feat(score_fn): make score_fn as a TrainableBase

gnes/base/__init__.py

@@ -66,7 +66,8 @@ class TrainableType(type):
         'batch_size': None,
         'work_dir': os.environ.get('GNES_VOLUME', os.getcwd()),
         'name': None,
-        'on_gpu': False
+        'on_gpu': False,
+        'unnamed_warning': True
     }
 
     def __new__(cls, *args, **kwargs):
@@ -180,11 +181,12 @@ def _post_init_wrapper(self):
         if not getattr(self, 'name', None) and os.environ.get('GNES_WARN_UNNAMED_COMPONENT', '1') == '1':
             _id = str(uuid.uuid4()).split('-')[0]
             _name = '%s-%s' % (self.__class__.__name__, _id)
-            self.logger.warning(
-                'this object is not named ("name" is not found under "gnes_config" in YAML config), '
-                'i will call it "%s". '
-                'naming the object is important as it provides an unique identifier when '
-                'serializing/deserializing this object.' % _name)
+            if self.unnamed_warning:
+                self.logger.warning(
+                    'this object is not named ("name" is not found under "gnes_config" in YAML config), '
+                    'i will call it "%s". '
+                    'naming the object is important as it provides an unique identifier when '
+                    'serializing/deserializing this object.' % _name)
             setattr(self, 'name', _name)
 
         _before = set(list(self.__dict__.keys()))

gnes/indexer/base.py

@@ -22,8 +22,11 @@
 
 
 class BaseIndexer(TrainableBase):
-    normalize_fn = ModifierFn()
-    score_fn = ModifierFn()
+    def __init__(self, normalize_fn=ModifierFn(),
+                 score_fn=ModifierFn(), *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.normalize_fn = normalize_fn
+        self.score_fn = score_fn
 
     def add(self, keys: Any, docs: Any, weights: List[float], *args, **kwargs):
         pass

gnes/score_fn/base.py

@@ -4,6 +4,7 @@
 from operator import mul, add
 from typing import Sequence
 
+from ..base import TrainableBase
 from ..proto import gnes_pb2
 
 
@@ -16,7 +17,8 @@ def get_unary_score(value: float, **kwargs):
     return score
 
 
-class BaseScoreFn:
+class BaseScoreFn(TrainableBase):
+    unnamed_warning = False
 
     def __call__(self, *args, **kwargs) -> 'gnes_pb2.Response.QueryResponse.ScoredResult.Score':
         raise NotImplementedError
@@ -37,10 +39,11 @@ def op(self, *args, **kwargs) -> float:
 class ScoreCombinedFn(BaseScoreFn):
     """Combine multiple scores into one score, defaults to 'multiply'"""
 
-    def __init__(self, score_mode: str = 'multiply'):
+    def __init__(self, score_mode: str = 'multiply', *args, **kwargs):
         """
         :param score_mode: specifies how the computed scores are combined
         """
+        super().__init__(*args, **kwargs)
         if score_mode not in {'multiply', 'sum', 'avg', 'max', 'min'}:
             raise AttributeError('score_mode=%s is not supported!' % score_mode)
         self.score_mode = score_mode
@@ -66,7 +69,9 @@ class ModifierFn(BaseScoreFn):
     score = modifier(factor * value)
     """
 
-    def __init__(self, modifier: str = 'none', factor: float = 1.0, factor_name: str = 'GivenConstant'):
+    def __init__(self, modifier: str = 'none', factor: float = 1.0, factor_name: str = 'GivenConstant', *args,
+                 **kwargs):
+        super().__init__(*args, **kwargs)
         if modifier not in {'none', 'log', 'log1p', 'log2p', 'ln', 'ln1p', 'ln2p', 'square', 'sqrt', 'reciprocal',
                             'reciprocal1p', 'abs'}:
             raise AttributeError('modifier=%s is not supported!' % modifier)

gnes/score_fn/normalize.py

@@ -40,7 +40,7 @@ def __init__(self, num_bytes: int):
 class Normalizer5(ModifierFn):
     """Do normalizing: score = 1 / (1 + sqrt(abs(score)))"""
 
-    def __init__(self, num_dim: int):
+    def __init__(self):
         super().__init__()
         self.modifier = 'reciprocal1p'
 

tests/test_annoyindexer.py

@@ -25,3 +25,4 @@ def test_search(self):
         self.assertEqual(top_1, list(range(10)))
         a.close()
         a.dump()
+        a.dump_yaml()