Quickstart

Get up and running with the Akkio API quickly using one of our convenient libraries.
Node.js
Python
Node.js
Installation
npm install akkio --save
Usage
const akkio = require('akkio')('your API key');
// get your API key at https://app.akk.io/team-settings
​
​
(async () => {
  // create a new dataset
  let newDataset = await akkio.createDataset('my new dataset');
​
  // populate it with some toy data
  let rows = [];
  for (var i = 0; i < 1000; i++) {
    let x = Math.random();
    rows.push({
      'x': x,
      'value larger than 0.5': x > 0.5,
    });
  }
  await akkio.addRowsToDataset(newDataset.dataset_id, rows);
​
  // train a model
  let model = await akkio.createModel(newDataset.dataset_id, ['value larger than 0.5'], [], {
    duration: 1
  });
​
  // field importance
  for (let field in model.field_importance) {
    console.log('field:', field, 'importance:', model.field_importance[field]);
  }
​
  // model stats
  for (let field of model.stats) {
    for (let outcome of field) {
      console.log(outcome);
    }
  }
​
  // use the trained model to make predictions
  let predictions = await akkio.makePrediction(model.model_id, [{
    'x': 0.25
  }, {
    'x': 0.75
  }], {
    explain: true
  });
  console.log(predictions);
​
})();
Python
Installation
pip install akkio
Usage
import akkio
akkio.api_key = 'YOUR-API-KEY-HERE' 
# get your API key at https://app.akk.io/team-settings
​
models = akkio.get_models()['models']
for model in models:
  print(model)
​
datasets = akkio.get_datasets()['datasets']
for dataset in datasets:
  print(dataset)
​
new_dataset = akkio.create_dataset('python api test')
print(new_dataset)
​
# create a toy dataset
import random
rows = []
for i in range(1000):
  rows.append({
    'x': random.random()
  })
  rows[-1]['y'] = rows[-1]['x'] > 0.5
​
akkio.add_rows_to_dataset(new_dataset['dataset_id'], rows)
​
new_model = akkio.create_model(new_dataset['dataset_id'], ['y'], [], {'duration': 1})
print(new_model)
prediction = akkio.make_prediction(new_model['model_id'], [{'x': 0.1}, {'x':0.7}], explain=True)
print(prediction)
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Last updated 3 months ago