danbooru-tag-expander 0.2.3

A tool for expanding Danbooru tags with their implications and aliases

Danbooru Tag Expander

A Python tool for expanding Danbooru tags with their implications and aliases. This tool helps you get a complete set of related tags when working with Danbooru's tagging system.

Features

• Expand tags with their implications and aliases
• High-performance semantic relationship methods for efficient tag processing
• Support for both command-line and programmatic usage
• Configurable output formats (text, JSON, CSV)
• Progress tracking and detailed logging
• Caching support for better performance

Performance Optimization

New in v0.2.3: High-performance semantic relationship methods that provide complete transitive relationships without the overhead of full tag expansion:

• 27,000+ tags/second throughput for cached relationships
• No API calls required for cached data
• Complete semantic relationships including transitive implications and aliases
• Ideal for large-scale processing of thousands of tags

Graph Theory Concepts

The tag expansion system can be understood through graph theory:

Tag Graph Structure

• Tags are nodes in a directed graph
• Two types of edges exist:
  1. Implications: Directed edges between different concepts (A → B means "A implies B")
  2. Aliases: Form equivalence classes (subgraphs) where all nodes represent the same concept

Frequency Calculation

• For implications:
  • Multiple implications to the same tag sum their frequencies
  • Example: If A implies X and B implies X, then freq(X) = freq(A) + freq(B)
• For aliases:
  • All nodes in an alias subgraph share the same frequency
  • Example: If X and Y are aliases, then freq(X) = freq(Y) = total frequency of their concept
  • This reflects that aliases are different names for the same underlying concept

Example

Given:
- Tags: [cat, feline, kitten]
- Aliases: cat ↔ feline (they're the same concept)
- Implications: kitten → cat

Results:
- Expanded tags: [cat, feline, kitten]
- Frequencies:
  - cat: 2 (1 from original + 1 from kitten implication)
  - feline: 2 (same as cat since they're aliases)
  - kitten: 1 (from original tag)

Installation

You can install the package using pip:

pip install danbooru-tag-expander

Usage

Command Line

# Basic usage with tags
danbooru-tag-expander --tags "1girl" "solo"

# Using a file containing tags
danbooru-tag-expander --file tags.txt

# Output in different formats
danbooru-tag-expander --tags "1girl" --format json
danbooru-tag-expander --tags "1girl" --format csv

# Control logging verbosity
danbooru-tag-expander --tags "1girl" --quiet
danbooru-tag-expander --tags "1girl" --log-level DEBUG

Python API

from danbooru_tag_expander.tag_expander import TagExpander

# Create an expander instance
expander = TagExpander(
    username="your-username",  # Optional, can be set via environment
    api_key="your-api-key",    # Optional, can be set via environment
    use_cache=True             # Enable caching for better performance
)

# Expand tags
expanded_tags, frequencies = expander.expand_tags(["1girl", "solo"])

# Print results
print(f"Original tags: 1girl, solo")
print(f"Expanded tags: {', '.join(expanded_tags)}")

Advanced Usage: High-Performance Semantic Relationships

For applications that need complete semantic relationships without the overhead of full tag expansion, use the new high-performance methods:

from danbooru_tag_expander import TagExpander

expander = TagExpander(
    username="your-username",
    api_key="your-api-key",
    use_cache=True
)

# First, ensure tags are cached (one-time cost)
expander.expand_tags(["aqua_bikini"])  # Populates cache via API

# Now use high-performance methods (no API calls, very fast)
tag = "aqua_bikini"

# Get direct implications only
direct_implications = expander.get_implications(tag)
# Returns: ["bikini", "swimwear", "clothing"]

# Get complete transitive implications (follows the full chain)
transitive_implications = expander.get_transitive_implications(tag)
# Returns: {"bikini", "swimwear", "clothing"} - includes all levels

# Get direct aliases
aliases = expander.get_aliases(tag)

# Get complete alias group (all equivalent tags)
alias_group = expander.get_alias_group(tag)

# Get comprehensive semantic relationships
relations = expander.get_semantic_relations(tag)
# Returns: {
#   'direct_implications': [...],
#   'transitive_implications': {...},
#   'direct_aliases': [...],
#   'alias_group': {...},
#   'all_related': {...}  # All semantically related tags
# }

# Check if tag relationships are cached
if expander.is_tag_cached(tag):
    # Safe to use high-performance methods
    all_related = expander.get_semantic_relations(tag)['all_related']
else:
    # Need to populate cache first
    expander.expand_tags([tag])

Performance Comparison

# Traditional approach (slower, includes frequency calculations)
expanded_tags, frequencies = expander.expand_tags(["aqua_bikini"])

# New high-performance approach (faster, semantic relationships only)
relations = expander.get_semantic_relations("aqua_bikini")
all_related = {tag}.union(relations['all_related'])

# Performance difference:
# - Traditional: ~4.6 tags/second (requires API calls + frequency calculation)
# - High-performance: 27,000+ tags/second (cached graph traversal only)

Use Cases

The high-performance semantic methods are ideal for:

• Building tag graphs for large datasets (thousands of tags)
• Real-time tag suggestion systems
• Semantic analysis without frequency calculations
• Batch processing where you need relationships but not frequencies
• Tag validation and expansion in user interfaces

Advanced Usage: External Graph Injection

For advanced use cases, you can inject an external DanbooruTagGraph instance from the separate danbooru-tag-graph package:

from danbooru_tag_expander.tag_expander import TagExpander
from danbooru_tag_graph import DanbooruTagGraph

# Create and populate an external graph
graph = DanbooruTagGraph()
graph.add_tag("cat", fetched=True)
graph.add_tag("animal", fetched=True)
graph.add_implication("cat", "animal")

# Use the external graph
expander = TagExpander(
    username="your-username",
    api_key="your-api-key",
    tag_graph=graph  # Inject external graph
)

# This will use the pre-populated graph data
expanded_tags, frequencies = expander.expand_tags(["cat"])

This approach is useful for:

• Pre-loading tag relationships from external sources
• Sharing graph instances between multiple expanders
• Custom caching strategies
• Integration with external tag management systems

The danbooru-tag-graph package can also be used independently for graph-based tag relationship management.

Configuration

The tool can be configured using environment variables or command-line arguments:

• DANBOORU_USERNAME: Your Danbooru username
• DANBOORU_API_KEY: Your Danbooru API key
• DANBOORU_SITE_URL: Custom Danbooru instance URL (optional)
• DANBOORU_CACHE_DIR: Custom cache directory location (optional)

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.