singhrahuldps

# get list of unique user ids
users = sorted(list(set(ratings[user_col].values)))

# get list of unique item ids
items = sorted(list(set(ratings[item_col].values)))

# generate dict of correponding indexes for the user ids
user2idx = list_2_dict(users)

# generate dict of correponding indexes for the item ids

singhrahuldps

# get a batch -> (user, item and rating arrays) from the dataframe
def get_batch(ratings, start:int, end:int):
    return ratings[user_col][start:end].values, ratings[item_col][start:end].values, ratings[rating_col][start:end].values

singhrahuldps

# splits ratings dataframe to training and validation dataframes
def get_data(ratings, valid_pct:float = 0.2):
    # shuffle the indexes
    ln = random.sample(range(0, len(ratings)), len(ratings))
    
    # split based on the given validation set percentage 
    part = int(len(ln)*valid_pct)
    
    valid_index = ln[0:part]
    train_index = ln[part:]

singhrahuldps

# this function returns a python dictionary
# which maps each id to a corresponding index value
def list_2_dict(id_list:list):
    d={}
    for id, index in zip(id_list, range(len(id_list))):
        d[id] = index
    return d

singhrahuldps

# laoding the table as a pandas dataframe
ratings = pd.read_csv('ratings.csv')

# getting the three column names from a pandas dataframe
user_col, item_col, rating_col = ratings.columns

singhrahuldps

# required libraries - numpy, pandas, pytorch
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import random

singhrahuldps

# required libraries - numpy, pandas, pytorch
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import random

# laoding the table as a pandas dataframe
ratings = pd.read_csv('ratings.csv')