Merge branch 'sphinx' into 'master'

Fixing sphinx warnings

See merge request !208

bob/bio/base/__init__.py

@@ -4,16 +4,19 @@ from . import preprocessor
 from . import extractor
 from . import algorithm
 from . import annotator
+from . import pipelines
 
 from . import script
 from . import test
 
+
 def get_config():
-  """Returns a string containing the configuration information.
+    """Returns a string containing the configuration information.
   """
-  import bob.extension
-  return bob.extension.get_config(__name__)
+    import bob.extension
+
+    return bob.extension.get_config(__name__)
 
 
 # gets sphinx autodoc done right - don't remove it
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]

bob/bio/base/database/__init__.py

-from .csv_dataset import CSVDatasetDevEval, CSVToSampleLoader, CSVDatasetCrossValidation
+from .csv_dataset import (
+    CSVDatasetDevEval,
+    CSVToSampleLoader,
+    CSVDatasetCrossValidation,
+    CSVBaseSampleLoader,
+)
 from .file import BioFile
 from .file import BioFileSet
 from .database import BioDatabase
@@ -9,7 +14,7 @@ from . import filelist
 
 # gets sphinx autodoc done right - don't remove it
 def __appropriate__(*args):
-  """Says object was actually declared here, and not in the import module.
+    """Says object was actually declared here, and not in the import module.
   Fixing sphinx warnings of not being able to find classes, when path is shortened.
   Parameters:
 
@@ -19,17 +24,18 @@ def __appropriate__(*args):
   <https://github.com/sphinx-doc/sphinx/issues/3048>`
   """
 
-  for obj in args:
-    obj.__module__ = __name__
+    for obj in args:
+        obj.__module__ = __name__
 
 
 __appropriate__(
     BioFile,
     BioFileSet,
     BioDatabase,
-    ZTBioDatabase,	
-	  CSVDatasetDevEval,
+    ZTBioDatabase,
+    CSVBaseSampleLoader,
+    CSVDatasetDevEval,
     CSVToSampleLoader,
-    CSVDatasetCrossValidation
+    CSVDatasetCrossValidation,
 )
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]

bob/bio/base/database/csv_dataset.py

@@ -67,25 +67,24 @@ class CSVToSampleLoader(CSVBaseSampleLoader):
     :any:`bob.pipelines.DelayedSample` or :any:`bob.pipelines.SampleSet`
     """
 
-    def __call__(self, filename):
-        def check_header(header):
-            """
-            A header should have at least "SUBJECT" AND "PATH"
-            """
-            header = [h.lower() for h in header]
-            if not "subject" in header:
-                raise ValueError(
-                    "The field `subject` is not available in your dataset."
-                )
+    def check_header(self, header):
+        """
+        A header should have at least "SUBJECT" AND "PATH"
+        """
+        header = [h.lower() for h in header]
+        if not "subject" in header:
+            raise ValueError("The field `subject` is not available in your dataset.")
 
-            if not "path" in header:
-                raise ValueError("The field `path` is not available in your dataset.")
+        if not "path" in header:
+            raise ValueError("The field `path` is not available in your dataset.")
+
+    def __call__(self, filename):
 
         with open(filename) as cf:
             reader = csv.reader(cf)
             header = next(reader)
 
-            check_header(header)
+            self.check_header(header)
             return [self.convert_row_to_sample(row, header) for row in reader]
 
     def convert_row_to_sample(self, row, header):
@@ -136,8 +135,8 @@ class CSVToSampleLoader(CSVBaseSampleLoader):
 
 class CSVDatasetDevEval:
     """
-    Generic filelist dataset for :any:`bob.bio.base.pipelines.VanillaBiometrics` pipeline.
-    Check :ref:`vanilla_biometrics_features` for more details about the Vanilla Biometrics Dataset
+    Generic filelist dataset for :any:` bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline` pipeline.
+    Check :any:`vanilla_biometrics_features` for more details about the Vanilla Biometrics Dataset
     interface.
 
