• data folder contains Weka dataset + some stats on full data & sampled data
• resources contains the Json dump of the Downloader & a sampled version.

• Run stats.py to get simple stats on downloader data.
• Run weka_converter.py to generate weka ARFF file from API downloader data.

Install modules :

• numpy
• scipy
• mdp
• liac-arff
• prettytable

Setup conf file lc_conf

The configuration file lc_conf contains the parameters used by the modules stats.py and weka_converter.py :

Attributes are the are the columns in the dataset to analyze. You can have NOMINAL or NUMERIC attributes in Weka.

• NOMINAL_ATTRIBUTES_NAMES are the attributes with discreet values. You must list the nominals values. The nominal values can't be modified. See CLASS_NOMINAL_VALUES and LOAN_GRADE_NOMINAL_VALUES below :

• CLASS_NOMINAL_VALUES cannot be changed. This parameter must be set to ['B', 'NB', 'NBY', 'C']. Those are the class labels used in the Weka dataset. Class Labels Description :

  • B = Note Bought
  • NB = Note Not Bought
  • C = Note Cancelled
  • NBY = Note Not Bought Yet

• LOAN_GRADE_NOMINAL_VALUES cannot be changed. This parameter must be set to ['A', 'B', 'C', 'D', 'E', 'F', 'G']

• NUMERIC_ATTRIBUTES_NAMES are the metrics on which you can do continous maths operations like mean, max, min, etc. /!\ make sure that the names correspond to a key in the raw downloader JSON.

• NUMERIC_ATTRIBUTES_METRICS is used to set the unit of each metric of NUMERIC_ATTRIBUTES_NAMES. /!\ list should match with METRICS order.

• INCLUDE_NBY is used to include or not the NBY class in the dataset to analyze.
• MAX_NB_BUCKETS to modify the max nb of buckets that will be generated for each continuous metric of the dataset (the goal being to discretize them).
• WEKA_FILE is the path to file where Weka will save converted downloader data.
• DATA_TO_CONVERT is used to change path of API downloader data file to analyze.