Numerical to Polynominal (RapidMiner Studio Core)

Synopsis

This operator changes the type of selected numeric attributes to a polynominal type. It also maps all values of these attributes to corresponding polynominal values. This operator simply changes the type of selected attributes; if you need a more sophisticated normalization method please use the discretization operators.

Description

The Numerical to Polynominal operator is used for changing the type of numeric attributes to a polynominal type. This operator not only changes the type of selected attributes but it also maps all values of these attributes to corresponding polynominal values. It simply changes the type of selected attributes i.e. every new numerical value is considered to be another possible value for the polynominal attribute. In other words, each numerical value is simply used as nominal value of the new attribute. As numerical attributes can have a huge number of different values even in a small range, converting such a numerical attribute to polynominal form will generate a huge number of possible values for the new attribute. Such a polynominal attribute may not be a very useful one and it may increase memory usage significantly. If you need a more sophisticated normalization method please use the discretization operators. The Discretization operators are at: "Data Transformation/ Type Conversion/ Discretization".

Input

  • 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 their meta data. The Retrieve operator provides meta data along-with data. The ExampleSet should have at least one numeric attribute because if there is no such attribute, use of this operator does not make sense.

Output

  • example set (Data Table)

    The ExampleSet with selected numeric attributes converted to nominal 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.

Parameters

  • 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 polynominal 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. (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 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. (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 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.
    Range: selection
  • 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 polynominal 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 Polynominal operator. If this parameter is set to true, Special attributes are also tested against conditions specified in the Nominal to Polynominal 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

Tutorial Processes

Converting numeric attributes of the Sonar data set to polynominal attributes

This Example Process mostly focuses on the working of this operator. All parameters of this operator 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 'Sonar' data set is loaded using the Retrieve operator. The Numerical to Polynominal operator is applied on it. All parameters are used with default values. The attribute filter type parameter is set to 'all', thus all numeric attributes of the 'Sonar' data set will be converted to nominal type. As you can see in the Results Workspace, before application of the Numerical to Polynominal operator, all attributes were of real type. After application of this operator they are now all changed to nominal type. But if you have a look at the examples, they are exactly the same i.e. just the type of the values has been changed not the actual values. Every new numerical value is considered to be another possible value for the polynominal attribute. In other words, each numerical value is simply used as nominal value of the new attribute. As there is a very large number of different values for almost all attributes in the 'Sonar' data set, converting these attributes to polynominal form generates a huge number of possible values for the new attributes. These new polynominal attributes may not be very useful and they may increase memory usage significantly. In such a scenario it is always better to use a more sophisticated normalization method i.e. the discretization operators.