Python

This is an example of how you can use the web API from Python.

import requests

from dffml_model_scikit import LinearRegressionModel

model = LinearRegressionModel(
    features=Features(
        Feature("Years", int, 1),
        Feature("Expertise", int, 1),
        Feature("Trust", float, 1),
    ),
    predict=Feature("Salary", int, 1),
    location="tempdir",
)

## Configure model

URL = "https://127.0.0.1:5000/configure/model/{model}/{label}".format(model = "fake", label = "mymodel")
PARAMS = {
  {
    "model": {
      "plugin": null,
      "config": {
        "location": {
          "plugin": [
            "/home/user/modeldirs/mymodel"
          ],
          "config": {}
        },
        "features": {
          "plugin": [
            {
              "name": "Years",
              "dtype": "int",
              "length": 1
            },
            {
              "name": "Expertise",
              "dtype": "int",
              "length": 1
            },
            {
              "name": "Trust",
              "dtype": "float",
              "length": 1
            }
          ],
          "config": {}
        }
      }
    }
  }
}

result = requests.post(url = URL, params = PARAMS)

*Note*: On successful creation and configuration the server will return  {"error": null}

## Context Creation

URL = "https://127.0.0.1:5000/context/mdoel/{label}/{ctx_label}".format(label = "mymodel", ctx_label = "ctx_mymodel")
result = requests.get(url = URL, params = {})

*Note*: On successful creation of a context the server will return {"error": null}

## Train the Model

URL = "https://127.0.0.1:5000/model/{ctx_label}/train".format(ctx_label = "ctx_mymodel")
params = {
  [
    "my_training_dataset"
  ]
}
result = requests.post(url = URL, params = PARAMS)

*Note*: On successful execution the server will return {"error": null}

## Assess Accuracy

URL = "https://127.0.0.1:5000/model/{ctx_label}/accuracy".format(ctx_label = "ctx_mymodel")
params = {
  [
    "my_test_dataset"
  ]
}
result = requests.post(url = URL, params = PARAMS)

*Note*: On successful execution the response will be a JSON object containing the accuracy as a float value : {"accuracy": 0.42}

## Make Prediction

URL = "https://127.0.0.1:5000/model/{ctx_label}/predict/0".format(ctx_label = "ctx_mymodel")
PARAMS = {
  {
    "42": {
      "features": {
        "by_ten": 420
      }
    }
  }
}

*Note*: The JSON passed as param maps key of the record to the JSON representation of dffml.record.Record as received by the source record endpoint

result = requests.post(url = URL, params = PARAMS)

*Note*: On successful execution the response will be a JSON object similar to this:

response = {
  "iterkey": null,
  "records": {
    "42": {
      "key": "42",
      "features": {
        "by_ten": 420
      },
      "prediction": {
        "confidence": 42,
        "value": 4200
      },
      "last_updated": "2019-10-15T08:19:41Z",
      "extra": {}
    }
  }
}