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personalization_analysis.py
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personalization_analysis.py
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import numpy as np
import argparse
import json
import pandas as pd
from experiment.personalization import personalization
from utils.io import load_numpy, find_best_hyperparameters, load_yaml
def main(args):
table_path = load_yaml('config/global.yml', key='path')['tables']
df = find_best_hyperparameters(table_path+args.problem, 'NDCG')
R_train = load_numpy(path=args.path, name=args.train)
R_valid = load_numpy(path=args.path, name=args.valid)
R_test = load_numpy(path=args.path, name=args.test)
R_train = R_train + R_valid
topK = [1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
personalization(R_train, R_test, df, topK, args.problem, args.model_folder, gpu_on=args.gpu)
if __name__ == "__main__":
# Commandline arguments
parser = argparse.ArgumentParser(description="Personalization")
parser.add_argument('-d', dest='path', default="datax/")
parser.add_argument('-t', dest='train', default='Rtrain.npz')
parser.add_argument('-v', dest='valid', default='Rvalid.npz')
parser.add_argument('-e', dest='test', default='Rtest.npz')
parser.add_argument('-p', dest='problem', default='movielens1m')
parser.add_argument('-s', dest='model_folder', default='latent') # Model saving folder
parser.add_argument('-gpu', dest='gpu', action='store_true')
args = parser.parse_args()
main(args)