Sample: do not check data by default; minor fixes

miplearn/features/sample.py

@@ -110,10 +110,12 @@ class MemorySample(Sample):
     def __init__(
         self,
         data: Optional[Dict[str, Any]] = None,
+        check_data: bool = False,
     ) -> None:
         if data is None:
             data = {}
         self._data: Dict[str, Any] = data
+        self._check_data = check_data
 
     @overrides
     def get_bytes(self, key: str) -> Optional[Bytes]:
@@ -142,19 +144,22 @@ class MemorySample(Sample):
     def put_scalar(self, key: str, value: Scalar) -> None:
         if value is None:
             return
-        self._assert_is_scalar(value)
+        if self._check_data:
+            self._assert_is_scalar(value)
         self._put(key, value)
 
     @overrides
     def put_vector(self, key: str, value: Vector) -> None:
         if value is None:
             return
-        self._assert_is_vector(value)
+        if self._check_data:
+            self._assert_is_vector(value)
         self._put(key, value)
 
     @overrides
     def put_vector_list(self, key: str, value: VectorList) -> None:
-        self._assert_is_vector_list(value)
+        if self._check_data:
+            self._assert_is_vector_list(value)
         self._put(key, value)
 
     def _get(self, key: str) -> Optional[Any]:
@@ -175,8 +180,14 @@ class Hdf5Sample(Sample):
     are actually accessed, and therefore it is more scalable.
     """
 
-    def __init__(self, filename: str, mode: str = "r+") -> None:
+    def __init__(
+        self,
+        filename: str,
+        mode: str = "r+",
+        check_data: bool = False,
+    ) -> None:
         self.file = h5py.File(filename, mode, libver="latest")
+        self._check_data = check_data
 
     @overrides
     def get_bytes(self, key: str) -> Optional[Bytes]:
@@ -230,27 +241,30 @@ class Hdf5Sample(Sample):
 
     @overrides
     def put_bytes(self, key: str, value: Bytes) -> None:
-        assert isinstance(
-            value, (bytes, bytearray)
-        ), f"bytes expected; found: {value}"  # type: ignore
+        if self._check_data:
+            assert isinstance(
+                value, (bytes, bytearray)
+            ), f"bytes expected; found: {value}"  # type: ignore
         self._put(key, np.frombuffer(value, dtype="uint8"), compress=True)
 
     @overrides
     def put_scalar(self, key: str, value: Any) -> None:
         if value is None:
             return
-        self._assert_is_scalar(value)
+        if self._check_data:
+            self._assert_is_scalar(value)
         self._put(key, value)
 
     @overrides
     def put_vector(self, key: str, value: Vector) -> None:
         if value is None:
             return
-        self._assert_is_vector(value)
+        if self._check_data:
+            self._assert_is_vector(value)
 
         for v in value:
             # Convert strings to bytes
-            if isinstance(v, str):
+            if isinstance(v, str) or v is None:
                 value = np.array(
                     [u if u is not None else b"" for u in value],
                     dtype="S",
@@ -266,7 +280,8 @@ class Hdf5Sample(Sample):
 
     @overrides
     def put_vector_list(self, key: str, value: VectorList) -> None:
-        self._assert_is_vector_list(value)
+        if self._check_data:
+            self._assert_is_vector_list(value)
         padded, lens = _pad(value)
         self.put_vector(f"{key}_lengths", lens)
         data = None
@@ -297,7 +312,6 @@ class Hdf5Sample(Sample):
 
 
 def _pad(veclist: VectorList) -> Tuple[VectorList, List[int]]:
-    veclist = deepcopy(veclist)
     lens = [len(v) if v is not None else -1 for v in veclist]
     maxlen = max(lens)