File size: 2,162 Bytes
9220556 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | 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()
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