def create_dataloader_fn(
number_features, category_features, sequence_features, batch_size,
train_df, label_col='label', test_df=None, num_workers=0):
features = Features(
number_features=number_features,
category_features=category_features,
sequence_features=sequence_features)
features = features.fit(train_df)
train_X_map = features.transform(train_df)
train_y = train_df[label_col].values
train_dataset = Dataset(features, train_X_map, train_y)
train_loader = data.DataLoader(
train_dataset, batch_size=batch_size,
shuffle=True, num_workers=num_workers)
test_loader = None
if test_df is not None:
test_X_map = features.transform(test_df)
test_y = None
if label_col in set(test_df.columns):
test_y = test_df[label_col].values
test_dataset = Dataset(features, test_X_map, test_y)
test_loader = data.DataLoader(
test_dataset, batch_size=batch_size,
shuffle=False, num_workers=num_workers)
return features, train_loader, test_loader
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
