[DelayedSample(Set)] make load and delayed_attributes private

bob/pipelines/sample.py

@@ -9,20 +9,20 @@ import numpy as np
 
 from bob.io.base import vstack_features
 
-SAMPLE_DATA_ATTRS = ("data", "load", "samples", "delayed_attributes")
+SAMPLE_DATA_ATTRS = ("data", "samples")
 
 
 def _copy_attributes(sample, parent, kwargs):
     """Copies attributes from a dictionary to self."""
     if parent is not None:
         for key in parent.__dict__:
-            if key in SAMPLE_DATA_ATTRS:
+            if key.startswith("__") or key in SAMPLE_DATA_ATTRS:
                 continue
 
             setattr(sample, key, getattr(parent, key))
 
     for key, value in kwargs.items():
-        if key in SAMPLE_DATA_ATTRS:
+        if key.startswith("__") or key in SAMPLE_DATA_ATTRS:
             continue
 
         setattr(sample, key, value)
@@ -120,24 +120,27 @@ class DelayedSample(_ReprMixin):
     """
 
     def __init__(self, load, parent=None, delayed_attributes=None, **kwargs):
-        self.delayed_attributes = delayed_attributes
         self.__running_init__ = True
+        self._delayed_attributes = delayed_attributes
         # create the delayed attributes but leave the their values as None for now.
         if delayed_attributes is not None:
             kwargs.update({k: None for k in delayed_attributes})
         _copy_attributes(self, parent, kwargs)
-        self.load = load
+        self._load = load
         del self.__running_init__
 
     def __getattribute__(self, name: str) -> Any:
-        delayed_attributes = super().__getattribute__("delayed_attributes")
+        try:
+            delayed_attributes = super().__getattribute__("_delayed_attributes")
+        except AttributeError:
+            delayed_attributes = None
         if delayed_attributes is None or name not in delayed_attributes:
             return super().__getattribute__(name)
         return delayed_attributes[name]()
 
     def __setattr__(self, name: str, value: Any) -> None:
         if name != "delayed_attributes" and "__running_init__" not in self.__dict__:
-            delayed_attributes = self.delayed_attributes
+            delayed_attributes = getattr(self, "_delayed_attributes", None)
             # if setting an attribute which was delayed, remove it from delayed_attributes
             if delayed_attributes is not None and name in delayed_attributes:
                 del delayed_attributes[name]
@@ -147,7 +150,7 @@ class DelayedSample(_ReprMixin):
     @property
     def data(self):
         """Loads the data from the disk file."""
-        return self.load()
+        return self._load()
 
 
 class SampleSet(MutableSequence, _ReprMixin):
@@ -178,12 +181,12 @@ class DelayedSampleSet(SampleSet):
     """A set of samples with extra attributes"""
 
     def __init__(self, load, parent=None, **kwargs):
-        self.load = load
+        self._load = load
         _copy_attributes(self, parent, kwargs)
 
     @property
     def samples(self):
-        return self.load()
+        return self._load()
 
 
 class SampleBatch(Sequence, _ReprMixin):

bob/pipelines/tests/test_wrappers.py

@@ -11,9 +11,10 @@ from sklearn.preprocessing import FunctionTransformer
 from sklearn.utils.estimator_checks import check_estimator
 from sklearn.utils.validation import check_array
 from sklearn.utils.validation import check_is_fitted
-from bob.pipelines.utils import hash_string
+
 import bob.pipelines as mario
-import tempfile
+
+from bob.pipelines.utils import hash_string
 
 
 def _offset_add_func(X, offset=1):
@@ -189,7 +190,7 @@ def test_checkpoint_function_sample_transfomer():
 
         features = transformer.transform(samples)
         # Checking if we have 8 chars in the second level
-        assert len(features[0].load.args[0].split("/")[-2]) == 8
+        assert len(features[0]._load.args[0].split("/")[-2]) == 8
         _assert_all_close_numpy_array(oracle, [s.data for s in features])
 
 

bob/pipelines/xarray.py

@@ -39,9 +39,9 @@ def _load_fn_to_xarray(load_fn, meta=None):
 
 def _one_sample_to_dataset(sample, meta=None):
     dataset = {}
-    delayed_attributes = getattr(sample, "delayed_attributes", None) or {}
+    delayed_attributes = getattr(sample, "_delayed_attributes", None) or {}
     for k in sample.__dict__:
-        if k in SAMPLE_DATA_ATTRS or k in delayed_attributes:
+        if k in SAMPLE_DATA_ATTRS or k in delayed_attributes or k.startswith("_"):
             continue
         dataset[k] = getattr(sample, k)