update 2.0.7

daisy/__init__.py

@@ -1,11 +1,11 @@
-from . import utils
-from . import model
-from . import config
+from daisy import utils
+from daisy import model
+from daisy import config
 
 
 __all__ = [
     'config', 'utils', 'model'
 ]
 
 
-__version__ = 'v2.0.6'
+__version__ = 'v2.0.7'

daisy/config/__init__.py

@@ -1,3 +1,3 @@
 from __future__ import absolute_import
 from __future__ import print_function
-from __future__ import division
+from __future__ import division

daisy/model/AbstractRecommender.py

@@ -4,8 +4,9 @@
 import torch.nn as nn
 import torch.optim as optim
 
-from ..utils.config import initializer_param_config, initializer_config
-from ..utils.loss import BPRLoss, TOP1Loss, HingeLoss
+from daisy.utils.config import initializer_param_config, initializer_config
+from daisy.utils.loss import BPRLoss, TOP1Loss, HingeLoss
+
 
 class AbstractRecommender(nn.Module):
     def __init__(self):

daisy/model/EASERecommender.py

@@ -12,6 +12,7 @@
 
 from daisy.model.AbstractRecommender import GeneralRecommender
 
+
 class EASE(GeneralRecommender):
     def __init__(self, config):
         super(EASE, self).__init__(config)

daisy/model/FMRecommender.py

@@ -13,6 +13,7 @@
 
 from daisy.model.AbstractRecommender import GeneralRecommender
 
+
 class FM(GeneralRecommender):
     def __init__(self, config):
         """

daisy/model/KNNCFRecommender.py

@@ -28,6 +28,7 @@
 
 from daisy.model.AbstractRecommender import GeneralRecommender
 
+
 def convert_df(user_num, item_num, df):
     """ 
     Convert DataFrame to make matrix to make similarity calculation available

daisy/model/MFRecommender.py

@@ -16,12 +16,12 @@
   year={2012}
 }
 '''
-
 import torch
 import torch.nn as nn
 
 from daisy.model.AbstractRecommender import GeneralRecommender
 
+
 class MF(GeneralRecommender):
     def __init__(self, config):
         """

daisy/model/NFMRecommender.py

@@ -13,6 +13,7 @@
 from daisy.model.AbstractRecommender import GeneralRecommender
 from daisy.utils.config import initializer_param_config, initializer_config
 
+
 class NFM(GeneralRecommender):
     def __init__(self, config):
         """

daisy/model/NGCFRecommender.py

@@ -12,7 +12,8 @@
 import torch.nn.functional as F
 
 from daisy.model.AbstractRecommender import GeneralRecommender
-from daisy.utils.config import initializer_param_config, initializer_config
+from daisy.utils.config import initializer_config
+
 
 class NGCF(GeneralRecommender):
     def __init__(self, config):

daisy/model/NeuMFRecommender.py

@@ -13,6 +13,7 @@
 from daisy.model.AbstractRecommender import GeneralRecommender
 from daisy.utils.config import initializer_param_config, initializer_config
 
+
 class NeuMF(GeneralRecommender):
     def __init__(self, config):
         """

daisy/model/PopRecommender.py

@@ -12,6 +12,7 @@
 
 from daisy.model.AbstractRecommender import GeneralRecommender
 
+
 class MostPop(GeneralRecommender):
     def __init__(self, config):
         """

daisy/model/PureSVDRecommender.py

@@ -14,6 +14,7 @@
 
 from daisy.model.AbstractRecommender import GeneralRecommender
 
+
 class PureSVD(GeneralRecommender):
     def __init__(self, config):
         """

daisy/model/SLiMRecommender.py

@@ -23,6 +23,7 @@
 
 from daisy.model.AbstractRecommender import GeneralRecommender
 
+
 class SLiM(GeneralRecommender):
     def __init__(self, config):
         """

