bemb 0.1.7

A Pytorch Backend Library for Choice Modelling with Bayesian Matrix Factorization

Bayesian Embedding (BEMB)

Authors: Tianyu Du and Ayush Kanodia; PI: Susan Athey; Contact: [e-mail]

BEMB is a flexible, fast Bayesian embedding model for modelling choice problems. The bemb package is built upon the torch_choice library.

The full documentation website for BEMB is https://gsbdbi.github.io/bemb/.

Installation

1. Install torch-choice following steps here.
2. The requirements.txt provide a combination of dependency versions that we have tested. However, we encourage users to install these packages manually (there are only 10 dependency libraries, you should have already installed things like numpy and matplotlib) because we wish the user to install the correct PyTorch version based on their specific CUDA versions. You should not do the traditional pip install -r requirements.txt because it installs all packages in parallel, but PyTorch must be installed first be installing torch-scatter.
3. The following script simulates a small dataset and train a simple BEMB model on it. You can run the following code snippet to check if the installation is successful.

import numpy as np
import pandas as pd
import torch
from torch_choice.data import ChoiceDataset
from bemb.model import LitBEMBFlex
from bemb.utils.run_helper import run
import matplotlib.pyplot as plt
import seaborn as sns

# simulate dataset
num_users = 1500
num_items = 50
data_size = 1000

user_index = torch.LongTensor(np.random.choice(num_users, size=data_size))
Us = np.arange(num_users)
Is = np.sin(np.arange(num_users) / num_users * 4 * np.pi)
Is = (Is + 1) / 2 * num_items
Is = Is.astype(int)

PREFERENCE = dict((u, i) for (u, i) in zip(Us, Is))

# construct users.
item_index = torch.LongTensor(np.random.choice(num_items, size=data_size))

for idx in range(data_size):
    if np.random.rand() <= 0.5:
        item_index[idx] = PREFERENCE[int(user_index[idx])]

user_obs = torch.zeros(num_users, num_items)
user_obs[torch.arange(num_users), Is] = 1

item_obs = torch.eye(num_items)

dataset = ChoiceDataset(user_index=user_index, item_index=item_index, user_obs=user_obs, item_obs=item_obs)

idx = np.random.permutation(len(dataset))
train_size = int(0.8 * len(dataset))
val_size = int(0.1 * len(dataset))
train_idx = idx[:train_size]
val_idx = idx[train_size: train_size + val_size]
test_idx = idx[train_size + val_size:]

dataset_list = [dataset[train_idx], dataset[val_idx], dataset[test_idx]]

bemb = LitBEMBFlex(
    learning_rate=0.03,  # set the learning rate, feel free to play with different levels.
    pred_item=True,  # let the model predict item_index, don't change this one.
    num_seeds=32,  # number of Monte Carlo samples for estimating the ELBO.
    utility_formula='theta_user * alpha_item',  # the utility formula.
    num_users=num_users,
    num_items=num_items,
    num_user_obs=dataset.user_obs.shape[1],
    num_item_obs=dataset.item_obs.shape[1],
    # whether to turn on obs2prior for each parameter.
    obs2prior_dict={'theta_user': True, 'alpha_item': True},
    # the dimension of latents, since the utility is an inner product of theta and alpha, they should have
    # the same dimension.
    coef_dim_dict={'theta_user': 10, 'alpha_item': 10}
)

bemb = bemb.to('cuda')

# use the provided run helper to train the model.
# we set batch size to be 5% of the data size, and train the model for 50 epochs.
# there would be 20*50=1,000 gradient update steps in total.
bemb = bemb.fit_model(dataset_list, batch_size=len(dataset) // 20, num_epochs=50)

Example Usage of BEMB

Here is a simulation exercise of using bemb.