Pushing the docs to dev/ for branch: main, commit bfcb5a400efcfc82483bc0e2f233214c63b9157e

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Shane Grigsby <[email protected]>\n#          Adrin Jalali <[email protected]>\n# License: BSD 3 clause\n\nfrom sklearn.cluster import OPTICS, cluster_optics_dbscan\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Generate sample data\n\nnp.random.seed(0)\nn_points_per_cluster = 250\n\nC1 = [-5, -2] + 0.8 * np.random.randn(n_points_per_cluster, 2)\nC2 = [4, -1] + 0.1 * np.random.randn(n_points_per_cluster, 2)\nC3 = [1, -2] + 0.2 * np.random.randn(n_points_per_cluster, 2)\nC4 = [-2, 3] + 0.3 * np.random.randn(n_points_per_cluster, 2)\nC5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)\nC6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)\nX = np.vstack((C1, C2, C3, C4, C5, C6))\n\nclust = OPTICS(min_samples=50, xi=0.05, min_cluster_size=0.05)\n\n# Run the fit\nclust.fit(X)\n\nlabels_050 = cluster_optics_dbscan(\n    reachability=clust.reachability_,\n    core_distances=clust.core_distances_,\n    ordering=clust.ordering_,\n    eps=0.5,\n)\nlabels_200 = cluster_optics_dbscan(\n    reachability=clust.reachability_,\n    core_distances=clust.core_distances_,\n    ordering=clust.ordering_,\n    eps=2,\n)\n\nspace = np.arange(len(X))\nreachability = clust.reachability_[clust.ordering_]\nlabels = clust.labels_[clust.ordering_]\n\nplt.figure(figsize=(10, 7))\nG = gridspec.GridSpec(2, 3)\nax1 = plt.subplot(G[0, :])\nax2 = plt.subplot(G[1, 0])\nax3 = plt.subplot(G[1, 1])\nax4 = plt.subplot(G[1, 2])\n\n# Reachability plot\ncolors = [\"g.\", \"r.\", \"b.\", \"y.\", \"c.\"]\nfor klass, color in zip(range(0, 5), colors):\n    Xk = space[labels == klass]\n    Rk = reachability[labels == klass]\n    ax1.plot(Xk, Rk, color, alpha=0.3)\nax1.plot(space[labels == -1], reachability[labels == -1], \"k.\", alpha=0.3)\nax1.plot(space, np.full_like(space, 2.0, dtype=float), \"k-\", alpha=0.5)\nax1.plot(space, np.full_like(space, 0.5, dtype=float), \"k-.\", alpha=0.5)\nax1.set_ylabel(\"Reachability (epsilon distance)\")\nax1.set_title(\"Reachability Plot\")\n\n# OPTICS\ncolors = [\"g.\", \"r.\", \"b.\", \"y.\", \"c.\"]\nfor klass, color in zip(range(0, 5), colors):\n    Xk = X[clust.labels_ == klass]\n    ax2.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)\nax2.plot(X[clust.labels_ == -1, 0], X[clust.labels_ == -1, 1], \"k+\", alpha=0.1)\nax2.set_title(\"Automatic Clustering\\nOPTICS\")\n\n# DBSCAN at 0.5\ncolors = [\"g\", \"greenyellow\", \"olive\", \"r\", \"b\", \"c\"]\nfor klass, color in zip(range(0, 6), colors):\n    Xk = X[labels_050 == klass]\n    ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3, marker=\".\")\nax3.plot(X[labels_050 == -1, 0], X[labels_050 == -1, 1], \"k+\", alpha=0.1)\nax3.set_title(\"Clustering at 0.5 epsilon cut\\nDBSCAN\")\n\n# DBSCAN at 2.\ncolors = [\"g.\", \"m.\", \"y.\", \"c.\"]\nfor klass, color in zip(range(0, 4), colors):\n    Xk = X[labels_200 == klass]\n    ax4.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)\nax4.plot(X[labels_200 == -1, 0], X[labels_200 == -1, 1], \"k+\", alpha=0.1)\nax4.set_title(\"Clustering at 2.0 epsilon cut\\nDBSCAN\")\n\nplt.tight_layout()\nplt.show()"