daisy/model/__init__.py

@@ -1,17 +1,17 @@
-from .EASERecommender import EASE
-from .FMRecommender import FM
-from .Item2VecRecommender import Item2Vec
-from .KNNCFRecommender import ItemKNNCF
-from .MFRecommender import MF
-from .NeuMFRecommender import NeuMF
-from .NFMRecommender import NFM
-from .NGCFRecommender import NGCF
-from .PopRecommender import MostPop
-from .PureSVDRecommender import PureSVD
-from .SLiMRecommender import SLiM
-from .VAECFRecommender import VAECF
+from daisy.model.EASERecommender import EASE
+from daisy.model.FMRecommender import FM
+from daisy.model.Item2VecRecommender import Item2Vec
+from daisy.model.KNNCFRecommender import ItemKNNCF
+from daisy.model.MFRecommender import MF
+from daisy.model.NeuMFRecommender import NeuMF
+from daisy.model.NFMRecommender import NFM
+from daisy.model.NGCFRecommender import NGCF
+from daisy.model.PopRecommender import MostPop
+from daisy.model.PureSVDRecommender import PureSVD
+from daisy.model.SLiMRecommender import SLiM
+from daisy.model.VAECFRecommender import VAECF
 
 __all__ = [
     'EASE', 'FM', 'Item2Vec', 'ItemKNNCF', 'MF', 'NeuMF', 'NFM', 'NGCF', 'MostPop',
     'PureSVD', 'SLiM', 'VAECF',
-]
+]

daisy/utils/__init__.py

@@ -1,11 +1,11 @@
-from .config import *
-from .dataset import *
-from .loader import *
-from .metrics import *
-from .parser import *
-from .sampler import *
-from .splitter import *
-from .utils import *
+from daisy.utils.config import *
+from daisy.utils.dataset import *
+from daisy.utils.loader import *
+from daisy.utils.metrics import *
+from daisy.utils.parser import *
+from daisy.utils.sampler import *
+from daisy.utils.splitter import *
+from daisy.utils.utils import *
 
 __all__ = [
     'log_colors_config', 'tune_params_config', 'param_type_config', 'metrics_config',
@@ -15,4 +15,4 @@
     'parse_args', 'Metric', 'Coverage', 'Popularity', 'Diversity', 'Precision', 'Recall',
     'MRR', 'MAP', 'NDCG', 'HR', 'AUC', 'F1', 'RawDataReader', 'Preprocessor', 'get_dataloader',
     'BasicDataset', 'CandidatesDataset', 'AEDataset'
-]
+]

daisy/utils/config.py

@@ -10,22 +10,22 @@
 import torch
 import torch.nn as nn
 
-from ..model.KNNCFRecommender import ItemKNNCF
-from ..model.PureSVDRecommender import PureSVD
-from ..model.SLiMRecommender import SLiM
-from ..model.PopRecommender import MostPop
-from ..model.MFRecommender import MF
-from ..model.FMRecommender import FM
-from ..model.Item2VecRecommender import Item2Vec
-from ..model.NeuMFRecommender import NeuMF
-from ..model.NFMRecommender import NFM
-from ..model.NGCFRecommender import NGCF
-from ..model.VAECFRecommender import VAECF
-from ..model.EASERecommender import EASE
-
-from .metrics import Precision, Recall, NDCG, MRR, MAP, HR, F1, AUC, Coverage, Diversity, Popularity
-from .parser import parse_args
-from .utils import ensure_dir, get_local_time
+from daisy.model.KNNCFRecommender import ItemKNNCF
+from daisy.model.PureSVDRecommender import PureSVD
+from daisy.model.SLiMRecommender import SLiM
+from daisy.model.PopRecommender import MostPop
+from daisy.model.MFRecommender import MF
+from daisy.model.FMRecommender import FM
+from daisy.model.Item2VecRecommender import Item2Vec
+from daisy.model.NeuMFRecommender import NeuMF
+from daisy.model.NFMRecommender import NFM
+from daisy.model.NGCFRecommender import NGCF
+from daisy.model.VAECFRecommender import VAECF
+from daisy.model.EASERecommender import EASE
+
+from daisy.utils.metrics import Precision, Recall, NDCG, MRR, MAP, HR, F1, AUC, Coverage, Diversity, Popularity
+from daisy.utils.parser import parse_args
+from daisy.utils.utils import ensure_dir, get_local_time
 
 log_colors_config = {
     'DEBUG': 'cyan',

daisy/utils/dataset.py

@@ -1,10 +1,12 @@
 import torch
 from torch.utils.data import Dataset, DataLoader
 
