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lightGCNModel_num_layers_MovieLens100K_checkpoint.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fa4a3960c3b792ea38bcba6e94ac96d85c95eb1fab88f0ed67e36060dea6ae0a
+size 2019334

processed_MVL_light.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1c19e3fab1ca895a2a7c25ce4692f9d19ace5179ccdce4b0786e2f61b1a24d44
+size 1772240

utils.py

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+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import random
+
+from tqdm import tqdm
+import pandas as pd
+from torch_geometric.datasets import AmazonBook, MovieLens100K, MovieLens1M
+from torch_geometric.nn import GCNConv, LGConv
+from torch_geometric.utils import degree
+from torch_geometric.nn.conv import MessagePassing
+from torch_geometric.data import HeteroData, Data
+import  torch_geometric.transforms as T
+
+def predict(model, device, data, num_users, num_items, user_id, train_edge_label_index, k=5):    
+    with torch.no_grad():
+        
+        ## ML100k
+        interaction_dataframe = pd.read_csv('./u1.base', delim_whitespace=True, header=None)
+        meta_dataframe = pd.read_csv('./u.item', sep='|', encoding='latin-1', header=None)
+        interaction_dataframe = interaction_dataframe[[0, 1]]
+        interaction_dataframe.columns = ['reviewerID', 'asin']
+
+        meta_dataframe = meta_dataframe[[0, 1]]
+        meta_dataframe.columns = ['asin', 'title']
+        
+
+        out = model.get_embedding(data.edge_index)
+        user_emb, item_emb = out[:num_users], out[num_users:]
+        logits = user_emb @ item_emb.t()
+        logits = torch.nn.Sigmoid()(logits)
+        logits[train_edge_label_index[0], train_edge_label_index[1]-num_users] = float('-inf')
+
+        # Create unique users to find the index of it in embedding table
+        unique_users = interaction_dataframe['reviewerID'].unique().tolist()
+        unique_items = interaction_dataframe['asin'].unique().tolist()
+        
+        random_row = random.randint(0, len(interaction_dataframe))
+        user_to_rec = interaction_dataframe.iloc[random_row]['reviewerID']
+        user_to_rec = user_id
+        #user_to_rec = 923
+        user_rates = logits[unique_users.index(user_to_rec)]
+        
+        # print(f"ID of user we want to recommend to: {user_to_rec}")
+        
+        ground_truth_asins = interaction_dataframe[interaction_dataframe['reviewerID'] == user_to_rec]['asin'].to_list()
+        
+        ground_truth_items = meta_dataframe[meta_dataframe['asin'].isin(ground_truth_asins)].head(5)
+        
+        
+        _, top_ratings = torch.topk(user_rates, k)
+        
+        recommended_items = []
+        for index in top_ratings:
+            asin_of_item = unique_items[index]
+            recommended_item = meta_dataframe[meta_dataframe['asin'] == asin_of_item]['title'].values
+            recommended_items.append(recommended_item)
+
+        return ground_truth_items.to_list(), recommended_items