Remove fiddling with bob.pipelines.Samples's internals

bob/bio/base/database/csv_dataset.py

@@ -44,7 +44,6 @@ class CSVBaseSampleLoader(metaclass=ABCMeta):
         self.data_loader = data_loader
         self.extension = extension
         self.dataset_original_directory = dataset_original_directory
-        self.excluding_attributes = ["_data", "load", "key"]
 
     @abstractmethod
     def __call__(self, filename):
@@ -104,15 +103,6 @@ class CSVToSampleLoader(CSVBaseSampleLoader):
     def convert_samples_to_samplesets(
         self, samples, group_by_subject=True, references=None
     ):
-        def get_attribute_from_sample(sample):
-            return dict(
-                [
-                    [attribute, sample.__dict__[attribute]]
-                    for attribute in list(sample.__dict__.keys())
-                    if attribute not in self.excluding_attributes
-                ]
-            )
-
         if group_by_subject:
 
             # Grouping sample sets
@@ -120,7 +110,7 @@ class CSVToSampleLoader(CSVBaseSampleLoader):
             for s in samples:
                 if s.subject not in sample_sets:
                     sample_sets[s.subject] = SampleSet(
-                        [s], **get_attribute_from_sample(s)
+                        [s], parent=s, references=references
                     )
                 else:
                     sample_sets[s.subject].append(s)
@@ -128,7 +118,7 @@ class CSVToSampleLoader(CSVBaseSampleLoader):
 
         else:
             return [
-                SampleSet([s], **get_attribute_from_sample(s), references=references)
+                SampleSet([s], parent=s, references=references)
                 for s in samples
             ]
 
@@ -174,7 +164,7 @@ class CSVDatasetDevEval:
        path_i,subject_j
        ...
 
-    
+
     You might want to ship metadata within your Samples (e.g gender, age, annotation, ...)
     To do so is simple, just do as below:
 
@@ -189,7 +179,7 @@ class CSVDatasetDevEval:
 
     The files `my_dataset/my_protocol/train.csv/eval_enroll.csv` and `my_dataset/my_protocol/train.csv/eval_probe.csv`
     are optional and it is used in case a protocol contains data for evaluation.
-    
+
     Finally, the content of the file `my_dataset/my_protocol/train.csv` is used in the case a protocol
     contains data for training (`bob.bio.base.pipelines.vanilla_biometrics.Database.background_model_samples`)
 
@@ -329,7 +319,7 @@ class CSVDatasetDevEval:
 
 class CSVDatasetCrossValidation:
     """
-    Generic filelist dataset for :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline` pipeline that 
+    Generic filelist dataset for :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline` pipeline that
     handles **CROSS VALIDATION**.
 
     Check :any:`vanilla_biometrics_features` for more details about the Vanilla Biometrics Dataset

bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py

@@ -3,7 +3,7 @@
 
 
 from abc import ABCMeta, abstractmethod
-from bob.pipelines.sample import Sample, SampleSet, DelayedSample
+from bob.pipelines.sample import SAMPLE_DATA_ATTRS, Sample, SampleSet, DelayedSample
 import functools
 import numpy as np
 import os
@@ -211,13 +211,7 @@ class BioAlgorithm(metaclass=ABCMeta):
 
                 scores_biometric_references.append(Sample(score, parent=ref))
 
-        # Fetching metadata from the probe
-        kwargs = dict(
-            (metadata, sampleset.__dict__[metadata])
-            for metadata in sampleset.__dict__.keys()
-            if metadata not in ["samples", "key", "data", "load", "_data"]
-        )
-        return SampleSet(scores_biometric_references, parent=sampleset, **kwargs)
+        return SampleSet(scores_biometric_references, parent=sampleset)
 
     @abstractmethod
     def score(self, biometric_reference, data):

bob/bio/base/pipelines/vanilla_biometrics/score_writers.py

@@ -4,6 +4,7 @@
 
 import os
 from bob.pipelines import SampleSet, DelayedSample
+from bob.pipelines.sample import SAMPLE_DATA_ATTRS
 from .abstract_classes import ScoreWriter
 import functools
 import csv
@@ -74,7 +75,7 @@ class CSVScoreWriter(ScoreWriter):
         self,
         path,
         n_sample_sets=1000,
-        exclude_list=["samples", "key", "data", "load", "_data", "references", "annotations"],
+        exclude_list=tuple(SAMPLE_DATA_ATTRS) + ("key", "references", "annotations"),
     ):
         super().__init__(path)
         self.n_sample_sets = n_sample_sets
@@ -92,13 +93,13 @@ class CSVScoreWriter(ScoreWriter):
             probe_dict = dict(
                 (k, f"probe_{k}")
                 for k in probe_sampleset.__dict__.keys()
-                if k not in self.exclude_list
+                if not (k in self.exclude_list or k.startswith("__"))
             )
 
             bioref_dict = dict(
                 (k, f"bio_ref_{k}")
                 for k in first_biometric_reference.__dict__.keys()
-                if k not in self.exclude_list
+                if not (k in self.exclude_list or k.startswith("__"))
             )
 
             header = (
@@ -130,7 +131,7 @@ class CSVScoreWriter(ScoreWriter):
 
             rows = []
             probe_row = [str(probe.key)] + [
-                str(probe.__dict__[k]) for k in probe_dict.keys()
+                str(getattr(probe, k)) for k in probe_dict.keys()
             ]
 
             # If it's delayed, load it
@@ -139,7 +140,7 @@ class CSVScoreWriter(ScoreWriter):
 
             for biometric_reference in probe:
                 bio_ref_row = [
-                    str(biometric_reference.__dict__[k])
+                    str(getattr(biometric_reference, k))
                     for k in list(bioref_dict.keys()) + ["data"]
                 ]
 
@@ -182,4 +183,4 @@ class CSVScoreWriter(ScoreWriter):
         if isinstance(score_paths, dask.bag.Bag):
             all_paths = dask.delayed(list)(score_paths)
             return dask.delayed(_post_process)(all_paths, path)
-        return _post_process(score_paths, path)        
+        return _post_process(score_paths, path)

bob/bio/base/test/test_vanilla_biometrics_score_norm.py

@@ -288,7 +288,7 @@ def test_norm_mechanics():
             z_normed_scores = _dump_scores_from_samples(
                 z_normed_score_samples, shape=(n_probes, n_references)
             )
-            assert np.allclose(z_normed_scores, z_normed_scores_ref)
+            np.testing.assert_allclose(z_normed_scores, z_normed_scores_ref)
 
             ############
             # TESTING T-NORM