So I saw the news at NY R Meetup and decided to have a go at Prediction API Package (which first started off as a blog post at
http://onertipaday.blogspot.com/2010/11/r-wrapper-for-google-prediction-api.html
1)My OS was Ubuntu 10.10 Netbook
Ubuntu has a slight glitch plus workaround for installing the RCurl package on which the Google Prediction API is dependent- you need to first install this Ubuntu package for RCurl to install libcurl4-gnutls-dev
Once you install that using Synaptic,
Simply start R
2) Install Packages rjson and Rcurl using install.packages and choosing CRAN
Since GooglePredictionAPI is not yet on CRAN
,
3) Download that package from
You need to copy this downloaded package to your “first library ” folder
When you start R, simply run
.libPaths()[1]
and thats the folder you copy the GooglePredictionAPI package you downloaded.
5) Now the following line works
- Under R prompt,
> install.packages("googlepredictionapi_0.1.tar.gz", repos=NULL, type="source")
6) Uploading data to Google Storage using the GUI (rather than gs util)
Just go to https://sandbox.google.com/storage/
and thats the Google Storage manager
Notes on Training Data-
Use a csv file
The first column is the score column (like 1,0 or prediction score)
There are no headers- so delete headers from data file and move the dependent variable to the first column (Note I used data from the kaggle contest for R package recommendation at
http://kaggle.com/R?viewtype=data )
6) The good stuff:
Once you type in the basic syntax, the first time it will ask for your Google Credentials (email and password)
It then starts showing you time elapsed for training.
Now you can disconnect and go off (actually I got disconnected by accident before coming back in a say 5 minutes so this is the part where I think this is what happened is why it happened, dont blame me, test it for yourself) –
and when you come back (hopefully before token expires) you can see status of your request (see below)
> library(rjson) > library(RCurl) Loading required package: bitops > library(googlepredictionapi) > my.model <- PredictionApiTrain(data="gs://numtraindata/training_data") The request for training has sent, now trying to check if training is completed Training on numtraindata/training_data: time:2.09 seconds Training on numtraindata/training_data: time:7.00 seconds
7)
Note I changed the format from the URL where my data is located- simply go to your Google Storage Manager and right click on the file name for link address ( https://sandbox.google.com/storage/numtraindata/training_data.csv)
to gs://numtraindata/training_data (that kind of helps in any syntax error)
8) From the kind of high level instructions at https://code.google.com/p/google-prediction-api-r-client/, you could also try this on a local file
Usage
## Load googlepredictionapi and dependent libraries library(rjson) library(RCurl) library(googlepredictionapi) ## Make a training call to the Prediction API against data in the Google Storage. ## Replace MYBUCKET and MYDATA with your data. my.model <- PredictionApiTrain(data="gs://MYBUCKET/MYDATA") ## Alternatively, make a training call against training data stored locally as a CSV file. ## Replace MYPATH and MYFILE with your data. my.model <- PredictionApiTrain(data="MYPATH/MYFILE.csv")
At the time of writing my data was still getting trained, so I will keep you posted on what happens.
Related Articles
- An R interface to the Google Prediction API (revolutionanalytics.com)
- Google Prediction Goes to the Movies (technoverseblog.com)
- 11 new APIs: Google Predictions, Amazon User Management (programmableweb.com)
- R at Google (r-bloggers.com)
- Google API Console Opens Up Millions of Queries Daily (programmableweb.com)
- Canonical Design Team: So, you want to provide an API for the world to use? (design.canonical.com)
DECISION STATS 