Use JSON-file from Prediction App with CHAP Core CLI

This document describes how to use the CHAP Core CLI to analyze the JSON file you get when pressing the “Download button” in the Prediction App. If you have not completed this step, you need to learn how to use the Prediction App to download a JSON file first.

The JSON file you download from the Prediction App should look similar to this:

{
  {
    "model_id": "EWARS-month",
    "features": [
      {
        "featureId": "population",
        "dhis2Id": "gJkrZH2vV8k",
        "data": [
          //removed for readability
        ]
      },
      {
        "featureId": "disease",
        "dhis2Id": "GPEAQwddr16",
        "data": [
          //removed for readability
        ]
      }
    ],
    "orgUnitsGeoJson": {
      "type": "FeatureCollection",
      "features": [
        //removed for readability
      ]
    }
  }
}

Convert the JSON data into a CHAP-DataSet

After downloading the JSON data from the Prediction App, it’s practical to first convert the data into a CHAP-DataSet. This fetches the climate data from the Google Earth Engine dataset ERA5-Land Daily Aggregated and harmonizes the data into a single dataSet.This is done by running the following command (replace the placeholders with the actual values):

$ chap-cli harmonize <path-to-json-file>.json <path-to-output-file>.csv

Evaluate models on the dataset

The next step is to evaluate existing models on the dataset to see if some of them perform well on your dataset. Ensure Docker is running. This is done by running the following command:

$ chap-cli evaluate <path-to-dataset-file>.csv <path-to-report-file>.pdf --model-id chap_ewars_monthly

This will generate a report with the evaluation results for the specified model (in this case chap_ewars).

Predict using the best model

After evaluating the models, you can predict the values for the dataset using the best model. This is done by running the predict command:

$ chap-cli predict <path-to-dataset-file>.csv <path-to-output-file>.csv --model-id chap_ewars_monthly --do-summary

This will generate a new dataset with the predicted values for the dataset.

An example of the full workflow would be (for input file ~/Downloads/chap_request_data_2024-09-24_two_provinces.json):

$ chap-cli harmonize ~/Downloads/chap_request_data_2024-09-24_two_provinces.json training_data.csv
$ chap-cli evaluate training_data.csv evaluation_report.pdf --model-id chap_ewars_monthly
$ chap-cli predict training_data.csv predictions.csv --model-id chap_ewars_monthly --do-summary