+
 def get_dataloader(ds, batch_size, shuffle, num_workers=4):  
     return DataLoader(
         ds, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
 
+
 class BasicDataset(Dataset):
     def __init__(self, samples):
         '''

daisy/utils/loader.py

@@ -8,7 +8,8 @@
 import scipy.io as sio
 from collections import Counter
 
-from .utils import ensure_dir
+from daisy.utils.utils import ensure_dir
+
 
 class RawDataReader(object):
     def __init__(self, config):

daisy/utils/loss.py

@@ -1,6 +1,7 @@
 import torch
 import torch.nn as nn
 
+
 class BPRLoss(nn.Module):
     def __init__(self, gamma=1e-10):
         super(BPRLoss, self).__init__()
@@ -11,6 +12,7 @@ def forward(self, pos_score, neg_score):
 
         return loss
 
+
 class HingeLoss(nn.Module):
     def __init__(self):
         super(HingeLoss, self).__init__()
@@ -20,6 +22,7 @@ def forward(self, pos_score, neg_score):
 
         return loss
 
+
 class TOP1Loss(nn.Module):
     def __init__(self):
         super(TOP1Loss, self).__init__()

daisy/utils/metrics.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from .config import metrics_config
+from daisy.utils.config import metrics_config
 
 class Metric(object):
     def __init__(self, config) -> None:

daisy/utils/utils.py

@@ -7,8 +7,8 @@
 import scipy.sparse as sp
 from collections import defaultdict
 
-from .metrics import Metric
-from .config import metrics_name_config
+from daisy.utils.metrics import Metric
+from daisy.utils.config import metrics_name_config
 
 def ensure_dir(path):
     if not os.path.exists(path):

setup.py

@@ -3,7 +3,6 @@
 
 classifiers = ["License :: OSI Approved :: MIT License"]
 
-
 long_description = 'daisyRec is a Python toolkit developed for benchmarking top-N recommendation task.' \
                    'The name DAISY stands for multi-Dimension fAirly comparIson for recommender SYstem.'
 
@@ -38,24 +37,24 @@
 print('=============================================')
 
 setup(
-    name = 'daisyRec',
-    packages = [package for package in find_packages() if package.startswith('daisy')],
+    name='daisyRec',
+    packages=[package for package in find_packages() if package.startswith('daisy')],
     # packages = find_packages(exclude=['tests*']),
-    version = 'v2.0.6',  # Ideally should be same as your GitHub release tag varsion
+    version='v2.0.7',  # Ideally should be same as your GitHub release tag varsion
     description=('An easy-to-use library for recommender systems.'),
-    long_description = long_description,
+    long_description=long_description,
     # long_description_content_type="text/markdown",
-    author = 'Yu Di',
-    author_email = '[email protected]',
-    url = 'https://github.com/AmazingDD/daisyRec',
-    download_url = 'https://github.com/AmazingDD/daisyRec/archive/refs/tags/v2.0.6.tar.gz',
-    keywords = ['ranking', 'recommendation'],
+    author='Yu Di',
+    author_email='[email protected]',
+    url='https://github.com/AmazingDD/daisyRec',
+    download_url='https://github.com/AmazingDD/daisyRec/archive/refs/tags/v2.0.7.tar.gz',
+    keywords=['ranking', 'recommendation'],
     include_package_data=True,
     install_requires=install_requires,
     setup_requires=setup_requires,
     extras_require=extras_require,
     zip_safe=False,
-    classifiers = classifiers,
+    classifiers=classifiers,
 )
 
-# python setup.py sdist upload -r pypi
+# python setup.py sdist upload -r pypi