     To create a new dataset, you need to provide a directory structure similar to the one below:
@@ -163,8 +162,8 @@ class CSVDatasetDevEval:
 
 
     Those csv files should contain in each row i-) the path to raw data and ii-) the subject label
-    for enrollment (:ref:`bob.bio.base.pipelines.vanilla_biometrics.abstract_classes.Database.references`) and
-    probing (:ref:`bob.bio.base.pipelines.vanilla_biometrics.abstract_classes.Database.probes`).
+    for enrollment (:any:`bob.bio.base.pipelines.vanilla_biometrics.Database.references`) and
+    probing (:any:`bob.bio.base.pipelines.vanilla_biometrics.Database.probes`).
     The structure of each CSV file should be as below:
 
     .. code-block:: text
@@ -192,7 +191,7 @@ class CSVDatasetDevEval:
     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 (:ref:`bob.bio.base.pipelines.vanilla_biometrics.abstract_classes.Database.background_model_samples`)
+    contains data for training (`bob.bio.base.pipelines.vanilla_biometrics.Database.background_model_samples`)
 
     Parameters
     ----------
@@ -203,8 +202,8 @@ class CSVDatasetDevEval:
         protocol_na,e: str
           The name of the protocol
 
-        csv_to_sample_loader: :any:`CSVBaseSampleLoader`
-            Base class that whose objective is to generate :any:`bob.pipelines.Samples`
+        csv_to_sample_loader: :any:`bob.bio.base.database.CSVBaseSampleLoader`
+            Base class that whose objective is to generate :any:`bob.pipelines.Sample`
             and/or :any:`bob.pipelines.SampleSet` from csv rows
 
     """
@@ -330,10 +329,10 @@ class CSVDatasetDevEval:
 
 class CSVDatasetCrossValidation:
     """
-    Generic filelist dataset for :any:`bob.bio.base.pipelines.VanillaBiometrics` pipeline that 
+    Generic filelist dataset for :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline` pipeline that 
     handles **CROSS VALIDATION**.
 
-    Check :ref:`vanilla_biometrics_features` for more details about the Vanilla Biometrics Dataset
+    Check :any:`vanilla_biometrics_features` for more details about the Vanilla Biometrics Dataset
     interface.
 
 
@@ -365,8 +364,8 @@ class CSVDatasetCrossValidation:
     samples_for_enrollment: float
       Number of samples used for enrollment
 
-    csv_to_sample_loader: :any:`CSVBaseSampleLoader`
-        Base class that whose objective is to generate :any:`bob.pipelines.Samples`
+    csv_to_sample_loader: :any:`bob.bio.base.database.CSVBaseSampleLoader`
+        Base class that whose objective is to generate :any:`bob.pipelines.Sample`
         and/or :any:`bob.pipelines.SampleSet` from csv rows
 
     """

bob/bio/base/database/file.py

@@ -2,6 +2,7 @@ import bob.db.base
 from bob.db.base.annotations import read_annotation_file
 from bob.pipelines.sample import _ReprMixin
 
+
 class BioFile(bob.db.base.File, _ReprMixin):
     """
     A simple base class that defines basic properties of File object for the use
@@ -29,7 +30,7 @@ class BioFile(bob.db.base.File, _ReprMixin):
         The extension of annotation files. Default is ``.json``
     annotation_type : str or None
         The type of the annotation file, see
-        :any:`bob.db.base.annotations.read_annotation_file`. Default is
+        :`bob.db.base.annotations.read_annotation_file`. Default is
         ``json``.
     """
 
@@ -82,14 +83,14 @@ class BioFile(bob.db.base.File, _ReprMixin):
         if original_extension is None:
             original_extension = self.original_extension
         # get the path
-        path = self.make_path(
-            original_directory or "", original_extension or ""
-        )
+        path = self.make_path(original_directory or "", original_extension or "")
         return bob.io.base.load(path)
 
     @property
     def annotations(self):
-        path = self.make_path(self.annotation_directory or "", self.annotation_extension or "")
+        path = self.make_path(
+            self.annotation_directory or "", self.annotation_extension or ""
+        )
         return read_annotation_file(path, annotation_type=self.annotation_type)
 
 

bob/bio/base/pipelines/__init__.py

 # see https://docs.python.org/3/library/pkgutil.html
 from pkgutil import extend_path
 
+
+from . import vanilla_biometrics
+
 __path__ = extend_path(__path__, __name__)

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

@@ -8,11 +8,59 @@ from .wrappers import (
     dask_vanilla_biometrics,
     checkpoint_vanilla_biometrics,
     dask_get_partition_size,
-    is_checkpointed
+    is_checkpointed,
 )
 
