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#!/usr/bin/env python3
"""
Script to build Item Space (PCA Projection) from Memory Embeddings.
Inputs:
- data/corpora/memory_embeddings.npy (M x 4096)
Outputs:
- data/corpora/item_projection.npz (P, mean, V)
"""
import sys
import os
import numpy as np
# Add src to sys.path
sys.path.append(os.path.join(os.path.dirname(__file__), "../src"))
from personalization.user_model.features import ItemProjection
def main():
emb_path = "data/corpora/memory_embeddings.npy"
out_path = "data/corpora/item_projection.npz"
if not os.path.exists(emb_path):
print(f"Error: {emb_path} not found. Run migrate_preferences.py first.")
sys.exit(1)
print(f"Loading embeddings from {emb_path}...")
E = np.load(emb_path)
print(f"Loaded shape: {E.shape}")
# Target dimension k=256
k = 256
print(f"Fitting PCA with k={k}...")
proj = ItemProjection.from_pca(E, k=k)
print("Transforming all embeddings to item space...")
V = proj.transform_embeddings(E)
print(f"Item vectors shape: {V.shape}")
print(f"Saving projection to {out_path}...")
np.savez(
out_path,
P=proj.P,
mean=proj.mean,
V=V
)
print("Done.")
if __name__ == "__main__":
main()
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