You are viewing the RapidMiner Studio documentation for version 8.1 - Check here for latest version
Aggregate (RapidMiner Studio Core)
SynopsisThis operator performs the aggregation functions known from SQL. This operator provides a lot of functionalities in the same format as provided by the SQL aggregation functions. SQL aggregation functions and GROUP BY and HAVING clauses can be imitated using this operator.
The Aggregate operator creates a new ExampleSet from the input ExampleSet showing the results of the selected aggregation functions. Many aggregation functions are supported including SUM, COUNT, MIN, MAX, AVERAGE and many other similar functions known from SQL. The functionality of the GROUP BY clause of SQL can be imitated by using the group by attributes parameter. You need to have a basic understanding of the GROUP BY clause of SQL for understanding the use of this parameter because it works exactly the same way. If you want to imitate the known HAVING clause from SQL, you can do that by applying the Filter Examples operator after the Aggregation operator. This operator imitates aggregation functions of SQL. It focuses on obtaining summary information, such as averages and counts etc. It can group examples in an ExampleSet into smaller sets and apply aggregation functions on those sets. Please study the attached Example Process for better understanding of this operator.
- example set (Data Table)
This input port expects an ExampleSet. It is output of the Filter Examples operator in the attached Example Process. Output of other operators can also be used as input.
- example set (Data Table)
The ExampleSet generated after applying the specified aggregation functions 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.
- use_default_aggregationThis parameter allows you to define the default aggregation for selected attributes. A number of parameters become available if this parameter is set to true. These parameters allow you to select the attributes and corresponding default aggregation function. Range: boolean
- 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 all examples 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. 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 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
- default_aggregation_functionThis parameter is only available when the use default aggregation parameter is set to true. It is used for specifying the default aggregation function for the selected attributes. Range:
- aggregation_attributesThis parameter is one of the most important parameters of the operator. It allows you to select attributes and the aggregation function to apply on them. Many aggregation functions are available including count, average, minimum, maximum variance and many more. Range:
- group_by_attributesThis operator can group examples of the input ExampleSet into smaller groups using this parameter. The aggregation functions are applied on these groups. This parameter allows the Aggregate operator to replicate the functionality of the known GROUP BY clause of SQL. From version 6.0.3 on the operator will cause an error if a given attribute can't be found in the example set. Range:
- count_all_combinationsThis parameter indicates if all possible combinations of the values of the group by attributes are counted, even if they don't occur. All possible combinations may result in a huge number so handle this parameter carefully. Range: boolean
- only_distinctThis parameter indicates if only examples with distinct values for the aggregation attribute should be used for the calculation of the aggregation function. Range: boolean
- ignore_missingsThis parameter indicates if missing values should be ignored and aggregation functions should be applied only on existing values. If this parameter is not set to true then the aggregated value will be a missing value in the presence of missing values in the selected attribute. Range: boolean
Imitating an SQL aggregation query using the Aggregate operator
This Example Process discusses an arbitrary scenario. Then describes how this scenario could be handled using SQL aggregation functions. Then the SQL's solution is imitated in RapidMiner. The Aggregate operator plays a key role in this process.
Let us assume a scenario where we want to apply certain aggregation functions on the Golf data set. We don't want to include examples where the Outlook attribute has the value 'overcast'. We group the remaining examples of the 'Golf' data set by values of the Play and Wind attributes. We wish to find the average Temperature and average Humidity for these groups. Once these averages have been calculated, we want to see only those examples where the average Temperature is above 71. Lastly, we want to see the results in ascending order of the average Temperature.
This problem can be solved by the following SQL query:
SELECT Play, Wind, AVG (Temperature), AVG (Humidity)
WHERE Outlook NOT LIKE 'overcast'
GROUP BY Play, Wind
HAVING AVG (Temperature)>71
ORDER BY AVG (Temperature)
The SELECT clause selects the attributes to be displayed. The FROM clause specifies the data set. The WHERE clause pre-excludes the examples where the Outlook attribute has value 'overcast'. The GROUP BY clause groups the data set according to the specified attributes. The HAVING clause filters the results after the aggregation functions have been applied. Finally the ORDER BY clause sorts the results in ascending order of the Temperature averages.
Here is how this scenario can be tackled using RapidMiner. First of all the Retrieve operator is used for loading the 'Golf' data set. This is similar to the FROM clause. Then the Select Attributes operator is applied on it to select the required attributes. This works a little different from the SQL query. If we select only the Play and Wind attributes as in the query, then the coming operators cannot be applied. Thus we select all attributes for now. You will see later that the attribute set will be reduced automatically, thus the Select Attributes operator is not really required here. Then the Filter Examples operator is applied to pre-exclude examples where the Outlook attribute has the value 'overcast'. This is similar to the WHERE clause of SQL. Then the Aggregate operator is applied on the remaining examples. The Aggregate operator performs a number of tasks here. Firstly, it specifies the aggregation functions using the aggregation attributes parameter. We need averages of the Temperature and Humidity attribute; this is specified using the aggregation attributes parameter. Secondly, we do not want the averages of the entire data set. We want the averages by groups, grouped by the Play and Wind attribute values. These groups are specified using the group by attributes parameter of the Aggregate operator. Thirdly, required attributes are automatically filtered by this operator. Only those attributes appear in the resultant data set that have been specified in the Aggregate operator. Next, we are interested only in those examples where the average Temperature is greater than 71. This condition can be applied using the Filter Examples operator. This step is similar to the HAVING clause. Lastly we want the results to be sorted. The Sort operator is used to do the required sorting. This step is very similar to the ORDER BY clause. Breakpoints are inserted after every operator in the Example Process so that you can understand the part played by each operator.