def test_ml1m() -> None:
if sys.platform == "win32":
pytest.skip("Skip on Windows.")
try:
with ZipFile(ZIPFILE_NAME, "w") as zf:
with zf.open("ml-1m/ratings.dat", "w") as ofs:
ofs.write("""1::2::5::0
1::3::5::86400
""".encode())
with zf.open("ml-1m/movies.dat", "w") as ofs:
ofs.write("""1::A fantastic movie (2020)::fantasy|thriller
1917::Vinni-Pukh(1969)::children
""".encode("latin-1"))
with zf.open("ml-1m/users.dat", "w") as ofs:
ofs.write("""1::M::32::0::1690074
2::F::4::1::1760013
""".encode())
loader = MovieLens1MDataManager(ZIPFILE_NAME)
df = loader.read_interaction()
movie_info = loader.read_item_info()
user_info = loader.read_user_info()
assert df.shape == (2, 4)
np.testing.assert_array_equal(df["userId"].values, [1, 1])
np.testing.assert_array_equal(df["movieId"].values, [2, 3])
np.testing.assert_array_equal(df["rating"].values, [5, 5])
np.testing.assert_array_equal(
df["timestamp"].values,
np.asarray(
[
"1970-01-01",
"1970-01-02",
],
dtype="datetime64[ns]",
),
)
np.testing.assert_array_equal(movie_info.index.values, [1, 1917])
np.testing.assert_array_equal(movie_info.release_year, [2020, 1969])
np.testing.assert_array_equal(user_info.index.values, [1, 2])
np.testing.assert_array_equal(user_info.gender, ["M", "F"])
finally:
ZIPFILE_NAME.unlink()
from irspack.evaluator import Evaluator
from irspack.optimizers import ( # BPRFMOptimizer, #requires lightFM; MultVAEOptimizer, #requires jax & haiku & optax; SLIMOptimizer,
AsymmetricCosineKNNOptimizer, BaseOptimizer, CosineKNNOptimizer,
DenseSLIMOptimizer, IALSOptimizer, P3alphaOptimizer, RP3betaOptimizer,
TopPopOptimizer, TverskyIndexKNNOptimizer,
)
from irspack.split import split_dataframe_partial_user_holdout
os.environ["OMP_NUM_THREADS"] = "8"
os.environ["IRSPACK_NUM_THREADS_DEFAULT"] = "8"
if __name__ == "__main__":
BASE_CUTOFF = 20
data_manager = MovieLens1MDataManager()
df_all = data_manager.read_interaction()
data_all, _ = split_dataframe_partial_user_holdout(
df_all,
"userId",
"movieId",
test_user_ratio=0.2,
val_user_ratio=0.2,
heldout_ratio_test=0.5,
heldout_ratio_val=0.5,
)
data_train = data_all["train"]
data_val = data_all["val"]
data_test = data_all["test"]
UserCBCosineKNNOptimizer,
UserColdStartEvaluator,
)
from irspack.utils.encoders import (
BinningEncoder,
CategoricalValueEncoder,
DataFrameEncoder,
)
os.environ["OMP_NUM_THREADS"] = "8"
os.environ["IRSPACK_NUM_THREADS_DEFAULT"] = "8"
if __name__ == "__main__":
BASE_CUTOFF = 20
loader = MovieLens1MDataManager()
user_df = loader.read_user_info()
ratings = loader.read_interaction()
user_ids_unique: List[int] = np.unique(ratings.userId)
movie_ids_unique: List[int] = np.unique(ratings.movieId)
movie_id_to_index = {id_: i for i, id_ in enumerate(movie_ids_unique)}
def df_to_sparse(df: pd.DataFrame) -> Tuple[UserIndexArray, sps.csr_matrix]:
unique_uids, row = np.unique(df.userId, return_inverse=True)
col = df.movieId.map(movie_id_to_index)
return (
unique_uids,
sps.csr_matrix(
(np.ones(df.shape[0]), (row, col)),
shape=(len(unique_uids), len(movie_id_to_index)),
),