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Cut (AI Studio Core)

Synopsis

This operator cuts the nominal values of the specified regular attributes. The resultant attributes have values that are substrings of the original attribute values.

Description

The Cut operator creates new attributes from nominal attributes where the new attributes contain only substrings of the original values. The range of characters to be cut is specified by the first character index and last character index parameters. The first character index parameter specifies the index of the first character and the last character index parameter specifies the index of the last character to be included. All characters of the attribute values that are at index equal to or greater than the first character index and less than or equal to the last character index are included in the resulting substring. Please note that the counting starts with 1 and that the first and the last character will be included in the resulting substring. For example, if the value is "RapidMiner" and the first index is set to 6 and the last index is set to 9 the result will be "Mine". If the last index is larger than the length of the word, the resulting substrings will end with the last character.

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.

Output

  • example set output (Data table)

    The ExampleSet with new attributes that have values that are substrings of the original 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. (Since RapidMiner 6.0.4 the Operator will fail if a selected Attribute is not in the ExampleSet)
    • 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. (Since RapidMiner 6.0.4 the Operator will fail if a selected Attribute is not in the ExampleSet)
    • 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 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
  • first_character_indexThis parameter specifies the index of the first character of the substring which should be kept. Please note that the counting starts with 1. Range: integer
  • last_character_indexThis parameter specifies the index of the last character of the substring which should be kept. Please note that the counting starts with 1. Range: integer

Tutorial Processes

Applying the Cut operator on label of the Iris data set

The 'Iris' data set is loaded using the Retrieve operator. A breakpoint is inserted here so that you can view the data set before application of the Cut operator. You can see that the label attribute has three possible values: 'Iris-setosa', 'Iris-versicolor' and 'Iris-virginica'. If we want to remove the 'Iris-' substring from the start of all the label values we can use the Cut operator. The Cut operator is applied on the Iris data set. The first character index parameter is set to 6 because we want to remove first 5 characters ('Iris-'). The last character index parameter can be set to any value greater than the length of longest possible value. Thus the last character index parameter can be safely set to 20 because if the last index is larger than the length of the word, the resulting substrings will end with the last character. Run the process and you can see that the substring 'Iris-' has been removed from the start of all possible values of the label attribute.

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