FlexMF Explicit

This page analyzes the hyperparameter tuning results for the FlexMF scorer in explicit-feedback mode (a biased matrix factorization model trained with PyTorch).

Parameter Search Space

/home/mde48/lenskit/lenskit-codex/.venv/lib/python3.12/site-packages/ray/tune/search/sample.py:700: RayDeprecationWarning: The `base` argument is deprecated. Please remove it as it is not actually needed in this method.
Parameter Type Distribution Values Selected
embedding_size Integer LogUniform 4 ≤ \(x\) ≤ 512 275
regularization Float LogUniform 0.0001 ≤ \(x\) ≤ 10 0.5
learning_rate Float LogUniform 0.001 ≤ \(x\) ≤ 0.1 0.00118
reg_method Categorical Uniform L2, AdamW AdamW

Final Result

Searching selected the following configuration:

{
    'embedding_size': 275,
    'regularization': 0.5000854696340721,
    'learning_rate': 0.0011840985940235055,
    'reg_method': 'AdamW',
    'epochs': 6
}

With these metrics:

{
    'RBP': 0.1836954179833178,
    'LogRBP': 2.099763739767223,
    'NDCG': 0.4290506910824616,
    'RecipRank': 0.3870147262075251,
    'RMSE': 0.7579250491011952,
    'TrainTask': '8c72cdb8-85c9-4569-9823-9dcfdfcc2359',
    'TrainTime': None,
    'TrainCPU': None,
    'max_epochs': 50,
    'done': False,
    'training_iteration': 6,
    'trial_id': 'f60bccb5',
    'date': '2025-05-04_22-22-46',
    'timestamp': 1746411766,
    'time_this_iter_s': 10.91522216796875,
    'time_total_s': 69.17381834983826,
    'pid': 2595285,
    'hostname': 'CCI-ws21',
    'node_ip': '10.248.127.152',
    'config': {
        'embedding_size': 275,
        'regularization': 0.5000854696340721,
        'learning_rate': 0.0011840985940235055,
        'reg_method': 'AdamW',
        'epochs': 6
    },
    'time_since_restore': 69.17381834983826,
    'iterations_since_restore': 6
}

Parameter Analysis

Embedding Size

The embedding size is the hyperparameter that most affects the model’s fundamental logic, so let’s look at performance as a fufnction of it:

Learning Parameters

Iteration Completion

How many iterations, on average, did we complete?

How did the metric progress in the best result?

How did the metric progress in the longest results?