├── .gitattributes
├── class_diagram.png
├── requirements.txt
├── skstab
    ├── __version__.py
    ├── __init__.py
    ├── metrics.py
    ├── datasets.py
    └── perturbation.py
├── setup.py
├── datasets
    ├── target
    │   ├── zoo.csv
    │   ├── exemple_scale_square_1.csv
    │   ├── iris.csv
    │   ├── tae.csv
    │   ├── wine.csv
    │   ├── wine_tnse.csv
    │   ├── wine_tsne.csv
    │   ├── wine_umap.csv
    │   ├── crabs.csv
    │   ├── exemple_scale_square_2.csv
    │   ├── seeds.csv
    │   ├── hepta.csv
    │   ├── thy.csv
    │   ├── spherical_5_2.csv
    │   ├── faithful.csv
    │   ├── spherical_6_2.csv
    │   ├── zelnik2.csv
    │   ├── exemple_scale_square_3.csv
    │   ├── haberman.csv
    │   ├── sonar.csv
    │   ├── tetra.csv
    │   ├── spherical_4_3.csv
    │   ├── exemple_scale_square_4.csv
    │   ├── iono.csv
    │   ├── arrhythmia.csv
    │   ├── ecoli.csv
    │   ├── ds4c2sc8.csv
    │   ├── elliptical_10_2.csv
    │   ├── exemple_scale_square_5.csv
    │   ├── exemples1_3g.csv
    │   ├── ds-577.csv
    │   ├── exemple_scale_square_6.csv
    │   ├── 4clusters_twins.csv
    │   ├── zelnik4.csv
    │   ├── r15.csv
    │   ├── 3clusters_elephant.csv
    │   ├── exemple_scale_square_7.csv
    │   ├── glass.csv
    │   ├── 2d-3c-no123.csv
    │   ├── diabetes.csv
    │   ├── balance-scale.csv
    │   ├── exemples7_elbow_3g.csv
    │   ├── twodiamonds.csv
    │   ├── exemple_scale_square_8.csv
    │   ├── ds-850.csv
    │   ├── 2d-4c-no4.csv
    │   ├── 2d-4c-no9.csv
    │   ├── st900.csv
    │   └── longsquare.csv
    └── data
    │   ├── real
    │       ├── iris.csv
    │       └── faithful.csv
    │   └── artificial
    │       ├── spherical_5_2.csv
    │       └── spherical_6_2.csv
├── example_modelexplorer.py
├── LICENSE
├── example_modelorderselection.py
├── .gitignore
├── example_stadion.py
└── README.md


/.gitattributes:
--------------------------------------------------------------------------------
1 | *.ipynb linguist-vendored
2 | 


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/class_diagram.png:
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https://raw.githubusercontent.com/FlorentF9/skstab/HEAD/class_diagram.png


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/requirements.txt:
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1 | joblib
2 | matplotlib
3 | numpy
4 | pandas
5 | scikit-learn
6 | scipy


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/skstab/__version__.py:
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1 | """
2 | skstab - Version
3 | """
4 | 
5 | __version__ = '1.0'
6 | 


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/skstab/__init__.py:
--------------------------------------------------------------------------------
 1 | """
 2 | skstab - Init file
 3 | 
 4 | @author Florent Forest, Alex Mourer
 5 | """
 6 | 
 7 | from .__version__ import __version__
 8 | 
 9 | from .stability import BaseStability, ReferenceComparisonStability, PairwiseComparisonStability, LabelTransferStability
10 | from .stability import StadionEstimator, ModelExplorer, ModelOrderSelection
11 | 
12 | __all__ = [
13 |     'BaseStability',
14 |     'ReferenceComparisonStability',
15 |     'PairwiseComparisonStability',
16 |     'LabelTransferStability',
17 |     'StadionEstimator',
18 |     'ModelExplorer',
19 |     'ModelOrderSelection'
20 | ]
21 | 


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/setup.py:
--------------------------------------------------------------------------------
 1 | from setuptools import setup, find_packages
 2 | 
 3 | with open('requirements.txt') as fp:
 4 |     install_reqs = [r.rstrip() for r in fp.readlines()
 5 |                     if not r.startswith('#') and not r.startswith('git+')]
 6 | 
 7 | with open('skstab/__version__.py') as fh:
 8 |     version = fh.readlines()[-1].split()[-1].strip('\'\'')
 9 | 
10 | setup(
11 |     name='skstab',
12 |     version=version,
13 |     description='Clustering stability analysis with a scikit-learn compatible API',
14 |     author='Florent Forest, Alex Mourer',
15 |     author_email='f@florentfo.rest',
16 |     packages=find_packages(),
17 |     install_requires=install_reqs,
18 |     url='https://github.com/FlorentF9/skstab'
19 | )
20 | 


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/datasets/target/zoo.csv:
--------------------------------------------------------------------------------
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102 | 2
103 | 


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/datasets/target/exemple_scale_square_1.csv:
--------------------------------------------------------------------------------
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103 | 1
104 | 


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/example_modelexplorer.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | from skstab import ModelExplorer
 3 | from skstab.datasets import load_dataset
 4 | from sklearn.cluster import KMeans
 5 | 
 6 | dataset = 'exemples2_5g'
 7 | X, y = load_dataset(dataset)
 8 | print('Dataset: {} (true number of clusters: K = {})'.format(dataset, len(np.unique(y))))
 9 | 
10 | algorithm = KMeans
11 | km_kwargs = {'init': 'k-means++', 'n_init': 10}
12 | 
13 | k_values = list(range(2, 11))
14 | print('Evaluated numbers of clusters:', k_values)
15 | 
16 | stab = ModelExplorer(X, algorithm,
17 |                      param_name='n_clusters',
18 |                      param_values=k_values,
19 |                      f=0.8,
20 |                      runs=100,
21 |                      algo_kwargs=km_kwargs,
22 |                      n_jobs=-1)
23 | 
24 | score = stab.score()
25 | print('Model explorer scores:\n', score)
26 | k_hat = stab.select_param()[0]
27 | print('Selected number of clusters: K =', k_hat)
28 | 


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2020 Florent Forest, Alex Mourer
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/datasets/target/tae.csv:
--------------------------------------------------------------------------------
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153 | 


--------------------------------------------------------------------------------
/example_modelorderselection.py:
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 1 | import numpy as np
 2 | from skstab import ModelOrderSelection
 3 | from skstab.datasets import load_dataset
 4 | from sklearn.cluster import KMeans
 5 | from sklearn.neighbors import KNeighborsClassifier
 6 | 
 7 | dataset = 'exemples2_5g'
 8 | X, y = load_dataset(dataset)
 9 | print('Dataset: {} (true number of clusters: K = {})'.format(dataset, len(np.unique(y))))
10 | 
11 | algorithm = KMeans
12 | km_kwargs = {'init': 'k-means++', 'n_init': 10}
13 | 
14 | k_values = list(range(2, 11))
15 | print('Evaluated numbers of clusters:', k_values)
16 | 
17 | stab = ModelOrderSelection(X, algorithm,
18 |                            param_name='n_clusters',
19 |                            param_values=k_values,
20 |                            classifier=KNeighborsClassifier,
21 |                            norm_samples=20,
22 |                            runs=20,
23 |                            algo_kwargs=km_kwargs,
24 |                            clf_kwargs={'n_neighbors': 1},
25 |                            n_jobs=-1)
26 | 
27 | score = stab.score()
28 | print('Model order selection scores:\n', score)
29 | k_hat = stab.select_param()[0]
30 | print('Selected number of clusters: K =', k_hat)
31 | 


