README.md

Project Documentation - Link

Data Scientist: Why is my cutting edge model still not in production?

Scenario 1:

IT Team: We are still implementing the scoring engine in Go/Java/C++.

Scenario 2:

IT Team: We are still figuring out how to read the model.pkl file you provided.

. .

and the push to production pang continues ..

New Features!

• titus2 home page and documentation has been migrated to https://realworldpython.github.io.

Changes in titus2 v1.2.1

View the complete changelog here.

Titus 2 - Portable Format for Analytics (PFA) implementation for Python 3.4-3.9

Titus 2 is a fork of the original Titus python library which resides in the (now defunct) Hadrian repository. Titus is not actively maintained by the Open Data Group and only supports Python 2, so this fork was created to actively support its development for Python 3.

Titus 2 is a complete, independent implementation of Portable Format for Analytics (PFA) in pure Python. PFA is a specification for scoring/inference engines: event-based processors that perform predictive or analytic calculations. It is a model interchange format which helps smoothen the transition from statistical model development to large-scale and/or online production. titus2 also focuses on model development, so it includes model producers and PFA manipulation tools in addition to runtime execution (scoring) engine.

Requirements

Titus 2 uses a number of open source projects to work properly:

• avro-python3 >= 1.8.2
• numpy == 1.14.0 (python 3.4 & 3.5) or numpy >=1.15.0, <1.20 (python 3.6+)
• pytz >= 2019.1
• pyyaml >= 5.1.2
• ply >= 3.11

The above packages are available via pip and are automatically installed during setup.

Installation

Titus requires Python 3.4+ to run. It can be installed via pip/pip3 as follows:

$ pip install titus2

or you can directly install the latest build from github repository via

$ pip install git+https://github.com/animator/titus2.git

After installation please run the following elementary example in python

from titus.genpy import PFAEngine

pfa = {"input": "double",
 "output": "double",
 "action": [
   {"+": ["input", 100]}
 ]}
engine, = PFAEngine.fromJson(pfa)

l = [1.0, 2.0, 3.0, 4.0, 5.0]

for num in l:
    print(num, engine.action(num))

User Guide and Tutorials

See the Project Documentation for user guide and tutorials.

Current Testing Framework

To run the tests, you can execute

python3 setup.py test

To generate the coverage report, you can execute

pip3 install coverage
coverage run setup.py test
coverage report

Testing Status:

• Unit testing status available here
• Conformance testing status available here

Issues, Questions and Feature Requests

Please raise an issue/question/request here.

Development

Want to contribute? Great!

Please raise an issue to discuss your ideas and send a pull request.

Todos

• Write MORE Tests for scripts/*.
• Add scikit-learn model export to PFA tutorials.