diff --git a/.github/workflows/cicd.yml b/.github/workflows/cicd.yml index 63cf6ad..ec3cd1c 100644 --- a/.github/workflows/cicd.yml +++ b/.github/workflows/cicd.yml @@ -6,7 +6,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.10', '3.11', '3.12'] + python-version: ['3.10', '3.11', '3.12', '3.13', '3.14'] steps: - uses: actions/checkout@v3 diff --git a/README.md b/README.md index 60d1ef2..64c14a1 100644 --- a/README.md +++ b/README.md @@ -1,29 +1,29 @@ # pyclustertend - - - - - [![Build Status](https://travis-ci.com/lachhebo/pyclustertend.svg?branch=master)](https://travis-ci.com/lachhebo/pyclustertend) [![PyPi Status](https://img.shields.io/pypi/v/pyclustertend.svg?color=brightgreen)](https://pypi.org/project/pyclustertend/) [![Documentation Status](https://readthedocs.org/projects/pyclustertend/badge/?version=master)](https://pyclustertend.readthedocs.io/en/master/) [![Downloads](https://pepy.tech/badge/pyclustertend)](https://pepy.tech/project/pyclustertend) [![codecov](https://codecov.io/gh/lachhebo/pyclustertend/branch/master/graph/badge.svg)](https://codecov.io/gh/lachhebo/pyclustertend) [![DOI](https://zenodo.org/badge/187477036.svg)](https://zenodo.org/badge/latestdoi/187477036) -pyclustertend is a python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset. - -Three methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator : +pyclustertend is a Python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset. - - -- [x] Hopkins Statistics -- [x] VAT -- [x] iVAT - -- [x] Metric based method (silhouette, calinksi, davies bouldin) +Three methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator: +- Hopkins Statistics [^1] +- VAT [^5] +- iVAT [^6] +- Metric based method (Silhouette [^2], Calinski-Harabasz [^3], Davies-Bouldin [^4]) ## Installation - + +The package is available on PyPi and can be installed using `pip`: +```shell +pip install pyclustertend ``` ## Usage @@ -31,22 +31,22 @@ Three methods for assessing cluster tendency are currently implemented and one a ### Example Hopkins ```python - >>>from sklearn import datasets - >>>from pyclustertend import hopkins - >>>from sklearn.preprocessing import scale - >>>X = scale(datasets.load_iris().data) - >>>hopkins(X,150) + >>> from sklearn import datasets + >>> from pyclustertend import hopkins + >>> from sklearn.preprocessing import scale + >>> X = scale(datasets.load_iris().data) + >>> hopkins(X,150) 0.18950453452838564 ``` ### Example VAT ```python - >>>from sklearn import datasets - >>>from pyclustertend import vat - >>>from sklearn.preprocessing import scale - >>>X = scale(datasets.load_iris().data) - >>>vat(X) + >>> from sklearn import datasets + >>> from pyclustertend import vat + >>> from sklearn.preprocessing import scale + >>> X = scale(datasets.load_iris().data) + >>> vat(X) ``` @@ -54,11 +54,11 @@ Three methods for assessing cluster tendency are currently implemented and one a ### Example iVat ```python - >>>from sklearn import datasets - >>>from pyclustertend import ivat - >>>from sklearn.preprocessing import scale - >>>X = scale(datasets.load_iris().data) - >>>ivat(X) + >>> from sklearn import datasets + >>> from pyclustertend import ivat + >>> from sklearn.preprocessing import scale + >>> X = scale(datasets.load_iris().data) + >>> ivat(X) ``` @@ -67,3 +67,25 @@ Three methods for assessing cluster tendency are currently implemented and one a It's preferable to scale the data before using hopkins or vat algorithm as they use distance between observations. Moreover, vat and ivat algorithms do not really fit to massive databases. A first solution is to sample the data before using those algorithms. + + + + +[^1]: Hopkins, Brian; Skellam, J.G. (1954). "A new method for determining the type of distribution of plant individuals". _Annals of Botany_. **18** (2). Annals Botany Co: 213–227. doi:10.1093/oxfordjournals.aob.a083391 + +[^2]: Rousseeuw, Peter J. (1987). "Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis". _Computational and Applied Mathematics_. **20**: 53–65. doi:10.1016/0377-0427(87)90125-7. + +[^3]: Caliński, Tadeusz; Harabasz, Jerzy (1974). "A dendrite method for cluster analysis". _Communications in Statistics_. **3** (1): 1–27. doi:10.1080/03610927408827101 + +[^4]: Davies, David L.; Bouldin, Donald W. (1979). "A Cluster Separation Measure". _IEEE Transactions on Pattern Analysis and Machine Intelligence_. PAMI-1 (2): 224–227. doi:10.1109/TPAMI.1979.4766909 + +[^5]: Bezdek, James C.; Hathaway, Richard J. (2002). "VAT: A Tool for Visual Assessment of (Cluster) Tendency". _Proceedings of the 2002 International Joint Conference on Neural Networks_. IJCNN '02. IEEE Computer Society. pp. 2225–2230. doi:10.1109/IJCNN.2002.1007487. + +[^6]: Wang, L., Nguyen, U. T., Bezdek, J. C., Leckie, C. A., Ramamohanarao, K. (2010). "iVAT and aVAT: enhanced visual analysis for cluster tendency assessment". In _Advances in Knowledge Discovery and Data Mining. PAKDD 2010_ (pp. 16-27). