Ubuntu Linux (or other Debian-based distro)
Open a terminal and change directory to the location of this file.
Run the install_dependencies script:
$ sh install_dependencies.sh
Best to install the python(x,y) bundle (http://code.google.com/p/pythonxy/)
Afterwards all the packages mentioned in the install_dependencies.sh file can be installed via pip. For example to install xlrd one would issue the following command inside Command Prompt:
$ pip install xlrd
Set the desired preferences in the configuration file:
Write down the crisis/normal years in the XLS file:
Position yourself inside the irb.foc.forecaster folder (pwd output just to show an example of the correct path):
$ cd irb.foc.forecaster
Run the Python interpreter with the entry script run.py as an argument:
$ python run.py
Lay of the code
forcaster - main module, use it to start the program
|- conf - configuration file with all the preferences
\- exceptions - all the custom exceptions are defined here
model - contains the data structures
|- country - code and list of indicators
\- indicator - internal representation: list of dates, list of values
sources - represents the data sources available online.
\- wb - extracts data from the World Bank
ai - classes regarding pattern recognition, train and test building etc.
|- input - parses XLS files to get crisis and normal period years
|- output - writes the dataset into a text file in a subgroup-discovery-friendly format
|- samples_set - the representation of the train and test datasets that can build samples based on the crisis/normal years input and indicators and countries specified in the conf file; fetches the data live from the World Bank API
|- preprocessor - processes the samples to extract useful features (min, max, slope...)
\- metadata - column labels and data type marks used when writing the dataset
tests - unit tests for individual modules
TODO: Brief introduction on what you do with files - including link to relevant help section.