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visualize_embeddings.py
Executable file
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visualize_embeddings.py
Executable file
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#!/usr/bin/env python3
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"""
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Visualisation des embeddings avec t-SNE ou UMAP.
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Ce script permet de visualiser les embeddings dans un espace 2D
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pour comprendre comment le modèle groupe les workflows similaires.
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"""
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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from PIL import Image
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sys.path.insert(0, str(Path(__file__).parent))
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from geniusia2.core.embedders import EmbeddingManager, FAISSIndex
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try:
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from sklearn.manifold import TSNE
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TSNE_AVAILABLE = True
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except ImportError:
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TSNE_AVAILABLE = False
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print("⚠️ scikit-learn not installed. Install with: pip install scikit-learn")
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try:
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import umap
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UMAP_AVAILABLE = True
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except ImportError:
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UMAP_AVAILABLE = False
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def create_sample_images():
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"""Crée des images d'exemple pour la visualisation."""
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from PIL import ImageDraw, ImageFont
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samples = []
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# Groupe 1: Boutons bleus (Submit, OK, Send)
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for text in ["Submit", "OK", "Send"]:
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img = Image.new('RGB', (200, 100), color=(240, 240, 240))
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draw = ImageDraw.Draw(img)
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draw.rectangle([20, 20, 180, 80], fill=(100, 150, 255), outline=(50, 50, 50), width=2)
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try:
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font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 16)
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except:
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font = ImageFont.load_default()
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bbox = draw.textbbox((0, 0), text, font=font)
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w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
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draw.text((100 - w//2, 50 - h//2), text, fill=(255, 255, 255), font=font)
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samples.append(('blue_button', text, img))
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# Groupe 2: Boutons rouges (Cancel, Close, Abort)
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for text in ["Cancel", "Close", "Abort"]:
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img = Image.new('RGB', (200, 100), color=(240, 240, 240))
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draw = ImageDraw.Draw(img)
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draw.rectangle([20, 20, 180, 80], fill=(255, 100, 100), outline=(50, 50, 50), width=2)
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try:
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font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 16)
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except:
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font = ImageFont.load_default()
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bbox = draw.textbbox((0, 0), text, font=font)
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w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
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draw.text((100 - w//2, 50 - h//2), text, fill=(255, 255, 255), font=font)
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samples.append(('red_button', text, img))
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# Groupe 3: Boutons verts (Apply, Confirm, Accept)
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for text in ["Apply", "Confirm", "Accept"]:
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img = Image.new('RGB', (200, 100), color=(240, 240, 240))
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draw = ImageDraw.Draw(img)
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draw.rectangle([20, 20, 180, 80], fill=(100, 200, 100), outline=(50, 50, 50), width=2)
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try:
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font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 16)
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except:
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font = ImageFont.load_default()
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bbox = draw.textbbox((0, 0), text, font=font)
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w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
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draw.text((100 - w//2, 50 - h//2), text, fill=(255, 255, 255), font=font)
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samples.append(('green_button', text, img))
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return samples
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def visualize_embeddings(method='tsne'):
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"""Visualise les embeddings en 2D."""
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print("\n" + "="*70)
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print("VISUALISATION DES EMBEDDINGS")
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print("="*70)
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# Check dependencies
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if method == 'tsne' and not TSNE_AVAILABLE:
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print("❌ t-SNE requires scikit-learn")
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print(" Install with: pip install scikit-learn")
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return 1
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if method == 'umap' and not UMAP_AVAILABLE:
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print("❌ UMAP requires umap-learn")
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print(" Install with: pip install umap-learn")
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print(" Falling back to t-SNE...")
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method = 'tsne'
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if not TSNE_AVAILABLE:
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return 1
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# 1. Create samples
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print("\n1. Création des échantillons...")
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samples = create_sample_images()
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print(f" ✓ Créé {len(samples)} images")
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# 2. Generate embeddings
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print("\n2. Génération des embeddings...")
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manager = EmbeddingManager(model_name="clip")
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embeddings = []
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labels = []
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texts = []
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for category, text, img in samples:
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emb = manager.embed(img)
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embeddings.append(emb)
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labels.append(category)
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texts.append(text)
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embeddings = np.array(embeddings)
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print(f" ✓ Généré {len(embeddings)} embeddings de dimension {embeddings.shape[1]}")
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# 3. Reduce dimensionality
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print(f"\n3. Réduction de dimensionnalité ({method.upper()})...")
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if method == 'tsne':
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reducer = TSNE(n_components=2, random_state=42, perplexity=min(5, len(embeddings)-1))
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else: # umap
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reducer = umap.UMAP(n_components=2, random_state=42)
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embeddings_2d = reducer.fit_transform(embeddings)
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print(f" ✓ Réduit à 2D: {embeddings_2d.shape}")
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# 4. Plot
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print("\n4. Création du graphique...")
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plt.figure(figsize=(12, 8))
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# Color map
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color_map = {
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'blue_button': 'blue',
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'red_button': 'red',
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'green_button': 'green'
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}
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# Plot points
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for category in set(labels):
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mask = np.array(labels) == category
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plt.scatter(
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embeddings_2d[mask, 0],
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embeddings_2d[mask, 1],
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c=color_map[category],
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label=category.replace('_', ' ').title(),
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s=200,
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alpha=0.6,
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edgecolors='black',
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linewidth=2
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)
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# Add text labels
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for i, (x, y) in enumerate(embeddings_2d):
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plt.annotate(
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texts[i],
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(x, y),
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xytext=(5, 5),
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textcoords='offset points',
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fontsize=9,
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bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.3)
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)
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plt.title(f'Visualisation des Embeddings ({method.upper()})\n'
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f'CLIP ViT-B/32 - {len(embeddings)} échantillons',
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fontsize=14, fontweight='bold')
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plt.xlabel('Dimension 1', fontsize=12)
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plt.ylabel('Dimension 2', fontsize=12)
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plt.legend(fontsize=10, loc='best')
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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# Save
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output_file = f'embeddings_visualization_{method}.png'
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plt.savefig(output_file, dpi=150, bbox_inches='tight')
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print(f" ✓ Sauvegardé: {output_file}")
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# Show
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print("\n5. Affichage...")
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print(" (Ferme la fenêtre pour continuer)")
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plt.show()
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print("\n✅ Visualisation terminée!")
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return 0
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def main():
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"""Main function."""
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import argparse
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parser = argparse.ArgumentParser(description='Visualiser les embeddings')
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parser.add_argument(
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'--method',
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choices=['tsne', 'umap'],
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default='tsne',
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help='Méthode de réduction de dimensionnalité (default: tsne)'
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)
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args = parser.parse_args()
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return visualize_embeddings(method=args.method)
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if __name__ == "__main__":
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sys.exit(main())
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