def test_test_loader(python_dataset_ncf):
train, test = python_dataset_ncf
data = Dataset(train=train, test=test, n_neg=N_NEG, n_neg_test=N_NEG_TEST)
# positive user-item dict, noting that the pool is train+test
positive_pool = {}
df = train.append(test)
for u in df[DEFAULT_USER_COL].unique():
positive_pool[u] = set(df[df[DEFAULT_USER_COL] == u][DEFAULT_ITEM_COL])
for batch in data.test_loader():
user, item, labels = batch
# shape
assert len(user) == N_NEG_TEST + 1
assert len(item) == N_NEG_TEST + 1
assert len(labels) == N_NEG_TEST + 1
label_list = []
for u, i, is_pos in zip(user, item, labels):
if is_pos:
assert i in positive_pool[u]
else:
assert i not in positive_pool[u]
label_list.append(is_pos)
# leave-one-out
assert sum(label_list) == 1
# right labels
assert len(label_list) == (N_NEG_TEST + 1) * sum(label_list)
def test_test_loader(python_dataset_ncf):
train, test = python_dataset_ncf
data = Dataset(train=train, test=test, n_neg=N_NEG, n_neg_test=N_NEG_TEST)
# positive user-item dict, noting that the pool is train+test
positive_pool = {}
df = train.append(test)
for u in df[DEFAULT_USER_COL].unique():
positive_pool[u] = set(df[df[DEFAULT_USER_COL] == u][DEFAULT_ITEM_COL])
for batch in data.test_loader():
user, item, labels = batch
# shape
assert len(user) == N_NEG_TEST + 1
assert len(item) == N_NEG_TEST + 1
assert len(labels) == N_NEG_TEST + 1
label_list = []
for u, i, is_pos in zip(user, item, labels):
if is_pos:
assert i in positive_pool[u]
else:
assert i not in positive_pool[u]
label_list.append(is_pos)
# leave-one-out
assert sum(label_list) == 1
# right labels
assert len(label_list) == (N_NEG_TEST + 1) * sum(label_list)
def test_fit(python_dataset_ncf, model_type):
train, test = python_dataset_ncf
data = Dataset(train=train, test=test, n_neg=N_NEG, n_neg_test=N_NEG_TEST)
model = NCF(
n_users=data.n_users, n_items=data.n_items, model_type=model_type, n_epochs=1
)
model.fit(data)
def test_data_preprocessing(python_dataset_ncf):
train, test = python_dataset_ncf
data = Dataset(train=train, test=test, n_neg=N_NEG, n_neg_test=N_NEG_TEST)
# shape
assert len(data.train) == len(train)
assert len(data.test) == len(test)
# index correctness for id2user, user2id, id2item, item2id
for data_row, row in zip(data.train.iterrows(), train.iterrows()):
assert data_row[1][DEFAULT_USER_COL] == data.user2id[
row[1][DEFAULT_USER_COL]]
assert row[1][DEFAULT_USER_COL] == data.id2user[data_row[1]
[DEFAULT_USER_COL]]
assert data_row[1][DEFAULT_ITEM_COL] == data.item2id[
row[1][DEFAULT_ITEM_COL]]
assert row[1][DEFAULT_ITEM_COL] == data.id2item[data_row[1]
[DEFAULT_ITEM_COL]]
for data_row, row in zip(data.test.iterrows(), test.iterrows()):
assert data_row[1][DEFAULT_USER_COL] == data.user2id[
row[1][DEFAULT_USER_COL]]
assert row[1][DEFAULT_USER_COL] == data.id2user[data_row[1]
[DEFAULT_USER_COL]]
assert data_row[1][DEFAULT_ITEM_COL] == data.item2id[
row[1][DEFAULT_ITEM_COL]]
assert row[1][DEFAULT_ITEM_COL] == data.id2item[data_row[1]
[DEFAULT_ITEM_COL]]
def test_train_loader(python_dataset_ncf):
train, test = python_dataset_ncf
data = Dataset(train=train, test=test, n_neg=N_NEG, n_neg_test=N_NEG_TEST)
# collect positvie user-item dict
positive_pool = {}
for u in train[DEFAULT_USER_COL].unique():
positive_pool[u] = set(train[train[DEFAULT_USER_COL] == u][DEFAULT_ITEM_COL])
# without negative sampling
for batch in data.train_loader(batch_size=BATCH_SIZE, shuffle=False):
user, item, labels = batch
#shape
assert len(user) == BATCH_SIZE
assert len(item) == BATCH_SIZE
assert len(labels) == BATCH_SIZE
assert max(labels) == min(labels)
# right labels
for u, i, is_pos in zip(user, item, labels):
if is_pos:
assert i in positive_pool[u]
else:
assert i not in positive_pool[u]
data.negative_sampling()
label_list = []
batches = []
for idx, batch in enumerate(data.train_loader(batch_size=1)):
user, item, labels = batch
assert len(user) == 1
assert len(item) == 1
assert len(labels) == 1
# right labels
for u, i, is_pos in zip(user, item, labels):
if is_pos:
assert i in positive_pool[u]
else:
assert i not in positive_pool[u]
label_list.append(is_pos)
# neagtive smapling
assert len(label_list) == (N_NEG + 1) * sum(label_list)
def test_predict(python_dataset_ncf, model_type):
# test data format
train, test = python_dataset_ncf
data = Dataset(train=train, test=test, n_neg=N_NEG, n_neg_test=N_NEG_TEST)
model = NCF(
n_users=data.n_users, n_items=data.n_items, model_type=model_type, n_epochs=1
)
model.fit(data)
test_users, test_items = list(test[DEFAULT_USER_COL]), list(test[DEFAULT_ITEM_COL])
assert type(model.predict(test_users[0], test_items[0])) == float
res = model.predict(test_users, test_items, is_list=True)
assert type(res) == list
assert len(res) == len(test)
def test_train_loader(python_dataset_ncf):
train, test = python_dataset_ncf
data = Dataset(train=train,
test=test,
n_neg=N_NEG,
n_neg_test=N_NEG_TEST,
seed=SEED)
# collect positvie user-item dict
positive_pool = {}
for u in train[DEFAULT_USER_COL].unique():
positive_pool[u] = set(
train[train[DEFAULT_USER_COL] == u][DEFAULT_ITEM_COL])
# without negative sampling
for batch in data.train_loader(batch_size=BATCH_SIZE, shuffle=False):
user, item, labels = batch
# shape
assert len(user) == BATCH_SIZE
assert len(item) == BATCH_SIZE
assert len(labels) == BATCH_SIZE
assert max(labels) == min(labels)
# right labels
for u, i, is_pos in zip(user, item, labels):
if is_pos:
assert i in positive_pool[u]
else:
assert i not in positive_pool[u]
data.negative_sampling()
label_list = []
for idx, batch in enumerate(data.train_loader(batch_size=1)):
user, item, labels = batch
assert len(user) == 1
assert len(item) == 1
assert len(labels) == 1
# right labels
for u, i, is_pos in zip(user, item, labels):
if is_pos:
assert i in positive_pool[u]
else:
assert i not in positive_pool[u]
label_list.append(is_pos)
# neagtive smapling
assert len(label_list) == (N_NEG + 1) * sum(label_list)