kuhyx/WUT_Computer_Science
1
0
Fork 0
mirror of https://github.com/kuhyx/WUT_Computer_Science.git synced 2026-07-06 19:23:07 +02:00
Code Issues Actions Packages Projects Releases Wiki Activity

feat: added argument support

Browse Source
This commit is contained in:
Krzysztof Rudnicki 2023-05-29 21:59:33 +02:00
parent e0b160f071
commit f4e00bf019
1 changed files with 24 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
midterm/code/main.py
View File

@ -4,6 +4,8 @@ recomends anime based on another anime entered by user
""" """
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import argparse
from sklearn.neighbors import NearestNeighbors from sklearn.neighbors import NearestNeighbors
from scipy.sparse import csr_matrix from scipy.sparse import csr_matrix
@ -184,8 +186,27 @@ def create_model(pivot_table):
return model return model
def handle_arguments():
parser = argparse.ArgumentParser(description='Example script with pyargs')
parser.add_argument('--data_limit', '-dl',
help='Specify data limit, Recommended at least 50k', required=False, type=int, default=-1)
parser.add_argument('--seed', '-s', help='Specify seed',
type=int, required=False, default=42)
parser.add_argument('--debug', '-d', help='Use debug (more information) prints',
type=bool, required=False, default=False)
parser.add_argument('--database', '-db', help='Specify database path',
required=False, default="database")
# Parse the command-line arguments
args = parser.parse_args()
# Access the values of the arguments
return args.seed, args.debug, args.data_limit, args.database
if __name__ == "__main__": if __name__ == "__main__":
RATING_DATA, ANIME_CONTACT_DATA = get_data(524288) seed, debug, data_limit, db = handle_arguments()
PIVOT_TABLE = preprocessing(RATING_DATA, ANIME_CONTACT_DATA)
RATING_DATA, ANIME_CONTACT_DATA = get_data(data_limit, db)
PIVOT_TABLE = preprocessing(RATING_DATA, ANIME_CONTACT_DATA, debug)
MODEL = create_model(PIVOT_TABLE) MODEL = create_model(PIVOT_TABLE)
predict(MODEL, PIVOT_TABLE) predict(MODEL, PIVOT_TABLE, seed)