build_tools.syllable_analysis.tsne_visualizer

t-SNE Visualization for Feature Signature Space

This build-time analysis tool creates a t-SNE (t-distributed Stochastic Neighbor Embedding) visualization of the feature signature space in the annotated syllable corpus.

t-SNE is a dimensionality reduction technique that projects high-dimensional feature vectors into 2D space while preserving local structure. This visualization helps identify: - Clustering patterns in the feature space - Syllable similarity based on phonetic features - Natural groupings and outliers in the corpus

The visualization uses: - Position (x, y): t-SNE projection of 12-dimensional feature vectors - Size: Syllable frequency (larger points = more common syllables) - Color: Syllable frequency (warmer colors = more common syllables)

Technical Details: - Uses Hamming distance metric (optimal for binary feature vectors) - Perplexity=30 (balances local vs global structure) - Fixed random seed for reproducibility (seed=42)

Output Formats: - Static PNG: High-resolution matplotlib visualization (always generated) - Interactive HTML: Plotly-based interactive visualization (optional, requires –interactive flag)

Usage:

# Generate static PNG visualization with default paths python -m build_tools.syllable_analysis.tsne_visualizer

# Generate both static PNG and interactive HTML python -m build_tools.syllable_analysis.tsne_visualizer

–interactive –save-mapping

# Custom input/output paths python -m build_tools.syllable_analysis.tsne_visualizer

–input data/annotated/syllables_annotated.json –output _working/analysis/tsne/ –interactive

# Adjust t-SNE parameters python -m build_tools.syllable_analysis.tsne_visualizer

–perplexity 50 –random-state 123 –interactive

# High-resolution output with interactive HTML python -m build_tools.syllable_analysis.tsne_visualizer

–dpi 600 –interactive –save-mapping

Programmatic Usage:
>>> from pathlib import Path
>>> from build_tools.syllable_analysis import (
...     run_tsne_visualization,
...     extract_feature_matrix
... )
>>> result = run_tsne_visualization(
...     input_path=Path("data/annotated/syllables_annotated.json"),
...     output_dir=Path("_working/analysis/tsne/"),
...     perplexity=30,
...     random_state=42,
...     interactive=True,
...     save_mapping=True
... )
>>> print(f"Static visualization: {result['output_path']}")
>>> print(f"Interactive HTML: {result['interactive_path']}")
Architecture:

This module orchestrates calls to specialized modules: - common.data_io: Load annotated syllables - common.paths: Default path configuration - common.output: Output directory and file management - dimensionality.feature_matrix: Extract feature matrices - dimensionality.tsne_core: Apply t-SNE reduction - dimensionality.mapping: Create and save coordinate mappings - plotting.static: Create and save matplotlib PNG visualizations - plotting.interactive: Create and save Plotly HTML visualizations

Functions

run_tsne_visualization(input_path, output_dir[, ...])

Run the complete t-SNE visualization pipeline.

create_argument_parser()

Create and return the argument parser for t-SNE visualization.

parse_args()

Parse command-line arguments.

main()

Main entry point for the t-SNE visualization tool.

Module Contents

build_tools.syllable_analysis.tsne_visualizer.run_tsne_visualization(input_path, output_dir, perplexity=30, random_state=42, dpi=300, verbose=False, save_mapping=False, interactive=False)[source]

Run the complete t-SNE visualization pipeline.

This is the main entry point for programmatic use. It handles the full workflow: 1. Load annotated syllables 2. Extract feature matrix 3. Apply t-SNE dimensionality reduction 4. Create visualization 5. Save outputs (PNG + optional HTML + optional mapping)

Parameters:

  • input_path (pathlib.Path) – Path to syllables_annotated.json

  • output_dir (pathlib.Path) – Directory to save visualization outputs

  • perplexity (int) – t-SNE perplexity parameter (default: 30)

  • random_state (int) – Random seed for reproducibility (default: 42)

  • dpi (int) – Output resolution in dots per inch (default: 300)

  • verbose (bool) – Print detailed progress information

  • save_mapping (bool) – Save syllable→features→coordinates mapping as JSON (default: False)

  • interactive (bool) – Generate interactive HTML visualization (requires Plotly, default: False)

Returns:

  • syllable_count: Number of syllables visualized

  • feature_count: Number of features (always 12)

  • output_path: Path to saved visualization PNG

  • metadata_path: Path to saved metadata file

  • tsne_coordinates: numpy array of 2D coordinates

  • mapping_path: Path to mapping JSON (None if save_mapping=False)

  • interactive_path: Path to interactive HTML (None if interactive=False or Plotly unavailable)

  • processing_time: Total processing time in seconds

Return type:

Dictionary containing

Raises:

Example

>>> from pathlib import Path
>>> result = run_tsne_visualization(
...     input_path=Path("data/annotated/syllables_annotated.json"),
...     output_dir=Path("_working/analysis/tsne/"),
...     interactive=True,
...     save_mapping=True
... )
>>> print(f"Visualized {result['syllable_count']} syllables")
>>> print(f"Interactive HTML: {result['interactive_path']}")
build_tools.syllable_analysis.tsne_visualizer.create_argument_parser()[source]

Create and return the argument parser for t-SNE visualization.

This function creates the ArgumentParser with all CLI options but does not parse arguments. This separation allows Sphinx documentation tools to introspect the parser and auto-generate CLI documentation.

Returns

argparse.ArgumentParser

Configured ArgumentParser ready to parse command-line arguments

build_tools.syllable_analysis.tsne_visualizer.parse_args()[source]

Parse command-line arguments.

Returns:

Parsed argument namespace with validated parameters

Return type:

argparse.Namespace

build_tools.syllable_analysis.tsne_visualizer.main()[source]

Main entry point for the t-SNE visualization tool.