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import json
import numpy as np
import pandas as pd
from jsonargparse import ArgumentParser
def parse_args():
"""Parse command-line arguments."""
parser = ArgumentParser()
parser.add_argument("--data_dir", type=str, required=True, default="./processed")
parser.add_argument("--weight_dir", type=str, required=True, default="./weight")
parser.add_argument("--info_path", type=str, required=True, default="./processed/summary_book.csv")
parser.add_argument("--user_id", required=True, default="276729")
parser.add_argument("--num", type=int, required=True, default=10)
parser.add_argument("--lb", type=int, required=True, default=0)
return vars(parser.parse_args())
def main(
data_dir,
weight_dir,
info_path,
user_id,
num,
lb,
**kwargs
):
# Load R matrix from file
R = np.load(f'{data_dir}/R.npy', allow_pickle=True)
# Load prediction
prediction = np.load(f'{weight_dir}/predicted.npy', allow_pickle=True)
# Load dictionary from JSON file
with open(f'{data_dir}/user_id_map.json', 'r') as file:
user2id = json.load(file)
with open(f'{data_dir}/book_id_map.json', 'r') as file:
book2id = json.load(file)
# Recommend
user_idx = user2id[str(user_id)]
predict = prediction[:, user_idx] # get prediction for user
predict_dict = {book: np.round(predict[idx], 2) for book, idx in book2id.items()}
# Load information about book
book_df = pd.read_csv(info_path)
book_df = book_df[book_df["Num-Rating"] > lb]
book_df['predict'] = book_df["ISBN"].map(predict_dict)
recommendations = book_df.nlargest(num, "predict").reset_index(drop=True)
recommendations["context"] = recommendations.apply(
lambda book: f"{book['Book-Title']} ({book['Year-Of-Publication']}) - by {book['Book-Author']}", axis=1
)
print(recommendations)
if __name__ == "__main__":
main(**parse_args())
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