Tutorials

In this section we outline a series of tutorials that will help you get started with the basics of using the pyreason library.

Contents

• PyReason Basic Tutorial
  • Graph
  • Rules
  • Facts
  • Running PyReason
  • Expected Output
• Advanced Tutorial
  • Graph
  • Rules
  • Facts
  • Running Pyreason
  • Expected Output
• PyReason Custom Threshold
  • Graph
  • Rules
  • Facts
  • Running PyReason
  • Expected output
• PyReason Infer Edges
  • Graph
  • Rules
  • Facts
  • Running PyReason
  • Expected output
• PyReason Annotation Functions
  • Average Annotation Function Example
    • Graph
    • Facts
    • Average Annotation Function
    • Rules
    • Running PyReason
    • Expected Output
  • Linear Combination Annotation Function
    • The map_interval Function
    • Graph
    • Facts
    • Linear Combination Function
    • Rules
    • Running PyReason
    • Expected Output
• Image Classifier to PyReason Tutorial
  • Overview
  • Step 1: Load the model
  • Step 2: Define allowed labels and rules
  • Step 3: Convert model output into facts
  • Step 4: Run reasoning and inspect output
  • Expected output: classifier (by image group)
    • Fish image trace
  • After classification: what PyReason does next
    • Sample end of run
  • Common Pitfalls
• Temporal Classifier Tutorial
  • Scenario
  • Setting Up the Classifier
  • Configuring the Interface
  • Wrapping with the Temporal Classifier
  • Rules
  • Running the Loop
  • Expected Output
• Cybersecurity Inconsistency Tutorial
  • Background
  • Graph
  • Rules
  • Facts
  • Inconsistency Demo 1: Monotonic Reasoning Violation
  • Inconsistency Demo 2: IPL Conflict via Facts
  • Inconsistency Demo 3: Rule-Triggered Inconsistency
  • Running PyReason
  • Expected Output
• Counterfactual Reasoning
  • What a Grounding Is
  • The Three Demos
  • Demo 1: Remove a Graph Edge
  • Demo 2: Inject a Contradicting Fact
  • Demo 3: Diagnose an Existing Inconsistency
  • Running the Code
  • Summary
• Load Rules and Facts From File
  • Introduction
  • Graph
  • Load Rules from CSV
  • Load Rules from JSON
  • Loading Facts from CSV
  • Loading Facts from JSON
  • Running PyReason
  • Expected Output
  • Further Details
• LLM Generated PyReason Rules
  • Introduction
  • Knowledge Graph
  • The Prompt
  • Generating the Rule
  • Validating the Rule
  • Running inference
  • Cross-LLM Consistency
• Natural Language to PyReason Tutorial
  • Setup
  • Step 1: Extract Facts and Rules in English
  • Step 2: Convert to PyReason Syntax
  • Step 3: Validate with PyReason
  • Testing on Other Paragraphs
  • Notes on Model Choice
  • What’s Next