.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gyexamples/plot_constraint_kmeans.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gyexamples_plot_constraint_kmeans.py: ================= Constraint KMeans ================= Simple example to show how to cluster keeping approximatively the same number of points in every cluster. .. contents:: :local: Data ==== .. GENERATED FROM PYTHON SOURCE LINES 16-37 .. code-block:: default from collections import Counter import numpy import matplotlib.pyplot as plt from sklearn.datasets import make_blobs from sklearn.cluster import KMeans from mlinsights.mlmodel import ConstraintKMeans n_samples = 100 data = make_blobs( n_samples=n_samples, n_features=2, centers=2, cluster_std=1.0, center_box=(-10.0, 0.0), shuffle=True, random_state=2) X1 = data[0] data = make_blobs( n_samples=n_samples // 2, n_features=2, centers=2, cluster_std=1.0, center_box=(0.0, 10.0), shuffle=True, random_state=2) X2 = data[0] X = numpy.vstack([X1, X2]) X.shape .. rst-class:: sphx-glr-script-out .. code-block:: none (150, 2) .. GENERATED FROM PYTHON SOURCE LINES 38-39 Plots. .. GENERATED FROM PYTHON SOURCE LINES 39-44 .. code-block:: default fig, ax = plt.subplots(1, 1, figsize=(4, 4)) ax.plot(X[:, 0], X[:, 1], '.') ax.set_title('4 clusters') .. image-sg:: /gyexamples/images/sphx_glr_plot_constraint_kmeans_001.png :alt: 4 clusters :srcset: /gyexamples/images/sphx_glr_plot_constraint_kmeans_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Text(0.5, 1.0, '4 clusters') .. GENERATED FROM PYTHON SOURCE LINES 45-47 Standard KMeans =============== .. GENERATED FROM PYTHON SOURCE LINES 47-63 .. code-block:: default km = KMeans(n_clusters=4) km.fit(X) cl = km.predict(X) hist = Counter(cl) colors = 'brgy' fig, ax = plt.subplots(1, 1, figsize=(4, 4)) for i in range(0, max(cl) + 1): ax.plot(X[cl == i, 0], X[cl == i, 1], colors[i] + '.', label='cl%d' % i) x = [km.cluster_centers_[i, 0], km.cluster_centers_[i, 0]] y = [km.cluster_centers_[i, 1], km.cluster_centers_[i, 1]] ax.plot(x, y, colors[i] + '+') ax.set_title(f'KMeans 4 clusters\n{hist!r}') ax.legend() .. image-sg:: /gyexamples/images/sphx_glr_plot_constraint_kmeans_002.png :alt: KMeans 4 clusters Counter({2: 50, 0: 50, 1: 27, 3: 23}) :srcset: /gyexamples/images/sphx_glr_plot_constraint_kmeans_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 64-66 Constraint KMeans ================= .. GENERATED FROM PYTHON SOURCE LINES 66-75 .. code-block:: default km1 = ConstraintKMeans(n_clusters=4, strategy='gain', balanced_predictions=True) km1.fit(X) km2 = ConstraintKMeans(n_clusters=4, strategy='distance', balanced_predictions=True) km2.fit(X) .. rst-class:: sphx-glr-script-out .. code-block:: none somewhere/workspace/mlinsights/mlinsights_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:1316: FutureWarning: algorithm='auto' is deprecated, it will be removed in 1.3. Using 'lloyd' instead. warnings.warn( somewhere/workspace/mlinsights/mlinsights_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:1316: FutureWarning: algorithm='auto' is deprecated, it will be removed in 1.3. Using 'lloyd' instead. warnings.warn( .. raw:: html 
ConstraintKMeans(balanced_predictions=True, n_clusters=4, strategy='distance')
