feat(encoder): add quantizer

gnes/encoder/numeric/quantizer.py

@@ -59,7 +59,7 @@ def _get_centroids(self):
                                      endpoint=False, retstep=False, dtype=None)[1:]
             coordinates = np.tile(axis_point, (self.dim_per_byte, 1))
         elif self.partition_method == 'random':
-            coordinates = np.random.randint(self.lower_bound, self.upper_bound,
+            coordinates = np.random.uniform(self.lower_bound, self.upper_bound,
                                                     size=[self.dim_per_byte, num_sample_per_dim])
         else:
             raise NotImplementedError
@@ -95,12 +95,12 @@ def _get_max_min_value(vecs):
 
     def _check_bound(self, max_value, min_value):
         if self.upper_bound < max_value:
-            self.logger.warning("upper bound (=%.3f) is smaller than max value of input data (=%.3f), you should choose"
+            raise Warning("upper bound (=%.3f) is smaller than max value of input data (=%.3f), you should choose"
                                 "a bigger value for upper bound" % (self.upper_bound, max_value))
         if self.lower_bound > min_value:
-            self.logger.warning("lower bound (=%.3f) is bigger than min value of input data (=%.3f), you should choose"
+            raise Warning("lower bound (=%.3f) is bigger than min value of input data (=%.3f), you should choose"
                                 "a smaller value for lower bound" % (self.lower_bound, min_value))
         if (self.upper_bound-self.lower_bound) >= 10*(max_value - min_value):
-            self.logger.warning("(upper bound - lower_bound) (=%.3f) is 10 times larger than (max value - min value) "
+            raise Warning("(upper bound - lower_bound) (=%.3f) is 10 times larger than (max value - min value) "
                                 "(=%.3f) of data, maybe you should choose a suitable bound" %
                                 ((self.upper_bound-self.lower_bound), (max_value - min_value)))

tests/test_quantizer_encoder.py

@@ -7,13 +7,22 @@
 
 class TestQuantizerEncoder(unittest.TestCase):
     def setUp(self):
-        self.vecs = np.random.randint(-150, 150, size=[1000, 160]).astype('float32')
         dirname = os.path.dirname(__file__)
         self.vanilla_quantizer_yaml = os.path.join(dirname, 'yaml', 'quantizer_encoder.yml')
 
     def test_vanilla_quantizer(self):
         encoder = BaseNumericEncoder.load_yaml(self.vanilla_quantizer_yaml)
         encoder.train()
-        out = encoder.encode(self.vecs)
-        print(out.shape)
+
+        vecs_1 = np.random.uniform(-150, 150, size=[1000, 160]).astype('float32')
+        out = encoder.encode(vecs_1)
+        self.assertEqual(len(out.shape), 2)
+        self.assertEqual(out.shape[0], 1000)
+        self.assertEqual(out.shape[1], 16)
+
+        vecs_2 = np.random.uniform(-1, 1, size=[1000, 160]).astype('float32')
+        self.assertRaises(Warning, encoder.encode, vecs_2)
+
+        vecs_3 = np.random.uniform(-1, 1000, size=[1000, 160]).astype('float32')
+        self.assertRaises(Warning, encoder.encode, vecs_3)
 

tests/yaml/quantizer_encoder.yml

@@ -1,7 +1,7 @@
 !QuantizerEncoder
 parameters:
-  upper_bound: 1000000
-  lower_bound: -100
+  upper_bound: 500
+  lower_bound: -200
   partition_method: 'random'
   cluster_per_byte: 255
   dim_per_byte: 10