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--------------------------------------------------------------------------------
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180 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/datasets/target/wine_tsne.csv:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/skstab/metrics.py:
--------------------------------------------------------------------------------
 1 | """
 2 | skstab - Clustering metrics
 3 | 
 4 | @author Florent Forest, Alex Mourer
 5 | """
 6 | 
 7 | import numpy as np
 8 | from scipy.optimize import linear_sum_assignment
 9 | 
10 | 
11 | def _contingency_matrix(y_true, y_pred):
12 |     w = np.zeros((y_true.max() + 1, y_pred.max() + 1), dtype=np.int64)
13 |     for c, k in zip(y_true, y_pred):
14 |         w[c, k] += 1  # w[c, k] = number of c-labeled samples in cluster k
15 |     return w
16 | 
17 | 
18 | def clustering_accuracy(y_true, y_pred):
19 |     """Unsupervised clustering accuracy.
20 | 
21 |     Can only be used if the number of target classes in y_true is equal to the number of clusters in y_pred.
22 | 
23 |     Parameters
24 |     ----------
25 |     y_true : array, shape = [n]
26 |         true labels.
27 |     y_pred : array, shape = [n]
28 |         predicted cluster ids.
29 | 
30 |     Returns
31 |     -------
32 |     accuracy : float in [0,1] (higher is better)
33 |         accuracy score.
34 |     """
35 |     y_true = y_true.astype(np.int64)
36 |     y_pred = y_pred.astype(np.int64)
37 |     w = _contingency_matrix(y_true, y_pred).T
38 |     row_ind, col_ind = linear_sum_assignment(w.max() - w)
39 |     return w[row_ind, col_ind].sum() * 1.0 / y_pred.size
40 | 
41 | 
42 | def minimum_matching_distance(y_true, y_pred):
43 |     """Minimum matching distance (MMD).
44 | 
45 |     Can only be used if the number of target classes in y_true is equal to the number of clusters in y_pred.
46 | 
47 |     Parameters
48 |     ----------
49 |     y_true : array, shape = [n]
50 |         true labels.
51 |     y_pred : array, shape = [n]
52 |         predicted cluster ids.
53 | 
54 |     Returns
55 |     -------
56 |     mmd : float in [0,1]
57 |         minimum matching distance.
58 |     """
59 |     return 1.0 - clustering_accuracy(y_true, y_pred)
60 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/datasets/target/exemple_scale_square_3.csv:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/datasets/target/haberman.csv:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | pip-wheel-metadata/
 24 | share/python-wheels/
 25 | *.egg-info/
 26 | .installed.cfg
 27 | *.egg
 28 | MANIFEST
 29 | 
 30 | # PyInstaller
 31 | #  Usually these files are written by a python script from a template
 32 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 33 | *.manifest
 34 | *.spec
 35 | 
 36 | # Installer logs
 37 | pip-log.txt
 38 | pip-delete-this-directory.txt
 39 | 
 40 | # Unit test / coverage reports
 41 | htmlcov/
 42 | .tox/
 43 | .nox/
 44 | .coverage
 45 | .coverage.*
 46 | .cache
 47 | nosetests.xml
 48 | coverage.xml
 49 | *.cover
 50 | *.py,cover
 51 | .hypothesis/
 52 | .pytest_cache/
 53 | 
 54 | # Translations
 55 | *.mo
 56 | *.pot
 57 | 
 58 | # Django stuff:
 59 | *.log
 60 | local_settings.py
 61 | db.sqlite3
 62 | db.sqlite3-journal
 63 | 
 64 | # Flask stuff:
 65 | instance/
 66 | .webassets-cache
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | target/
 76 | 
 77 | # Jupyter Notebook
 78 | .ipynb_checkpoints
 79 | 
 80 | # IPython
 81 | profile_default/
 82 | ipython_config.py
 83 | 
 84 | # pyenv
 85 | .python-version
 86 | 
 87 | # pipenv
 88 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 89 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 90 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 91 | #   install all needed dependencies.
 92 | #Pipfile.lock
 93 | 
 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 95 | __pypackages__/
 96 | 
 97 | # Celery stuff
 98 | celerybeat-schedule
 99 | celerybeat.pid
100 | 
101 | # SageMath parsed files
102 | *.sage.py
103 | 
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 | 
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 | 
117 | # Rope project settings
118 | .ropeproject
119 | 
120 | # mkdocs documentation
121 | /site
122 | 
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 | 
128 | # Pyre type checker
129 | .pyre/
130 | 


--------------------------------------------------------------------------------
/skstab/datasets.py:
--------------------------------------------------------------------------------
  1 | """
  2 | skstab - Data loading utility functions
  3 | 
  4 | @author Florent Forest, Alex Mourer
  5 | """
  6 | 
  7 | import os
  8 | import pandas as pd
  9 | from sklearn.preprocessing import StandardScaler
 10 | 
 11 | 
 12 | ARTIFICIAL_DATASETS = [
 13 |     '2d_10c', 
 14 |     'r15', 
 15 |     'd31', 
 16 |     'elliptical_10_2', 
 17 |     'twenty',
 18 |     'fourty',
 19 |     'hepta', 
 20 |     'tetra',
 21 |     '2d-3c-no123', 
 22 |     '2d-4c',
 23 |     '2d-4c-no4',
 24 |     '2d-4c-no9',
 25 |     'curves1',
 26 |     'diamond9',
 27 |     'ds-577',
 28 |     'ds-850',
 29 |     'elly-2d10c13s',
 30 |     'engytime',
 31 |     'ds4c2sc8',
 32 |     'long1',
 33 |     'long2',
 34 |     'long3',
 35 |     'longsquare',
 36 |     'sizes1',
 37 |     'sizes2',
 38 |     'sizes3',
 39 |     'sizes4',
 40 |     'sizes5',
 41 |     'spherical_4_3',
 42 |     'spherical_5_2',
 43 |     'spherical_6_2',
 44 |     'square1',
 45 |     'square2',
 46 |     'square3',
 47 |     'square4',
 48 |     'square5',
 49 |     'st900',
 50 |     'triangle1',
 51 |     'triangle2',
 52 |     'twodiamonds',
 53 |     'wingnut',
 54 |     'xclara',
 55 |     'zelnik2',
 56 |     'zelnik4',
 57 |     'exemples5_overlap2_3g',
 58 |     'exemples4_overlap_3g',
 59 |     'exemples8_Overlap_Uvar_5g',
 60 |     'exemples9_YoD_6g',
 61 |     'exemples6_quicunx_4g',
 62 |     'exemples10_WellS_3g',
 63 |     'exemples3_Uvar_4g',
 64 |     'exemples1_3g',
 65 |     'exemples2_5g',
 66 |     'exemples7_elbow_3g',
 67 |     '3clusters_elephant',
 68 |     '4clusters_corner',
 69 |     '4clusters_twins',
 70 |     '5clusters_stars',
 71 |     'a1',
 72 |     'a2',
 73 |     'g2-2',
 74 |     'g2-16',
 75 |     'g2-64',
 76 |     's1',
 77 |     's2',
 78 |     's3',
 79 |     's4',
 80 | ]
 81 | 
 82 | REAL_DATASETS = [
 83 |     'crabs',
 84 |     'iris',
 85 |     'oldfaithful',
 86 |     'wine_umap',
 87 |     'mfds_umap',
 88 |     'usps_umap',
 89 |     'mnist_umap'
 90 | ]
 91 | 
 92 | 
 93 | def load_dataset(name, path='./datasets'):
 94 |     if name in ARTIFICIAL_DATASETS:
 95 |         data_path = os.path.join(path, 'data/artificial')
 96 |     elif name in REAL_DATASETS:
 97 |         data_path = os.path.join(path, 'data/real')
 98 |     else:
 99 |         raise ValueError('Unknown dataset!')
100 |     target_path = os.path.join(path, 'target')
101 |     x = pd.read_csv(os.path.join(data_path, '{}.csv'.format(name)), sep=';', decimal=',').values
102 |     y = pd.read_csv(os.path.join(target_path, '{}.csv'.format(name))).values.ravel()
103 |     x_scaled = StandardScaler().fit_transform(x)
104 |     del x
105 |     return x_scaled, y
106 | 