+        "# Authors: Shane Grigsby <[email protected]>\n#          Adrin Jalali <[email protected]>\n# License: BSD 3 clause\n\nfrom sklearn.cluster import OPTICS, cluster_optics_dbscan\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Generate sample data\n\nnp.random.seed(0)\nn_points_per_cluster = 250\n\nC1 = [-5, -2] + 0.8 * np.random.randn(n_points_per_cluster, 2)\nC2 = [4, -1] + 0.1 * np.random.randn(n_points_per_cluster, 2)\nC3 = [1, -2] + 0.2 * np.random.randn(n_points_per_cluster, 2)\nC4 = [-2, 3] + 0.3 * np.random.randn(n_points_per_cluster, 2)\nC5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)\nC6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)\nX = np.vstack((C1, C2, C3, C4, C5, C6))\n\nclust = OPTICS(min_samples=50, xi=0.05, min_cluster_size=0.05)\n\n# Run the fit\nclust.fit(X)\n\nlabels_050 = cluster_optics_dbscan(\n    reachability=clust.reachability_,\n    core_distances=clust.core_distances_,\n    ordering=clust.ordering_,\n    eps=0.5,\n)\nlabels_200 = cluster_optics_dbscan(\n    reachability=clust.reachability_,\n    core_distances=clust.core_distances_,\n    ordering=clust.ordering_,\n    eps=2,\n)\n\nspace = np.arange(len(X))\nreachability = clust.reachability_[clust.ordering_]\nlabels = clust.labels_[clust.ordering_]\n\nplt.figure(figsize=(10, 7))\nG = gridspec.GridSpec(2, 3)\nax1 = plt.subplot(G[0, :])\nax2 = plt.subplot(G[1, 0])\nax3 = plt.subplot(G[1, 1])\nax4 = plt.subplot(G[1, 2])\n\n# Reachability plot\ncolors = [\"g.\", \"r.\", \"b.\", \"y.\", \"c.\"]\nfor klass, color in zip(range(0, 5), colors):\n    Xk = space[labels == klass]\n    Rk = reachability[labels == klass]\n    ax1.plot(Xk, Rk, color, alpha=0.3)\nax1.plot(space[labels == -1], reachability[labels == -1], \"k.\", alpha=0.3)\nax1.plot(space, np.full_like(space, 2.0, dtype=float), \"k-\", alpha=0.5)\nax1.plot(space, np.full_like(space, 0.5, dtype=float), \"k-.\", alpha=0.5)\nax1.set_ylabel(\"Reachability (epsilon distance)\")\nax1.set_title(\"Reachability Plot\")\n\n# OPTICS\ncolors = [\"g.\", \"r.\", \"b.\", \"y.\", \"c.\"]\nfor klass, color in zip(range(0, 5), colors):\n    Xk = X[clust.labels_ == klass]\n    ax2.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)\nax2.plot(X[clust.labels_ == -1, 0], X[clust.labels_ == -1, 1], \"k+\", alpha=0.1)\nax2.set_title(\"Automatic Clustering\\nOPTICS\")\n\n# DBSCAN at 0.5\ncolors = [\"g.\", \"r.\", \"b.\", \"c.\"]\nfor klass, color in zip(range(0, 4), colors):\n    Xk = X[labels_050 == klass]\n    ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)\nax3.plot(X[labels_050 == -1, 0], X[labels_050 == -1, 1], \"k+\", alpha=0.1)\nax3.set_title(\"Clustering at 0.5 epsilon cut\\nDBSCAN\")\n\n# DBSCAN at 2.\ncolors = [\"g.\", \"m.\", \"y.\", \"c.\"]\nfor klass, color in zip(range(0, 4), colors):\n    Xk = X[labels_200 == klass]\n    ax4.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)\nax4.plot(X[labels_200 == -1, 0], X[labels_200 == -1, 1], \"k+\", alpha=0.1)\nax4.set_title(\"Clustering at 2.0 epsilon cut\\nDBSCAN\")\n\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -88,10 +88,10 @@
 ax2.set_title("Automatic Clustering\nOPTICS")
 
 # DBSCAN at 0.5
-colors = ["g", "greenyellow", "olive", "r", "b", "c"]
-for klass, color in zip(range(0, 6), colors):
+colors = ["g.", "r.", "b.", "c."]
+for klass, color in zip(range(0, 4), colors):
     Xk = X[labels_050 == klass]
-    ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3, marker=".")
+    ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
 ax3.plot(X[labels_050 == -1, 0], X[labels_050 == -1, 1], "k+", alpha=0.1)
 ax3.set_title("Clustering at 0.5 epsilon cut\nDBSCAN")