def test_get_normalized_scores(header):
train = pd.DataFrame({
header["col_user"]: [1, 1, 1, 1, 2, 2, 2, 2],
header["col_item"]: [1, 2, 3, 4, 1, 5, 6, 7],
header["col_rating"]: [3.0, 4.0, 5.0, 4.0, 3.0, 2.0, 1.0, 5.0],
header["col_timestamp"]: [1, 20, 30, 400, 50, 60, 70, 800],
})
test = pd.DataFrame({
header["col_user"]: [1, 1, 1, 2, 2, 2],
header["col_item"]: [5, 6, 7, 2, 3, 4],
header["col_rating"]: [2.0, 1.0, 5.0, 3.0, 4.0, 5.0],
})
model = SARSingleNode(**header, timedecay_formula=True, normalize=True)
model.fit(train)
actual = model.score(test, remove_seen=True)
expected = np.array([
[
-np.inf, -np.inf, -np.inf, -np.inf, 1.23512374, 1.23512374,
1.23512374
],
[
-np.inf, 1.23512374, 1.23512374, 1.23512374, -np.inf, -np.inf,
-np.inf
],
])
assert actual.shape == (2, 7)
assert isinstance(actual, np.ndarray)
assert np.isclose(expected, np.asarray(actual)).all()
actual = model.score(test)
expected = np.array([
[
3.11754872,
4.29408577,
4.29408577,
4.29408577,
1.23512374,
1.23512374,
1.23512374,
],
[
2.5293308,
1.23511758,
1.23511758,
1.23511758,
3.11767458,
3.11767458,
3.11767458,
],
])
assert actual.shape == (2, 7)
assert isinstance(actual, np.ndarray)
assert np.isclose(expected, np.asarray(actual)).all()
def test_get_item_based_topk(header, pandas_dummy):
sar = SARSingleNode(**header)
sar.fit(pandas_dummy)
# test with just items provided
expected = pd.DataFrame(
dict(UserId=[0, 0, 0], MovieId=[8, 7, 6], prediction=[2.0, 2.0, 2.0])
)
items = pd.DataFrame({header["col_item"]: [1, 5, 10]})
actual = sar.get_item_based_topk(items, top_k=3)
assert_frame_equal(expected, actual, check_dtype=False)
# test with items and users
expected = pd.DataFrame(
dict(
UserId=[100, 100, 100, 1, 1, 1],
MovieId=[8, 7, 6, 4, 3, 10],
prediction=[2.0, 2.0, 2.0, 2.0, 2.0, 1.0],
)
)
items = pd.DataFrame(
{
header["col_user"]: [100, 100, 1, 100, 1, 1],
header["col_item"]: [1, 5, 1, 10, 2, 6],
}
)
actual = sar.get_item_based_topk(items, top_k=3, sort_top_k=True)
assert_frame_equal(expected, actual, check_dtype=False)
# test with items, users, and ratings
expected = pd.DataFrame(
dict(
UserId=[100, 100, 100, 1, 1, 1],
MovieId=[2, 4, 3, 4, 3, 10],
prediction=[5.0, 5.0, 5.0, 8.0, 8.0, 4.0],
)
).set_index(["UserId", "MovieId"])
items = pd.DataFrame(
{
header["col_user"]: [100, 100, 1, 100, 1, 1],
header["col_item"]: [1, 5, 1, 10, 2, 6],
header["col_rating"]: [5, 1, 3, 1, 5, 4],
}
)
actual = sar.get_item_based_topk(items, top_k=3).set_index(["UserId", "MovieId"])
assert_frame_equal(expected, actual, check_like=True)
def test_fit(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)
train, test = python_random_split(data, 0.75)
# instantiate the SAR algorithm and set the index
header = {
"col_user": "******",
"col_item": "MovieId",
"col_rating": "Rating",
"col_timestamp": "Timestamp",
}
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)-8s %(message)s')
model = SARSingleNode(remove_seen=True,
similarity_type="jaccard",
time_decay_coefficient=30,
time_now=None,
timedecay_formula=True,
**header)
# train the SAR model
start_time = time.time()
model.fit(train)
train_time = time.time() - start_time
run.log(name="Training time", value=train_time)
start_time = time.time()
top_k = model.recommend_k_items(test)
def train_sar(params, data):
model = SARSingleNode(**params)
model.set_index(data)
with Timer() as t:
model.fit(data)
return model, t
def train_sar(params, data):
model = SARSingleNode(**params)
model.set_index(data)
with Timer() as t:
model.fit(data)
return model, t
