breaking: rename numpy_to_record_serializer to RecordSerializer (#1690)

Author: bveeramani

src/sagemaker/amazon/common.py

@@ -22,31 +22,37 @@
 
 from sagemaker.amazon.record_pb2 import Record
 from sagemaker.deserializers import BaseDeserializer
+from sagemaker.serializers import BaseSerializer
 from sagemaker.utils import DeferredError
 
 
-class numpy_to_record_serializer(object):
-    """Placeholder docstring"""
+class RecordSerializer(BaseSerializer):
+    """Serialize a NumPy array for an inference request."""
 
-    def __init__(self, content_type="application/x-recordio-protobuf"):
-        """
-        Args:
-            content_type:
-        """
-        self.content_type = content_type
+    CONTENT_TYPE = "application/x-recordio-protobuf"
+
+    def serialize(self, data):
+        """Serialize a NumPy array into a buffer containing RecordIO records.
 
-    def __call__(self, array):
-        """
         Args:
-            array:
+            data (numpy.ndarray): The data to serialize.
+
+        Returns:
+            io.BytesIO: A buffer containing the data serialized as records.
         """
-        if len(array.shape) == 1:
-            array = array.reshape(1, array.shape[0])
-        assert len(array.shape) == 2, "Expecting a 1 or 2 dimensional array"
-        buf = io.BytesIO()
-        write_numpy_to_dense_tensor(buf, array)
-        buf.seek(0)
-        return buf
+        if len(data.shape) == 1:
+            data = data.reshape(1, data.shape[0])
+
+        if len(data.shape) != 2:
+            raise ValueError(
+                "Expected a 1D or 2D array, but got a %dD array instead." % len(data.shape)
+            )
+
+        buffer = io.BytesIO()
+        write_numpy_to_dense_tensor(buffer, data)
+        buffer.seek(0)
+
+        return buffer
 
 
 class RecordDeserializer(BaseDeserializer):

src/sagemaker/amazon/factorization_machines.py

@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
-from sagemaker.amazon.common import numpy_to_record_serializer, RecordDeserializer
+from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import gt, isin, ge
 from sagemaker.predictor import Predictor
@@ -289,7 +289,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
         super(FactorizationMachinesPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=numpy_to_record_serializer(),
+            serializer=RecordSerializer(),
             deserializer=RecordDeserializer(),
         )
 

src/sagemaker/amazon/kmeans.py

@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
-from sagemaker.amazon.common import numpy_to_record_serializer, RecordDeserializer
+from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import gt, isin, ge, le
 from sagemaker.predictor import Predictor
@@ -222,7 +222,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
         super(KMeansPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=numpy_to_record_serializer(),
+            serializer=RecordSerializer(),
             deserializer=RecordDeserializer(),
         )
 

src/sagemaker/amazon/knn.py

@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
-from sagemaker.amazon.common import numpy_to_record_serializer, RecordDeserializer
+from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, isin
 from sagemaker.predictor import Predictor
@@ -210,7 +210,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
         super(KNNPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=numpy_to_record_serializer(),
+            serializer=RecordSerializer(),
             deserializer=RecordDeserializer(),
         )
 

src/sagemaker/amazon/lda.py

@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
-from sagemaker.amazon.common import numpy_to_record_serializer, RecordDeserializer
+from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import gt
 from sagemaker.predictor import Predictor
@@ -194,7 +194,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
         super(LDAPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=numpy_to_record_serializer(),
+            serializer=RecordSerializer(),
             deserializer=RecordDeserializer(),
         )
 

src/sagemaker/amazon/linear_learner.py

@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
-from sagemaker.amazon.common import numpy_to_record_serializer, RecordDeserializer
+from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import isin, gt, lt, ge, le
 from sagemaker.predictor import Predictor
@@ -453,7 +453,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
         super(LinearLearnerPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=numpy_to_record_serializer(),
+            serializer=RecordSerializer(),
             deserializer=RecordDeserializer(),
         )
 

src/sagemaker/amazon/ntm.py

@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
-from sagemaker.amazon.common import numpy_to_record_serializer, RecordDeserializer
+from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, le, isin
 from sagemaker.predictor import Predictor
@@ -224,7 +224,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
         super(NTMPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=numpy_to_record_serializer(),
+            serializer=RecordSerializer(),
             deserializer=RecordDeserializer(),
         )
 

src/sagemaker/amazon/pca.py

@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
-from sagemaker.amazon.common import numpy_to_record_serializer, RecordDeserializer
+from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import gt, isin
 from sagemaker.predictor import Predictor
@@ -206,7 +206,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
         super(PCAPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=numpy_to_record_serializer(),
+            serializer=RecordSerializer(),
             deserializer=RecordDeserializer(),
         )
 

src/sagemaker/amazon/randomcutforest.py

@@ -14,7 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
-from sagemaker.amazon.common import numpy_to_record_serializer, RecordDeserializer
+from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, le
 from sagemaker.predictor import Predictor
@@ -183,7 +183,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
         super(RandomCutForestPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=numpy_to_record_serializer(),
+            serializer=RecordSerializer(),
             deserializer=RecordDeserializer(),
         )
 

tests/unit/test_common.py

@@ -21,26 +21,26 @@
     RecordDeserializer,
     write_numpy_to_dense_tensor,
     read_recordio,
-    numpy_to_record_serializer,
+    RecordSerializer,
     write_spmatrix_to_sparse_tensor,
 )
 from sagemaker.amazon.record_pb2 import Record
 
 
 def test_serializer():
-    s = numpy_to_record_serializer()
+    s = RecordSerializer()
     array_data = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]]
-    buf = s(np.array(array_data))
+    buf = s.serialize(np.array(array_data))
     for record_data, expected in zip(read_recordio(buf), array_data):
         record = Record()
         record.ParseFromString(record_data)
         assert record.features["values"].float64_tensor.values == expected
 
 
 def test_serializer_accepts_one_dimensional_array():
-    s = numpy_to_record_serializer()
+    s = RecordSerializer()
     array_data = [1.0, 2.0, 3.0]
-    buf = s(np.array(array_data))
+    buf = s.serialize(np.array(array_data))
     record_data = next(read_recordio(buf))
     record = Record()
     record.ParseFromString(record_data)
@@ -49,8 +49,8 @@ def test_serializer_accepts_one_dimensional_array():
 
 def test_deserializer():
     array_data = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]]
-    s = numpy_to_record_serializer()
-    buf = s(np.array(array_data))
+    s = RecordSerializer()
+    buf = s.serialize(np.array(array_data))
     d = RecordDeserializer()
     for record, expected in zip(d.deserialize(buf, "who cares"), array_data):
         assert record.features["values"].float64_tensor.values == expected