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5. Declare Missing Value

Declare Missing Value (RapidMiner Studio Core)

Synopsis

This operator declares the specified values of the selected attributes as missing values.

Description

The Declare Missing Value operator replaces the specified values of the selected attributes by Double.NaN, thus these values will become missing values. These values will be treated as missing values by the subsequent operators. The desired values can be selected through nominal, numeric or regular expression mode. This behavior can be controlled by the mode parameter.

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.

Output

• example set output (Data Table)

  The specified values of the selected attributes are replaced by missing values and the resultant ExampleSet is delivered through 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 the required 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 the ExampleSet are present in the list; required attributes can be easily selected. This option will not work if the 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 a basic understanding of type hierarchy when selecting attributes through this option. When it 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. 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 desired attribute can be selected from this option. The attribute name can be selected from the drop down box of attribute parameter 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 on which the conversion from nominal to numeric will take place; all other attributes will remain unchanged. Range: string
• regular_expressionThe attributes whose name matches 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. This menu also allows you to try different expressions and preview the results simultaneously. This will enhance your concept of regular expressions. Range: string
• use_except_expressionIf enabled, an exception to the selected type can be specified. When this option is selected another parameter (except value type) 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 expression (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. One of the following types can be chosen: nominal, text, binominal, polynominal, file_path. Range: selection
• use_value_type_exception If enabled, an exception to the selected type can be specified. When this option is selected another parameter (except value type) becomes visible in the Parameters panel. Range: boolean
• except_value_typeThe attributes matching this type will be removed from the final output even if they matched the previously mentioned type i.e. value type parameter's value. One of the following types can be selected here: nominal, text, binominal, polynominal, file_path. Range: selection
• block_typeThe block type of attributes to be selected can be chosen from a drop down list. The only possible value here is 'single_value' 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 removed from the final output even if they matched the previously mentioned block type. 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. 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
• modeThis parameter specifies the type of the value that should be set to missing value. The type can be nominal or numeric or it can be specified through a regular expression. Range: selection
• numeric_valueThis parameter specifies the numerical value that should be declared as missing value. Range: real
• nominal_valueThis parameter specifies the nominal value that should be declared as missing value. Range: string
• expression_valueThis parameter specifies the value that should be declared as missing value through an expression. Range: string

Tutorial Processes

Declaring a nominal value as missing value

The 'Golf' data set is loaded using the Retrieve operator. A breakpoint is inserted here so that you can have a look at the ExampleSet. You can see that the 'Outlook' attribute has three possible values i.e. 'sunny', 'rain' and 'overcast'. The Declare Missing Value operator is applied on this ExampleSet to change the 'overcast' value of the 'Outlook' attribute to a missing value. The attribute filter type parameter is set to 'single' and the attribute parameter is set to 'Outlook'. The mode parameter is set to 'nominal' and the nominal value parameter is set to 'overcast'. Run the process and compare the resultant ExampleSet with the original ExampleSet. You can clearly see that the value 'overcast' has been replaced by missing values.

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Source: https://docs.rapidminer.com/latest/studio/operators/cleansing/missing/declare_missing_value.html

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