Welcome to Auto-Surprise’s documentation!¶

Auto-Surprise is an easy-to-use python AutoRecommenderSystem (AutoRecSys) library. It automates algorithm selection and hyperparameter tuning to build an optimized recommendation model. It uses the popular scikit library Surprise for recommender algorithms and Hyperopt for hyperparameter tuning.

Unfortunately, currently only linux systems are supported, but you can use WSL in windows as well.

To get started with Auto-Surprise, check out the Quick Start guide. If you have any issues or doubts, head over to the Github repository

Quick Start¶

Installing¶

You will require Python >=3.6 and a linux based OS. With pip, installing Auto-Surprise is as easy as

pip install auto-surprise

Thats it. You are ready to get started

Quick Example¶

Here’s a quick example of using Auto-Surprise to determine the best algorithm and hyperparameters for the Movielens 100k dataset.

# Import required libraries
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(verbose=True)

# Start the trainer
best_algo, best_params, best_score, tasks = engine.train(
    data=data,
    target_metric='test_rmse',
    cpu_time_limit=60 * 60, # Run for 1 hour
    max_evals=100
)

Thats it, after about 1 hour you should have the best algorithm along with the best parameters. To learn more, continue with the Manual

Manual¶

Here, we will cover in more detail the usage for Auto-Surprise. We will start with an example, and go through each section

import hyperopt
from surprise import Reader, Dataset
from auto_surprise.engine import Engine

# Load the movielens dataset
file_path = os.path.expanduser('./ml-100k/u.data')
reader = Reader(line_format='user item rating timestamp', sep='\t', rating_scale=(1, 5))
data = Dataset.load_from_file(file_path, reader=reader)

# Intitialize auto surprise engine
engine = Engine(verbose=True)

# Start the trainer
best_algo, best_params, best_score, tasks = engine.train(
    data=data,
    target_metric='test_rmse',
    cpu_time_limit=60*60*2,
    max_evals=100,
    hpo_algo=hyperopt.tpe.suggest
)

# Build the model using the best algorithm and hyperparameters
best_model = engine.build_model(best_algo, best_params)

Loading the dataset¶

Auto-Surprise requires your dataset to be an instance of surprise.dataset.DatasetAutoFolds. You can learn more about this by reading the Surprise Dataset Docs

Initializing Auto-Surprise Engine¶

Engine is the main class for Auto-Surprise. You will need to initialize it once before you start using it.

engine = Engine(verbose=True, algorithms=['svd', 'svdpp', 'knn_basic', 'knn_baseline'])

verbose : By default set to True. Controls the verbosity of Auto-Surprise.
algorithms : The algorithms to be optimized. Must be in the form of an array of strings. Available choices are [‘svd’, ‘svdpp’, ‘nmf’, ‘knn_basic’, ‘knn_baseline’, ‘knn_with_means’, ‘knn_with_z_score’, ‘co_clustering’, ‘slope_one’, ‘baseline_only’]

Starting the Optimization process¶

To start the optimization method, you can use the train method of Engine. This will return the best algorithm, hyperparameters, best score, and tasks completed.

best_algo, best_params, best_score, tasks = engine.train(
    data=data,
    target_metric='test_rmse',
    cpu_time_limit=60*60*2,
    max_evals=100,
    hpo_algo=hyperopt.tpe.suggest
)

There are a few parameters you can use.

data : The data as an instance of surprise.dataset.DatasetAutoFolds.
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, 1-2 hours 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.

Building the best Model¶

You can use the best alogithm and best hypermaters you got from the train method to build a model.

best_model = engine.build_model(best_algo, best_params)

You can pickle this model to save it and use it elsewhere.