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"""
Add user-scoped reasoning vectors to their own namespace.
Usage:
python scripts/add_user_model.py \
--model_path ~/user_model.pkl \
--csv ~/user_data.csv \
--features f1,f2,f3 \
--namespace data/user_indices/user_123_my_model \
--limit 500
"""
import argparse
import pandas as pd
import numpy as np
from core.model_loader import load_model, predict
from core.explain import explain_instance
from core.storage import ensure_dir, init_matrix_files, append_case
from core.utils import to_numpy, case_id_from_vector
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--model_path", required=True)
ap.add_argument("--csv", required=True)
ap.add_argument("--features", required=True)
ap.add_argument("--namespace", required=True)
ap.add_argument("--limit", type=int, default=1000)
args = ap.parse_args()
feat_names = args.features.split(",")
df = pd.read_csv(args.csv)
if args.limit < len(df):
df = df.sample(args.limit, random_state=1337)
X = df[feat_names].values.astype("float32")
model = load_model(args.model_path)
ensure_dir(args.namespace)
init_matrix_files(args.namespace, feature_dim=X.shape[1], shap_dim=X.shape[1])
bg = df[feat_names].sample(min(100, len(df)), random_state=0).values.astype("float32")
for _, row in df.iterrows():
x = row[feat_names].values.astype("float32")
y_pred, _ = predict(model, x.reshape(1, -1))
exp = explain_instance(model, x, feat_names, background_X=bg, top_k=8)
cid = case_id_from_vector(x, prefix="usercase")
append_case(args.namespace, cid, x, np.array(exp["shap_values"], dtype="float32"), {"y_pred": float(y_pred[0])})
print(f"User namespace populated at: {args.namespace}")
if __name__ == "__main__":
main()
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