Pushing the docs to dev/ for branch: main, commit 28a054c7c3f907cb37a6780e41eaea2ca3c0eceb

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -13,15 +13,17 @@
 from sklearn import metrics
 from sklearn.datasets import make_blobs
 
-# #############################################################################
+# %%
 # Generate sample data
+# --------------------
 centers = [[1, 1], [-1, -1], [1, -1]]
 X, labels_true = make_blobs(
     n_samples=300, centers=centers, cluster_std=0.5, random_state=0
 )
 
-# #############################################################################
+# %%
 # Compute Affinity Propagation
+# ----------------------------
 af = AffinityPropagation(preference=-50, random_state=0).fit(X)
 cluster_centers_indices = af.cluster_centers_indices_
 labels = af.labels_
@@ -42,8 +44,9 @@
     % metrics.silhouette_score(X, labels, metric="sqeuclidean")
 )
 
-# #############################################################################
+# %%
 # Plot result
+# -----------
 import matplotlib.pyplot as plt
 from itertools import cycle
 

dev/_downloads/2f930acda654766f8ba0fee08887bb41/plot_affinity_propagation.py

@@ -26,7 +26,61 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.cluster import AffinityPropagation\nfrom sklearn import metrics\nfrom sklearn.datasets import make_blobs\n\n# #############################################################################\n# Generate sample data\ncenters = [[1, 1], [-1, -1], [1, -1]]\nX, labels_true = make_blobs(\n    n_samples=300, centers=centers, cluster_std=0.5, random_state=0\n)\n\n# #############################################################################\n# Compute Affinity Propagation\naf = AffinityPropagation(preference=-50, random_state=0).fit(X)\ncluster_centers_indices = af.cluster_centers_indices_\nlabels = af.labels_\n\nn_clusters_ = len(cluster_centers_indices)\n\nprint(\"Estimated number of clusters: %d\" % n_clusters_)\nprint(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\nprint(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\nprint(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\nprint(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\nprint(\n    \"Adjusted Mutual Information: %0.3f\"\n    % metrics.adjusted_mutual_info_score(labels_true, labels)\n)\nprint(\n    \"Silhouette Coefficient: %0.3f\"\n    % metrics.silhouette_score(X, labels, metric=\"sqeuclidean\")\n)\n\n# #############################################################################\n# Plot result\nimport matplotlib.pyplot as plt\nfrom itertools import cycle\n\nplt.close(\"all\")\nplt.figure(1)\nplt.clf()\n\ncolors = cycle(\"bgrcmykbgrcmykbgrcmykbgrcmyk\")\nfor k, col in zip(range(n_clusters_), colors):\n    class_members = labels == k\n    cluster_center = X[cluster_centers_indices[k]]\n    plt.plot(X[class_members, 0], X[class_members, 1], col + \".\")\n    plt.plot(\n        cluster_center[0],\n        cluster_center[1],\n        \"o\",\n        markerfacecolor=col,\n        markeredgecolor=\"k\",\n        markersize=14,\n    )\n    for x in X[class_members]:\n        plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)\n\nplt.title(\"Estimated number of clusters: %d\" % n_clusters_)\nplt.show()"
+        "from sklearn.cluster import AffinityPropagation\nfrom sklearn import metrics\nfrom sklearn.datasets import make_blobs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generate sample data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "centers = [[1, 1], [-1, -1], [1, -1]]\nX, labels_true = make_blobs(\n    n_samples=300, centers=centers, cluster_std=0.5, random_state=0\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compute Affinity Propagation\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "af = AffinityPropagation(preference=-50, random_state=0).fit(X)\ncluster_centers_indices = af.cluster_centers_indices_\nlabels = af.labels_\n\nn_clusters_ = len(cluster_centers_indices)\n\nprint(\"Estimated number of clusters: %d\" % n_clusters_)\nprint(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\nprint(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\nprint(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\nprint(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\nprint(\n    \"Adjusted Mutual Information: %0.3f\"\n    % metrics.adjusted_mutual_info_score(labels_true, labels)\n)\nprint(\n    \"Silhouette Coefficient: %0.3f\"\n    % metrics.silhouette_score(X, labels, metric=\"sqeuclidean\")\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot result\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nfrom itertools import cycle\n\nplt.close(\"all\")\nplt.figure(1)\nplt.clf()\n\ncolors = cycle(\"bgrcmykbgrcmykbgrcmykbgrcmyk\")\nfor k, col in zip(range(n_clusters_), colors):\n    class_members = labels == k\n    cluster_center = X[cluster_centers_indices[k]]\n    plt.plot(X[class_members, 0], X[class_members, 1], col + \".\")\n    plt.plot(\n        cluster_center[0],\n        cluster_center[1],\n        \"o\",\n        markerfacecolor=col,\n        markeredgecolor=\"k\",\n        markersize=14,\n    )\n    for x in X[class_members]:\n        plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)\n\nplt.title(\"Estimated number of clusters: %d\" % n_clusters_)\nplt.show()"
       ]
     }
   ],