(AI Studio Core)
Synopsis
This operator transforms the data generated by the Log operator into an ExampleSet which can then be used by other operators of the process.Description
The Log operator stores information into the log table. This information can be almost anything including parameter values of operators, apply-count of operators, execution time etc. The Log operator is mostly used when you want to see the values calculated during the execution of the process that are otherwise not visible. For example you want to see values of different parameters in all iterations of any Loop operator. In such scenarios the ideal operator is the Log operator. A large variety of information can be stored using this operator. The information stored in the log table can be viewed in the Results View. But this information is not directly accessible in the process. To solve this problem, the Log to Data operator provides the information in the Log table in form of an ExampleSet. This ExampleSet can be used in the process like any other ExampleSet. The type of attributes of this ExampleSet is automatically guessed and all attributes have regular role. The type and role can be changed by using the corresponding operators.
Input
through (IOObject)It is not compulsory to connect any object with this port. Any object connected at this port is delivered without any modifications to the output port. This operator can have multiple inputs. When one input is connected, another through input port becomes available which is ready to accept another input (if any). The order of inputs remains the same. The object supplied at the first through input port of the Log to Data operator is available at the first through output port.
Output
- example set (Data table)
The data generated by the Log operator is delivered as an ExampleSet through this port.
- through (IOObject)
The objects that were given as input are passed without changing to the output through this port. It is not compulsory to attach this port to any other port. The Log to Data operator can have multiple outputs. When one output is connected, another through output port becomes available which is ready to deliver another output (if any). The order of outputs remains the same. The object delivered at the first through input port of the Log to Data operator is delivered at the first through output port
Parameters
- log nameThis parameter specifies the name of the Log operator that generated the log data which should be returned as an ExampleSet. If this parameter is left blank then the first found data table is returned as an ExampleSet.
Tutorial Processes
Accessing Training vs Testing error using the Log and Log to Data operators
The 'Weighting' data set is loaded using the Retrieve operator. The Loop Parameters operator is applied on it. The parameters of the Loop Parameters operator are set such that this operator loops 25 times. Thus its subprocess is executed 25 times. In every iteration, the value of the C parameter of the Support Vector Machine (LibSVM) operator is changed. The value of the C parameter is 0.001 in the first iteration. The value is increased logarithmically until it reaches 100000 in the last iteration.
Have a look at the subprocess of the Loop Parameters operator. First the data is split into two equal partitions using the Split Data operator. The Support Vector Machine (LibSVM) operator is applied on one partition. The resultant classification model is applied using two Apply Model operators on both the partitions. The statistical performance of the SVM model on both testing and training partitions is measured using the Performance (Classification) operators. At the end the Log operator is used to store the required results.
The log parameter of the Log operator stores four things. The iterations of the Loop Parameters operator are counted by apply-count of the SVM operator. This is stored in a column named 'Count'. The value of the classification error parameter of the Performance (Classification) operator that was applied on the Training partition is stored in a column named 'Training Error'. The value of the classification error parameter of the Performance (Classification) operator that was applied on the Testing partition is stored in a column named 'Testing Error'. The value of the C parameter of the Support Vector Machine (LibSVM) operator is stored in a column named 'SVM C'.
In the main process, the Log to Data operator is used for providing the log values in form of an ExampleSet. The resultant ExampleSet is connected to the result port of the process and it can be seen in the Results Workspace. You can see the meta data of the ExampleSet in the Meta Data View and the values of the ExampleSet can be seen in the Data View. This ExampleSet can be used to study how classification errors in training and testing partitions behave with the increase in the value of the C parameter of the Support Vector Machine (LibSVM) operator. To view these results in graphical form, switch to the Plot View. Select an appropriate plotter. You can use 'Series Multiple' plotter with 'SVM-C' as the 'Index Dimension'. Select 'Training Error' and 'Testing Error' in the 'Plot Series'. The 'scatter multiple' plotter can also be used. Now you can analyze how the training and testing error behaved with the increase in the parameter C. More importantly this ExampleSet is available in the process, so information stored in it can be used by other operators of the process.
Please note that since RapidMiner version 8.0, the Loop Parameters Operator has been updated to a) be parallel and b) log the parameter set and performance automatically. Please see the help of that operator for more information.
