get_results.py

def get_results(client, session_id):
    # First wait on the results to be complete by checking periodically
    while True:
        current = client.sessions.get(session_id)
        if current.status == session.Status.completed or current.status == session.Status.failed:
            break
        # waiting to not exhaust the call per minute limit on the API
        time.sleep(5)

    # Then get results and find the model_id that was generated
    results = client.sessions.get(session_id)
    return results.model_id

gettingstarted.py

import json
import time

from nexosisapi import session
from nexosisapi import Client


if __name__ == '__main__':
    # You must add a reference to the `nexosisapi` package: https://pypi.python.org/pypi/nexosisapi/
    # $ pip install nexosisapi

    client = Client('YOUR-API-KEY-GOES-HERE')

    dataset_name = 'mpg-dataset'
    upload_data(client, dataset_name)
    session_id = request_model(client, dataset_name)
    model_id = get_results(client, session_id)
    features = {
        'Cylinders': 8,
        'Displacement': 307,
        'Horsepower': 135,
        'Weight': 3624,
        'Acceleration': 13,
        'ModelYear': 70,
        'Origin': 1,
        'Make': 'chevrolet'
    }
    prediction = client.models.predict(model_id, [features])
    print(prediction.data[0]['MPG'])

request_model.py

def request_model(client, dataset_name):
    # This call starts the model building process.
    # Here we are looking to predict the mpg column
    # on the dataset that was previously uploaded.
    trainer = client.sessions.train_regression_model(dataset_name)
    return trainer.session_id

upload_data.py

def upload_data(client, dataset_name):
    # auto-mpg.data.json is a json file that can be pulled from:
    # https://s3.us-east-2.amazonaws.com/nexosis-api-sample-data/auto-mpg.data.json
    with open('auto-mpg.data.json', 'r') as data:
        ds = json.load(data)
    client.datasets.create(dataset_name, ds['data'], ds['columns'])