[pre-commit] update configs

.flake8

 [flake8]
-max-line-length = 88
-select = B,C,E,F,W,T4,B9,B950
-ignore = E501, W503, E203
+max-line-length = 80
+ignore = E501,W503,E302,E402,E203

.pre-commit-config.yaml

@@ -2,20 +2,20 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/timothycrosley/isort
-    rev: 5.8.0
+    rev: 5.9.3
     hooks:
       - id: isort
-      args: [--sl, --line-length, "88"]
+        args: [--settings-path, "pyproject.toml"]
   - repo: https://github.com/psf/black
-    rev: 20.8b1
+    rev: 21.7b0
     hooks:
       - id: black
   - repo: https://gitlab.com/pycqa/flake8
-    rev: 3.9.0
+    rev: 3.9.2
     hooks:
       - id: flake8
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.4.0
+    rev: v4.0.1
     hooks:
       - id: check-ast
       - id: check-case-conflict
@@ -23,21 +23,5 @@ repos:
       - id: end-of-file-fixer
       - id: debug-statements
       - id: check-added-large-files
-  - repo: local
-    hooks:
-      - id: sphinx-build
-        name: sphinx build
-        entry: python -m sphinx.cmd.build
-        args: [-a, -E, -W, doc, sphinx]
-        language: system
-        files: ^doc/
-        types: [file]
-        pass_filenames: false
-      - id: sphinx-doctest
-        name: sphinx doctest
-        entry: python -m sphinx.cmd.build
-        args: [-a, -E, -b, doctest, doc, sphinx]
-        language: system
-        files: ^doc/
-        types: [file]
-        pass_filenames: false
+      - id: check-yaml
+        exclude: .*/meta.yaml

bob/bio/gmm/algorithm/GMM.py

@@ -13,8 +13,7 @@ This adds the notions of models, probes, enrollment, and scores to GMM.
 import copy
 import logging
 
-from typing import Callable
-from typing import Union
+from typing import Callable, Union
 
 import dask.array as da
 import numpy as np
@@ -23,10 +22,7 @@ from h5py import File as HDF5File
 from sklearn.base import BaseEstimator
 
 from bob.bio.base.pipelines.vanilla_biometrics import BioAlgorithm
-from bob.learn.em import GMMMachine
-from bob.learn.em import GMMStats
-from bob.learn.em import KMeansMachine
-from bob.learn.em import linear_scoring
+from bob.learn.em import GMMMachine, GMMStats, KMeansMachine, linear_scoring
 
 logger = logging.getLogger(__name__)
 
@@ -153,7 +149,9 @@ class GMM(BioAlgorithm, BaseEstimator):
             or feature.ndim != 2
             or feature.dtype != np.float64
         ):
-            raise ValueError(f"The given feature is not appropriate: \n{feature}")
+            raise ValueError(
+                f"The given feature is not appropriate: \n{feature}"
+            )
         if self.ubm is not None and feature.shape[1] != self.ubm.shape[1]:
             raise ValueError(
                 "The given feature is expected to have %d elements, but it has %d"
@@ -165,7 +163,11 @@ class GMM(BioAlgorithm, BaseEstimator):
         # Saves the UBM to file
         logger.debug("Saving model to file '%s'", ubm_file)
 
-        hdf5 = ubm_file if isinstance(ubm_file, HDF5File) else HDF5File(ubm_file, "w")
+        hdf5 = (
+            ubm_file
+            if isinstance(ubm_file, HDF5File)
+            else HDF5File(ubm_file, "w")
+        )
         self.ubm.save(hdf5)
 
     def load_model(self, ubm_file):
@@ -273,7 +275,9 @@ class GMM(BioAlgorithm, BaseEstimator):
             The probe data to compare to the models.
         """
 
-        stats = self.project(probe) if not isinstance(probe, GMMStats) else probe
+        stats = (
+            self.project(probe) if not isinstance(probe, GMMStats) else probe
+        )
         return self.scoring_function(
             models_means=biometric_references,
             ubm=self.ubm,
@@ -291,7 +295,9 @@ class GMM(BioAlgorithm, BaseEstimator):
         if array[0].ndim == 2:
             array = np.vstack(array)
 
