raid-tool 5.1.0

RAID: Rapid Automated Interpretability Datasets tool

RAID (Rapid Automated Interpretability Datasets) tool

Designed to generate binary and multiclass datasets rapidly using regular expressions, AST labels, and semantic concepts. This tool enabled us to perform fine-grained analyses of concepts learned by the model through its latent representations.

Features

1. Labels of different granularities: tokens, phrases, blocks, other semantic chunks.
2. Corresponding activations for tokens, phrases, blocks.
3. B-I-0 Labelling for higher-level semantic concepts (Phrase and Block level chunks)
4. Activation aggregation for higher-level semantic concepts (Phrase and Block level chunks): You can generate activations once and experiment with different granularities by aggregating the activations.
5. Integration with static analysis tools to create custom labels a. Tree-sitter parsers (Abstract Syntax Tree based labels) - Syntactic b. CK metrics (Object Oriented metrics/Design patterns) - Structural c. CFG, DFG, AST Nesting/Parent-child relationships, etc -Hierarchical d. SE datasets, Ontologies, Design Patterns - Semantic e. Regular Expressions to create datasets, filter datasets, edit datasets.

Installation Instructions

Install RAID and its dependencies:

pip install git+https://github.com/arushisharma17/NeuroX.git@fe7ab9c2d8eb1b4b3f93de73b8eaae57a6fc67b7
pip install raid-tool

Usage

Run RAID on a Java file:

raid path/to/your/file.java --model bert-base-uncased --device cpu --binary_filter "set:public,static" --output_prefix output --aggregation_method mean --label class_body 

Required Arguments:

• input_file: Path to the Java source file to analyze

Optional Arguments:

• --model: Transformer model to use (default: 'bert-base-uncased')
• --device: Computing device to use ('cpu' or 'cuda', default: 'cpu')
• --binary_filter: Filter for token labeling
  • Format: "type:pattern"
  • Types:
    • set: Comma-separated list (e.g., "set:public,static")
    • re: Regular expression pattern
• --output_prefix: Prefix for output files (default: 'output')
• --aggregation_method: Method to aggregate activations
  • Options: mean, max, sum, concat (default: mean)
• --label: Type of AST label to analyze
  • Options: program, class_declaration, class_body, method_declaration, etc.
• --layer: Specific transformer layer to analyze (0-12, default: all layers)

Available Labels

The following labels are supported for the --label parameter:

• program
• class_declaration
• class_body
• method_declaration
• formal_parameters
• block
• method_invocation
• leaves

Link to Colab notebook for tutorial and initial instructions

https://colab.research.google.com/drive/1MfTbOMrZnQ_FkC65CCJyUE4v21u5pJ4G?usp=sharing

Note: Please keep updating readme as you add code.

Features

1. Integrate static analysis tools.
2. Generate AST nodes and labels
3. Extract layerwise and 'all' layers activations
4. Split phrasal nodes into B-I-O tokens
5. Aggregate activations
6. Lexical Patterns or features
7. Support for multiple languages
8. Code Ontologies/HPC Ontologies
9. Software engineering datasets
10. Hierarchical Properties/Structural Properties
11. Add support for autoencoders NeuroX
12. Train, validation, test splits for probing tasks support.