Decision Tree Studio

A comprehensive PyQt5-based desktop application for designing, visualizing, and simulating decision trees. This tool provides an intuitive graphical interface for creating complex decision logic flows with visual execution tracing.

Features

Core Features

• 🎨 Visual Node Editor: Drag-and-drop interface for creating decision trees
• 🔀 Multiple Node Types: Start, Decision, Action, and End nodes
• ⚙️ Condition Builder: Create complex conditions with various operators
• 🎯 Execution Tracing: Step-by-step visualization of decision tree execution
• 📊 Statistics Panel: Real-time tree metrics and analysis
• 💾 Import/Export: Save and load trees in JSON format
• 🔍 Zoom & Pan: Navigate large trees with ease

Node Types

1. Start Node: Entry point of the decision tree (green, rounded)
2. Decision Node: Conditional branching based on variables (blue, diamond)
3. Action Node: Execute actions or operations (yellow, rectangle)
4. End Node: Terminal node in the tree (red, rounded)

Condition Operators

• == Equal to
• != Not equal to
• > Greater than
• < Less than
• >= Greater than or equal to
• <= Less than or equal to
• contains String containment check

Installation

Prerequisites

• Python 3.6 or higher
• pip package manager

Install Dependencies

pip install -r requirements.txt

Or install PyQt5 directly:

pip install PyQt5>=5.15.0

Usage

Running the Application

python main.py

Or make it executable (Linux/Mac):

chmod +x main.py
./main.py

Creating a Decision Tree

1. Add Nodes:

   • Right-click on canvas → "Add Node" → Select node type
   • Or use toolbar buttons to add nodes at center
   • Or use Edit menu → "Add Node"

2. Edit Nodes:

   • Right-click on a node → "Edit Node"
   • Configure label, conditions (for decision nodes), or actions (for action nodes)

3. Connect Nodes:

   • Right-click on a node → "Connect To" → Select target node
   • For decision nodes: Choose "Connect True Branch" or "Connect False Branch"

4. Move Nodes:

   • Click and drag nodes to reposition them
   • Use mouse wheel to zoom in/out
   • Click and drag on empty space to pan

Executing a Decision Tree

1. Click "Execute" in toolbar or press F5
2. Enter variable values in the execution dialog:

   age = 25
   score = 85.5
   name = John
   

3. Click OK to execute
4. View execution path highlighted in red on the canvas
5. Check execution trace in the bottom panel

Saving and Loading

• New Tree: File → New (Ctrl+N)
• Open Tree: File → Open (Ctrl+O)
• Save Tree: File → Save (Ctrl+S)
• Save As: File → Save As (Ctrl+Shift+S)
• Export JSON: File → Export JSON
• Import JSON: File → Import JSON

File Format

Trees are saved in JSON format with the following structure:

{
  "name": "My Decision Tree",
  "root_id": "node_id",
  "nodes": {
    "node_id": {
      "id": "node_id",
      "type": "decision",
      "label": "Check Age",
      "position": [100, 200],
      "condition": {
        "variable": "age",
        "operator": ">=",
        "value": "18"
      },
      "true_child": "true_node_id",
      "false_child": "false_node_id"
    }
  }
}

Keyboard Shortcuts

• Ctrl+N - New tree
• Ctrl+O - Open tree
• Ctrl+S - Save tree
• Ctrl+Shift+S - Save tree as
• F5 - Execute tree
• Ctrl++ - Zoom in
• Ctrl+- - Zoom out
• Ctrl+0 - Reset zoom
• Ctrl+Q - Quit application

Example Use Cases

Age Verification System

Start → Check Age → (age >= 18) → True: Grant Access / False: Deny Access → End

Grading System

Start → Check Score → (score >= 90) → True: Grade A
                    → False: Check Score → (score >= 80) → True: Grade B
                                         → False: Check Score → (score >= 70) → True: Grade C
                                                              → False: Grade F

Product Recommendation

Start → Check Budget → (budget > 1000) → True: Premium Products
                                       → False: Check Category → (category == "electronics") → ...

Project Structure

qt-decision-tree-studio/
├── main.py              # Application entry point
├── main_window.py       # Main window with menus and panels
├── models.py            # Data models (TreeNode, DecisionTree, Condition)
├── graphics.py          # Graphical node and edge widgets
├── canvas.py            # Canvas scene and view for tree visualization
├── dialogs.py           # Dialog windows (node editor, execution)
├── requirements.txt     # Python dependencies
├── README.md            # This file
└── LICENSE              # MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Max Base

Acknowledgments

• Built with PyQt5
• Inspired by visual programming tools and decision tree algorithms