-from .zt_norm import ZTNormPipeline, ZTNormDaskWrapper, ZTNormCheckpointWrapper
+from .abstract_classes import BioAlgorithm, Database, ScoreWriter
+
+from .zt_norm import ZTNormPipeline, ZTNormDaskWrapper, ZTNormCheckpointWrapper, ZTNorm
 
 from .legacy import BioAlgorithmLegacy, DatabaseConnector
 
 from .vanilla_biometrics import execute_vanilla_biometrics
+
+
+# gets sphinx autodoc done right - don't remove it
+def __appropriate__(*args):
+    """Says object was actually declared here, and not in the import module.
+    Fixing sphinx warnings of not being able to find classes, when path is
+    shortened.
+
+    Parameters
+    ----------
+    *args
+        An iterable of objects to modify
+
+    Resolves `Sphinx referencing issues
+    <https://github.com/sphinx-doc/sphinx/issues/3048>`
+    """
+
+    for obj in args:
+        obj.__module__ = __name__
+
+
+__appropriate__(
+    VanillaBiometricsPipeline,
+    Distance,
+    FourColumnsScoreWriter,
+    CSVScoreWriter,
+    BioAlgorithmCheckpointWrapper,
+    BioAlgorithmDaskWrapper,
+    dask_vanilla_biometrics,
+    checkpoint_vanilla_biometrics,
+    dask_get_partition_size,
+    is_checkpointed,
+    ZTNormPipeline,
+    ZTNormDaskWrapper,
+    ZTNormCheckpointWrapper,
+    BioAlgorithmLegacy,
+    DatabaseConnector,
+    execute_vanilla_biometrics,
+    BioAlgorithm,
+    Database,
+    ScoreWriter,
+    ZTNorm,
+)
+
+__all__ = [_ for _ in dir() if not _.startswith("_")]
+

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

@@ -21,14 +21,14 @@ class Distance(BioAlgorithm):
 
         Enrolls the model by storing all given input vectors.
 
-        Parameters:
-        -----------
+        Parameters
+        ----------
 
         ``enroll_features`` : [:py:class:`numpy.ndarray`]
           The list of projected features to enroll the model from.
 
-        Returns:
-        --------
+        Returns
+        -------
 
         ``model`` : 2D :py:class:`numpy.ndarray`
           The enrolled model.
@@ -43,8 +43,8 @@ class Distance(BioAlgorithm):
 
         Computes the distance of the model to the probe using the distance function specified in the constructor.
 
-        Parameters:
-        -----------
+        Parameters
+        ----------
 
         ``model`` : 2D :py:class:`numpy.ndarray`
           The model storing all enrollment features
@@ -52,8 +52,8 @@ class Distance(BioAlgorithm):
         ``probe`` : :py:class:`numpy.ndarray`
           The probe feature vector
 
-        Returns:
-        --------
+        Returns
+        -------
 
         ``score`` : float
           A similarity value between ``model`` and ``probe``

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

@@ -52,7 +52,7 @@ class DatabaseConnector(Database):
 
     protocol : str
         The name of the protocol to generate samples from.
-        To be plugged at :py:method:`bob.db.base.Database.objects`.
+        To be plugged at `bob.db.base.Database.objects`.
 
     allow_scoring_with_all_biometric_references: bool
         If True will allow the scoring function to be performed in one shot with multiple probes.
@@ -200,7 +200,7 @@ class BioAlgorithmLegacy(BioAlgorithm):
     -------
         >>> from bob.bio.base.pipelines.vanilla_biometrics import BioAlgorithmLegacy
         >>> from bob.bio.base.algorithm import PCA
-        >>> biometric_algorithm = BioAlgorithmLegacy(PCA())
+        >>> biometric_algorithm = BioAlgorithmLegacy(PCA(subspace_dimension=0.99), base_dir="./", projector_file="Projector.hdf5")
 
     """
 
@@ -294,7 +294,7 @@ class BioAlgorithmLegacy(BioAlgorithm):
 
     def write_scores(self, samples, path):
         os.makedirs(os.path.dirname(path), exist_ok=True)
-        #open(path, "wb").write(pickle.dumps(samples))
+        # open(path, "wb").write(pickle.dumps(samples))
         joblib.dump(samples, path, compress=4)
 
     def _score_sample_set(
@@ -304,7 +304,7 @@ class BioAlgorithmLegacy(BioAlgorithm):
         allow_scoring_with_all_biometric_references=False,
     ):
         def _load(path):
-            #return pickle.loads(open(path, "rb").read())
+            # return pickle.loads(open(path, "rb").read())
             return joblib.load(path)
 
         def _make_name(sampleset, biometric_references):
@@ -315,7 +315,8 @@ class BioAlgorithmLegacy(BioAlgorithm):
             return name + suffix
 
         path = os.path.join(
-            self.score_dir, _make_name(sampleset, biometric_references) + self._score_extension
+            self.score_dir,
+            _make_name(sampleset, biometric_references) + self._score_extension,
         )
 
         if self.force or not os.path.exists(path):
@@ -329,8 +330,9 @@ class BioAlgorithmLegacy(BioAlgorithm):
 
             self.write_scores(scored_sample_set.samples, path)
 
