pre-commit required changes

bob/__init__.py

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

bob/fusion/__init__.py

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

bob/fusion/base/__init__.py

-from . import algorithm
-from . import preprocessor
-from . import tools
-from . import config
-from . import script
+from . import algorithm, config, preprocessor, script, tools  # noqa: F401
+
 
 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__)
 
+
 # gets sphinx autodoc done right - don't remove it
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]

bob/fusion/base/algorithm/AND.py

 #!/usr/bin/env python
 
-from __future__ import division
-from __future__ import absolute_import
+from __future__ import absolute_import, division
+
+import logging
 
 import numpy
 
 from .Algorithm import Algorithm
 
-import logging
-
 logger = logging.getLogger(__name__)
 
 
@@ -18,14 +17,14 @@ class AND(Algorithm):
     def __init__(self, thresholds=None, **kwargs):
         super(AND, self).__init__(classifier=self, **kwargs)
         self.thresholds = thresholds
-        self.str['thresholds'] = thresholds
+        self.str["thresholds"] = thresholds
 
     def fit(self, X, y):
         pass
 
     def decision_function(self, scores):
         if self.thresholds is None:
-            ValueError('No threshold was specified.')
+            ValueError("No threshold was specified.")
 
         for i, th in enumerate(self.thresholds):
             mask = scores[:, i + 1] < th
@@ -33,5 +32,5 @@ class AND(Algorithm):
 
         mask = numpy.sum(numpy.isnan(scores[:, 1:]), axis=1, dtype=bool)
         new_scores = numpy.array(scores[0])
-        new_scores[mask] = numpy.finfo('float16').min
+        new_scores[mask] = numpy.finfo("float16").min
         return new_scores

bob/fusion/base/algorithm/Algorithm.py

 #!/usr/bin/env python
 
-from __future__ import division
-from __future__ import absolute_import
+from __future__ import absolute_import, division
 
-import numpy as np
+import logging
 import pickle
 
-import logging
+import numpy as np
 
 logger = logging.getLogger(__name__)
 
@@ -22,11 +21,7 @@ class Algorithm(object):
         A dictionary that its content will printed in the __str__ method.
     """
 
-    def __init__(self,
-                 preprocessors=None,
-                 classifier=None,
-                 **kwargs
-                 ):
+    def __init__(self, preprocessors=None, classifier=None, **kwargs):
         """
         Parameters
         ----------
@@ -44,9 +39,9 @@ class Algorithm(object):
         super(Algorithm, self).__init__(**kwargs)
         self.classifier = classifier
         self.preprocessors = preprocessors
-        self.str = {'preprocessors': preprocessors}
+        self.str = {"preprocessors": preprocessors}
         if classifier is not self:
-            self.str['classifier'] = classifier
+            self.str["classifier"] = classifier
 
     def train_preprocessors(self, X, y=None):
         """Train preprocessors in order.
@@ -81,7 +76,7 @@ class Algorithm(object):
         """
         train_scores = np.vstack((train_neg, train_pos))
         neg_len = train_neg.shape[0]
-        y = np.zeros((train_scores.shape[0],), dtype='bool')
+        y = np.zeros((train_scores.shape[0],), dtype="bool")
         y[neg_len:] = True
         self.classifier.fit(train_scores, y)
 
@@ -107,9 +102,16 @@ class Algorithm(object):
             A string containing the full information of all parameters of this
                 (and the derived) class.
         """
-        return "%s(%s)" % (str(self.__class__), ", ".join(
-            ["%s=%s" % (key, value) for key, value in
-             self.str.items() if value is not None]))
+        return "%s(%s)" % (
+            str(self.__class__),
+            ", ".join(
+                [
+                    "%s=%s" % (key, value)
+                    for key, value in self.str.items()
+                    if value is not None
+                ]
+            ),
+        )
 
     def save(self, model_file):
         """Save the instance of the algorithm.
@@ -140,6 +142,6 @@ class Algorithm(object):
         with open(model_file, "rb") as f:
             algo_class = pickle.load(f)
             algo = algo_class()
-            if not hasattr(algo, 'custom_save'):
+            if not hasattr(algo, "custom_save"):
                 return pickle.load(f)
         return algo.load(model_file)

bob/fusion/base/algorithm/AlgorithmBob.py

 #!/usr/bin/env python
 
-from __future__ import division
-from __future__ import absolute_import
+from __future__ import absolute_import, division
 
+import logging
 import pickle
-from .Algorithm import Algorithm
+
 from h5py import File as HDF5File
 
-import logging
+from .Algorithm import Algorithm
 
 logger = logging.getLogger(__name__)
 
@@ -16,13 +16,13 @@ class AlgorithmBob(Algorithm):
     """A class to be used in score fusion using bob machines."""
 
