We'll use (a fast hybrid matrix factorization library) but you could swap it for Spark ALS, TensorFlow, or PyTorch.
Below we define each component as a . You can also use the KFP v2 “component‑function” style – the code snippets include both approaches. kfp movie
# RMSE (approx, using predicted scores) pred = model.predict(interactions_test.tocsr().row, interactions_test.tocsr().col) rmse = np.sqrt(mean_squared_error(test["rating"], pred)) precision = precision_at_k(model, interactions_test, k=10).mean() recall = recall_at_k(model, interactions_test, k=10).mean() We'll use (a fast hybrid matrix factorization library)
The "Skadoosh" Factor: Why the Kung Fu Panda Movie Series is an Animated Masterpiece # RMSE (approx, using predicted scores) pred = model
# Dockerfile.evaluate FROM python:3.9-slim WORKDIR /app RUN pip install pandas pyarrow lightfm numpy scikit-learn COPY evaluate.py . ENTRYPOINT ["python", "evaluate.py"] # evaluate.py import os, pandas as pd, numpy as np, pickle from lightfm import LightFM from lightfm.evaluation import auc_score, precision_at_k, recall_at_k from sklearn.metrics import mean_squared_error
Goal: Pull the latest MovieLens dataset (e.g., ml‑latest‑small ) from the public URL and store it in a shared volume ( /mnt/data ).