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)
wide_features = ['title', 'genres']
deep_features = ['clickedMovieIds', 'clickedMovieTopGenres']
dataloader, _ = prepare_dataloader(features)
model = WideDeep(features,
wide_features,
deep_features, [],
num_classes=2,
embedding_size=4,
hidden_layers=(8, 4),
final_activation='sigmoid',
dropout=0.3)
model(next(iter(dataloader)))
def test_without_number_feature():
number_features = []
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_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)
deep_fm = DeepFM(features,
num_classes=2,
embedding_size=4,
hidden_layers=(8, 4),
final_activation='sigmoid',
dropout=0.3)
deep_fm(next(iter(dataloader)))
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))
]
attention_groups = [
AttentionGroup(name='group1',
pairs=[{
'ad': 'movieId',
'pos_hist': 'clickedMovieIds'
}, {
'ad': 'topGenre',
'pos_hist': 'clickedMovieTopGenres'
}],
hidden_layers=[8, 4])
]
features = Features(number_features=number_features,
category_features=category_features,
sequence_features=sequence_features)
dataloader = prepare_dataloader(features)
model = DIN(features,
attention_groups=attention_groups,
num_classes=2,
embedding_size=4,
hidden_layers=(16, 8),
final_activation='sigmoid',
dropout=0.3)
model(next(iter(dataloader)))
def test_column_flow_define():
number_features = [
Number('age', None),
Number('height', ColumnFlow([StandardScaler()]))
]
category_features = [
Category('gender', ColumnFlow([CategoryEncoder(min_cnt=1)]))
]
sequence_features = [
Sequence('likes', ColumnFlow([SequenceEncoder(sep=' ', min_cnt=1)]))
]
features = Features(number_features, category_features, sequence_features)
features.fit(__TEST_DATA)
actual = features.transform(__TEST_DATA)
expected_age = np.array([23, 43, 35, 41, 16, 32, 26, 76])
expected_gender = np.array([2, 2, 1, 2, 1, 1, 2, 2])
expected_height = np.array([
0.1159659, 0.85814767, -0.99730676, -0.06957954, -1.73948853,
-0.34789771, 0.48705679, 1.69310217
])
assert len(actual) == 4
assert features.number_feature_names() == ['age', 'height']
assert features.category_feature_names() == ['gender']
assert features.sequence_feature_names() == ['likes']
np.testing.assert_array_equal(actual['age'], expected_age)
np.testing.assert_array_equal(actual['gender'], expected_gender)
np.testing.assert_array_almost_equal(actual['height'], expected_height)
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)
features.fit(__SAMPLE_DF)
X_map = features.transform(__SAMPLE_DF)
dataset = Dataset(features, X_map, __SAMPLE_DF.label.values)
assert dataset[0]['userId'] == 1
assert dataset[0]['movieId'] == 1
assert dataset[0]['genres'].tolist() == [8, 9, 0, 0]
assert dataset[0]['__genres_length'] == 2
assert dataset[0]['label'] == 1
def create_test_data():
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)),
Sequence('noClickedMovieIds',
SequenceEncoder(sep='|', min_cnt=1, max_len=5)),
Sequence('noClickedMovieTopGenres',
SequenceEncoder(sep='|', min_cnt=1, max_len=5))
]
attention_groups = [
AttentionGroup(name='group1',
pairs=[{
'ad': 'movieId',
'pos_hist': 'clickedMovieIds',
'neg_hist': 'noClickedMovieIds'
}, {
'ad': 'topGenre',
'pos_hist': 'clickedMovieTopGenres',
'neg_hist': 'noClickedMovieTopGenres'
}],
hidden_layers=[8, 4])
]
features = Features(number_features=number_features,
category_features=category_features,
sequence_features=sequence_features)
dataloader = prepare_dataloader(features)
return dataloader, features, attention_groups
def create_test_data_with_sharing_emb():
number_features = [
Number('userAge', StandardScaler()),
Number('rating', StandardScaler())
]
# provide word to index mapping
movie_word2idx = {
'__PAD__': 0,
'4226': 1,
'5971': 2,
'6291': 3,
'7153': 4,
'30707': 5,
'3242': 6,
'42': 7,
'32': 8,
'34': 9,
'233': 10,
'291': 11,
'324': 12,
'325': 13,
'3542': 14,
'322': 15,
'33': 16,
'45': 17,
'__UNKNOWN__': 18
}
movie_idx2word = {index: word for word, index in movie_word2idx.items()}
category_features = [
Category(
'movieId',
CategoryEncoder(word2idx=movie_word2idx, idx2word=movie_idx2word)),
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='|',
max_len=5,
word2idx=movie_word2idx,
idx2word=movie_idx2word)),
Sequence(
'noClickedMovieIds',
SequenceEncoder(sep='|',
max_len=5,
word2idx=movie_word2idx,
idx2word=movie_idx2word))
]
attention_groups = [
AttentionGroup(name='group1',
pairs=[{
'ad': 'movieId',
'pos_hist': 'clickedMovieIds',
'neg_hist': 'noClickedMovieIds'
}],
hidden_layers=[8, 4])
]
embedding_ref = EmbeddingRef({
'clickedMovieIds': 'movieId',
'noClickedMovieIds': 'movieId'
})
features = Features(number_features=number_features,
category_features=category_features,
sequence_features=sequence_features)
dataloader = prepare_dataloader(features)
return dataloader, features, attention_groups, embedding_ref
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)))