     def _get_hdf5_file(self, model_file):
-        return model_file[:-3] + 'hdf5'
+        return model_file[:-3] + "hdf5"
 
     def custom_save(self, model_file):
         # dump preprocessors in a pickle file because
         # we don't know how they look like
         # saves the class to create it later.
-        with open(model_file, 'wb') as f:
+        with open(model_file, "wb") as f:
             pickle.dump(type(self), f)
             pickle.dump(self.preprocessors, f)
             # just for consistent string representation

bob/fusion/base/algorithm/Empty.py

 #!/usr/bin/env python
 
-from __future__ import division
-from __future__ import absolute_import
+from __future__ import absolute_import, division
 
-from .Algorithm import Algorithm
 import logging
 
+from .Algorithm import Algorithm
+
 logger = logging.getLogger(__name__)
 
 
@@ -15,9 +15,7 @@ class Empty(Algorithm):
     preprocessors."""
 
     def __init__(self, **kwargs):
-        super(Empty, self).__init__(
-            classifier=self,
-            **kwargs)
+        super(Empty, self).__init__(classifier=self, **kwargs)
 
     def fit(self, X, y):
         pass

bob/fusion/base/algorithm/GMM.py

 #!/usr/bin/env python
 
-from __future__ import division
-from __future__ import absolute_import
+from __future__ import absolute_import, division
+
+import logging
 
 import numpy as np
 
 from bob.learn.em.mixture import GMMMachine
-from .AlgorithmBob import AlgorithmBob
 
-import logging
+from .AlgorithmBob import AlgorithmBob
 
 logger = logging.getLogger("bob.fusion.base")
 
@@ -64,12 +64,10 @@ class GMM(AlgorithmBob):
         if self.n_gaussians is None:
             self.n_gaussians = array.shape[1] + 1
             logger.warning(
-                "Number of Gaussians was None. " "Using {}.".format(self.n_gaussians)
+                "Number of Gaussians was None. "
+                "Using {}.".format(self.n_gaussians)
             )
 
-        # Computes input size
-        input_size = array.shape[1]
-
         # Creates the machines (KMeans and GMM)
         logger.debug("Training GMM machine")
         self.machine = GMMMachine(
@@ -84,4 +82,8 @@ class GMM(AlgorithmBob):
         self.machine.fit(array)
 
     def decision_function(self, scores):
-        return np.fromiter((self.machine.log_likelihood(s) for s in scores), np.float, scores.shape[0])
+        return np.fromiter(
+            (self.machine.log_likelihood(s) for s in scores),
+            np.float,
+            scores.shape[0],
+        )

bob/fusion/base/algorithm/Weighted_Sum.py

 #!/usr/bin/env python
 
-from __future__ import division
-from __future__ import absolute_import
+from __future__ import absolute_import, division
+
+import logging
 
 import numpy
 
 from .Algorithm import Algorithm
 
-import logging
-
 logger = logging.getLogger(__name__)
 
 
@@ -16,11 +15,9 @@ class Weighted_Sum(Algorithm):
     """weighted sum (default: mean)"""
 
     def __init__(self, weights=None, **kwargs):
-        super(Weighted_Sum, self).__init__(
-            classifier=self,
-            **kwargs)
+        super(Weighted_Sum, self).__init__(classifier=self, **kwargs)
         self.weights = weights
-        self.str['weights'] = weights
+        self.str["weights"] = weights
 
     def fit(self, X, y):
         pass

bob/fusion/base/algorithm/__init__.py

 from .Algorithm import Algorithm
 from .AlgorithmBob import AlgorithmBob
-from .Weighted_Sum import Weighted_Sum
-from .GMM import GMM
 from .Empty import Empty
+from .GMM import GMM
+from .Weighted_Sum import Weighted_Sum
 
 
 # gets sphinx autodoc done right - don't remove it
@@ -28,4 +28,4 @@ __appropriate__(
     GMM,
     Empty,
 )
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]

bob/fusion/base/config/algorithm/gmm.py

 #!/usr/bin/env python
 
-import bob.fusion.base
 from sklearn.preprocessing import StandardScaler
 
+import bob.fusion.base
+
 algorithm = bob.fusion.base.algorithm.GMM(preprocessors=[StandardScaler()])
 
 algorithm_tanh = bob.fusion.base.algorithm.GMM(
-    preprocessors=[bob.fusion.base.preprocessor.Tanh()])
+    preprocessors=[bob.fusion.base.preprocessor.Tanh()]
+)

bob/fusion/base/config/algorithm/llr_skl.py

 #!/usr/bin/env python
 
-import bob.fusion.base
-from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+
+import bob.fusion.base
 
 algorithm = bob.fusion.base.algorithm.Algorithm(
-    preprocessors=[StandardScaler()],
-    classifier=LogisticRegression())
+    preprocessors=[StandardScaler()], classifier=LogisticRegression()
+)
 
