vogon101 / FamineBackend

Famine Project Backend

Installation/usage

This needs python 3 along with the following modules:

• Pystan: https://pystan.readthedocs.io/en/latest/getting_started.html
• numpy, pandas
• flask, flask-restful

To use simply run the server.py script from the src directory

Endpoints

Data

• GET /data/all - Returns all data and predicted data for every region
• GET /data/region/<region> - Returns all data and predicted data for a given region
• GET /data/region_list - Returns list of all regions and fitted regions

Predictions

{
  "changes" : [
    {
      "source"  : "Maize (white) - Borama | Awdal - Fatalities due to Conflict | ...",
      "year"    : 2015,
      "month"   : 5,
      "column"  : "Price | Temperature | Fatalities",
      "value"   : 123.456
    }, ...
  ]
}

• GET /prediction/summary - Returns predictions for every region (no changes)
• GET /prediction/region/<region> - Returns predictions for a specific region (no changes)
• POST /prediction/region/<region> - Returns predictions for a specific region with the specified changes array as defined above
  • Within the changes list, source refers to the feature name, that is the key to a given data frame returned by /data/ endpoints
    • These names can be found in the _feature_names list
  • The column field specifies the type of data being changed
    • Food data - "Price"
    • Temperature data - "Temperature"
    • Conflict data - "Fatalities"
    • The mapping from feature names to columns can be found in the _value_columns list

Predictions look like this:

{
  "success" : true,
  "region"  : "Awdal",
  "data"    : {
    "202001" : {
      "P2" : {
        "68" : [0.2, 0.3],
        "95" : [0.1, 0.4],
        "mean" : 0.25
      },
      "P3" : {...},
      "P4" : {...}
    },
    "202002" : {...},
    ...
  }
}

The dates are in YYYYQQ format (QQ = quater from 01 to 04). The 95 and 68 percent confidence intervals are given