Merge pull request #482 from ShanleiMu/master

FEA: update setuptools

conda/build.sh

@@ -0,0 +1 @@
+$PYTHON setup.py install

conda/meta.yaml

@@ -0,0 +1,39 @@
+package:
+  name: recbole
+  version: 0.1.0
+
+source:
+  path: ../
+
+requirements:
+  build:
+    - python
+  host:
+    - python
+    - numpy >=1.17.2
+    - scipy >=1.3.1
+    - pandas >=1.0.5
+    - tqdm >=4.48.2
+    - pyyaml >=5.1.0
+    - matplotlib >=3.1.3
+    - scikit-learn >=0.23.2
+    - pytorch
+  run:
+    - python
+    - numpy >=1.17.2
+    - scipy >=1.3.1
+    - pandas >=1.0.5
+    - tqdm >=4.48.2
+    - pyyaml >=5.1.0
+    - matplotlib >=3.1.3
+    - scikit-learn >=0.23.2
+    - pytorch
+
+test:
+  imports:
+    - recbole
+
+about:
+  home: https://recbole.io/
+  license: MIT
+  summary: "A unified, comprehensive and efficient recommendation library"

recbole/trainer/hyper_tuning.py

@@ -10,19 +10,13 @@
 """
 
 import numpy as np
-
-import hyperopt
 from functools import partial
-from hyperopt import fmin, tpe, hp, pyll
-from hyperopt.base import miscs_update_idxs_vals
-from hyperopt.pyll.base import dfs, as_apply
-from hyperopt.pyll.stochastic import implicit_stochastic_symbols
-from hyperopt.pyll.base import Apply
 
 from recbole.utils.utils import dict2str
 
 
 def _recursiveFindNodes(root, node_type='switch'):
+    from hyperopt.pyll.base import Apply
     nodes = []
     if isinstance(root, (list, tuple)):
         for node in root:
@@ -75,6 +69,8 @@ class ExhaustiveSearchError(Exception):
 
 
 def _validate_space_exhaustive_search(space):
+    from hyperopt.pyll.base import dfs, as_apply
+    from hyperopt.pyll.stochastic import implicit_stochastic_symbols
     supported_stochastic_symbols = ['randint', 'quniform', 'qloguniform', 'qnormal', 'qlognormal', 'categorical']
     for node in dfs(as_apply(space)):
         if node.name in implicit_stochastic_symbols:
@@ -87,6 +83,8 @@ def exhaustive_search(new_ids, domain, trials, seed, nbMaxSucessiveFailures=1000
     r""" This is for exhaustive search in HyperTuning.
 
     """
+    from hyperopt import pyll
+    from hyperopt.base import miscs_update_idxs_vals
     # Build a hash set for previous trials
     hashset = set([hash(frozenset([(key, value[0]) if len(value) > 0 else ((key, None))
                                    for key, value in trial['misc']['vals'].items()])) for trial in trials.trials])
@@ -139,7 +137,7 @@ class HyperTuning(object):
     """
 
     def __init__(self, objective_function, space=None, params_file=None, fixed_config_file_list=None,
-                 algo=tpe.suggest, max_evals=100):
+                 algo='exhaustive', max_evals=100):
         self.best_score = None
         self.best_params = None
         self.best_test_result = None
@@ -165,6 +163,7 @@ def __init__(self, objective_function, space=None, params_file=None, fixed_confi
 
     @staticmethod
     def _build_space_from_file(file):
+        from hyperopt import hp
         space = {}
         with open(file, 'r') as fp:
             for line in fp:
@@ -230,6 +229,7 @@ def trial(self, params):
         Args:
             params (dict): the parameter dictionary
         """
+        import hyperopt
         config_dict = params.copy()
         params_str = self.params2str(params)
         print('running parameters:', config_dict)
@@ -261,4 +261,5 @@ def run(self):
         r""" begin to search the best parameters
 
         """
+        from hyperopt import fmin
         fmin(self.trial, self.space, algo=self.algo, max_evals=self.max_evals)

setup.py

@@ -13,6 +13,17 @@
 
 extras_require = {}
 
+classifiers = ["License :: OSI Approved :: MIT License"]
+
+long_description = 'RecBole is developed based on Python and PyTorch for ' \
+                   'reproducing and developing recommendation algorithms in ' \
+                   'a unified, comprehensive and efficient framework for ' \
+                   'research purpose. In the first version, Our library ' \
+                   'includes 53 recommendation algorithms, covering four ' \
+                   'major categories: General Recommendation, Sequential ' \
+                   'Recommendation, Context-aware Recommendation and ' \
+                   'Knowledge-based Recommendation.'
+
 # Readthedocs requires Sphinx extensions to be specified as part of
 # install_requires in order to build properly.
 on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
@@ -23,7 +34,9 @@
     name='recbole',
     version=
     '0.1.0',  # please remember to edit recbole/__init__.py in response, once updating the version
-    description='A package for building recommender systems',
+    description='A unified, comprehensive and efficient recommendation library',
+    long_description=long_description,
+    long_description_content_type="text/markdown",
     url='https://github.com/RUCAIBox/RecBole',
     author='RecBoleTeam',
     author_email='[email protected]',
@@ -35,4 +48,6 @@
     install_requires=install_requires,
     setup_requires=setup_requires,
     extras_require=extras_require,
-    zip_safe=False)
+    zip_safe=False,
+    classifiers=classifiers,
+)