diff --git a/bob/bio/base/algorithm/Algorithm.py b/bob/bio/base/algorithm/Algorithm.py
index 38834b8fa45d6484f1c9927d250f2cd5e654bb78..c15cc9e0fd80af78842e6c4b4a655ec32d2588d9 100644
--- a/bob/bio/base/algorithm/Algorithm.py
+++ b/bob/bio/base/algorithm/Algorithm.py
@@ -172,7 +172,8 @@ class Algorithm (object):
probe : object
The probe object to compare the model with.
- The ``probe`` was read using the :py:meth:`read_probe` function.
+ The ``probe`` was read using the :py:meth:`read_feature` function
+ (or the :py:meth:`bob.bio.base.extractor.Extractor.read_feature` function, if this algorithm does not perform projection.
**Returns:**
@@ -330,29 +331,6 @@ class Algorithm (object):
return utils.load(model_file)
- def read_probe(self, probe_file):
- """read_probe(probe_file) -> probe
-
- Reads the probe feature from file.
- By default, the probe feature is identical to the projected feature.
- Hence, this base class implementation simply calls :py:meth:`read_feature`.
-
- If your algorithm requires different behavior, please overwrite this function.
-
- **Parameters:**
-
- probe_file : str or :py:class:`bob.io.base.HDF5File`
- The file open for reading, or the file name to read from.
-
- **Returns:**
-
- probe : object
- The probe that was read from file.
- """
- return self.read_feature(probe_file)
-
-
-
def train_projector(self, training_features, projector_file):
"""This function can be overwritten to train the feature projector.
If you do this, please also register the function by calling this base class constructor
diff --git a/bob/bio/base/algorithm/BIC.py b/bob/bio/base/algorithm/BIC.py
index c15b5e58c04ec064db1c14886e4e1febc8799326..42aa785c53af8fd36898f6f4818567b2bb633782 100644
--- a/bob/bio/base/algorithm/BIC.py
+++ b/bob/bio/base/algorithm/BIC.py
@@ -230,25 +230,6 @@ class BIC(Algorithm):
i += 1
return model
- def read_probe(self, probe_file):
- """read_probe(probe_file) -> probe
-
- Reads the probe feature from the given HDF5 file.
-
- To read the feature, the ``read_function`` specified in the constructor is employed.
-
- **Parameters:**
-
- probe_file : str or :py:class:`bob.io.base.HDF5File`
- The file (open for reading) or the name of an existing file to read from.
-
- **Returns:**
-
- probe : object
- The read probe, which is a feature.
- """
- return self.read_function(bob.io.base.HDF5File(probe_file))
-
def score(self, model, probe):
"""score(model, probe) -> float
diff --git a/bob/bio/base/algorithm/Distance.py b/bob/bio/base/algorithm/Distance.py
index f902dcd25c559204b8ab331eb05a0f0e97f9a17d..c6a235960d97f7691a2c01631125261ed2bf782e 100644
--- a/bob/bio/base/algorithm/Distance.py
+++ b/bob/bio/base/algorithm/Distance.py
@@ -73,24 +73,6 @@ class Distance (Algorithm):
return numpy.vstack([f.flatten() for f in enroll_features])
- def read_probe(self, probe_file):
- """read_probe(probe_file) -> probe
-
- Reads the probe feature from the given HDF5 file.
-
- **Parameters:**
-
- probe_file : str or :py:class:`bob.io.base.HDF5File`
- The file (open for reading) or the name of an existing file to read from.
-
- **Returns:**
-
- probe : object
- The probe.
- """
- return utils.load(probe_file)
-
-
def score(self, model, probe):
"""score(model, probe) -> float
diff --git a/bob/bio/base/algorithm/PLDA.py b/bob/bio/base/algorithm/PLDA.py
index e2350d6b2bb4e2dc4c68f7adebbbd3bccc1f0aa1..712b9d0f352be4e90c4399ab1285718de7847e77 100644
--- a/bob/bio/base/algorithm/PLDA.py
+++ b/bob/bio/base/algorithm/PLDA.py
@@ -168,10 +168,6 @@ class PLDA (Algorithm):
plda_machine = bob.learn.em.PLDAMachine(bob.io.base.HDF5File(model_file), self.plda_base)
return plda_machine
- def read_probe(self, probe_file):
- """Reads the probe using :py:func:`bob.bio.base.load`."""
- return bob.bio.base.load(probe_file)
-
def score(self, model, probe):
"""Computes the PLDA score for the given model and probe"""
diff --git a/bob/bio/base/test/test_algorithms.py b/bob/bio/base/test/test_algorithms.py
index 08e117dc4657d53b8db4793636059dc6523c527e..8b03c5e32599244677fe5e2ed5db437680f7a3b7 100644
--- a/bob/bio/base/test/test_algorithms.py
+++ b/bob/bio/base/test/test_algorithms.py
@@ -109,7 +109,7 @@ def test_pca():
_compare(model, pkg_resources.resource_filename('bob.bio.base.test', 'data/pca_model.hdf5'), pca1.write_model, pca1.read_model)
# compare model with probe
- probe = pca1.read_probe(pkg_resources.resource_filename('bob.bio.base.test', 'data/pca_projected.hdf5'))
+ probe = pca1.read_feature(pkg_resources.resource_filename('bob.bio.base.test', 'data/pca_projected.hdf5'))
reference_score = -251.53563107
assert abs(pca1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (pca1.score(model, probe), reference_score)
assert abs(pca1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
@@ -183,7 +183,7 @@ def test_lda():
_compare(model, pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_model.hdf5'), lda1.write_model, lda1.read_model)
# compare model with probe
- probe = lda1.read_probe(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5'))
+ probe = lda1.read_feature(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5'))
reference_score = -233.30450012
assert abs(lda1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (lda1.score(model, probe), reference_score)
assert abs(lda1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
@@ -226,7 +226,7 @@ def test_distance():
# compare model with probe
enroll = utils.random_training_set(5, 5, 0., 255., seed=21);
model = numpy.mean(distance.enroll(enroll),axis=0)
- probe = distance.read_probe(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5'))
+ probe = bob.io.base.load(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5'))
reference_score = -0.1873371
assert abs(distance.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (distance.score(model, probe), reference_score)
@@ -358,7 +358,7 @@ def test_plda():
reference_score = 0.
