[pre-commit.ci] pre-commit suggestions (#41)

* [pre-commit.ci] pre-commit suggestions
* deprecated KMeans args
* pre-commit: no further update
* Apply suggestions from code review

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka <jirka.borovec@seznam.cz>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ default_language_version:
 ci:
   autofix_prs: true
   autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
-  autoupdate_schedule: quarterly
+  # autoupdate_schedule: quarterly
   # submodules: true
 
 repos:
@@ -23,7 +23,7 @@ repos:
   #      name: fixing PEP8
 
   - repo: https://github.com/PyCQA/isort
-    rev: 5.9.1
+    rev: 5.9.3
     hooks:
       - id: isort
         name: imports
@@ -38,7 +38,7 @@ repos:
         require_serial: false
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.7
+    rev: 0.7.10
     hooks:
       - id: mdformat
         additional_dependencies:

imsegm/classification.py

@@ -1127,7 +1127,7 @@ def down_sample_dict_features_kmean(dict_features, nb_samples):
         if len(features) <= nb_samples:
             dict_features_new[label] = features.copy()
             continue
-        kmeans = cluster.KMeans(n_clusters=nb_samples, init='random', n_init=3, max_iter=5, n_jobs=-1)
+        kmeans = cluster.KMeans(n_clusters=nb_samples, init='random', n_init=3, max_iter=5)
         dist = kmeans.fit_transform(features)
         find_min = np.argmin(dist, axis=0)
         dict_features_new[label] = features[find_min, :]

imsegm/graph_cuts.py

@@ -127,7 +127,7 @@ def estim_class_model(features, nb_classes, estim_model='GMM', pca_coef=None, us
         if init_type == 'kmeans':
             mm.set_params(n_init=1)
             # http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
-            kmeans = cluster.KMeans(n_clusters=nb_classes, init='k-means++', n_jobs=-1)
+            kmeans = cluster.KMeans(n_clusters=nb_classes, init='k-means++')
             y = kmeans.fit_predict(features)
         elif init_type == 'Otsu':
             mm.set_params(n_init=1)
@@ -209,7 +209,7 @@ def compute_multivarian_otsu(features):
 #     mm = mixture.GMM(n_components=nb_classes, covariance_type='full', n_iter=999)
 #     if init == 'kmeans':
 #         # http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
-#         kmeans = cluster.KMeans(n_clusters=nb_classes, init='k-means++', n_jobs=-1)
+#         kmeans = cluster.KMeans(n_clusters=nb_classes, init='k-means++')
 #         y = kmeans.fit_predict(features)
 #         mm.fit(features, y)
 #     else:
@@ -240,7 +240,7 @@ def estim_class_model_gmm(features, nb_classes, init='kmeans'):
     gmm = mixture.GaussianMixture(n_components=nb_classes, covariance_type='full', max_iter=99)
     if init == 'kmeans':
         # http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
-        kmeans = cluster.KMeans(n_clusters=nb_classes, init='k-means++', n_jobs=-1)
+        kmeans = cluster.KMeans(n_clusters=nb_classes, init='k-means++')
         y = kmeans.fit_predict(features)
         gmm.fit(features, y)
     else:
@@ -275,10 +275,10 @@ def estim_class_model_kmeans(features, nb_classes, init_type='k-means++', max_it
     if init_type == 'quantiles':
         quntiles = np.linspace(5, 95, nb_classes).tolist()
         init_perc = np.array(np.percentile(features, quntiles, axis=0))
-        kmeans = cluster.KMeans(nb_classes, init=init_perc, max_iter=2, n_jobs=-1)
+        kmeans = cluster.KMeans(nb_classes, init=init_perc, max_iter=2)
     else:
         nb_inits = max(1, int(np.sqrt(max_iter)))
-        kmeans = cluster.KMeans(nb_classes, init=init_type, max_iter=max_iter, n_init=nb_inits, n_jobs=-1)
+        kmeans = cluster.KMeans(nb_classes, init=init_type, max_iter=max_iter, n_init=nb_inits)
     y = kmeans.fit_predict(features)
     gmm = mixture.GaussianMixture(n_components=nb_classes, covariance_type='full', max_iter=1)
     gmm.fit(features, y)

requirements.txt

@@ -3,9 +3,9 @@ Cython >= 0.28  # 0.27 fails with python 3.7
 numpy >= 1.15.0  # version 1.16 breaks skimage 0.14
 scipy >= 1.2.0
 pandas >= 0.23.0
-Pillow>=8.1.2
+Pillow >= 8.1.2
 matplotlib >= 3.0.0
-scikit-learn >= 0.19.0
+scikit-learn >= 0.19.0, !=1.0  # fails with segmentation fault on MacOS
 scikit-image >= 0.14.0
 tqdm >= 4.7.4
 olefile == 0.44  # new version - ImportError: No module named OleFileIO_PL

tests/requirements.txt

@@ -1,4 +1,3 @@
-nose>=1.3.7
 coverage
 codecov
 pytest>4.0
@@ -8,3 +7,4 @@ codacy-coverage
 check-manifest
 flake8>3.0
 yapf
+isort