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.. GENERATED FROM PYTHON SOURCE LINES 76-78 This algorithm tries to exchange points between clusters. .. GENERATED FROM PYTHON SOURCE LINES 78-103 .. code-block:: default cl1 = km1.predict(X) hist1 = Counter(cl1) cl2 = km2.predict(X) hist2 = Counter(cl2) fig, ax = plt.subplots(1, 2, figsize=(10, 4)) for i in range(0, max(cl1) + 1): ax[0].plot(X[cl1 == i, 0], X[cl1 == i, 1], colors[i] + '.', label='cl%d' % i) ax[1].plot(X[cl2 == i, 0], X[cl2 == i, 1], colors[i] + '.', label='cl%d' % i) x = [km1.cluster_centers_[i, 0], km1.cluster_centers_[i, 0]] y = [km1.cluster_centers_[i, 1], km1.cluster_centers_[i, 1]] ax[0].plot(x, y, colors[i] + '+') x = [km2.cluster_centers_[i, 0], km2.cluster_centers_[i, 0]] y = [km2.cluster_centers_[i, 1], km2.cluster_centers_[i, 1]] ax[1].plot(x, y, colors[i] + '+') ax[0].set_title(f'ConstraintKMeans 4 clusters (gains)\n{hist1!r}') ax[0].legend() ax[1].set_title(f'ConstraintKMeans 4 clusters (distances)\n{hist2!r}') ax[1].legend() .. image-sg:: /gyexamples/images/sphx_glr_plot_constraint_kmeans_003.png :alt: ConstraintKMeans 4 clusters (gains) Counter({2: 39, 1: 37, 3: 37, 0: 37}), ConstraintKMeans 4 clusters (distances) Counter({0: 38, 2: 38, 3: 37, 1: 37}) :srcset: /gyexamples/images/sphx_glr_plot_constraint_kmeans_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 104-106 Another algorithm tries to extend the area of attraction of each cluster. .. GENERATED FROM PYTHON SOURCE LINES 106-114 .. code-block:: default km = ConstraintKMeans(n_clusters=4, strategy='weights', max_iter=1000, history=True) km.fit(X) cl = km.predict(X) hist = Counter(cl) .. rst-class:: sphx-glr-script-out .. code-block:: none somewhere/workspace/mlinsights/mlinsights_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:1316: FutureWarning: algorithm='auto' is deprecated, it will be removed in 1.3. Using 'lloyd' instead. warnings.warn( .. GENERATED FROM PYTHON SOURCE LINES 115-116 Let's plot Delaunay edges as well. .. GENERATED FROM PYTHON SOURCE LINES 116-146 .. code-block:: default def plot_delaunay(ax, edges, points): for a, b in edges: ax.plot(points[[a, b], 0], points[[a, b], 1], '--', color="#555555") edges = km.cluster_edges() fig, ax = plt.subplots(1, 2, figsize=(10, 4)) for i in range(0, max(cl) + 1): ax[0].plot(X[cl == i, 0], X[cl == i, 1], colors[i] + '.', label='cl%d' % i) x = [km.cluster_centers_[i, 0], km.cluster_centers_[i, 0]] y = [km.cluster_centers_[i, 1], km.cluster_centers_[i, 1]] ax[0].plot(x, y, colors[i] + '+') ax[0].set_title(f"ConstraintKMeans 4 clusters\nstrategy='weights'\n{hist!r}") ax[0].legend() cls = km.cluster_centers_iter_ ax[1].plot(X[:, 0], X[:, 1], '.', label='X', color='#AAAAAA', ms=3) for i in range(0, max(cl) + 1): ms = numpy.arange( cls.shape[-1]).astype(numpy.float64) / cls.shape[-1] * 50 + 1 ax[1].scatter(cls[i, 0, :], cls[i, 1, :], color=colors[i], s=ms, label='cl%d' % i) plot_delaunay(ax[1], edges, km.cluster_centers_) ax[1].set_title("Centers movement") plt.show() .. image-sg:: /gyexamples/images/sphx_glr_plot_constraint_kmeans_004.png :alt: ConstraintKMeans 4 clusters strategy='weights' Counter({2: 49, 1: 49, 0: 48, 3: 4}), Centers movement :srcset: /gyexamples/images/sphx_glr_plot_constraint_kmeans_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 8.263 seconds) .. _sphx_glr_download_gyexamples_plot_constraint_kmeans.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_constraint_kmeans.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_constraint_kmeans.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_