--------------------------------------------------------------------------------
/datasets/target/sonar.csv:
--------------------------------------------------------------------------------
  1 | "x"
  2 | "Rock"
  3 | "Rock"
  4 | "Rock"
  5 | "Rock"
  6 | "Rock"
  7 | "Rock"
  8 | "Rock"
  9 | "Rock"
 10 | "Rock"
 11 | "Rock"
 12 | "Rock"
 13 | "Rock"
 14 | "Rock"
 15 | "Rock"
 16 | "Rock"
 17 | "Rock"
 18 | "Rock"
 19 | "Rock"
 20 | "Rock"
 21 | "Rock"
 22 | "Rock"
 23 | "Rock"
 24 | "Rock"
 25 | "Rock"
 26 | "Rock"
 27 | "Rock"
 28 | "Rock"
 29 | "Rock"
 30 | "Rock"
 31 | "Rock"
 32 | "Rock"
 33 | "Rock"
 34 | "Rock"
 35 | "Rock"
 36 | "Rock"
 37 | "Rock"
 38 | "Rock"
 39 | "Rock"
 40 | "Rock"
 41 | "Rock"
 42 | "Rock"
 43 | "Rock"
 44 | "Rock"
 45 | "Rock"
 46 | "Rock"
 47 | "Rock"
 48 | "Rock"
 49 | "Rock"
 50 | "Rock"
 51 | "Rock"
 52 | "Rock"
 53 | "Rock"
 54 | "Rock"
 55 | "Rock"
 56 | "Rock"
 57 | "Rock"
 58 | "Rock"
 59 | "Rock"
 60 | "Rock"
 61 | "Rock"
 62 | "Rock"
 63 | "Rock"
 64 | "Rock"
 65 | "Rock"
 66 | "Rock"
 67 | "Rock"
 68 | "Rock"
 69 | "Rock"
 70 | "Rock"
 71 | "Rock"
 72 | "Rock"
 73 | "Rock"
 74 | "Rock"
 75 | "Rock"
 76 | "Rock"
 77 | "Rock"
 78 | "Rock"
 79 | "Rock"
 80 | "Rock"
 81 | "Rock"
 82 | "Rock"
 83 | "Rock"
 84 | "Rock"
 85 | "Rock"
 86 | "Rock"
 87 | "Rock"
 88 | "Rock"
 89 | "Rock"
 90 | "Rock"
 91 | "Rock"
 92 | "Rock"
 93 | "Rock"
 94 | "Rock"
 95 | "Rock"
 96 | "Rock"
 97 | "Rock"
 98 | "Rock"
 99 | "Mine"
100 | "Mine"
101 | "Mine"
102 | "Mine"
103 | "Mine"
104 | "Mine"
105 | "Mine"
106 | "Mine"
107 | "Mine"
108 | "Mine"
109 | "Mine"
110 | "Mine"
111 | "Mine"
112 | "Mine"
113 | "Mine"
114 | "Mine"
115 | "Mine"
116 | "Mine"
117 | "Mine"
118 | "Mine"
119 | "Mine"
120 | "Mine"
121 | "Mine"
122 | "Mine"
123 | "Mine"
124 | "Mine"
125 | "Mine"
126 | "Mine"
127 | "Mine"
128 | "Mine"
129 | "Mine"
130 | "Mine"
131 | "Mine"
132 | "Mine"
133 | "Mine"
134 | "Mine"
135 | "Mine"
136 | "Mine"
137 | "Mine"
138 | "Mine"
139 | "Mine"
140 | "Mine"
141 | "Mine"
142 | "Mine"
143 | "Mine"
144 | "Mine"
145 | "Mine"
146 | "Mine"
147 | "Mine"
148 | "Mine"
149 | "Mine"
150 | "Mine"
151 | "Mine"
152 | "Mine"
153 | "Mine"
154 | "Mine"
155 | "Mine"
156 | "Mine"
157 | "Mine"
158 | "Mine"
159 | "Mine"
160 | "Mine"
161 | "Mine"
162 | "Mine"
163 | "Mine"
164 | "Mine"
165 | "Mine"
166 | "Mine"
167 | "Mine"
168 | "Mine"
169 | "Mine"
170 | "Mine"
171 | "Mine"
172 | "Mine"
173 | "Mine"
174 | "Mine"
175 | "Mine"
176 | "Mine"
177 | "Mine"
178 | "Mine"
179 | "Mine"
180 | "Mine"
181 | "Mine"
182 | "Mine"
183 | "Mine"
184 | "Mine"
185 | "Mine"
186 | "Mine"
187 | "Mine"
188 | "Mine"
189 | "Mine"
190 | "Mine"
191 | "Mine"
192 | "Mine"
193 | "Mine"
194 | "Mine"
195 | "Mine"
196 | "Mine"
197 | "Mine"
198 | "Mine"
199 | "Mine"
200 | "Mine"
201 | "Mine"
202 | "Mine"
203 | "Mine"
204 | "Mine"
205 | "Mine"
206 | "Mine"
207 | "Mine"
208 | "Mine"
209 | "Mine"
210 | 


