Working with Function Finding Tools
Loading Data
 
Before evolving a model with GeneXproTools 4.0 you must first load the input data for the learning algorithm. GeneXproTools 4.0 allows you to work either with databases/Excel or text files and, for text files, accepts two different data matrix formats.

The first is the standard Samples x Variables format where samples are in rows and variables in columns, with the dependent variable occupying the rightmost position. In the small example below with five samples, PRODUCTION is the dependent variable and LABOR, MATERIAL, and CAPITAL are the dependent variables:

LABOR MATERIAL CAPITAL PRODUCTION
0.88287491 0.70249262 0.64540872 0.73044339
0.76598265 0.59360711 0.62686264 0.64924032
0.64562062 0.56965563 0.53160265 0.58497820
0.72908206 0.58913859 0.52247179 0.62160041
0.50690655 0.23498841 0.42968111 0.36493144

And the second, is the Gene Expression Matrix format commonly used in DNA microarrays studies where samples are in columns and variables in rows, with the dependent variable occupying the topmost position. For instance, in Gene Expression Matrix format, the small dataset above corresponds to:

PRODUCTION 0.73044339 0.64924032 0.5849782 0.62160041 0.36493144
LABOR 0.88287491 0.76598265 0.64562062 0.72908206 0.50690655
MATERIAL 0.70249262 0.59360711 0.56965563 0.58913859 0.23498841
CAPITAL 0.64540872 0.62686264 0.53160265 0.52247179 0.42968111

which is not visually very appealing, but of course very handy for datasets with a relatively small number of samples and thousands of variables. Note, however, that for Excel files this format is not supported and if your data is kept in this format in Excel, you must copy it to a text file so that it can be loaded into GeneXproTools.

GeneXproTools uses the Samples x Variables format throughout and therefore all formats are automatically converted and shown in this format.

GeneXproTools supports the standard separators (space, tab, comma, semicolon, and pipe) and detects them automatically. The use of labels to identify your variables is optional and GeneXproTools also detects automatically whether they are present or not. If you use them, however, you will be able to generate more intelligible code where each variable is identified by its name, by checking the Use Labels box in the Model Panel.

To Load Input Data for Modeling

  1. Click the File Menu and then choose New.
    The New Run Wizard appears. You must give a name to your new run file (the default filename extension of GeneXproTools 4.0 run files is .gep) and then choose Function Finding in the Problem Category box and the kind of source file in the Data Source Type box. GeneXproTools 4.0 allows you to work either with Excel/databases or text files.
  2. Then go to the Training Data window by clicking the Next button.
    Choose the path for the training set by browsing the Open dialog box and choose the appropriate data matrix format. Irrespective of the data format used, GeneXproTools shows the loaded data in the standard Samples x Variables format, with the dependent variable occupying the rightmost position.
  3. Then go to the Testing Data window by clicking the Next button.
    Repeat the same steps of the previous point if you wish to use a testing set to evaluate the predictive accuracy of your model.
  4. Click the Finish button to save your new run file.
    The Save As dialog box appears and after choosing the directory where you want your new run file to be saved, the GeneXproTools modeling environment appears. Then you just have to click the Evolve button to create a model as GeneXproTools automatically chooses, from a gallery of templates, default settings that will enable you to evolve a model immediately.

In data mining, be it performed by learning algorithms or conventional statistical methods, it really pays to take a good look at your data before embarking on a complex, usually time consuming modeling process. It's true that evolutionary algorithms are particularly well equipped to deal with noisy data, but the better the data you feed them the better the models they produce.

GeneXproTools helps you find missing and invalid (usually nominal) values in your data sets and prompts you to fix them before they are used for modeling. But the preparation of a well balanced data set should be done before loading the data into GeneXproTools, and we recommend you to particularly take care of the following:

  • Avoid using duplicated samples for they can bias the modeling process considerably.
  • Choose a well balanced data set.
  • Choose a reasonable number of samples for training.
    An excessively large data set will slow the modeling process unnecessarily. If you have access to huge data bases, it’s good practice to use the surplus samples for testing instead. A good rule of thumb consists of using about 8-10 samples for each independent variable in your training data.
  • Check your data sets carefully for inaccurate values. Typographical or measurement errors generally cause outliers that can be detected by graphing one variable at a time, a task that can be easily accomplished by GeneXproTools in the Data Panel.

The graphical visualization tools of GeneXproTools 4.0 make it easy to identify outliers, which may well represent errors in the data files. After loading your data into GeneXproTools, in the Data Panel you can visualize the distribution of values for each variable and also plot each independent variable against the dependent variable.


Home | Contents | Previous | Next