| from __future__ import annotations |
|
|
| from pathlib import Path |
|
|
| import gradio as gr |
| import numpy as np |
| import plotly.graph_objects as go |
|
|
| ARTIFACT_DIR = Path(__file__).resolve().parent / "artifacts" / "factorial-code-forge" |
| DATA = np.load(ARTIFACT_DIR / "latent_comparison.npz") |
| METHODS = { |
| "Observed mixtures": "observations", |
| "Autoencoder control": "autoencoder_control", |
| "Neural adversarial ablation": "neural_adversarial_ablation", |
| "Predictability minimization": "predictability_minimization", |
| "PCA whitened": "pca_whitened", |
| "FastICA": "fastica", |
| } |
|
|
|
|
| def explore(method: str, points: int) -> tuple[go.Figure, dict]: |
| key = METHODS[method] |
| code = DATA[key][: int(points)] |
| sources = DATA["sources"][: int(points)] |
| figure = go.Figure( |
| go.Scattergl( |
| x=code[:, 0], |
| y=code[:, 1], |
| mode="markers", |
| marker={ |
| "size": 4, |
| "color": sources[:, 0], |
| "colorscale": "Turbo", |
| "opacity": 0.65, |
| }, |
| ) |
| ) |
| figure.update_layout( |
| title=f"{method}: learned two-dimensional code", |
| xaxis_title="Coordinate 1", |
| yaxis_title="Coordinate 2", |
| template="plotly_dark", |
| ) |
| return figure, { |
| "absolute_correlation": round(float(abs(np.corrcoef(code.T)[0, 1])), 5), |
| "points": len(code), |
| "color": "true source 1 (evaluation only)", |
| } |
|
|
|
|
| with gr.Blocks(title="Factorial Code Forge") as demo: |
| gr.Markdown( |
| "# Factorial Code Forge\n" |
| "Inspect how different unsupervised methods reorganize two mixed, " |
| "independent non-Gaussian sources." |
| ) |
| with gr.Row(): |
| method = gr.Dropdown( |
| list(METHODS), value="Predictability minimization", label="Code" |
| ) |
| points = gr.Slider(250, 5_000, 2_000, step=250, label="Points") |
| run = gr.Button("Reveal code", variant="primary") |
| scatter = gr.Plot() |
| metrics = gr.JSON() |
| run.click(explore, [method, points], [scatter, metrics]) |
| demo.load(explore, [method, points], [scatter, metrics]) |
|
|
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
