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{
"name": "Simple JSON key/val extraction",
"description": "Given a dataset field containing JSON documents and a key, this WhizzML script creates a new feature with the JSON values. This is a hack and *NOT* a valid JSON parser",
"inputs": [
{
"name": "dataset-in",
"type": "dataset-id",
"description": "Dataset to transform by extracting JSON values."
},
{
@petersen-poul
petersen-poul / highways.py
Created November 26, 2014 21:54
US Primary Interstates - Feature Engineering Example
#!/usr/bin/env python
import bigml
from bigml.api import BigML
# You need to define BIGML_USERNAME and BIGML_API_KEY in your environment, or
# add them here:
#api = BigML(username, api_key, dev_mode=True)
api = BigML(dev_mode=True)
import bigml, sys, csv, time
from bigml.api import BigML
api = BigML(dev_mode=True)
source = api.create_source("s3://bigml-public/csv/lc_sample.csv.gz", { "name": "LC Source"} )
api.ok(source)
soure = api.get_source(source)
lc_dataset = api.create_dataset(source, { "name": "LC Dataset" })
@petersen-poul
petersen-poul / Active Learning - Diabetes Example
Last active August 29, 2015 14:02
A simplified example of Active Learning using clustering and decision tress for the diabetes dataset. This iPython notebook was used for a demonstration during the BigML Spring 2014 Webinar which can be viewed here: http://youtu.be/uG-vXFyCcms
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{