--------------------------------------------------------------------------------
/datasets/data/real/iris.csv:
--------------------------------------------------------------------------------
  1 | "V1";"V2";"V3";"V4"
  2 | 4,8;3,4;1,9;0,2
  3 | 4,5;2,3;1,3;0,3
  4 | 4,6;3,4;1,4;0,3
  5 | 6,8;3;5,5;2,1
  6 | 5;3,4;1,6;0,4
  7 | 6,2;2,9;4,3;1,3
  8 | 5,7;2,8;4,5;1,3
  9 | 5,6;2,9;3,6;1,3
 10 | 5,4;3,9;1,3;0,4
 11 | 7,4;2,8;6,1;1,9
 12 | 6,7;3,1;5,6;2,4
 13 | 5,8;2,7;5,1;1,9
 14 | 6,1;2,9;4,7;1,4
 15 | 4,9;2,5;4,5;1,7
 16 | 6;2,2;4;1
 17 | 5,8;2,8;5,1;2,4
 18 | 5;3,5;1,6;0,6
 19 | 6,4;3,2;4,5;1,5
 20 | 4,3;3;1,1;0,1
 21 | 6,4;2,8;5,6;2,2
 22 | 6,5;3;5,2;2
 23 | 6,7;2,5;5,8;1,8
 24 | 5,4;3,4;1,5;0,4
 25 | 5,8;2,7;5,1;1,9
 26 | 6,3;2,3;4,4;1,3
 27 | 6,3;3,3;4,7;1,6
 28 | 4,7;3,2;1,3;0,2
 29 | 6,9;3,1;4,9;1,5
 30 | 5,2;2,7;3,9;1,4
 31 | 5,8;4;1,2;0,2
 32 | 5,2;3,4;1,4;0,2
 33 | 5,7;3,8;1,7;0,3
 34 | 6,6;3;4,4;1,4
 35 | 7;3,2;4,7;1,4
 36 | 7,2;3,2;6;1,8
 37 | 5,8;2,6;4;1,2
 38 | 7,3;2,9;6,3;1,8
 39 | 6,1;3;4,9;1,8
 40 | 6;2,9;4,5;1,5
 41 | 6,5;3;5,5;1,8
 42 | 4,8;3,4;1,6;0,2
 43 | 5,5;2,4;3,7;1
 44 | 5,5;4,2;1,4;0,2
 45 | 6,4;3,1;5,5;1,8
 46 | 5;3,5;1,3;0,3
 47 | 5,5;2,4;3,8;1,1
 48 | 4,4;3,2;1,3;0,2
 49 | 5,2;3,5;1,5;0,2
 50 | 5,6;3;4,1;1,3
 51 | 6,3;2,8;5,1;1,5
 52 | 5,9;3;5,1;1,8
 53 | 6,5;3;5,8;2,2
 54 | 5,9;3;4,2;1,5
 55 | 5,1;3,8;1,9;0,4
 56 | 5,4;3,4;1,7;0,2
 57 | 4,8;3;1,4;0,3
 58 | 5,6;2,5;3,9;1,1
 59 | 5;2,3;3,3;1
 60 | 6,8;3,2;5,9;2,3
 61 | 6;3;4,8;1,8
 62 | 5,4;3;4,5;1,5
 63 | 7,6;3;6,6;2,1
 64 | 5,2;4,1;1,5;0,1
 65 | 7,7;3,8;6,7;2,2
 66 | 6,5;3,2;5,1;2
 67 | 6,2;2,8;4,8;1,8
 68 | 6;3,4;4,5;1,6
 69 | 4,6;3,1;1,5;0,2
 70 | 5;3,3;1,4;0,2
 71 | 5,1;3,8;1,6;0,2
 72 | 6,3;2,5;4,9;1,5
 73 | 5,1;2,5;3;1,1
 74 | 5,3;3,7;1,5;0,2
 75 | 5,7;2,5;5;2
 76 | 5,6;2,7;4,2;1,3
 77 | 6,3;3,4;5,6;2,4
 78 | 6,7;3;5;1,7
 79 | 6,1;2,8;4,7;1,2
 80 | 5,4;3,9;1,7;0,4
 81 | 5,5;2,6;4,4;1,2
 82 | 6,3;3,3;6;2,5
 83 | 7,2;3;5,8;1,6
 84 | 6,9;3,1;5,4;2,1
 85 | 4,6;3,2;1,4;0,2
 86 | 5,1;3,4;1,5;0,2
 87 | 4,9;3;1,4;0,2
 88 | 6,9;3,2;5,7;2,3
 89 | 5,1;3,8;1,5;0,3
 90 | 6,1;2,8;4;1,3
 91 | 6,2;2,2;4,5;1,5
 92 | 6,1;2,6;5,6;1,4
 93 | 5,1;3,5;1,4;0,2
 94 | 4,9;3,1;1,5;0,1
 95 | 6,3;2,5;5;1,9
 96 | 7,7;3;6,1;2,3
 97 | 6,8;2,8;4,8;1,4
 98 | 6,4;2,9;4,3;1,3
 99 | 5;3;1,6;0,2
100 | 4,8;3;1,4;0,1
101 | 5,8;2,7;3,9;1,2
102 | 6,4;2,7;5,3;1,9
103 | 6,7;3,1;4,4;1,4
104 | 5;3,6;1,4;0,2
105 | 5,8;2,7;4,1;1
106 | 6,2;3,4;5,4;2,3
107 | 6,3;2,9;5,6;1,8
108 | 7,9;3,8;6,4;2
109 | 5,1;3,7;1,5;0,4
110 | 5;3,4;1,5;0,2
111 | 5,1;3,3;1,7;0,5
112 | 4,4;2,9;1,4;0,2
113 | 5,5;2,3;4;1,3
114 | 6,7;3;5,2;2,3
115 | 5,7;2,6;3,5;1
116 | 4,4;3;1,3;0,2
117 | 6;2,2;5;1,5
118 | 5;2;3,5;1
119 | 6,6;2,9;4,6;1,3
120 | 7,2;3,6;6,1;2,5
121 | 5,7;3;4,2;1,2
122 | 4,8;3,1;1,6;0,2
123 | 5;3,2;1,2;0,2
124 | 5,5;3,5;1,3;0,2
125 | 5,4;3,7;1,5;0,2
126 | 5,1;3,5;1,4;0,3
127 | 7,7;2,8;6,7;2
128 | 5,5;2,5;4;1,3
129 | 6,1;3;4,6;1,4
130 | 6,7;3,3;5,7;2,5
131 | 7,7;2,6;6,9;2,3
132 | 6;2,7;5,1;1,6
133 | 6,4;2,8;5,6;2,1
134 | 6,7;3,1;4,7;1,5
135 | 6,3;2,7;4,9;1,8
136 | 4,9;2,4;3,3;1
137 | 6,4;3,2;5,3;2,3
138 | 5,9;3,2;4,8;1,8
139 | 4,7;3,2;1,6;0,2
140 | 4,9;3,1;1,5;0,1
141 | 4,6;3,6;1;0,2
142 | 5,7;2,8;4,1;1,3
143 | 4,9;3,1;1,5;0,1
144 | 5,6;2,8;4,9;2
145 | 7,1;3;5,9;2,1
146 | 5,6;3;4,5;1,5
147 | 6,7;3,3;5,7;2,1
148 | 6,5;2,8;4,6;1,5
149 | 6,9;3,1;5,1;2,3
150 | 5,7;4,4;1,5;0,4
151 | 5,7;2,9;4,2;1,3
152 | 


--------------------------------------------------------------------------------
/datasets/target/tetra.csv:
--------------------------------------------------------------------------------
  1 | "x"
  2 | 1
  3 | 1
  4 | 1
  5 | 1
  6 | 1
  7 | 1
  8 | 1
  9 | 1
 10 | 1
 11 | 1
 12 | 1
 13 | 1
 14 | 1
 15 | 1
 16 | 1
 17 | 1
 18 | 1
 19 | 1
 20 | 1
 21 | 1
 22 | 1
 23 | 1
 24 | 1
 25 | 1
 26 | 1
 27 | 1
 28 | 1
 29 | 1
 30 | 1
 31 | 1
 32 | 1
 33 | 1
 34 | 1
 35 | 1
 36 | 1
 37 | 1
 38 | 1
 39 | 1
 40 | 1
 41 | 1
 42 | 1
 43 | 1
 44 | 1
 45 | 1
 46 | 1
 47 | 1
 48 | 1
 49 | 1
 50 | 1
 51 | 1
 52 | 1
 53 | 1
 54 | 1
 55 | 1
 56 | 1
 57 | 1
 58 | 1
 59 | 1
 60 | 1
 61 | 1
 62 | 1
 63 | 1
 64 | 1
 65 | 1
 66 | 1
 67 | 1
 68 | 1
 69 | 1
 70 | 1
 71 | 1
 72 | 1
 73 | 1
 74 | 1
 75 | 1
 76 | 1
 77 | 1
 78 | 1
 79 | 1
 80 | 1
 81 | 1
 82 | 1
 83 | 1
 84 | 1
 85 | 1
 86 | 1
 87 | 1
 88 | 1
 89 | 1
 90 | 1
 91 | 1
 92 | 1
 93 | 1
 94 | 1
 95 | 1
 96 | 1
 97 | 1
 98 | 1
 99 | 1
100 | 1
101 | 1
102 | 2
103 | 2
104 | 2
105 | 2
106 | 2
107 | 2
108 | 2
109 | 2
110 | 2
111 | 2
112 | 2
113 | 2
114 | 2
115 | 2
116 | 2
117 | 2
118 | 2
119 | 2
120 | 2
121 | 2
122 | 2
123 | 2
124 | 2
125 | 2
126 | 2
127 | 2
128 | 2
129 | 2
130 | 2
131 | 2
132 | 2
133 | 2
134 | 2
135 | 2
136 | 2
137 | 2
138 | 2
139 | 2
140 | 2
141 | 2
142 | 2
143 | 2
144 | 2
145 | 2
146 | 2
147 | 2
148 | 2
149 | 2
150 | 2
151 | 2
152 | 2
153 | 2
154 | 2
155 | 2
156 | 2
157 | 2
158 | 2
159 | 2
160 | 2
161 | 2
162 | 2
163 | 2
164 | 2
165 | 2
166 | 2
167 | 2
168 | 2
169 | 2
170 | 2
171 | 2
172 | 2
173 | 2
174 | 2
175 | 2
176 | 2
177 | 2
178 | 2
179 | 2
180 | 2
181 | 2
182 | 2
183 | 2
184 | 2
185 | 2
186 | 2
187 | 2
188 | 2
189 | 2
190 | 2
191 | 2
192 | 2
193 | 2
194 | 2
195 | 2
196 | 2
197 | 2
198 | 2
199 | 2
200 | 2
201 | 2
202 | 3
203 | 3
204 | 3
205 | 3
206 | 3
207 | 3
208 | 3
209 | 3
210 | 3
211 | 3
212 | 3
213 | 3
214 | 3
215 | 3
216 | 3
217 | 3
218 | 3
219 | 3
220 | 3
221 | 3
222 | 3
223 | 3
224 | 3
225 | 3
226 | 3
227 | 3
228 | 3
229 | 3
230 | 3
231 | 3
232 | 3
233 | 3
234 | 3
235 | 3
236 | 3
237 | 3
238 | 3
239 | 3
240 | 3
241 | 3
242 | 3
243 | 3
244 | 3
245 | 3
246 | 3
247 | 3
248 | 3
249 | 3
250 | 3
251 | 3
252 | 3
253 | 3
254 | 3
255 | 3
256 | 3
257 | 3
258 | 3
259 | 3
260 | 3
261 | 3
262 | 3
263 | 3
264 | 3
265 | 3
266 | 3
267 | 3
268 | 3
269 | 3
270 | 3
271 | 3
272 | 3
273 | 3
274 | 3
275 | 3
276 | 3
277 | 3
278 | 3
279 | 3
280 | 3
281 | 3
282 | 3
283 | 3
284 | 3
285 | 3
286 | 3
287 | 3
288 | 3
289 | 3
290 | 3
291 | 3
292 | 3
293 | 3
294 | 3
295 | 3
296 | 3
297 | 3
298 | 3
299 | 3
300 | 3
301 | 3
302 | 4
303 | 4
304 | 4
305 | 4
306 | 4
307 | 4
308 | 4
309 | 4
310 | 4
311 | 4
312 | 4
313 | 4
314 | 4
315 | 4
316 | 4
317 | 4
318 | 4
319 | 4
320 | 4
321 | 4
322 | 4
323 | 4
324 | 4
325 | 4
326 | 4
327 | 4
328 | 4
329 | 4
330 | 4
331 | 4
332 | 4
333 | 4
334 | 4
335 | 4
336 | 4
337 | 4
338 | 4
339 | 4
340 | 4
341 | 4
342 | 4
343 | 4
344 | 4
345 | 4
346 | 4
347 | 4
348 | 4
349 | 4
350 | 4
351 | 4
352 | 4
353 | 4
354 | 4
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454 | 


