def test_load_movielens_1m_ratings(self):
test_rating_file = os.path.join(TEST_DATA_DIR, TEST_ML_1M_RATING_FILE)
ratings = load_movielens_1m_ratings(test_rating_file)
n_row, n_col = ratings.shape
self.assertEqual(n_row, 1000)
self.assertEqual(n_col, 3)
np_test.assert_array_equal(ratings[0], [1, 1193, 5])
np_test.assert_array_equal(ratings[-1], [10, 1022, 5])
def test_load_movielens_1m_ratings(self):
test_rating_file = os.path.join(TEST_DATA_DIR, TEST_ML_1M_RATING_FILE)
ratings = load_movielens_1m_ratings(test_rating_file)
n_row, n_col = ratings.shape
self.assertEqual(n_row, 1000)
self.assertEqual(n_col, 3)
np_test.assert_array_equal(ratings[0], [1, 1193, 5])
np_test.assert_array_equal(ratings[-1], [10, 1022, 5])
zip_name, _ = urllib.request.urlretrieve(ML_1M_URL, "ml-1m.zip")
with zipfile.ZipFile(zip_name, 'r') as zf:
file_path = zf.extract('ml-1m/ratings.dat')
# check file
statinfo = os.stat(file_path)
if statinfo.st_size == file_size:
print('verify success: %s' % file_path)
else:
raise Exception('verify failed: %s' % file_path)
return file_path
# load or download MovieLens 1M dataset
rating_file = ml_1m_download(ML_1M_FOLDER, file_size=ML_1M_ZIP_SIZE)
ratings = load_movielens_1m_ratings(rating_file)
n_user = max(ratings[:, 0])
n_item = max(ratings[:, 1])
# shift user_id & movie_id by 1. let user_id & movie_id start from 0
ratings[:, (0, 1)] -= 1
plt.imshow(ratings, cmap='jet', interpolation='nearest')
# split data to training & testing
train_pct = 0.9
rand_state.shuffle(ratings)
train_size = int(train_pct * ratings.shape[0])
train = ratings[:train_size]
validation = ratings[train_size:]
# -*- coding: utf-8 -*-
"""
Created on Sun May 26 17:34:22 2019
@author: dblab
"""
import numpy as np
from recommend.bpmf import BPMF
from recommend.utils.evaluation import RMSE
from recommend.utils.datasets import load_movielens_1m_ratings
# load user ratings
ratings = load_movielens_1m_ratings('ml-1m/ratings.dat')
n_user = max(ratings[:, 0])
n_item = max(ratings[:, 1])
ratings[:,
(0, 1)] -= 1 # shift ids by 1 to let user_id & movie_id start from 0
# fit model
bpmf = BPMF(n_user=n_user,
n_item=n_item,
n_feature=10,
max_rating=5.,
min_rating=1.,
seed=0).fit(ratings, n_iters=20)
RMSE(bpmf.predict(ratings[:, :2]), ratings[:, 2]) # training RMSE
# predict ratings for user 0 and item 0 to 9:
print(bpmf.predict(np.array([[0, i] for i in range(10)])))
print("file %s not exists. downloading..." % file_path)
zip_name, _ = urllib.request.urlretrieve(ML_1M_URL, "ml-1m.zip")
with zipfile.ZipFile(zip_name, 'r') as zf:
file_path = zf.extract('ml-1m/ratings.dat')
# check file
statinfo = os.stat(file_path)
if statinfo.st_size == file_size:
print('verify success: %s' % file_path)
else:
raise Exception('verify failed: %s' % file_path)
return file_path
# load or download MovieLens 1M dataset
rating_file = ml_1m_download(ML_1M_FOLDER, file_size=ML_1M_ZIP_SIZE)
ratings = load_movielens_1m_ratings(rating_file)
n_user = max(ratings[:, 0])
n_item = max(ratings[:, 1])
# shift user_id & movie_id by 1. let user_id & movie_id start from 0
ratings[:, (0, 1)] -= 1
# split data to training & testing
train_pct = 0.9
rand_state.shuffle(ratings)
train_size = int(train_pct * ratings.shape[0])
train = ratings[:train_size]
validation = ratings[train_size:]
# models settings
n_feature = 10