hw6_best.sh

hw6/hw6_best.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+# $1: the data directory including test.csv, users.csv, movies.csv
+# $2: prediction filename
+
+python3 test_best.py $1 $2

hw6/test_best.py

@@ -0,0 +1,74 @@
+import os
+import sys
+import argparse
+import numpy as np
+import pandas as pd
+from Model import build_cf_model, build_deep_model, rate
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description='HW6: Matrix Factorization')
+ parser.add_argument('data_dir', type=str)
+ parser.add_argument('output', type=str)
+ return parser.parse_args()
+
+
+def predict_rating(trained_model, userid, movieid):
+ return rate(trained_model, userid - 1, movieid - 1)
+
+
+def ensure_dir(file_path):
+ directory = os.path.dirname(file_path)
+ if len(directory) == 0: return
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+
+
+def main(args):
+ users = pd.read_csv(USERS_CSV, sep='::', engine='python',
+ usecols=['UserID', 'Gender', 'Age', 'Occupation', 'Zip-code'])
+ print('{} description of {} users loaded'.format(len(users), max_userid))
+
+ movies = pd.read_csv(MOVIES_CSV, sep='::', engine='python',
+ usecols=['movieID', 'Title', 'Genres'])
+ print('{} descriptions of {} movies loaded'.format(len(movies), max_movieid))
+
+ test_data = pd.read_csv(TEST_CSV, usecols=['UserID', 'MovieID'])
+ print('{} testing data loaded.'.format(test_data.shape[0]))
+
+ trained_model = build_cf_model(max_userid, max_movieid, DIM, isBest=True)
+ print('Loading model weights...')
+ trained_model.load_weights(MODEL_WEIGHTS_FILE)
+ print('Loading model done!!!')
+
+ recommendations = pd.read_csv(TEST_CSV, usecols=['TestDataID'])
+ recommendations['Rating'] = test_data.apply(lambda x: predict_rating(trained_model, x['UserID'], x['MovieID']), axis=1)
+ # print(recommendations)
+
+ ensure_dir(args.output)
+ recommendations.to_csv(args.output, index=False, columns=['TestDataID', 'Rating'])
+
+
+if __name__ == '__main__':
+ args = parse_args()
+
+ MODEL_DIR = './model'
+ MAX_CSV = 'max_best.csv'
+ TEST_CSV = 'test.csv'
+ USERS_CSV = 'users.csv'
+ MOVIES_CSV = 'movies.csv'
+ MODEL_WEIGHTS_FILE = 'weights_add_const_dim15.h5'
+
+ DATA_DIR = args.data_dir
+ TEST_CSV = os.path.join(DATA_DIR, TEST_CSV)
+ USERS_CSV = os.path.join(DATA_DIR, USERS_CSV)
+ MOVIES_CSV = os.path.join(DATA_DIR, MOVIES_CSV)
+
+ MODEL_WEIGHTS_FILE = os.path.join(MODEL_DIR, MODEL_WEIGHTS_FILE)
+ MAX_CSV = os.path.join(MODEL_DIR, MAX_CSV)
+ info = pd.read_csv(MAX_CSV)
+ DIM = list(info['dim'])[0]
+ max_userid = list(info['max_userid'])[0]
+ max_movieid = list(info['max_movieid'])[0]
+
+ main(args)