--------------------------------------------------------------------------------
/datasets/target/ecoli.csv:
--------------------------------------------------------------------------------
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338 | 


--------------------------------------------------------------------------------
/datasets/target/ds4c2sc8.csv:
--------------------------------------------------------------------------------
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355 | 6
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386 | 7
387 | 7
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389 | 7
390 | 7
391 | 7
392 | 7
393 | 7
394 | 7
395 | 7
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398 | 7
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400 | 7
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402 | 7
403 | 7
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405 | 7
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411 | 7
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414 | 7
415 | 7
416 | 7
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419 | 7
420 | 7
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422 | 7
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427 | 7
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451 | 8
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456 | 8
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464 | 8
465 | 8
466 | 8
467 | 8
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470 | 8
471 | 8
472 | 8
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474 | 8
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477 | 8
478 | 8
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480 | 8
481 | 8
482 | 8
483 | 8
484 | 8
485 | 8
486 | 8
487 | 


--------------------------------------------------------------------------------
/datasets/data/real/faithful.csv:
--------------------------------------------------------------------------------
  1 | "V2";"V3"
  2 | 3,6;79
  3 | 1,8;54
  4 | 3,333;74
  5 | 2,283;62
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 24 | 3,45;78
 25 | 3,067;69
 26 | 4,533;74
 27 | 3,6;83
 28 | 1,967;55
 29 | 4,083;76
 30 | 3,85;78
 31 | 4,433;79
 32 | 4,3;73
 33 | 4,467;77
 34 | 3,367;66
 35 | 4,033;80
 36 | 3,833;74
 37 | 2,017;52
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 41 | 4,783;90
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 43 | 1,883;58
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 46 | 4,533;73
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 49 | 2,1;53
 50 | 4,633;82
 51 | 2;59
 52 | 4,8;75
 53 | 4,716;90
 54 | 1,833;54
 55 | 4,833;80
 56 | 1,733;54
 57 | 4,883;83
 58 | 3,717;71
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 62 | 2,233;59
 63 | 4,5;84
 64 | 1,75;48
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 67 | 4,4;92
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 69 | 4,7;78
 70 | 2,067;65
 71 | 4,7;73
 72 | 4,033;82
 73 | 1,967;56
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 75 | 4;71
 76 | 1,983;62
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 78 | 2,017;60
 79 | 4,567;78
 80 | 3,883;76
 81 | 3,6;83
 82 | 4,133;75
 83 | 4,333;82
 84 | 4,1;70
 85 | 2,633;65
 86 | 4,067;73
 87 | 4,933;88
 88 | 3,95;76
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 90 | 2,167;48
 91 | 4;86
 92 | 2,2;60
 93 | 4,333;90
 94 | 1,867;50
 95 | 4,817;78
 96 | 1,833;63
 97 | 4,3;72
 98 | 4,667;84
 99 | 3,75;75
100 | 1,867;51
101 | 4,9;82
102 | 2,483;62
103 | 4,367;88
104 | 2,1;49
105 | 4,5;83
106 | 4,05;81
107 | 1,867;47
108 | 4,7;84
109 | 1,783;52
110 | 4,85;86
111 | 3,683;81
112 | 4,733;75
113 | 2,3;59
114 | 4,9;89
115 | 4,417;79
116 | 1,7;59
117 | 4,633;81
118 | 2,317;50
119 | 4,6;85
120 | 1,817;59
121 | 4,417;87
122 | 2,617;53
123 | 4,067;69
124 | 4,25;77
125 | 1,967;56
126 | 4,6;88
127 | 3,767;81
128 | 1,917;45
129 | 4,5;82
130 | 2,267;55
131 | 4,65;90
132 | 1,867;45
133 | 4,167;83
134 | 2,8;56
135 | 4,333;89
136 | 1,833;46
137 | 4,383;82
138 | 1,883;51
139 | 4,933;86
140 | 2,033;53
141 | 3,733;79
142 | 4,233;81
143 | 2,233;60
144 | 4,533;82
145 | 4,817;77
146 | 4,333;76
147 | 1,983;59
148 | 4,633;80
149 | 2,017;49
150 | 5,1;96
151 | 1,8;53
152 | 5,033;77
153 | 4;77
154 | 2,4;65
155 | 4,6;81
156 | 3,567;71
157 | 4;70
158 | 4,5;81
159 | 4,083;93
160 | 1,8;53
161 | 3,967;89
162 | 2,2;45
163 | 4,15;86
164 | 2;58
165 | 3,833;78
166 | 3,5;66
167 | 4,583;76
168 | 2,367;63
169 | 5;88
170 | 1,933;52
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172 | 1,917;49
173 | 2,083;57
174 | 4,583;77
175 | 3,333;68
176 | 4,167;81
177 | 4,333;81
178 | 4,5;73
179 | 2,417;50
180 | 4;85
181 | 4,167;74
182 | 1,883;55
183 | 4,583;77
184 | 4,25;83
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196 | 3,966;77
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200 | 2,25;51
201 | 4,667;78
202 | 2,1;60
203 | 4,35;82
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205 | 1,867;53
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207 | 1,783;46
208 | 4,367;77
209 | 3,85;84
210 | 1,933;49
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212 | 2,383;71
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214 | 1,867;49
215 | 3,833;75
216 | 3,417;64
217 | 4,233;76
218 | 2,4;53
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220 | 2;55
221 | 4,15;76
222 | 1,867;50
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224 | 1,75;54
225 | 4,483;75
226 | 4;78
227 | 4,117;79
228 | 4,083;78
229 | 4,267;78
230 | 3,917;70
231 | 4,55;79
232 | 4,083;70
233 | 2,417;54
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235 | 2,217;50
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237 | 1,883;54
238 | 1,85;54
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240 | 3,95;79
241 | 2,333;64
242 | 4,15;75
243 | 2,35;47
244 | 4,933;86
245 | 2,9;63
246 | 4,583;85
247 | 3,833;82
248 | 2,083;57
249 | 4,367;82
250 | 2,133;67
251 | 4,35;74
252 | 2,2;54
253 | 4,45;83
254 | 3,567;73
255 | 4,5;73
256 | 4,15;88
257 | 3,817;80
258 | 3,917;71
259 | 4,45;83
260 | 2;56
261 | 4,283;79
262 | 4,767;78
263 | 4,533;84
264 | 1,85;58
265 | 4,25;83
266 | 1,983;43
267 | 2,25;60
268 | 4,75;75
269 | 4,117;81
270 | 2,15;46
271 | 4,417;90
272 | 1,817;46
273 | 4,467;74
274 | 


