the Python library for sensible AI

sensAI provides a framework for AI and machine learning applications, integrating industry-standard libraries and providing additional abstractions that facilitate rapid implementation, experimentation as well as deployment.

In particular, sensAI provides ...

• machine learning methods
  • regression and classification models
    • unified interface to models and algorithms of other machine learning libraries, particularly scikit-learn, PyTorch and TensorFlow
    • additional implementations of our own, e.g. for k-nearest neighbour models and naive Bayes models
  • mechanisms for feature generation, which serve to decouple externally provided input data from the data that is actually required as input to particular models
  • mechanisms for model-specific (input and output) data transformation, enabling, for example, convenient model-specific scaling/normalisation or encodings of features
  • (parallelised) hyper-parameter optimisation methods
  • cloud-based tracking of experimental results (with direct support for Microsoft Azure)
• combinatorial optimisation
  • stochastic local search methods, including (adaptive) simulated annealing and parallel tempering
• general utilities, including ...
  • extensive caching mechanisms (using SQLite, pickle and MySQL as backends)
  • multi-processing tools, e.g. a debugger for pickle errors

Source code documentation and tutorials can be found here

sensAI may be integrated into your project in several ways:

1. Install it as a library with pip install sensai. Choose this option if you do not intend to make changes to sensAI in the context of your project.
2. Include sensAI's source code as a package within your project (e.g. in src/sensai), which you synchronise with a sensAI branch. Choose this option if you intend to make changes to sensAI as you develop your project. When using this option, you (and others) may even make changes to sensAI in several branches of your project and even several projects using the same inclusion mechanism at the same time. See below for details on how synchronisation works.
3. Clone sensAI and add its source directory to your PYTHONPATH. Choose this option if you potentially intend to make changes to sensAI but no one else working on your project will do the same and you will be modifying sensAI's source in no more than one branch at a time.

We support the synchronisation of a branch in the sensAI repository with a directory within the git repository of your project which is to contain the sensAI source code (i.e. alternative #2 from above) via a convenient scripting solution.

We consider two local repositories: the sensAI repository in directory sensAI/ and your project in, for instance, directory sensAI/../myprj/. Let us assume that we want to synchronise branch myprj-branch in the sensAI repository with directory myprj/src/sensai.

To perform the synchronisation, please create a script as follows, which you should save to sensAI/sync.py:

import os
from repo_dir_sync import LibRepo, OtherRepo

r = LibRepo()
r.add(OtherRepo("myprj", "myprj-branch", os.path.join("..", "myprj", "src", "sensai")))
r.runMain()

You can add multiple other repositories if you so desire in the future.

From directory sensAI/ you can use the script in order to

• Push: Update your project (i.e. myprj/src/sensai) with changes that were made in other projects by running python sync.py myprj push
• Pull: Update myprj-branch in the sensAI repository with changes made in your project by running python sync.py myprj pull

To initialise the synchronisation, proceed as follows:

1. Create the branch myprj-branch in the sensAI repository, i.e. in sensAI/ run this command: git branch myprj-branch master

2. Create the directory myprj/src/sensai.

3. Make sure you have a .gitignore file in myprj/ with at least the following entries:

   *.pyc
   __pycache__
   *.bak
   *.orig
   

   Otherwise you may end up with unwanted tracked files after a synchronisation.

4. Perform the initial push, i.e. in sensAI/ run this command: python sync.py myprj push

• Both push and pull operations are always performed based on the branch that is currently checked out in myprj/. The best practice is to only use one branch for synchronisation, e.g. master.
• Push and pull operations will make git commits in both repositories. Should an operation ever go wrong/not do what you intended, use git reset --hard to go back to the commits before the operation in both repositories.

sensAI is being developed by the artificial intelligence group at jambit GmbH.

The main contributors are Dominik Jain, Michael Panchenko, Kristof Schröder and Magnus Winter.

External contributions are welcome! Please issue a pull request.

We deliberately do not comply with PEP 8 and do not intend to adapt in the future. If you decide to contribute, please strive for consistency.