 algorithm_tanh = bob.fusion.base.algorithm.Algorithm(
     preprocessors=[bob.fusion.base.preprocessor.Tanh()],
-    classifier=LogisticRegression())
+    classifier=LogisticRegression(),
+)

bob/fusion/base/config/algorithm/mean.py

 #!/usr/bin/env python
 
-import bob.fusion.base
 from sklearn.preprocessing import StandardScaler
 
+import bob.fusion.base
+
 algorithm = bob.fusion.base.algorithm.Weighted_Sum(
-    preprocessors=[StandardScaler()])
+    preprocessors=[StandardScaler()]
+)
 
 algorithm_tanh = bob.fusion.base.algorithm.Weighted_Sum(
-    preprocessors=[bob.fusion.base.preprocessor.Tanh()])
+    preprocessors=[bob.fusion.base.preprocessor.Tanh()]
+)

bob/fusion/base/config/algorithm/plr_2.py

 #!/usr/bin/env python
 
+from sklearn.preprocessing import PolynomialFeatures, StandardScaler
+
 import bob.fusion.base
-from sklearn.preprocessing import StandardScaler, PolynomialFeatures
 
 algorithm = bob.fusion.base.algorithm.LLR(
-    preprocessors=[StandardScaler(), PolynomialFeatures(degree=2)])
+    preprocessors=[StandardScaler(), PolynomialFeatures(degree=2)]
+)
 
 algorithm_tanh = bob.fusion.base.algorithm.LLR(
-    preprocessors=[bob.fusion.base.preprocessor.Tanh(),
-                   PolynomialFeatures(degree=2)])
+    preprocessors=[
+        bob.fusion.base.preprocessor.Tanh(),
+        PolynomialFeatures(degree=2),
+    ]
+)

bob/fusion/base/config/algorithm/plr_3.py

 #!/usr/bin/env python
 
+from sklearn.preprocessing import PolynomialFeatures, StandardScaler
+
 import bob.fusion.base
-from sklearn.preprocessing import StandardScaler, PolynomialFeatures
 
 algorithm = bob.fusion.base.algorithm.LLR(
-    preprocessors=[StandardScaler(), PolynomialFeatures(degree=3)])
+    preprocessors=[StandardScaler(), PolynomialFeatures(degree=3)]
+)
 
 algorithm_tanh = bob.fusion.base.algorithm.LLR(
-    preprocessors=[bob.fusion.base.preprocessor.Tanh(),
-                   PolynomialFeatures(degree=3)])
+    preprocessors=[
+        bob.fusion.base.preprocessor.Tanh(),
+        PolynomialFeatures(degree=3),
+    ]
+)

bob/fusion/base/preprocessor/Tanh.py

-from sklearn.preprocessing import StandardScaler
 import numpy as np
 
+from sklearn.preprocessing import StandardScaler
+
 # to fix the sphinx docs
-StandardScaler.__module__ = 'sklearn.preprocessing'
+StandardScaler.__module__ = "sklearn.preprocessing"
 
 
 class Tanh(StandardScaler):
@@ -22,7 +23,8 @@ class Tanh(StandardScaler):
     def __init__(self, copy=True, **kwargs):
         """Initialize self. See help(type(self)) for accurate signature."""
         super(Tanh, self).__init__(
-            copy=copy, with_mean=True, with_std=True, **kwargs)
+            copy=copy, with_mean=True, with_std=True, **kwargs
+        )
 
     def fit(self, X, y=None):
         """Estimates the mean and standard deviation of samples.

bob/fusion/base/preprocessor/ZNorm.py

-from sklearn.preprocessing import StandardScaler
 import numpy as np
 
+from sklearn.preprocessing import StandardScaler
+
 # to fix the sphinx docs
-StandardScaler.__module__ = 'sklearn.preprocessing'
+StandardScaler.__module__ = "sklearn.preprocessing"
 
 
 class ZNorm(StandardScaler):
@@ -16,7 +17,8 @@ class ZNorm(StandardScaler):
     def __init__(self, copy=True, **kwargs):
         """Initialize self. See help(type(self)) for accurate signature."""
         super(ZNorm, self).__init__(
-            copy=copy, with_mean=True, with_std=True, **kwargs)
+            copy=copy, with_mean=True, with_std=True, **kwargs
+        )
 
     def fit(self, X, y=None):
         """Estimates the mean and standard deviation of samples.

bob/fusion/base/preprocessor/__init__.py

@@ -22,4 +22,4 @@ __appropriate__(
     Tanh,
     ZNorm,
 )
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]

bob/fusion/base/script/__init__.py

-from .fuse import routine_fusion
+from .fuse import routine_fusion  # noqa: F401
 
 # gets sphinx autodoc done right - don't remove it
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]