The ŷhat Cloud Toolbox is a suite of development tools for integrating predictive analytics into existing Web and mobile applications. ŷhat deploys your models as RESTful APIs, so there's no need to port, translate, or adapt models for Integration with existing systems. It is currently in beta testing and an invite only stage. You can request an invite here.
ŷhat supports most regression and classification algorithms in popular scientific projects like scikit-learn and Statsmodels. There is just a two step procedure to start using it.
- Request an an API Key
- Install Yhat-You can install
Yhat
using pip
The use cases that are included are -
- Building new and improving existing predictive models
- Porting models to Python or R (e.g. from SAS or Matlab)
Yhat has two parts to it's API - the core API and the real-time Predictions API. We are listing a summary of how they work.
ŷhat Core API
ŷhat claims to empowers data science teams to turn isolated analytical work into predictive APIs which can be immediately consumed by other software systems.
Five Steps to Deploy:
- Import the ŷhat Library
- Create a subclass of
yhat.Basemodel
- Define
transform
andpredict
functions. These tell ŷhat what to do when the API is called to make predictions. Thetransform
step is just the code we wrote to convert to tf-idf vector representation and ourpredict
step is just the predict Function from theMultinomialNB
classifier we're using. - Authenticate by passing your
Username
andAPIKEY
toyhat.Yhat
- Train your model and pass it to
yh.upload
to deploy it.
ŷhat Real-Time Predictions API
Models deployed to ŷhat can be consumed from any environment using any programming language. You can also make calls to your models using the Python library.
- Authentication- Authenticate by passing your username and API Key to
yhat.Yhat
. - Listing models deployed to ŷhat -Once authenticated, you can call
yhat.Yhat.show_models
. - Making predictions using the ŷhat Python library- It's easy to make requests to your models
yhat.Yhat.predict
available in the ŷhat Python library. - Making requests from non-Python environments -You can also execute models independently of Python via Web Service calls.
While yhat has clearly a Pythonic focus (especially numPy , Pandas and scikits) , it is very good news for people wanting to use a open source solution for predictive models. Existing providers in hosted models include RevoDeploy API covered here, Google Prediction API covered here, BigML.com API who we have covered here and the pioneer in PMML based cloud scoring Zementis's ADAPA.
The Documentation is clearly a work in active progress. There is an example there of training a Naive Bayes classifier to perform text classification, but as the site expands hopefully there will be more examples or a gallery section. The gallery section in BigML.com API is clearly an outstanding example.
Models to predict ? Just another Yhat API call away!