assert abs(plda1.score(model, feature) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (plda1.score(model, feature), reference_score)
assert abs(plda1.score_for_multiple_probes(model, [feature, feature]) - reference_score) < 1e-5
-
+
def test_plda_nopca():
temp_file = bob.io.base.test_utils.temporary_filename()
plda_ref = bob.bio.base.load_resource("plda", "algorithm", preferred_package = 'bob.bio.base')
@@ -373,7 +373,7 @@ def test_plda_nopca():
# train the projector
try:
# train projector
- plda.train_enroller(train_set, temp_file)
+ plda.train_enroller(train_set, temp_file)
assert os.path.exists(temp_file)
if regenerate_refs: shutil.copy(temp_file, reference_file)
@@ -396,4 +396,3 @@ def test_plda_nopca():
reference = plda.read_model(reference)
assert model.is_similar_to(reference)
-
diff --git a/bob/bio/base/test/test_scripts.py b/bob/bio/base/test/test_scripts.py
index e028cb1e8eeb792f504b66983ba9cf9dc37a8cef..67d0a8d51d50a460e0835d1a1615d0bb0f2e6bb3 100644
--- a/bob/bio/base/test/test_scripts.py
+++ b/bob/bio/base/test/test_scripts.py
@@ -337,12 +337,60 @@ def test_internal_raises():
'--imports', 'bob.bio.base.test.dummy'
]
- from bob.bio.base.script.verify import main
- for option, value in (("--group", "dev"), ("--model-type", "N"), ("--score-type", "A")):
- internal = parameters + [option, value]
+ try:
+ from bob.bio.base.script.verify import main
+ for option, value in (("--group", "dev"), ("--model-type", "N"), ("--score-type", "A")):
+ internal = parameters + [option, value]
+
+ nose.tools.assert_raises(ValueError, main, internal)
+ finally:
+ shutil.rmtree(test_dir)
+
+
+def test_verify_generate_config():
+ # tests the config file generation
+ test_dir = tempfile.mkdtemp(prefix='bobtest_')
+ config_file = os.path.join(test_dir, 'config.py')
+ # define dummy parameters
+ parameters = [
+ '-H', config_file
+ ]
+ try:
+ from bob.bio.base.script.verify import main
+ nose.tools.assert_raises(SystemExit, main, parameters)
+ assert os.path.exists(config_file)
+ from bob.bio.base.tools.command_line import _required_list, _common_list, _optional_list
+ assert all(a in _required_list for a in ['database', 'preprocessor', 'extractor', 'algorithm', 'sub_directory'])
+ assert all(a in _common_list for a in ['protocol', 'grid', 'parallel', 'verbose', 'groups', 'temp_directory', 'result_directory', 'zt_norm', 'allow_missing_files', 'dry_run', 'force'])
+ assert all(a in _optional_list for a in ['preprocessed_directory', 'extracted_directory', 'projected_directory', 'model_directories', 'extractor_file', 'projector_file', 'enroller_file'])
+ # todo: this list is actually much longer...
+ _rare_list = ['imports', 'experiment_info_file', 'write_compressed_score_files', 'skip_preprocessing', 'skip_calibration', 'execute_only']
+
+ lines = open(config_file).readlines()
+
+ # split into four lists (required, common, optional, rare)
+ last_lines = None
+ split_lines = []
+ for line in lines:
+ if line.startswith("#####"):
+ if last_lines:
+ split_lines.append(last_lines)
+ last_lines = []
+ else:
+ if last_lines is not None:
+ last_lines.append(line)
+ split_lines.append(last_lines)
+ assert len(split_lines) == 4
+
+ for _list, lines in zip((_required_list, _common_list, _optional_list, _rare_list), split_lines):
+ for a in _list:
+ assert any(l.startswith("#%s =" %a) for l in lines), a
+ finally:
+ shutil.rmtree(test_dir)
+
+
+
- nose.tools.assert_raises(ValueError, main, internal)
- shutil.rmtree(test_dir)
def test_fusion():
diff --git a/doc/implementation.rst b/doc/implementation.rst
index 142cd551de228bcb4c734691af7b8e2f33854fbb..4d9221fc5160b9b8a0f1f8cabeb7473f84065f94 100644
--- a/doc/implementation.rst
+++ b/doc/implementation.rst
@@ -173,10 +173,6 @@ If the ``score`` function expects models and probe features to be of a different
* ``write_model(self, model, model_file)``: writes the model (as returned by the ``enroll`` function).
* ``read_model(self, model_file) -> model``: reads the model (as written by the ``write_model`` function) from file.
-* ``read_probe(self, probe_file) -> feature``: reads the probe feature from file.
-
- .. note::
- In many cases, the ``read_feature`` and ``read_probe`` functions are identical (if both are present).
Finally, the :py:class:`bob.bio.base.algorithm.Algorithm` class provides default implementations for the case that models store several features, or that several probe features should be combined into one score.
These two functions are: