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)