def test_normal():
number_features = [
Number('userAge', StandardScaler()),
Number('rating', StandardScaler())
]
category_features = [
Category('userId', CategoryEncoder(min_cnt=1)),
Category('movieId', CategoryEncoder(min_cnt=1)),
Category('topGenre', CategoryEncoder(min_cnt=1))
]
sequence_features = [
Sequence('title', SequenceEncoder(sep='|', min_cnt=1)),
Sequence('genres', SequenceEncoder(sep='|', min_cnt=1)),
Sequence('clickedMovieIds',
SequenceEncoder(sep='|', min_cnt=1, max_len=5)),
Sequence('clickedMovieTopGenres',
SequenceEncoder(sep='|', min_cnt=1, max_len=5))
]
features = Features(number_features=number_features,
category_features=category_features,
sequence_features=sequence_features)
dataloader = prepare_dataloader(features)
model = DNN(features,
num_classes=2,
embedding_size=4,
hidden_layers=(8, 4),
final_activation='sigmoid',
dropout=0.3)
model(next(iter(dataloader)))
def test_without_category_feature():
number_features = []
category_features = []
sequence_features = [
Sequence('title', SequenceEncoder(sep='|', min_cnt=1)),
Sequence('genres', SequenceEncoder(sep='|', min_cnt=1)),
Sequence('clickedMovieIds',
SequenceEncoder(sep='|', min_cnt=1, max_len=5)),
Sequence('clickedMovieTopGenres',
SequenceEncoder(sep='|', min_cnt=1, max_len=5))
]
features = Features(number_features=number_features,
category_features=category_features,
sequence_features=sequence_features)
dataloader = prepare_dataloader(features)
model = DNN(features,
num_classes=2,
embedding_size=4,
hidden_layers=(8, 4),
final_activation='sigmoid',
dropout=0.3)
model(next(iter(dataloader)))
def test_only_with_number_features():
number_features = [
Number('userAge', StandardScaler()),
Number('rating', StandardScaler())
]
category_features = []
sequence_features = []
features = Features(number_features=number_features,
category_features=category_features,
sequence_features=sequence_features)
dataloader = prepare_dataloader(features)
model = DNN(features,
num_classes=2,
embedding_size=4,
hidden_layers=(8, 4),
final_activation='sigmoid',
dropout=0.3)
model(next(iter(dataloader)))
def test_shared_embedding():
number_features = []
movie_enc = SequenceEncoder(sep='|', min_cnt=1, max_len=5)
genre_enc = SequenceEncoder(sep='|', min_cnt=1, max_len=5)
movie_enc.fit(
np.concatenate(
(_SAMPLE_DF.clickedMovieIds.values, _SAMPLE_DF.movieId.values),
axis=None))
genre_enc.fit(
np.concatenate((_SAMPLE_DF.clickedMovieTopGenres.values,
_SAMPLE_DF.topGenre.values),
axis=None))
category_features = [
Category('userId', CategoryEncoder(min_cnt=1)),
Category('movieId',
CategoryEncoder(min_cnt=1,
word2idx=movie_enc.word2idx,
idx2word=movie_enc.idx2word),
embedding_name='movieId'),
Category('topGenre',
CategoryEncoder(min_cnt=1,
word2idx=genre_enc.word2idx,
idx2word=genre_enc.idx2word),
embedding_name='topGenre',
embedding_size=8)
]
sequence_features = [
Sequence('title', SequenceEncoder(sep='|', min_cnt=1)),
Sequence('genres', SequenceEncoder(sep='|', min_cnt=1)),
Sequence('clickedMovieIds',
SequenceEncoder(sep='|',
min_cnt=1,
max_len=5,
word2idx=movie_enc.word2idx,
idx2word=movie_enc.idx2word),
embedding_name='movieId'),
Sequence('clickedMovieTopGenres',
SequenceEncoder(sep='|',
min_cnt=1,
max_len=5,
word2idx=genre_enc.word2idx,
idx2word=genre_enc.idx2word),
embedding_name='topGenre',
embedding_size=8)
]
features = Features(number_features=number_features,
category_features=category_features,
sequence_features=sequence_features)
dataloader, _ = prepare_dataloader(features)
model = DNN(features,
num_classes=2,
embedding_size=16,
hidden_layers=(8, 4),
final_activation='sigmoid',
dropout=0.3)
model(next(iter(dataloader)))