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-13657-3_5. \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 5e717e8..0342b7f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.2 and should not be changed by hand. 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Open, modern and actively maintained alternative to Chardet." optional = false python-versions = ">=3.7.0" +groups = ["dev"] files = [ {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4f9fc98dad6c2eaa32fc3af1417d95b5e3d08aff968df0cd320066def971f9a6"}, {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0de7b687289d3c1b3e8660d0741874abe7888100efe14bd0f9fd7141bcbda92b"}, @@ -145,6 +148,7 @@ version = "2.1.13" description = "Hosted coverage reports for GitHub, Bitbucket and Gitlab" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +groups = ["dev"] files = [ {file = "codecov-2.1.13-py2.py3-none-any.whl", hash = "sha256:c2ca5e51bba9ebb43644c43d0690148a55086f7f5e6fd36170858fa4206744d5"}, {file = "codecov-2.1.13.tar.gz", hash = "sha256:2362b685633caeaf45b9951a9b76ce359cd3581dd515b430c6c3f5dfb4d92a8c"}, @@ -160,6 +164,8 @@ version = "0.4.6" description = "Cross-platform colored terminal text." optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" +groups = ["main", "dev"] +markers = "sys_platform == \"win32\"" files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, @@ -171,6 +177,7 @@ version = "1.3.1" description = "Python library for calculating contours of 2D quadrilateral grids" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "contourpy-1.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a045f341a77b77e1c5de31e74e966537bba9f3c4099b35bf4c2e3939dd54cdab"}, {file = "contourpy-1.3.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:500360b77259914f7805af7462e41f9cb7ca92ad38e9f94d6c8641b089338124"}, @@ -244,6 +251,7 @@ version = "7.6.9" description = "Code coverage measurement for Python" 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np.ndarray): # pragma: no cover +def compute_ordered_dis_njit( + matrix_of_pairwise_distance: np.ndarray, +): # pragma: no cover """ The ordered dissimilarity matrix is used by visual assessment of tendency. It is a just a a reordering of the dissimilarity matrix. @@ -114,11 +116,11 @@ def compute_ordered_dis_njit(matrix_of_pairwise_distance: np.ndarray): # pragma for column_index_of_maximum_value in range(ordered_matrix.shape[0]): for j in range(ordered_matrix.shape[1]): - ordered_matrix[ - column_index_of_maximum_value, j - ] = matrix_of_pairwise_distance[ - list_of_int[column_index_of_maximum_value], list_of_int[j] - ] + ordered_matrix[column_index_of_maximum_value, j] = ( + matrix_of_pairwise_distance[ + list_of_int[column_index_of_maximum_value], list_of_int[j] + ] + ) # Step 4 : diff --git a/pyproject.toml b/pyproject.toml index bee8978..8cd31e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,14 +10,20 @@ exclude = ["tests/"] repository = "https://github.com/lachhebo/pyclustertend" keywords = ["cluster tendency", "clustering", "machine learning", "hopkins", "VAT"] - [tool.poetry.dependencies] -python = "^3.10, <3.13" -numba = "^0.60.0" -pandas = "^2.2.3" -matplotlib = "^3.10.0" +python = ">=3.10, <3.15" +numba = "0.64.0" +numpy = ">=2.2.6" +pandas = "^2.3.3" +matplotlib = "^3.10.3" pytest = "^8.3.4" -scikit-learn = "^1.6.0" +scikit-learn = "^1.7.2" +scipy = [ + { version = ">=1.11,<1.14", markers = "python_version >= '3.10' and python_version < '3.11'" }, + { version = ">=1.14,<1.17", markers = "python_version >= '3.11' and python_version < '3.14'" }, + { version = ">=1.17", markers = "python_version >= '3.14'" } +] +pillow = ">=10.0.0,<13.0.0" [tool.poetry.group.dev.dependencies] flake8 = "^7.1.1" @@ -26,4 +32,3 @@ coverage = "^7.6.9" codecov = "^2.1.13" pytest-cov = "^6.0.0" pytest = "^8.3.4" -