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Nominal to Binominal (RapidMiner Studio Core)
SynopsisThis operator changes the type of selected nominal attributes to a binominal type. It also maps all values of these attributes to binominal values.
The Nominal to Binominal operator is used for changing the type of nominal attributes to a binominal type. This operator not only changes the type of selected attributes but it also maps all values of these attributes to binominal values i.e. true and false. For example, if a nominal attribute with name 'costs' and possible nominal values 'low', 'moderate', and 'high' is transformed, the result is a set of three binominal attributes 'costs = low', 'costs = moderate', and 'costs = high'. Only the value of one of these attributes is true for a specific example, the value of the other attributes is false. Examples of the original ExampleSet where the 'costs' attribute had value 'low', in the new ExampleSet these examples will have attribute 'costs=low' value set to 'true', value of 'cost=moderate' and ' cost=high' attributes will be 'false'. Numeric attributes of the input ExampleSet remain unchanged.
- example set (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 its meta data. The Retrieve operator provides meta data along-with data. The ExampleSet should have at least one nominal attribute because if there is no such attribute, use of this operator does not make sense.
- example set (Data Table)
The ExampleSet with selected nominal attributes converted to binominal type 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.
- preprocessing model
This port delivers the preprocessing model, which has information regarding the parameters of this operator in the current process.
- create_view It is possible to create a View instead of changing the underlying data. Simply select this parameter to enable this option. The transformation that would be normally performed directly on the data will then be computed every time a value is requested and the result is returned without changing the data. Range: boolean
- attribute_filter_typeThis parameter allows you to select the attribute selection filter; the method you want to use for selecting attributes that you want to convert to binominal form. 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 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 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 not selected.
- 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.
- attributeThe required attribute can be selected from this option. The attribute name can be selected from the drop down box of 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. Attributes can be shifted to the right list which is the list of selected attributes on which the conversion from nominal to binominal will take place; all other attributes will remain unchanged. Range: string
- regular_expressionThe attributes whose name match this expression will be selected. Regular expression is 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. This will enhance your concept of regular expressions. 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 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. Range: selection
- use_value_type_exceptionIf 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. 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 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. By default all special attributes are selected irrespective of the conditions in the Nominal to Binominal operator. If this parameter is set to true, Special attributes are also tested against conditions specified in the Nominal to Binominal 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 removed prior to selection of this parameter. After selection of this parameter 'att1' will be removed and 'att2' will be selected. Range: boolean
- transform_binominalThis parameter indicates if attributes which are already binominal should be dichotomized i.e. they should be split in two columns with values true and false. Range: boolean
- use_underscore_in_nameThis parameter indicates if underscores should be used in the new attribute names instead of empty spaces and '='. Although the resulting names are harder to read for humans it might be more appropriate to use these if the data should be written into a database system. Range: boolean
Nominal to Binominal conversion of attributes of Golf data set
This Example Process mostly focuses on the transform binominal parameter. All remaining parameters are mostly for selecting the attributes. The Select Attributes operator also has many similar parameters for selection of attributes. You can study the Example Process of the Select Attributes operator if you want an understanding of these parameters.
The Retrieve operator is used to load the Golf data set. A breakpoint is inserted at this point so that you can have look at the data set before application of the Nominal to Binominal operator. You can see that the 'Outlook' attribute has three possible values i.e. 'sunny', 'rain' and 'overcast'. The 'Wind' attribute has two possible values i.e. 'true' and 'false'. All parameters of the Nominal to Binominal operator are used with default values. Run the process. First you will see the Golf data set. Press the run button again and you will see the final results. You can see that the 'Outlook' attribute is replaced by three binominal attributes, one for each possible value of the original 'Outlook' attribute. These attributes are ' Outlook = sunny', ' Outlook = rain', and ' Outlook = overcast'. Only the value of one of these attributes is true for a specific example, the value of the other attributes is false. Examples whose 'Outlook ' attribute had the value 'sunny' in the original ExampleSet, will have the attribute ' Outlook =sunny' value set to 'true'in the new ExampleSet, the value of the 'Outlook =overcast' and 'Outlook =rain' attributes will be 'false'. The numeric attributes of the input ExampleSet remain unchanged.
The 'Wind' attribute was not replaced by two binominal attributes, one for each possible value of the 'Wind' attribute because this attribute is already binominal. Still if you want to break it into two separate binominal attributes, this can be done by setting the transform binominal parameter to true.