-            scored_sample_set = DelayedSampleSet(functools.partial(_load, path),
-                                                 parent=scored_sample_set)
+            scored_sample_set = DelayedSampleSet(
+                functools.partial(_load, path), parent=scored_sample_set
+            )
 
         else:
             scored_sample_set = SampleSet(_load(path), parent=sampleset)

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

@@ -43,25 +43,28 @@ class VanillaBiometricsPipeline(object):
 
     Example
     -------
+       >>> from bob.pipelines.transformers import Linearize
        >>> from sklearn.pipeline import make_pipeline
-       >>> from bob.bio.base.pipelines.vanilla_biometrics.implemented import Distance
-       >>> transformer = make_pipeline(estimator_1, estimator_2)
+       >>> from bob.bio.base.pipelines.vanilla_biometrics import Distance, VanillaBiometricsPipeline
+       >>> estimator_1 = Linearize()
+       >>> transformer = make_pipeline(estimator_1)
        >>> biometric_algoritm = Distance()
-       >>> pipeline = VanillaBiometrics(transformer, biometric_algoritm)
-       >>> pipeline(samples_for_training_back_ground_model, samplesets_for_enroll, samplesets_for_scoring)
+       >>> pipeline = VanillaBiometricsPipeline(transformer, biometric_algoritm)
+       >>> pipeline(samples_for_training_back_ground_model, samplesets_for_enroll, samplesets_for_scoring)  # doctest: +SKIP
 
 
     To run this pipeline using Dask, used the function :py:func:`dask_vanilla_biometrics`.
 
     Example
     -------
-      >>> pipeline = VanillaBiometrics(transformer, biometric_algoritm)
+      >>> from bob.bio.base.pipelines.vanilla_biometrics import dask_vanilla_biometrics
+      >>> pipeline = VanillaBiometricsPipeline(transformer, biometric_algoritm)
       >>> pipeline = dask_vanilla_biometrics(pipeline)
-      >>> pipeline(samples_for_training_back_ground_model, samplesets_for_enroll, samplesets_for_scoring).compute()
+      >>> pipeline(samples_for_training_back_ground_model, samplesets_for_enroll, samplesets_for_scoring).compute()  # doctest: +SKIP
 
 
-    Parameters:
-    -----------
+    Parameters
+    ----------
 
       transformer: :py:class`sklearn.pipeline.Pipeline` or a `sklearn.base.BaseEstimator`
         Transformer that will preprocess your data
@@ -69,8 +72,8 @@ class VanillaBiometricsPipeline(object):
       biometric_algorithm: :py:class:`bob.bio.base.pipelines.vanilla_biometrics.abstract_classes.BioAlgorithm`
         Biometrics algorithm object that implements the methods `enroll` and `score` methods
 
-      score_writer: :any:`bob.bio.base.pipelines.vanilla_biometrics.abstract_classe.ScoreWriter`
-          Format to write scores. Default to :any:`FourColumnsScoreWriter`
+      score_writer: :any:`bob.bio.base.pipelines.vanilla_biometrics.ScoreWriter`
+          Format to write scores. Default to :any:`bob.bio.base.pipelines.vanilla_biometrics.FourColumnsScoreWriter`
 
     """
 

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

@@ -17,7 +17,6 @@ from bob.pipelines.utils import isinstance_nested
 logger = logging.getLogger(__name__)
 
 
-
 def compute_scores(result, dask_client):
     if isinstance(result, Delayed) or isinstance(result, dask.bag.Bag):
         if dask_client is not None:
@@ -33,7 +32,6 @@ def post_process_scores(pipeline, scores, path):
     return pipeline.post_process(written_scores, path)
 
 
-
 def execute_vanilla_biometrics(
     pipeline,
     database,

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

@@ -31,8 +31,8 @@ class BioAlgorithmCheckpointWrapper(BioAlgorithm):
 
