Include small-size datasets for benchmarking

[jairodelgado]

We need to include a set of benchmark datasets to compare the accuracy and efficiency of our neural implementations. In this case, we are looking for small-size datasets (less that 1k examples) that deals either classification or regression. Probably, a good starting point are the following publicly available datasets:

• Breast cancer
• Contact lenses
• CPU
• Credit
• Diabetes
• Glass
• Ionosphere
• Iris
• Labor
• Soy bean
• Supermarket
• Unbalanced
• Vote
• Wheather

We need to create a notebook transforming the data as follows:

• Dataset format should be CSV with header information (coma-separated, header contains attribute names).
• Nominal and ordinal values should be transformed into numerical one (create dummies for simplicity).
• Normalize numerical values (scale the values to [0, 1])
• Ordinal cyclic variables such as day of the week should be transformed using sin and cos transformations.
• Remove missing data.
• Identify and remove outliers.

Every steep should be documented and explained in the notebook. Include in the notebook the following information for each dataset:

• Number of input and output variables.
• Variable type: nominal, ordinal, numeric, etc.
• Number of examples.
• Are the examples balanced or not?
• Number of examples removed due to missing values.
• Number of outliers.
• Best reported accuracy so far, describe the model used (include an ISO 690 reference to the paper).

The output of this issue should be a new directory structure within docs folder. Create the following hierarchy of folders:

• docs/bench_data/small/original/: with all the .ARFF original files.
• docs/bench_data/small/transformed/: with all the transformed files.
• docs/bench_data/small/notebooks: with the transform.ipynb and transform.html notebook that can be used to generate the docs/bench_data/small/transformed/ files from the files in docs/bench_data/small/original/.