PSyKI

Platform for Symbolic Knowledge Injection

Some quick links:

• GitHub Repository
• PyPi Repository
• Issues

Reference paper

Matteo Magnini, Giovanni Ciatto, Andrea Omicini. "[On the Design of PSyKI: A Platform for Symbolic Knowledge Injection into Sub-Symbolic Predictors]", in: Proceedings of the 4th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems, 2022.

Bibtex:

@inproceedings{PsykiExtraamas2022,
	keywords = {Symbolic Knowledge Injection,  Explainable AI, XAI, Neural Networks, PSyKI},
	year = 2022,
	talk = {Talks.PsykiExtraamas2022},
	author = {Magnini, Matteo and Ciatto, Giovanni and Omicini, Andrea},
	venue_e = {Events.Extraamas2022},
	sort = {inproceedings},
	publisher = {Springer},
	status = {In press},
	title = {On the Design of PSyKI: a Platform for Symbolic Knowledge Injection into Sub-Symbolic Predictors},
	booktitle = {Proceedings of the 4th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems}
}

Intro

PSyKI (Platform for Symbolic Knowledge Injection) is a python library for symbolic knowledge injection (SKI). In the literature, SKI may also be referred as neuro-symbolic integration. PSyKI offers SKI algorithms (injectors) along with quality of service metrics (QoS) and other utility functionalities. Finally, the library is open to extendability.

More in detail

An Injector is a SKI algorithm that may -- or may not -- take a sub-symbolic predictor in conjunction with prior symbolic knowledge to create a new predictor through the inject method. We refer to the new predictor as educated, while predictors that are not affected by symbolic knowledge are called uneducated.

Knowledge can be represented in many ways. The most common is the representation via logic formulae. PSyKI integrates 2ppy, a python porting of 2p-kt (a multi-paradigm logic programming framework). Thanks to this integration, PSyKI supports logic formulae written with the formalism of Prolog. Therefore, all subsets of the Prolog language (including Prolog itself) are potentially supported (e.g., propositional logic, Datalog, etc.). It is worth noting that each injector may have its own requirements on the knowledge representation.

List of available injectors:

• KBANN, one of the first injector introduced in literature;
• KILL, constrains a NN by altering its predictions using the knowledge;
• KINS, structure the knowledge adding ad-hoc layers into a NN.

High level architecture

Class diagram representing the relations between Injector, Theory and Fuzzifier classes

The core abstractions of PSyKI are the following:

• Injector: a SKI algorithm;
• Theory: symbolic knowledge plus additional information about the domain;
• Fuzzifier: entity that transforms (fuzzify) symbolic knowledge into a sub-symbolic data structure.

The class Theory is built upon the symbolic knowledge and the metadata of the dataset (extracted by a Pandas DataFrame). The knowledge can be generated by an adapter that parses the Prolog theory (e.g., a .pl file, a string) and generates a list of Formula objects. Each Injector has one Fuzzifier. The Fuzzifier is used to transform the Theory into a sub-symbolic data structure (e.g., ad-hoc layers of a NN). Different fuzzifiers encode the knowledge in different ways.

To avoid confusion, we use the following terminology:

• rule is a single logic clause;
• knowledge is the set of rules;
• theory is the knowledge plus metadata.

Hello world

The following example shows how to use PSyKI to inject knowledge into a NN.

import pandas as pd
from tensorflow.keras.models import Model
from psyki.logic import Theory
from psyki.ski import Injector


def create_uneducated_predictor() -> Model:
...

dataset = pd.read_csv("path_to_dataset.csv")  # load dataset
knowledge_file = "path_to_knowledge_file.pl"  # load knowledge
theory = Theory(knowledge_file, dataset)      # create a theory
uneducated = create_uneducated_predictor()    # create a NN
injector = Injector.kins(uneducated)          # create an injector
educated = injector.inject(theory)            # inject knowledge into the NN

# From now on you can use the educated predictor as you would use the uneducated one

For more detailed examples, please refer to the demos in the demo-psyki-python repository.

Users

PSyKI is deployed as a library on Pypi, and it can therefore be installed as Python package by running:

pip install psyki

Requirements

• python 3.9+
• java 11
• 2ppy 0.4.0
• tensorflow 2.7.0
• numpy 1.22.3
• scikit-learn 1.0.2
• pandas 1.4.2
• codecarbon 2.1.4

Developers

Working with PSyKI codebase requires a number of tools to be installed:

• Python 3.9+
• JDK 11+ (please ensure the JAVA_HOME environment variable is properly configured)
• Git 2.20+

Develop PSyKI with PyCharm

To participate in the development of PSyKI, we suggest the PyCharm IDE.

Importing the project

1. Clone this repository in a folder of your preference using git_clone appropriately
2. Open PyCharm
3. Select Open
4. Navigate your file system and find the folder where you cloned the repository
5. Click Open

Developing the project

Contributions to this project are welcome. Just some rules:

• We use git flow, so if you write new features, please do so in a separate feature/ branch
• We recommend forking the project, developing your stuff, then contributing back vie pull request
• Commit often
• Stay in sync with the develop (or main | master) branch (pull frequently if the build passes)
• Do not introduce low quality or untested code

Issue tracking

If you meet some problem in using or developing PSyKI, you are encouraged to signal it through the project "Issues" section on GitHub.