[sphinx] Fixed doctests

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

@@ -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")
 
     """
 

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

@@ -43,21 +43,24 @@ 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

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

@@ -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
 
     """
 

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

@@ -31,11 +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
     ----------