def test_testset():
"""Test TestSet"""
data_file = './tests/data.txt'
u_col = 0
i_col = 1
r_col = 2
sep = '\t'
triplet_data = Reader.read_uir_triplets(data_file,
u_col,
i_col,
r_col,
sep,
skip_lines=0)
test_set = TestSet.from_uir_triplets(triplet_data,
pre_uid_map={},
pre_iid_map={},
pre_ui_set=set())
assert test_set.get_uid('768') == 1
assert test_set.get_iid('195') == 7
assert all([a == b for a, b in zip(test_set.get_users(), range(10))])
assert all([a == b for a, b in zip(test_set.get_ratings(2), [(2, 4)])])
test_set = TestSet.from_uir_triplets(triplet_data,
pre_uid_map={},
pre_iid_map={},
pre_ui_set=set([('76', '93')]),
verbose=True)
assert len(test_set.get_users()) == 9
def test_txt_to_triplets():
"""Test txt_to_triplets function"""
data_file = './tests/data.txt'
u_col = 0
i_col = 1
r_col = 2
sep = '\t'
triplet_data = Reader.read_uir_triplets(data_file, u_col, i_col, r_col, sep, skip_lines=0)
assert len(triplet_data) == 10
assert triplet_data[4][2] == 3
assert triplet_data[6][1] == '478'
assert triplet_data[8][0] == '543'
try:
Reader.read_uir_triplets(data_file, 10)
except IndexError:
assert True
def test_from_provided():
data_file = './tests/data.txt'
data = Reader.read_uir_triplets(data_file)
try:
BaseMethod.from_provided(train_data=None, test_data=None)
except ValueError:
assert True
try:
BaseMethod.from_provided(train_data=data, test_data=None)
except ValueError:
assert True
bm = BaseMethod.from_provided(train_data=data, test_data=data)
assert bm.total_users == 10
assert bm.total_items == 10
def test_matrix_trainset_uir_iter():
data_file = './tests/data.txt'
triplet_data = Reader.read_uir_triplets(data_file)
train_set = MatrixTrainSet.from_uir_triplets(triplet_data,
pre_uid_map={},
pre_iid_map={},
pre_ui_set=set(),
verbose=True)
users = [batch_users for batch_users, _, _ in train_set.uir_iter()]
assert all([a == b for a, b in zip(users, range(10))])
items = [batch_items for _, batch_items, _ in train_set.uir_iter()]
assert all([a == b for a, b in zip(items, range(10))])
ratings = [batch_ratings for _, _, batch_ratings in train_set.uir_iter()]
assert all(
[a == b for a, b in zip(ratings, [4, 4, 4, 4, 3, 4, 4, 5, 3, 4])])
def test_matrix_trainset_uij_iter():
data_file = './tests/data.txt'
triplet_data = Reader.read_uir_triplets(data_file)
train_set = MatrixTrainSet.from_uir_triplets(triplet_data,
pre_uid_map={},
pre_iid_map={},
pre_ui_set=set(),
verbose=True)
users = [batch_users for batch_users, _, _ in train_set.uij_iter()]
assert all([a == b for a, b in zip(users, range(10))])
pos_items = [
batch_pos_items for _, batch_pos_items, _ in train_set.uij_iter()
]
assert all([a == b for a, b in zip(pos_items, range(10))])
neg_items = [
batch_neg_items for _, _, batch_neg_items in train_set.uij_iter()
]
assert all([a != b for a, b in zip(neg_items, range(10))])
from cornac.data import Reader
from cornac.eval_methods import BaseMethod
from cornac.utils.download_utils import DownloadItem
# Download MovieLens 100K provided training and test splits
train_path = DownloadItem(
url='http://files.grouplens.org/datasets/movielens/ml-100k/u1.base',
relative_path='u1.base',
sub_dir='datasets/ml_100k').download_if_needed(True)
test_path = DownloadItem(
url='http://files.grouplens.org/datasets/movielens/ml-100k/u1.test',
relative_path='u1.test',
sub_dir='datasets/ml_100k').download_if_needed(True)
# Load data using Reader
train_data = Reader.read_uir_triplets(train_path)
test_data = Reader.read_uir_triplets(test_path)
# Construct base evaluation method with given data
eval_method = BaseMethod.from_provided(train_data=train_data,
test_data=test_data,
exclude_unknowns=False,
verbose=True)
# Model
mf = cn.models.MF(k=10,
max_iter=25,
learning_rate=0.01,
lambda_reg=0.02,
use_bias=True,
early_stop=True,
def test_matrix_trainset():
"""Test MatrixTrainSet"""
data_file = './tests/data.txt'
u_col = 0
i_col = 1
r_col = 2
sep = '\t'
triplet_data = Reader.read_uir_triplets(data_file,
u_col,
i_col,
r_col,
sep,
skip_lines=0)
train_set = MatrixTrainSet.from_uir_triplets(triplet_data,
pre_uid_map={},
pre_iid_map={},
pre_ui_set=set(),
verbose=True)
assert train_set.matrix.shape == (10, 10)
assert train_set.min_rating == 3
assert train_set.max_rating == 5
assert int(train_set.global_mean) == int((3 * 2 + 4 * 7 + 5) / 10)
assert all([
a == b for a, b in zip(train_set.item_ppl_rank,
[7, 9, 6, 5, 3, 2, 1, 0, 8, 4])
])
assert train_set.num_users == 10
assert train_set.num_items == 10
assert train_set.is_unk_user(7) == False
assert train_set.is_unk_user(13) == True
assert train_set.is_unk_item(3) == False
assert train_set.is_unk_item(16) == True
assert train_set.get_uid('768') == 1
assert train_set.get_iid('195') == 7
assert all([a == b for a, b in zip(train_set.get_uid_list(), range(10))])
assert all([
a == b for a, b in zip(train_set.get_raw_uid_list(), [
'76', '768', '642', '930', '329', '633', '716', '871', '543', '754'
])
])
assert all([a == b for a, b in zip(train_set.get_iid_list(), range(10))])
assert all([
a == b for a, b in zip(train_set.get_raw_iid_list(), [
'93', '257', '795', '709', '705', '226', '478', '195', '737', '282'
])
])
train_set = MatrixTrainSet.from_uir_triplets(triplet_data,
pre_uid_map={},
pre_iid_map={},
pre_ui_set=set([('76', '93')
]),
verbose=True)
assert train_set.num_users == 9
assert train_set.num_items == 9
