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reproduce_paper_results.py
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reproduce_paper_results.py
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import argparse
import pandas as pd
from experiment.execute import execute
from utils.io import load_numpy, save_dataframe_csv, find_best_hyperparameters, load_yaml
from utils.modelnames import models
from plots.rec_plots import precision_recall_curve
import timeit
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 = [5, 10, 15, 20, 50]
frame = []
for idx, row in df.iterrows():
start = timeit.default_timer()
row = row.to_dict()
row['metric'] = ['R-Precision', 'NDCG', 'Precision', 'Recall', "MAP"]
row['topK'] = topK
result = execute(R_train, R_test, row, models[row['model']],
measure=row['similarity'], gpu_on=args.gpu, folder=args.model_folder)
stop = timeit.default_timer()
print('Time: ', stop - start)
frame.append(result)
results = pd.concat(frame)
save_dataframe_csv(results, table_path, args.name)
precision_recall_curve(results, topK, save=True, folder='analysis/'+args.problem)
if __name__ == "__main__":
# Commandline arguments
parser = argparse.ArgumentParser(description="Reproduce")
parser.add_argument('-n', dest='name', default="final_result.csv")
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)