--------------------------------------------------------------------------------
/example_stadion.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | import matplotlib.pyplot as plt
 3 | import numpy as np
 4 | from skstab import StadionEstimator
 5 | from skstab.datasets import load_dataset
 6 | from sklearn.cluster import KMeans
 7 | 
 8 | dataset = 'exemples2_5g'
 9 | X, y = load_dataset(dataset)
10 | print('Dataset: {} (true number of clusters: K = {})'.format(dataset, len(np.unique(y))))
11 | 
12 | algorithm = KMeans
13 | km_kwargs = {'init': 'k-means++', 'n_init': 10}
14 | 
15 | k_values = list(range(1, 11))
16 | omega = list(range(2, 6))
17 | print('Evaluated numbers of clusters:', k_values)
18 | 
19 | stab = StadionEstimator(X, algorithm,
20 |                         param_name='n_clusters',
21 |                         param_values=k_values,
22 |                         omega=omega,
23 |                         extended=True,
24 |                         runs=10,
25 |                         perturbation='uniform',
26 |                         perturbation_kwargs='auto',
27 |                         algo_kwargs=km_kwargs,
28 |                         n_jobs=-1)
29 | 
30 | score = stab.score(strategy='max', crossing=True)
31 | print('Stadion-max scores:\n', score)
32 | k_hat = stab.select_param()[0]
33 | print('Selected number of clusters: K =', k_hat)
34 | 
35 | """
36 | Generate plots
37 | """
38 | # These are the "Tableau 20" colors as RGB.
39 | tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
40 |              (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
41 |              (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
42 |              (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
43 |              (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
44 | # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.
45 | for i in range(len(tableau20)):
46 |     r, g, b = tableau20[i]
47 |     tableau20[i] = (r / 255., g / 255., b / 255.)
48 | epsilons = [kwargs['eps'] for kwargs in stab.perturbation_kwargs]
49 | bstab_path = 'stabB_paths_{}_{}.pdf'.format(algorithm.__name__, dataset)
50 | wstab_path = 'stabW_paths_{}_{}.pdf'.format(algorithm.__name__, dataset)
51 | stadion_path = 'stadion_paths_{}_{}.pdf'.format(algorithm.__name__, dataset)
52 | 
53 | plt.figure(figsize=(10, 8))
54 | for i, p in enumerate(k_values):
55 |     plt.plot(epsilons, stab.between_cluster_stability_paths[i, :, 0], label='{} = {}'.format(stab.param_name, p),
56 |              color=tableau20[(2 * i) % 20])
57 | plt.legend()
58 | plt.xlabel('$\epsilon$', fontsize=16)
59 | plt.ylabel('Between-cluster Stability', fontsize=16)
60 | plt.title('{} (dataset = {} / true K = {})'.format(algorithm.__name__, dataset, len(np.unique(y))), fontsize=16)
61 | plt.savefig(bstab_path, bbox_inches='tight')
62 | 
63 | plt.figure(figsize=(10, 8))
64 | for i, p in enumerate(k_values):
65 |     plt.plot(epsilons, stab.within_cluster_stability_paths[i, :, 0], label='{} = {}'.format(stab.param_name, p),
66 |              color=tableau20[(2 * i) % 20])
67 | plt.legend()
68 | plt.xlabel('$\epsilon$', fontsize=16)
69 | plt.ylabel('Within-cluster Stability', fontsize=16)
70 | plt.title('{} (dataset = {} / true K = {})'.format(algorithm.__name__, dataset, len(np.unique(y))), fontsize=16)
71 | plt.savefig(wstab_path, bbox_inches='tight')
72 | 
73 | plt.figure(figsize=(10, 8))
74 | for i, p in enumerate(k_values):
75 |     plt.plot(epsilons, stab.stadion_paths[i, :, 0], label='{} = {}'.format(stab.param_name, p),
76 |              color=tableau20[(2 * i) % 20])
77 | plt.legend()
78 | plt.xlabel('$\epsilon$', fontsize=16)
79 | plt.ylabel('Stadion', fontsize=16)
80 | plt.title('{} (dataset = {} / true K = {})'.format(algorithm.__name__, dataset, len(np.unique(y))), fontsize=16)
81 | plt.savefig(stadion_path, bbox_inches='tight')
82 | print('Paths saved to {}, {} and {}!'.format(bstab_path, wstab_path, stadion_path))
83 | 


--------------------------------------------------------------------------------
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340 | 2
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--------------------------------------------------------------------------------
/datasets/data/artificial/spherical_5_2.csv:
--------------------------------------------------------------------------------
  1 | "V1";"V2"
  2 | 11,11;10,14
  3 | 8,68;9,76
  4 | 10,04;10,63
  5 | 8,79;10,13
  6 | 10,53;8,95
  7 | 10,58;9,72
  8 | 11,19;11,6
  9 | 8,14;10,21
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 49 | 10,38;8,92
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103 | 14,63;11,17
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109 | 14,46;8,7
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111 | 13,45;10,2
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113 | 14,32;9,79
114 | 15,39;9,18
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116 | 15,09;10,79
117 | 12,85;9,3
118 | 14,13;8,91
119 | 13,19;9,97
120 | 13,8;8,84
121 | 14,08;10,93
122 | 14,53;11,83
123 | 13,61;10,37
124 | 15,69;9,18
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126 | 13,99;9,22
127 | 15,71;9,4
128 | 13,77;10,07
129 | 14,85;9,43
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132 | 13,28;11,25
133 | 13,1;11,83
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137 | 14,52;10,94
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139 | 14,88;10,75
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141 | 12,27;9,45
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143 | 13,51;10,42
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146 | 12,56;9,02
147 | 13,09;8,48
148 | 15,18;9,13
149 | 15,31;9,71
150 | 13,01;8,33
151 | 14,64;8,21
152 | 11,12;13,31
153 | 11,43;13,45
154 | 11,2;15,49
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156 | 11,19;13,24
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158 | 9,94;12,17
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161 | 10,31;15,26
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184 | 10,56;14,87
185 | 9,47;15,62
186 | 8,32;13,62
187 | 10,03;15,79
188 | 9,41;13,6
189 | 11,67;13,58
190 | 9,29;12,55
191 | 9,93;12,91
192 | 10,88;12,93
193 | 9,73;14,31
194 | 9,92;12,07
195 | 8,84;13,29
196 | 11,48;14,49
197 | 10,65;13,06
198 | 11,53;14,4
199 | 11,23;12,72
200 | 8,89;12,49
201 | 9,16;14,12
202 | 7,21;9,66
203 | 4,79;10,99
204 | 5,38;8,74
205 | 7,96;9,76
206 | 7,1;11,72
207 | 5,49;11,63
208 | 6,82;11,73
209 | 7,26;9,27
210 | 5,69;8,95
211 | 4,47;9,53
212 | 5,06;8,59
213 | 5,52;11,29
214 | 4,69;9,5
215 | 6,02;9,05
216 | 4,36;10,93
217 | 6,96;9,41
218 | 6,61;9,98
219 | 7,27;9,04
220 | 6,17;11,69
221 | 6,14;8,38
222 | 6,61;9,06
223 | 4,98;8,77
224 | 5,03;9,42
225 | 4,65;9,12
226 | 5,08;9,74
227 | 4,95;8,49
228 | 7,48;10,64
229 | 6,54;10,76
230 | 5,6;9,53
231 | 6,78;8,98
232 | 5,11;8,75
233 | 4,6;9,7
234 | 4,82;9,54
235 | 4,8;10,34
236 | 5,54;9,4
237 | 5,26;10,29
238 | 5,48;10,88
239 | 6,92;10,55
240 | 6,55;10,25
241 | 5,41;10,15
242 | 4,45;10,14
243 | 5;8,43
244 | 6,62;8,4
245 | 7,7;11,15
246 | 5,45;9,99
247 | 7,01;9,11
248 | 6,95;10,75
249 | 6,7;9,87
250 | 5,71;8,26
251 | 5,79;10,67
252 | 


