is a very powerful tool but needs a detailed explanation to beginners. It is always good to specify the regular expression through the edit and preview regular expression menu. The replace by parameter can be defined as an arbitrary string. Empty strings are also allowed. Capturing groups of the regular expression of the replace what parameter can be accessed with $1, $2, $3 etc. Please study the attached Example Process for more understanding.

Please keep in mind that attribute names must be unique. The Rename by Replacing operator has no impact on the type or role of an attribute. For example if you have an attribute named 'alpha' of integer type and regular role. Renaming the attribute to 'beta' will just change its name. It will retain its type integer and role regular. To change the role of an operator, use the Set Role operator. Many type conversion operators are available for changing the type of an attribute at 'Data Transformation/Type Conversion'.

Input

  • example set input (Data table)

    This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input. It is essential that meta data should be attached with the data for the input because attributes are specified in their meta data.

Output

  • example set output (Data table)

    The ExampleSet with renamed attributes is output of this port.

  • original (Data table)

    The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.

Parameters

  • attribute_filter_typeThis parameter allows you to select the attribute selection filter; the method you want to use for selecting attributes. It has the following options:
    • all: This option simply selects all the attributes of the ExampleSet.This is the default option.
    • single: This option allows selection of a single attribute. When this option is selected another parameter (attribute) becomes visible in the Parameters panel.
    • subset: This option allows selection of multiple attributes through a list. All attributes of ExampleSet are present in the list; required attributes can be easily selected. This option will not work if meta data is not known. When this option is selected another parameter becomes visible in the Parameters panel.
    • regular_expression: This option allows you to specify a regular expression for attribute selection. When this option is selected some other parameters (regular expression, use except expression) become visible in the Parameters panel.
    • value_type: This option allows selection of all the attributes of a particular type. It should be noted that types are hierarchical. For example real and integer types both belong to the numeric type. Users should have basic understanding of type hierarchy when selecting attributes through this option. When this option is selected some other parameters (value type, use value type exception) become visible in the Parameters panel.
    • block_type: This option is similar in working to the value_type option. This option allows selection of all the attributes of a particular block type. It should be noted that block types may be hierarchical. For example value_series_start and value_series_end block types both belong to the value_series block type. When this option is selected some other parameters (block type, use block type exception) become visible in the Parameters panel.
    • no_missing_values: This option simply selects all the attributes of the ExampleSet which don't contain a missing value in any example. Attributes that have even a single missing value are removed.
    • numeric value filter: When this option is selected another parameter (numeric condition) becomes visible in the Parameters panel. All numeric attributes whose examples all satisfy the mentioned numeric condition are selected. Please note that all nominal attributes are also selected irrespective of the given numerical condition.
    Range: selection
  • attributeThe required attribute can be selected from this option. The attribute name can be selected from the drop down box of the parameter attribute if the meta data is known. Range: string
  • attributesThe required attributes can be selected from this option. This opens a new window with two lists. All attributes are present in the left list and can be shifted to the right list, which is the list of selected attributes. Range: string
  • regular_expressionThe attributes whose name match this expression will be selected. Regular expression is a very powerful tool but needs a detailed explanation to beginners. It is always good to specify the regular expression through the edit and preview regular expression menu. This menu gives a good idea of regular expressions and it also allows you to try different expressions and preview the results simultaneously. Range: string
  • use_except_expressionIf enabled, an exception to the first regular expression can be specified. When this option is selected another parameter (except regular expression) becomes visible in the Parameters panel. Range: boolean
  • except_regular_expressionThis option allows you to specify a regular expression. Attributes matching this expression will be filtered out even if they match the first regular expression (regular expression that was specified in the regular expression parameter). Range: string
  • value_typeThe type of attributes to be selected can be chosen from a drop down list. Range: selection
  • use_value_type_exception If enabled, an exception to the selected type can be specified. When this option is enabled, another parameter (except value type) becomes visible in the Parameters panel. Range: boolean
  • except_value_typeThe attributes matching this type will not be selected even if they match the previously mentioned type i.e. value type parameter's value. Range: selection
  • block_typeThe block type of attributes to be selected can be chosen from a drop down list. Range: selection
  • use_block_type_exception If enabled, an exception to the selected block type can be specified. When this option is selected another parameter (except block type) becomes visible in the Parameters panel. Range: boolean
  • except_block_typeThe attributes matching this block type will be not be selected even if they match the previously mentioned block type i.e. block type parameter's value. Range: selection
  • numeric_conditionThe numeric condition for testing examples of numeric attributes is specified here. For example the numeric condition '> 6' will keep all nominal attributes and all numeric attributes having a value of greater than 6 in every example. A combination of conditions is possible: '> 6 && < 11' or '<= 5 || < 0'. But && and || cannot be used together in one numeric condition. Conditions like '(> 0 && < 2) || (>10 && < 12)' are not allowed because they use both && and ||. Use a blank space after '>', '=' and '<' e.g. '<5' will not work, so use '< 5' instead. Range: string
  • include_special_attributesThe special attributes are attributes with special roles which identify the examples. In contrast regular attributes simply describe the examples. Special attributes are: id, label, prediction, cluster, weight and batch. By default all special attributes are selected irrespective of the conditions in the Select Attribute operator. If this parameter is set to true, Special attributes are also tested against conditions specified in the Select Attribute operator and only those attributes are selected that satisfy the conditions. Range: boolean
  • invert_selectionIf this parameter is set to true, it acts as a NOT gate, it reverses the selection. In that case all the selected attributes are unselected and previously unselected attributes are selected. For example if attribute 'att1' is selected and attribute 'att2' is unselected prior to checking of this parameter. After checking of this parameter 'att1' will be unselected and 'att2' will be selected. Range: boolean
  • replace_whatThe replace what parameter defines that part of the attribute name that should be replaced. It can be defined as a regular expression. Capturing groups of the regular expression of the replace what parameter can be accessed in the replace by parameter with $1, $2, $3 etc. Range: string
  • replace_by The replace by parameter can be defined as an arbitrary string. Empty strings are also allowed. Capturing groups of the regular expression of the replace what parameter can be accessed with $1, $2, $3 etc. Range: string

Tutorial Processes

Renaming attributes of the Sonar data set

The 'Sonar' data set is loaded using the Retrieve operator. A breakpoint is inserted here so that you can view the ExampleSet. You can see that the ExampleSet has 60 regular attributes with names like attribute_1, atribute_2 etc. The Rename by Replacing operator is applied on it. The attribute filter type parameter is set to 'all' thus all attributes can be renamed by this operator. The replace what parameter is set to the regular expression: '(att)ribute_'. The brackets are used for specifying the capturing group which can be accessed in the replace by parameter with $1. The replace by parameter is set to '$1-'. Wherever 'attribute_' is found in names of the 'Sonar' attributes, it is replaced by the first capturing group and a dash i.e. 'att-'. Thus attributes are renamed to format att-1, att-2 and so on. This can be verified by seeing the results in the Results Workspace.