def test_transform_fails_for_unknown_elements(sample_dataset):
users, items = sample_dataset[:, 0], sample_dataset[:, 1]
n = int(users.shape[0] / 4)
with pytest.raises(KeyError):
a_users, a_items = users[:n], items[:n]
b_users, b_items = users[n:], items[n:]
d = DatasetEncoder()
d.fit(users=a_users, items=a_items)
d.transform(users=b_users, items=b_items)
def test_to_df_fails_with_mismatched_inputs(sample_dataset):
users, items = sample_dataset[:, 0], sample_dataset[:, 1]
d = DatasetEncoder()
d.fit(users=users, items=items)
encoded = d.transform(users=users, items=items)
recommended = [
np.random.choice(encoded["items"], 10, replace=False)
for _ in range(int(len(users) / 2))
]
with pytest.raises(ValueError):
d.to_df(users, recommended)
def test_fit_reversible_user_item_transform(sample_dataset):
users, items = sample_dataset[:, 0], sample_dataset[:, 1]
d = DatasetEncoder()
d.fit(users=users, items=items)
r = d.inverse_transform(**d.transform(users=users, items=items))
assert r["users"].shape == users.shape
assert r["items"].shape == items.shape
assert np.all(r["users"] == users)
assert np.all(r["items"] == items)
def test_to_df_succeeds(sample_dataset):
users, items = sample_dataset[:, 0], sample_dataset[:, 1]
d = DatasetEncoder()
d.fit(users=users, items=items)
encoded = d.transform(users=users, items=items)
recommended = [
np.random.choice(encoded["items"], 10, replace=False) for _ in users
]
rdf = d.to_df(encoded["users"], recommended, target_col="user")
assert np.all(rdf["user"].values == users)
for i, row in enumerate(rdf.values[:, 1:]):
assert np.all(row == d.inverse_transform(
items=recommended[i])["items"])
def test_partial_fit_item_user_succeeds(sample_dataset):
users, items = sample_dataset[:, 0], sample_dataset[:, 1]
n = int(users.shape[0] / 2)
a_users, a_items = users[:n], items[:n]
b_users, b_items = users[n:], items[n:]
d = DatasetEncoder()
d.partial_fit(users=a_users, items=a_items)
d.partial_fit(users=b_users, items=b_items)
r = d.transform(users=users, items=items)
assert r["users"].shape == users.shape
assert r["items"].shape == items.shape
assert np.unique(r["users"]).shape == np.unique(users).shape
assert np.unique(r["items"]).shape == np.unique(items).shape
def test_inverse_fit_transform_using_metadata(sample_metadata_dataset):
users = sample_metadata_dataset[:, 0]
items = sample_metadata_dataset[:, 1]
user_meta = sample_metadata_dataset[:, 2]
item_meta = sample_metadata_dataset[:, 3]
d = DatasetEncoder()
d.fit(users=users, items=items, user_tags=user_meta, item_tags=item_meta)
r = d.inverse_transform(**d.transform(
users=users, items=items, user_tags=user_meta, item_tags=item_meta))
assert r["users"].shape == users.shape
assert r["items"].shape == items.shape
assert r["user_tags"].shape == user_meta.shape
assert r["item_tags"].shape == item_meta.shape
assert np.all(r["users"] == users)
assert np.all(r["items"] == items)
assert np.all(r["user_tags"] == user_meta)
assert np.all(r["item_tags"] == item_meta)