gobrec 0.5.0

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gobrec

GOBRec: GPU Optimized Bandits Recommender

Usage

Using a MAB Algorithm individually to generate arm scores

import numpy as np
# Import LinUCB as an example, it could be also LinTS or LinGreedy
from gobrec.mabs.lin_mabs import LinUCB

# A batch of contexts for training
contexts = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
# Corresponding decisions (items) taken, it can be str or int
decisions = np.array(['a', 1, 2])
# Corresponding rewards (ratings) received                     
rewards = np.array([1, 0, 1])

# Initialize the bandit. A seed is set for reproducibility and GPU usage can be switched
bandit = LinUCB(seed=42, use_gpu=True)

# Fit the model with the training data
bandit.fit(contexts, decisions, rewards)

# Predict scores for each arm (item) given a batch of contexts
bandit.predict(np.array([[1, 1, 0], [0, 1, 1]]))

Using a MAB Algorithm to generate recommendations

import numpy as np
import gobrec
# Import LinUCB as an example, it could be also LinTS or LinGreedy
from gobrec.mabs.lin_mabs import LinUCB

# A batch of contexts for training.
contexts = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
# Corresponding decisions (items) taken, it can be str or int
decisions = np.array(['a', 1, 2])
# Corresponding rewards (ratings) received
rewards = np.array([1, 0, 1])

recommender = gobrec.Recommender(
    # The recommender can use any implementation following the MABAlgo interface
    mab_algo=LinUCB(seed=42, use_gpu=True),
    # Number of items to recommend
    top_k=2
)

# Fit the model with the training data
recommender.fit(contexts, decisions, rewards)

# Recommend top_k items given a batch of contexts
recommender.recommend(np.array([[1, 1, 0], [0, 1, 1]]))

Documentation

sphinx-apidoc -o docsrc ./gobrec

make html