--------------------------------------------------------------------------------
/datasets/target/ds-577.csv:
--------------------------------------------------------------------------------
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/README.md:
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  1 | # skstab
  2 | 
  3 | **skstab** is a module for clustering stability analysis in Python with a scikit-learn compatible API.
  4 | 
  5 | If this repository was useful, please cite following conference paper:
  6 | 
  7 | ```
  8 | @inproceedings{mourer_stadion_2023,
  9 |     title = {Selecting the Number of Clusters K with a Stability Trade-off: an Internal Validation Criterion},
 10 |     author = {Mourer, Alex and Forest, Florent and Lebbah, Mustapha and Azzag, Hanane and Lacaille, Jérôme},
 11 |     year = {2023},
 12 |     booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
 13 |     address = {Osaka, Japan}
 14 |     url = {https://arxiv.org/abs/2006.08530}
 15 | }
 16 | ```
 17 | 
 18 | ## What is clustering stability?
 19 | 
 20 | Clustering stability is a method for model selection in clustering, based on the principle that if we repeatedly 
 21 | perturb a data set, a good clustering algorithm should output similar partitions. In particular, it allows to 
 22 | select the correct number of clusters in an unlabeled data set. See for example [4] for an overview.
 23 | 
 24 | ## How to use skstab for model selection
 25 | 
 26 | The API is compatible with any clustering algorithm with an interface similar to scikit-sklearn, i.e. a 
 27 | `fit` method for training, and a `labels_` field or `predict` method to obtain cluster memberships.
 28 | 
 29 | skstab allows to build and tune a variety of stability estimation methods. As of today, three concrete methods are 
 30 | implemented and can be used off-the-shelf:
 31 | 
 32 | * **Model explorer** from Ben-Hur et al, 2002 [1]
 33 | * **Model order selection** from Lange et al, 2004 [2]
 34 | * **Stadion** from Mourer et al, 2023 [3]
 35 | 
 36 | Each method has a usage example script (simply run `example_modelexplorer.py`/`example_modelorderselection.py`/`example_stadion.py`).
 37 | Let us start with the K-means algorithm and Stadion [3], since it effectively selects the number of clusters K even in 
 38 | the case where data is not clusterable (i.e. K=1):
 39 | 
 40 | ```python
 41 | from skstab import StadionEstimator
 42 | from skstab.datasets import load_dataset
 43 | from sklearn.cluster import KMeans
 44 | 
 45 | # 1. Load data
 46 | dataset = 'exemples2_5g' # example data set with 5 Gaussians
 47 | X, y = load_dataset(dataset)
 48 | 
 49 | # 2. Choose clustering algorithm
 50 | algorithm = KMeans
 51 | km_kwargs = {'init': 'k-means++', 'n_init': 10}  # algorithm settings
 52 | 
 53 | # 3. Fix range of parameters and Stadion hyperparameter omega
 54 | k_values = list(range(1, 11))  # evaluate K=1 to K=10
 55 | omega = list(range(2, 6))  # keep this fixed to 2:5 or 2:10
 56 | 
 57 | # 4. Define stability estimation class
 58 | stab = StadionEstimator(X, algorithm,
 59 |                         param_name='n_clusters',
 60 |                         param_values=k_values,
 61 |                         omega=omega,
 62 |                         extended=True,
 63 |                         runs=10,
 64 |                         perturbation='uniform',
 65 |                         perturbation_kwargs='auto',
 66 |                         algo_kwargs=km_kwargs,
 67 |                         n_jobs=-1)
 68 | 
 69 | # 5. Evaluate stability scores
 70 | score = stab.score(strategy='max', crossing=True)  # estimate Stadion scores
 71 | k_hat = stab.select_param()[0]  # selected number of clusters
 72 | ```
 73 | 
 74 | The correct number of clusters (K=5) is selected. The example script `example_stadion.py` also outputs visualizations 
 75 | called _stability paths_, representing stability as a function of the level of perturbation (see 
 76 | [3] for more details).
 77 | 
 78 | ## Installation
 79 | 
 80 | `$ python3 setup.py install`
 81 | 
 82 | skstab was written for Python 3 and depends on joblib, matplotlib, numpy, pandas, scikit-learn and scipy.
 83 | 
 84 | ## Architecture
 85 | 
 86 | ![Class diagram](class_diagram.png)
 87 | 
 88 | ## References
 89 | 
 90 | > [1] Ben-Hur, A., Elisseeff, A., & Guyon, I. (2002). A stability based method for discovering structure in clustered
 91 |     data. Pacific Symposium on Biocomputing. https://doi.org/10.1142/9789812799623_0002
 92 | 
 93 | > [2] Lange, T., Roth, V., Braun, M. L., & Buhmann, J. M. (2004). Stability-based validation of clustering solutions.
 94 |     Neural Computation. https://doi.org/10.1162/089976604773717621
 95 | 
 96 | > [3] Mourer, A., Forest, F., Lebbah, M., Azzag, H., & Lacaille, J. (2023). Selecting the Number of Clusters K with a
 97 |     Stability Trade-off: an Internal Validation Criterion. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). https://arxiv.org/abs/2006.08530
 98 | 
 99 | > [4] Von Luxburg, U. (2009). Clustering stability: An overview. Foundations and Trends in Machine Learning. https://doi.org/10.1561/2200000008
100 | 


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/datasets/data/artificial/spherical_6_2.csv:
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301 | 21,15;5,52
302 | 


--------------------------------------------------------------------------------
/datasets/target/3clusters_elephant.csv:
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--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/datasets/target/exemples7_elbow_3g.csv:
--------------------------------------------------------------------------------
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124 | 2
125 | 2
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128 | 2
129 | 2
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135 | 2
136 | 2
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140 | 2
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415 | 3
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--------------------------------------------------------------------------------
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862 | 3
863 | 3
864 | 3
865 | 


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/datasets/target/2d-4c-no9.csv:
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877 | 3
878 | 