     Parameters
     ----------
-        biometric_algorithm: :any:`BioAlgorithm`
-           An implemented :any:`BioAlgorithm`
+        biometric_algorithm: :any:`bob.bio.base.pipelines.vanilla_biometrics.BioAlgorithm`
+           An implemented :any:`bob.bio.base.pipelines.vanilla_biometrics.BioAlgorithm`
     
         base_dir: str
            Path to store biometric references and scores
@@ -46,9 +46,9 @@ class BioAlgorithmCheckpointWrapper(BioAlgorithm):
     Examples
     --------
 
-    >>> from bob.bio.base.pipelines.vanilla_biometrics.biometric_algorithm import BioAlgCheckpointWrapper, Distance    
-    >>> biometric_algorithm = BioAlgCheckpointWrapper(Distance(), base_dir="./")
-    >>> biometric_algorithm.enroll(sample)
+    >>> from bob.bio.base.pipelines.vanilla_biometrics import BioAlgorithmCheckpointWrapper, Distance    
+    >>> biometric_algorithm = BioAlgorithmCheckpointWrapper(Distance(), base_dir="./")
+    >>> biometric_algorithm.enroll(sample) # doctest: +SKIP
 
     """
 
@@ -182,7 +182,7 @@ class BioAlgorithmCheckpointWrapper(BioAlgorithm):
 
 class BioAlgorithmDaskWrapper(BioAlgorithm):
     """
-    Wrap :any:`BioAlgorithm` to work with DASK
+    Wrap :any:`bob.bio.base.pipelines.vanilla_biometrics.BioAlgorithm` to work with DASK
     """
 
     def __init__(self, biometric_algorithm, **kwargs):
@@ -235,20 +235,20 @@ class BioAlgorithmDaskWrapper(BioAlgorithm):
 
 def dask_vanilla_biometrics(pipeline, npartitions=None, partition_size=None):
     """
-    Given a :any:`VanillaBiometrics`, wraps :any:`VanillaBiometrics.transformer` and
-    :any:`VanillaBiometrics.biometric_algorithm` to be executed with dask
+    Given a :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline`, wraps :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline` and
+    :any:`bob.bio.base.pipelines.vanilla_biometrics.BioAlgorithm` to be executed with dask
 
     Parameters
     ----------
 
-    pipeline: :any:`VanillaBiometrics`
+    pipeline: :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline`
        Vanilla Biometrics based pipeline to be dasked
 
     npartitions: int
-       Number of partitions for the initial :any:`dask.bag`
+       Number of partitions for the initial `dask.bag`
 
     partition_size: int
-       Size of the partition for the initial :any:`dask.bag`
+       Size of the partition for the initial `dask.bag`
     """
 
     if isinstance(pipeline, ZTNormPipeline):
@@ -294,11 +294,11 @@ def dask_get_partition_size(cluster, n_objects):
     The heuristics is pretty simple, given the max number of possible workers to be run
     in a queue (not the number of current workers running) and a total number objects to be processed do n_objects/n_max_workers:
 
-    Parameters:
-    -----------
+    Parameters
+    ----------
 
-        cluster:  :any:`bob.pipelines.distributed.SGEMultipleQueuesCluster`
-            Cluster of the type :any:`bob.pipelines.distributed.SGEMultipleQueuesCluster`
+        cluster:  :any:`bob.pipelines.distributed.sge.SGEMultipleQueuesCluster`
+            Cluster of the type :any:`bob.pipelines.distributed.sge.SGEMultipleQueuesCluster`
 
         n_objects: int
             Number of objects to be processed
@@ -313,13 +313,13 @@ def dask_get_partition_size(cluster, n_objects):
 
 def checkpoint_vanilla_biometrics(pipeline, base_dir, biometric_algorithm_dir=None):
     """
-    Given a :any:`VanillaBiometrics`, wraps :any:`VanillaBiometrics.transformer` and
-    :any:`VanillaBiometrics.biometric_algorithm` to be checkpointed
+    Given a :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline`, wraps :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline` and
+    :any:`bob.bio.base.pipelines.vanilla_biometrics.BioAlgorithm` to be checkpointed
 
