def test_predict(
similarity_type, timedecay_formula, train_test_dummy_timestamp, header
):
model = SARSingleNode(
similarity_type=similarity_type, timedecay_formula=timedecay_formula, **header
)
trainset, testset = train_test_dummy_timestamp
model.fit(trainset)
preds = model.predict(testset)
assert len(preds) == 2
assert isinstance(preds, pd.DataFrame)
assert preds[header["col_user"]].dtype == trainset[header["col_user"]].dtype
assert preds[header["col_item"]].dtype == trainset[header["col_item"]].dtype
assert preds[DEFAULT_PREDICTION_COL].dtype == trainset[header["col_rating"]].dtype
def test_predict_all_items(train_test_dummy_timestamp, header):
model = SARSingleNode(**header)
trainset, _ = train_test_dummy_timestamp
model.fit(trainset)
user_items = itertools.product(
trainset[header["col_user"]].unique(), trainset[header["col_item"]].unique()
)
testset = pd.DataFrame(user_items, columns=[header["col_user"], header["col_item"]])
preds = model.predict(testset)
assert len(preds) == len(testset)
assert isinstance(preds, pd.DataFrame)
assert preds[header["col_user"]].dtype == trainset[header["col_user"]].dtype
assert preds[header["col_item"]].dtype == trainset[header["col_item"]].dtype
assert preds[DEFAULT_PREDICTION_COL].dtype == trainset[header["col_rating"]].dtype
def test_predict(similarity_type, timedecay_formula,
train_test_dummy_timestamp, header):
model = SARSingleNode(remove_seen=True,
similarity_type=similarity_type,
timedecay_formula=timedecay_formula,
**header)
trainset, testset = train_test_dummy_timestamp
_apply_sar_hash_index(model, trainset, testset, header)
model.fit(trainset)
preds = model.predict(testset)
assert len(preds) == 2
assert isinstance(preds, pd.DataFrame)
assert preds[header["col_user"]].dtype == object
assert preds[header["col_item"]].dtype == object
assert preds[PREDICTION_COL].dtype == float