Issue when Re-implement Matrix Factorization in Pytorch

Question

I try to implement matrix factorization in Pytorch as the data extractor and model.

The original model is written in mxnet. Here I try to use the same idea in Pytorch.

Here is my code, it can be runned directly in codelab

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader

import collections
from collections import defaultdict
from IPython import display
import math
from matplotlib import pyplot as plt
import os
import pandas as pd
import random
import re
import shutil
import sys
import tarfile
import time
import requests
import zipfile
import hashlib



# ============data obtained, not change the original code
DATA_HUB= {}

# Defined in file: ./chapter_multilayer-perceptrons/kaggle-house-price.md
def download(name, cache_dir=os.path.join('..', 'data')):
    """Download a file inserted into DATA_HUB, return the local filename."""
    assert name in DATA_HUB, f"{name} does not exist in {DATA_HUB}."
    url, sha1_hash = DATA_HUB[name]
    os.makedirs(cache_dir, exist_ok=True)
    fname = os.path.join(cache_dir, url.split('/')[-1])
    if os.path.exists(fname):
        sha1 = hashlib.sha1()
        with open(fname, 'rb') as f:
            while True:
                data = f.read(1048576)
                if not data:
                    break
                sha1.update(data)
        if sha1.hexdigest() == sha1_hash:
            return fname  # Hit cache
    print(f'Downloading {fname} from {url}...')
    r = requests.get(url, stream=True, verify=True)
    with open(fname, 'wb') as f:
        f.write(r.content)
    return fname




# Defined in file: ./chapter_multilayer-perceptrons/kaggle-house-price.md
def download_extract(name, folder=None):
    """Download and extract a zip/tar file."""
    fname = download(name)
    base_dir = os.path.dirname(fname)
    data_dir, ext = os.path.splitext(fname)
    if ext == '.zip':
        fp = zipfile.ZipFile(fname, 'r')
    elif ext in ('.tar', '.gz'):
        fp = tarfile.open(fname, 'r')
    else:
        assert False, 'Only zip/tar files can be extracted.'
    fp.extractall(base_dir)
    return os.path.join(base_dir, folder) if folder else data_dir


#1. obtain dataset
DATA_HUB['ml-100k'] = ('http://files.grouplens.org/datasets/movielens/ml-100k.zip',
    'cd4dcac4241c8a4ad7badc7ca635da8a69dddb83')


def read_data_ml100k():
    data_dir = download_extract('ml-100k')
    names = ['user_id', 'item_id', 'rating', 'timestamp']
    data = pd.read_csv(os.path.join(data_dir, 'u.data'), '\t', names=names,
                       engine='python')
    num_users = data.user_id.unique().shape[0]
    num_items = data.item_id.unique().shape[0]
    return data, num_users, num_items


# 2. Split data
#@save
def split_data_ml100k(data, num_users, num_items,
                      split_mode='random', test_ratio=0.1):
    """Split the dataset in random mode or seq-aware mode."""
    if split_mode == 'seq-aware':
        train_items, test_items, train_list = {}, {}, []
        for line in data.itertuples():
            u, i, rating, time = line[1], line[2], line[3], line[4]
            train_items.setdefault(u, []).append((u, i, rating, time))
            if u not in test_items or test_items[u][-1] < time:
                test_items[u] = (i, rating, time)
        for u in range(1, num_users + 1):
            train_list.extend(sorted(train_items[u], key=lambda k: k[3]))
        test_data = [(key, *value) for key, value in test_items.items()]
        train_data = [item for item in train_list if item not in test_data]
        train_data = pd.DataFrame(train_data)
        test_data = pd.DataFrame(test_data)
    else:
        mask = [True if x == 1 else False for x in np.random.uniform(
            0, 1, (len(data))) < 1 - test_ratio]
        neg_mask = [not x for x in mask]
        train_data, test_data = data[mask], data[neg_mask]
    return train_data, test_data

#@save
def load_data_ml100k(data, num_users, num_items, feedback='explicit'):
    users, items, scores = [], [], []
    inter = np.zeros((num_items, num_users)) if feedback == 'explicit' else {}
    for line in data.itertuples():
        user_index, item_index = int(line[1] - 1), int(line[2] - 1)
        score = int(line[3]) if feedback == 'explicit' else 1
        users.append(user_index)
        items.append(item_index)
        scores.append(score)
        if feedback == 'implicit':
            inter.setdefault(user_index, []).append(item_index)
        else:
            inter[item_index, user_index] = score
    return users, items, scores, inter


