Auto-Surprise

Auto-Surprise is built as a wrapper around the Python Surprise recommender-system library. It automates algorithm selection and hyper parameter optimization in a highly parallelized manner. Full documentation is available at Auto-Surprise ReadTheDocs

AutoSurprise is currently in development.

Setup

To setup Auto-Surprise, you will require Python3 installed on a linux system. Auto-Surprise can be installed using pip

pip install auto-surprise

Usage

Basic usage of AutoSurprise is given below.

from surprise import Dataset
from auto_surprise.engine import Engine

# Load the dataset
data = Dataset.load_builtin('ml-100k')

# Intitialize auto surprise engine
engine = Engine(debug=False)

# Start the trainer
best_algo, best_params, best_score, tasks = engine.train(data=data, target_metric='test_rmse', cpu_time_limit=720, max_evals=100)

In the above example, we first initialize the Engine. We then run engine.train() to begin training our model. To train the model we need to pass the following

• data : The data as an instance of surprise.dataset.DatasetAutoFolds. Please read Surprise Dataset docs
• target_metric : The metric we seek to minimize. Available options are test_rmse and test_mae.
• cpu_time_limit : The time limit we want to train. This is in seconds. For datasets like Movielens 100k, 1 hour is sufficient. But you may want to increase this based on the size of your dataset
• max_evals: The maximum number of evaluations each algorithm gets for hyper parameter optimization.
• hpo_algo: Auto-Surprise uses Hyperopt for hyperparameter tuning. By default, it's set to use TPE, but you can change this to any algorithm supported by hyperopt, such as Adaptive TPE or Random search.

# Example for setting the HPO algorithm to adaptive TPE
import hyperopt

...

engine = Engine(debug=False)
engine.train(
    data=data,
    target_metric='test_rmse',
    cpu_time_limit=720,
    max_evals=100,
    hpo_algo=hyperopt.atpe.suggest
)