pharmine

1. Put File widget (Data) to canvas and configure it to load a dataset from a file.
2. Put Data Sampler widget (Data) to canvas and connect the output port from the File widget to its input port.
• Configure it by choosing Random sampling and setting the Sample size to 80%.
• Click on the Sample Data button.
3. Put Save widget (Data) on the canvas. Connect the Examples output port from the Data Sampler node to the input node of the Save widget and configure it to save the training set to a file. Then click on the Save current data button.
4. Put Save widget (Data) on the canvas. Connect the Remaining Examples output port from the Data Sampler node to the input node of the Save and configure it to save the testing set to a file. Then click on the Save current data button.

As can be seen from the above procedure, it is very easy to partition a dataset randomly into a training set and testing set. However, Orange does not seems to contain other algorithms, like the Kennard and Stone algorithm, for partitioning datasets.

Orange (Snapshot 11 April 2008)

From their official website, “Orange is a component-based data mining software. It includes a range of preprocessing, modelling and data exploration techniques. It is based on C++ components, that are accessed either directly (not very common), through Python scripts (easier and better), or through GUI objects called Orange Widgets”. Orange is distributed under GPL.

If you install the current version of Orange, you will have a total of 77 widgets, with the following nodes distribution:

• Data: 15
• Classify: 14
• Evaluate: 6
• Visualize: 13
• Associate: 13
• Prototypes: 13
• Regression: 3

However, since I am interested in using it for QSAR experiments, I will only examine those nodes that are relevant. Basically, Orange can read data from five sources: text-delimited files (which include csv files), C4.5 files, and three other formats which I am not familar with. Orange cannot read data from SVM^light files, LIBSVM files or Microsoft Excel files. The lack of support for Microsoft Excel files is no big deal since you can easily convert them to csv format using Microsoft Excel. However, the lack of support for SVM^light and LIBSVM files will inconvenient users who are already using these two popular support vector machine softwares.

Orange has a few filter descriptor selection methods such as ReliefF, Information gain, Gain ratio and Gini gain.

Currently, Orange contains one algorithm for developing regression models and 10 algorithms for constructing classification models. It seems strange that Orange does not have multiple linear regression algorithm, which is the most basic of regression algorithms.

Orange has a Data Sampler widget that provides validation methods like cross-validation and leave-one-out.

Overall, my first impression of Orange is that it has a nice graphical user interface but it seems quite inadequate for QSAR experiments.

The previous post already provides the steps for model validation using cross-validation. TANAGRA does not provide any functionality for loading another dataset into the same diagram, or saving and loading a model. Thus TANAGRA is unable to validate an independent validation set (TANAGRA is able to validate on a testing set only if the testing set is derived using its Sampling operator).