     Parameters
     ----------
 
-    pipeline: :any:`VanillaBiometrics`
+    pipeline: :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline`
        Vanilla Biometrics based pipeline to be checkpointed
 
     base_dir: str
@@ -362,7 +362,9 @@ def checkpoint_vanilla_biometrics(pipeline, base_dir, biometric_algorithm_dir=No
         ):
             save_func = estimator.estimator.instance.write_feature
             load_func = estimator.estimator.instance.read_feature
-            estimator.estimator.projector_file = os.path.join(bio_ref_scores_dir,"Projector.hdf5")
+            estimator.estimator.projector_file = os.path.join(
+                bio_ref_scores_dir, "Projector.hdf5"
+            )
 
         wraped_estimator = bob.pipelines.wrap(
             ["checkpoint"],
@@ -374,7 +376,6 @@ def checkpoint_vanilla_biometrics(pipeline, base_dir, biometric_algorithm_dir=No
 
         sk_pipeline.steps[i] = (name, wraped_estimator)
 
-
     if isinstance(pipeline.biometric_algorithm, BioAlgorithmLegacy):
         pipeline.biometric_algorithm.base_dir = bio_ref_scores_dir
     else:
@@ -387,13 +388,13 @@ def checkpoint_vanilla_biometrics(pipeline, base_dir, biometric_algorithm_dir=No
 
 def is_checkpointed(pipeline):
     """
-    Check if :any:`VanillaBiometrics` is checkpointed
+    Check if :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline` is checkpointed
 
 
     Parameters
     ----------
 
-    pipeline: :any:`VanillaBiometrics`
+    pipeline: :any:`bob.bio.base.pipelines.vanilla_biometrics.VanillaBiometricsPipeline`
        Vanilla Biometrics based pipeline to be checkpointed
 
     """
@@ -402,7 +403,7 @@ def is_checkpointed(pipeline):
     # If it BioAlgorithmLegacy and the transformer of BioAlgorithmLegacy is also checkpointable
     return isinstance_nested(
         pipeline, "biometric_algorithm", BioAlgorithmCheckpointWrapper
-    ) or \
-    (   isinstance_nested(pipeline, "biometric_algorithm", BioAlgorithmLegacy) and \
-        isinstance_nested(pipeline, "transformer", CheckpointWrapper)
+    ) or (
+        isinstance_nested(pipeline, "biometric_algorithm", BioAlgorithmLegacy)
+        and isinstance_nested(pipeline, "transformer", CheckpointWrapper)
     )

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

@@ -3,7 +3,7 @@
 # vim: set fileencoding=utf-8 :
 
 Implementation of a pipeline and an algorithm that 
-computes Z, T and ZT Score Normalization of a :any:`BioAlgorithm`
+computes Z, T and ZT Score Normalization of a :any:`bob.bio.base.pipelines.vanilla_biometrics.BioAlgorithm`
 """
 
 from bob.pipelines import DelayedSample, Sample, SampleSet, DelayedSampleSet
@@ -17,6 +17,7 @@ import copy
 import joblib
 import logging
 from .pipelines import check_valid_pipeline
+
 logger = logging.getLogger(__name__)
 
 
@@ -30,11 +31,15 @@ class ZTNormPipeline(object):
 
     Example
     -------
-       >>> transformer = make_pipeline([])
+       >>> from bob.pipelines.transformers import Linearize
+       >>> from sklearn.pipeline import make_pipeline
+       >>> from bob.bio.base.pipelines.vanilla_biometrics import Distance, VanillaBiometricsPipeline, ZTNormPipeline
+       >>> estimator_1 = Linearize()
+       >>> transformer = make_pipeline(estimator_1)
        >>> biometric_algorithm = Distance()
        >>> vanilla_biometrics_pipeline = VanillaBiometricsPipeline(transformer, biometric_algorithm)
-       >>> zt_pipeline = ZTNormVanillaBiometricsPipeline(vanilla_biometrics_pipeline)
-       >>> zt_pipeline(...)
+       >>> zt_pipeline = ZTNormPipeline(vanilla_biometrics_pipeline)
+       >>> zt_pipeline(...) #doctest: +SKIP
 
     Parameters
     ----------
@@ -265,7 +270,7 @@ class ZTNormPipeline(object):
 
 class ZTNorm(object):
     """
-    Computes Z, T and ZT Score Normalization of a :any:`BioAlgorithm`
+    Computes Z, T and ZT Score Normalization of a `:any:`bob.bio.base.pipelines.vanilla_biometrics.BioAlgorithm`
 
     Reference bibliography from: A Generative Model for Score Normalization in Speaker Recognition
     https://arxiv.org/pdf/1709.09868.pdf
@@ -496,12 +501,12 @@ class ZTNorm(object):
 
 class ZTNormDaskWr