feat(score_fn): use numpy for score fn

gnes/score_fn/base.py

@@ -14,11 +14,10 @@
 #  limitations under the License.
 
 import json
-from functools import reduce
-from math import log, log1p, log10, sqrt
-from operator import mul, add
 from typing import Sequence
 
+import numpy as np
+
 from ..base import TrainableBase
 from ..proto import gnes_pb2
 
@@ -66,19 +65,19 @@ def __init__(self, score_mode: str = 'multiply', *args, **kwargs):
     @property
     def supported_ops(self):
         return {
-            'multiply': lambda v: reduce(mul, v),
-            'sum': lambda v: reduce(add, v),
-            'max': lambda v: reduce(max, v),
-            'min': lambda v: reduce(min, v),
-            'avg': lambda v: reduce(add, v) / len(v),
+            'multiply': np.prod,
+            'sum': np.sum,
+            'max': np.max,
+            'min': np.min,
+            'avg': np.mean,
         }
 
     def post_init(self):
         self.op = self.supported_ops[self.score_mode]
 
     def __call__(self, *last_scores) -> 'gnes_pb2.Response.QueryResponse.ScoredResult.Score':
         return self.new_score(
-            value=self.op(s.value for s in last_scores),
+            value=self.op([s.value for s in last_scores]),
             operands=last_scores,
             score_mode=self.score_mode)
 
@@ -102,17 +101,17 @@ def __init__(self, modifier: str = 'none', factor: float = 1.0, factor_name: str
     def supported_ops(self):
         return {
             'none': lambda x: x,
-            'log': log10,
-            'log1p': lambda x: log(x + 1, 10),
-            'log2p': lambda x: log(x + 2, 10),
-            'ln': log,
-            'ln1p': log1p,
-            'ln2p': lambda x: log(x + 2),
-            'square': lambda x: x * x,
-            'sqrt': sqrt,
-            'reciprocal': lambda x: 1 / x,
-            'reciprocal1p': lambda x: 1 / (1 + x),
-            'abs': abs,
+            'log': np.log10,
+            'log1p': lambda x: np.log10(x + 1),
+            'log2p': lambda x: np.log10(x + 2),
+            'ln': np.log,
+            'ln1p': np.log1p,
+            'ln2p': lambda x: np.log(x + 2),
+            'square': np.square,
+            'sqrt': np.sqrt,
+            'reciprocal': np.reciprocal,
+            'reciprocal1p': lambda x: np.reciprocal(1 + x),
+            'abs': np.abs,
             'invert': lambda x: - x,
             'invert1p': lambda x: 1 - x
         }