-        logger.debug(f"Creating UBM machine with {self.number_of_gaussians} gaussians and {len(array)} samples")
+        logger.debug(
+            f"Creating UBM machine with {self.number_of_gaussians} gaussians and {len(array)} samples"
+        )
 
         self.ubm = GMMMachine(
             n_gaussians=self.number_of_gaussians,

bob/bio/gmm/test/test_gmm.py

@@ -27,8 +27,7 @@ import bob.bio.gmm
 
 from bob.bio.base.test import utils
 from bob.bio.gmm.algorithm import GMM
-from bob.learn.em import GMMMachine
-from bob.learn.em import GMMStats
+from bob.learn.em import GMMMachine, GMMStats
 
 logger = logging.getLogger(__name__)
 
@@ -44,7 +43,8 @@ def test_class():
     )
     assert isinstance(gmm1, GMM)
     assert isinstance(
-        gmm1, bob.bio.base.pipelines.vanilla_biometrics.abstract_classes.BioAlgorithm
+        gmm1,
+        bob.bio.base.pipelines.vanilla_biometrics.abstract_classes.BioAlgorithm,
     )
     assert gmm1.number_of_gaussians == 512
     assert "bob_fit_supports_dask_array" in gmm1._get_tags()
@@ -117,7 +117,9 @@ def test_enroll():
     )
     # Create a GMM object with that UBM
     gmm1 = GMM(
-        number_of_gaussians=2, enroll_update_means=True, enroll_update_variances=True
+        number_of_gaussians=2,
+        enroll_update_means=True,
+        enroll_update_variances=True,
     )
     gmm1.ubm = ubm
     # Enroll the biometric reference from random features
@@ -136,7 +138,9 @@ def test_enroll():
     gmm2 = gmm1.read_biometric_reference(reference_file)
     assert biometric_reference.is_similar_to(gmm2)
 
-    with tempfile.NamedTemporaryFile(prefix="bob_", suffix="_bioref.hdf5") as fd:
+    with tempfile.NamedTemporaryFile(
+        prefix="bob_", suffix="_bioref.hdf5"
+    ) as fd:
         temp_file = fd.name
         gmm1.write_biometric_reference(biometric_reference, temp_file)
         assert GMMMachine.from_hdf5(temp_file, ubm).is_similar_to(gmm2)
@@ -148,11 +152,15 @@ def test_score():
         pkg_resources.resource_filename("bob.bio.gmm.test", "data/gmm_ubm.hdf5")
     )
     biometric_reference = GMMMachine.from_hdf5(
-        pkg_resources.resource_filename("bob.bio.gmm.test", "data/gmm_enrolled.hdf5"),
+        pkg_resources.resource_filename(
+            "bob.bio.gmm.test", "data/gmm_enrolled.hdf5"
+        ),
         ubm=gmm1.ubm,
     )
     probe = GMMStats.from_hdf5(
-        pkg_resources.resource_filename("bob.bio.gmm.test", "data/gmm_projected.hdf5")
+        pkg_resources.resource_filename(
+            "bob.bio.gmm.test", "data/gmm_projected.hdf5"
+        )
     )
     probe_data = utils.random_array((20, 45), -5.0, 5.0, seed=seed_value)
 

doc/conf.py

@@ -8,8 +8,7 @@ import pkg_resources
 import sphinx_rtd_theme
 
 # For inter-documentation mapping:
-from bob.extension.utils import link_documentation
-from bob.extension.utils import load_requirements
+from bob.extension.utils import link_documentation, load_requirements
 
 # -- General configuration -----------------------------------------------------
 
@@ -130,9 +129,7 @@ pygments_style = "sphinx"
 
 # Some variables which are useful for generated material
 project_variable = project.replace(".", "_")
-short_description = (
-    u"Tools for running biometric recognition experiments using GMM-based approximation"
-)
+short_description = u"Tools for running biometric recognition experiments using GMM-based approximation"
 owner = [u"Idiap Research Institute"]
 
 

pyproject.toml

 [build-system]
     requires = ["setuptools", "wheel", "bob.extension"]
     build-backend = "setuptools.build_meta"
+
+[tool.isort]
+    profile = "black"
+    line_length = 80
+    order_by_type = true
+    lines_between_types = 1
+
+[tool.black]
+    line-length = 80

setup.py

@@ -33,11 +33,9 @@
 # allows you to test your package with new python dependencies w/o requiring
 # administrative interventions.
 
-from setuptools import dist
-from setuptools import setup
+from setuptools import dist, setup
 
-from bob.extension.utils import find_packages
-from bob.extension.utils import load_requirements
+from bob.extension.utils import find_packages, load_requirements
 
 dist.Distribution(dict(setup_requires=["bob.extension"]))