#@save
def split_and_load_ml100k(split_mode='seq-aware', feedback='explicit',
                          test_ratio=0.1, batch_size=256):
    data, num_users, num_items = read_data_ml100k()
    train_data, test_data = split_data_ml100k(data, num_users, num_items, split_mode, test_ratio)
    train_u, train_i, train_r, _ = load_data_ml100k(train_data, num_users, num_items, feedback)
    test_u, test_i, test_r, _ = load_data_ml100k(test_data, num_users, num_items, feedback)

    # Create Dataset
    train_set = MyData(np.array(train_u), np.array(train_i), np.array(train_r))
    test_set = MyData(np.array(test_u), np.array(test_i), np.array(test_r))

    # Create Dataloader
    train_iter = DataLoader(train_set, shuffle=True, batch_size=batch_size)
    test_iter = DataLoader(test_set, batch_size=batch_size)

    return num_users, num_items, train_iter, test_iter


class MyData(Dataset):
  def __init__(self, user, item, score):
    self.user = torch.tensor(user)
    self.item = torch.tensor(item)
    self.score = torch.tensor(score)
  
  def __len__(self):
    return len(self.user)
  
  def __getitem__(self, idx):
    return self.user[idx], self.item[idx], self.score[idx]


# create a nn class (just-for-fun choice :-) 
class RMSELoss(nn.Module):
    def __init__(self, eps=1e-6):
        '''You should be careful with NaN which will appear if the mse=0, adding self.eps'''
        super().__init__()
        self.mse = nn.MSELoss()
        self.eps = eps
        
    def forward(self,yhat,y):
        loss = torch.sqrt(self.mse(yhat,y) + self.eps)
        return loss



class MF(nn.Module):
    def __init__(self, num_factors, num_users, num_items, **kwargs):
        super(MF, self).__init__(**kwargs)
        self.P = nn.Embedding(num_embeddings=num_users, embedding_dim=num_factors)
        self.Q = nn.Embedding(num_embeddings=num_items, embedding_dim=num_factors)
        self.user_bias = nn.Embedding(num_users, 1)
        self.item_bias = nn.Embedding(num_items, 1)

    def forward(self, user_id, item_id):
        P_u = self.P(user_id)
        Q_i = self.Q(item_id)
        

        b_u = self.user_bias(user_id)
        b_i = self.item_bias(item_id)

        outputs = (P_u * Q_i).sum() + b_u.squeeze() + b_i.squeeze()
        return outputs
        



# train
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyper parameters
num_epochs = 50
batch_size = 512
lr = 0.001


num_users, num_items, train_iter, test_iter = split_and_load_ml100k(test_ratio=0.1, batch_size=batch_size)

model = MF(30, num_users, num_items).to(device)

# Loss and Optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=lr)
criterion = RMSELoss()

# Train the Model
train_rmse = []
test_rmse = []
for epoch in range(num_epochs):
    train_loss = 0
    num_train = 0
    model.train()
    for users, items, scores in train_iter:
        users = users.to(device)
        items = items.to(device)
        scores = scores.float().to(device)

        # Forward pass
        outputs = model(users, items)
        loss = criterion(outputs, scores)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        num_train += scores.shape[0]
        
    train_rmse.append(train_loss / num_train)    

    model.eval()
    test_loss = 0
    num_test = 0
    with torch.no_grad():
        for users, items, scores in test_iter:
            users = users.to(device)
            items = items.to(device)
            scores = scores.float().to(device)

            outputs = model(users, items)
            loss = criterion(outputs, scores)
            
            test_loss += loss.item()
            num_test += scores.shape[0]
    
    test_rmse.append(test_loss / num_test)


# plot
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')

x = list(range(num_epochs))
fig = plt.figure()
ax = plt.axes()

plt.plot(x, train_rmse, label='train_rmse');
plt.plot(x, test_rmse, label='test_rmse');

leg = ax.legend();

I got the result

The MXNET result is here

Why I cannot get a beautiful shape. And my train_rmse is larger than test_rmse.

Answer 1

I modified your code a bit and got a similar result with mxnet's. Here is the code in colab.

1. model. you missed axis=1 in the summation operation.

outputs = (P_u * Q_i).sum(axis=1) + b_u.squeeze() + b_i.squeeze()

The default sum operation will sum all the elements in a tensor and produces a scalar. It is fine to add a scalar to a tensor so you didn't catch an error.

2. optimizer. I used the same optimizer - Adam as the mxnet's implementation. Similarly, I also added weight decay.

optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)

3. initialization. Initialize the weights with normal distribution.

nn.init.normal_(self.P.weight, std=0.01)
nn.init.normal_(self.Q.weight, std=0.01)
nn.init.normal_(self.user_bias.weight, std=0.01)
nn.init.normal_(self.item_bias.weight, std=0.01)

Other,

You don't need to add num_train with the batch size. The loss has already been divided by the batch size in the MSELoss.

num_train += 1