--------------------------------------------------------------------------------
/skstab/perturbation.py:
--------------------------------------------------------------------------------
  1 | """
  2 | skstab - Perturbation functions
  3 | 
  4 | @author Florent Forest, Alex Mourer
  5 | """
  6 | 
  7 | import numpy as np
  8 | 
  9 | 
 10 | """
 11 | Generic perturbations
 12 | """
 13 | 
 14 | 
 15 | def subsample(x, f, return_indices=False):
 16 |     """Sample a fraction f of the data set without replacement.
 17 | 
 18 |     Parameters
 19 |     ----------
 20 |     x : array
 21 |         input data matrix.
 22 |     f : float in [0, 1]
 23 |         fraction of input data set.
 24 |     return_indices : bool (default = False)
 25 |         return the indices of the sample.
 26 | 
 27 |     Returns
 28 |     -------
 29 |     x_sub[, idx] : array[, array]
 30 |         output data matrix and corresponding indices (if return_indices equals True).
 31 |     """
 32 |     idx = np.random.choice(x.shape[0], size=int(f * x.shape[0]), replace=False)
 33 |     if return_indices:
 34 |         return x[idx], idx
 35 |     else:
 36 |         return x[idx]
 37 | 
 38 | 
 39 | def bootstrap(x, return_indices=False):
 40 |     """Bootstrap the data set with replacement.
 41 | 
 42 |     Parameters
 43 |     ----------
 44 |     x : array
 45 |         input data matrix.
 46 |     return_indices : bool (default = False)
 47 |         return the indices of the sample.
 48 | 
 49 |     Returns
 50 |     -------
 51 |     x_sub[, idx] : array[, array]
 52 |         output data matrix and corresponding indices (if return_indices equals True).
 53 |     """
 54 |     idx = np.random.choice(x.shape[0], size=x.shape[0], replace=True)
 55 |     if return_indices:
 56 |         return x[idx], idx
 57 |     else:
 58 |         return x[idx]
 59 | 
 60 | 
 61 | def uniform_additive_noise(x, eps):
 62 |     """Uniform additive noise.
 63 | 
 64 |     Parameters
 65 |     ----------
 66 |     x : array
 67 |         input data matrix.
 68 |     eps : float
 69 |         noise amplitude. Noise values are sampled uniformely in [-eps, +eps] for each input feature.
 70 | 
 71 |     Returns
 72 |     -------
 73 |     x_noise : array
 74 |         output data matrix.
 75 |     """
 76 |     noise = np.random.uniform(low=-eps, high=eps, size=x.shape)
 77 |     return x + noise
 78 | 
 79 | 
 80 | def gaussian_additive_noise(x, sigma):
 81 |     """Gaussian additive noise.
 82 | 
 83 |     Parameters
 84 |     ----------
 85 |     x : array
 86 |         input data matrix.
 87 |     sigma : float
 88 |         noise standard deviation. Noise values are sampled from N(0, sigma) for each input feature.
 89 | 
 90 |     Returns
 91 |     -------
 92 |     x_noise : array
 93 |         output data matrix.
 94 |     """
 95 |     noise = np.random.normal(0.0, sigma, size=x.shape)
 96 |     return x + noise
 97 | 
 98 | 
 99 | """
100 | Time series perturbations (univariate)
101 | """
102 | 
103 | 
104 | def random_shift(x, alpha):
105 |     """Random temporal shifting of a fraction of the time series length.
106 | 
107 |     Parameters
108 |     ----------
109 |     x : array
110 |         input data matrix.
111 |     alpha : float
112 |         shift amplitude. A fraction of input length is drawn uniformely in [-alpha, +alpha].
113 | 
114 |     Returns
115 |     -------
116 |     x_shifted : array
117 |         output data matrix.
118 |     """
119 |     shift = (x.shape[1] * np.random.uniform(low=-alpha, high=alpha, size=x.shape[0])).astype('int')
120 |     x_shifted = np.zeros_like(x)
121 |     # pad with first and last values
122 |     for i in range(x.shape[0]):
123 |         if shift[i] > 0:
124 |             x_shifted[i, :] = np.pad(x[i, :-shift[i]], (shift[i], 0), mode='edge')
125 |         else:
126 |             x_shifted[i, :] = np.pad(x[i, -shift[i]:], (0, -shift[i]), mode='edge')
127 |     return x_shifted
128 | 
129 | 
130 | def random_offset(x, eps):
131 |     """Random vertical offset.
132 | 
133 |     Parameters
134 |     ----------
135 |     x : array
136 |         input data matrix.
137 |     eps : float
138 |         offset amplitude. The offset value is drawn uniformely in [-eps, +eps].
139 | 
140 |     Returns
141 |     -------
142 |     x_offset : array
143 |         output data matrix.
144 |     """
145 |     offset = np.random.uniform(low=-eps, high=eps, size=x.shape[0])
146 |     return x + offset[:, None]
147 | 
148 | 
149 | def random_scale(x, eps):
150 |     """Random vertical scaling.
151 | 
152 |     Parameters
153 |     ----------
154 |     x : array
155 |         input data matrix.
156 |     eps : float
157 |         scaling factor. The scaling factor is drawn uniformely in [1/(1+eps), 1+eps].
158 | 
159 |     Returns
160 |     -------
161 |     x_scaled : array
162 |         output data matrix.
163 |     """
164 |     scale = np.random.uniform(low=1.0 / (1.0 + eps), high=1.0 + eps, size=x.shape[0])
165 |     return x * scale[:, None]
166 | 
167 | 
168 | def random_warp(x, alpha, eps):
169 |     """Random local warping of a fraction of the time series. Location of the warping is drawn uniformely inside
170 |     the input time series.
171 | 
172 |     Parameters
173 |     ----------
174 |     x : array
175 |         input data matrix.
176 |     alpha : float
177 |         fraction of input time series that will be warped. A fraction of input length is drawn
178 |         uniformely in [-alpha, +alpha].
179 |     eps : float
180 |         scaling factor of the warping. The scaling factor is drawn uniformely in [1/(1+eps), 1+eps].
181 | 
182 |     Returns
183 |     -------
184 |     x_warped : array
185 |         output data matrix.
186 |     """
187 |     start = np.random.choice(x.shape[1], size=x.shape[0])  # random position in the time series
188 |     end = start + 1 + (x.shape[1] * np.random.uniform(low=0, high=alpha, size=x.shape[0])).astype('int')
189 |     end = np.minimum(end, x.shape[1])  # clip end at end of time series
190 |     warp = np.random.uniform(low=1.0 / (1.0 + eps), high=1.0 + eps, size=x.shape[0])  # warping level
191 |     warped_end = start + (warp * (end - start)).astype('int')
192 |     warped_end = np.minimum(warped_end, x.shape[1])  # clip warp end at end of time series
193 |     x_warped = np.zeros_like(x)
194 |     for i in range(x.shape[0]):
195 |         segment = x[i, start[i]:end[i]]
196 |         warped_segment = np.interp(np.linspace(0, 1, num=(warped_end[i] - start[i])), np.linspace(0, 1, num=segment.size), segment)
197 |         x_warped[i, :start[i]] = x[i, :start[i]]
198 |         x_warped[i, start[i]:warped_end[i]] = warped_segment
199 |         if warped_end[i] < x.shape[1]:  # if the end of the time series was not reached
200 |             if warp[i] >= 1.0:  # dilation: fill with end of original time series
201 |                 x_warped[i, warped_end[i]:] = x[i, end[i]:end[i] + x.shape[1] - warped_end[i]]
202 |             else:  # reduction: end of original time series is not long enough, pad with last value
203 |                 x_warped[i, warped_end[i]:warped_end[i] + x.shape[1] - end[i]] = x[i, end[i]:]
204 |                 x_warped[i, warped_end[i] + x.shape[1] - end[i]:] = x[i, -1] # pad the rest with last value
205 |     return x_warped
206 | 


--------------------------------------------------------------------------------
/datasets/target/st900.csv:
--------------------------------------------------------------------------------
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600 | 3
601 | 3
602 | 4
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866 | 5
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891 | 5
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893 | 5
894 | 5
895 | 5
896 | 5
897 | 5
898 | 5
899 | 5
900 | 5
901 | 5
902 | 


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