Pushing the docs to dev/ for branch: main, commit 1e965785fd529e9c3f12953782c165eaa90f2171

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -13,40 +13,51 @@
 #          Alexandre Gramfort, 2011
 # License: BSD 3 clause
 
-import time as time
-
-import numpy as np
-from scipy.ndimage import gaussian_filter
-
-import matplotlib.pyplot as plt
+# %%
+# Generate data
+# -------------
 
 from skimage.data import coins
-from skimage.transform import rescale
 
-from sklearn.feature_extraction.image import grid_to_graph
-from sklearn.cluster import AgglomerativeClustering
-
-
-# #############################################################################
-# Generate data
 orig_coins = coins()
 
+# %%
 # Resize it to 20% of the original size to speed up the processing
 # Applying a Gaussian filter for smoothing prior to down-scaling
 # reduces aliasing artifacts.
+
+import numpy as np
+from scipy.ndimage import gaussian_filter
+from skimage.transform import rescale
+
 smoothened_coins = gaussian_filter(orig_coins, sigma=2)
 rescaled_coins = rescale(
-    smoothened_coins, 0.2, mode="reflect", anti_aliasing=False, multichannel=False
+    smoothened_coins,
+    0.2,
+    mode="reflect",
+    anti_aliasing=False,
 )
 
 X = np.reshape(rescaled_coins, (-1, 1))
 
-# #############################################################################
-# Define the structure A of the data. Pixels connected to their neighbors.
+# %%
+# Define structure of the data
+# ----------------------------
+#
+# Pixels are connected to their neighbors.
+
+from sklearn.feature_extraction.image import grid_to_graph
+
 connectivity = grid_to_graph(*rescaled_coins.shape)
 
-# #############################################################################
+# %%
 # Compute clustering
+# ------------------
+
+import time as time
+
+from sklearn.cluster import AgglomerativeClustering
+
 print("Compute structured hierarchical clustering...")
 st = time.time()
 n_clusters = 27  # number of regions
@@ -55,12 +66,20 @@
 )
 ward.fit(X)
 label = np.reshape(ward.labels_, rescaled_coins.shape)
-print("Elapsed time: ", time.time() - st)
-print("Number of pixels: ", label.size)
-print("Number of clusters: ", np.unique(label).size)
+print(f"Elapsed time: {time.time() - st:.3f}s")
+print(f"Number of pixels: {label.size}")
+print(f"Number of clusters: {np.unique(label).size}")
 
-# #############################################################################
+# %%
 # Plot the results on an image
+# ----------------------------
+#
+# Agglomerative clustering is able to segment each coin however, we have had to
+# use a ``n_cluster`` larger than the number of coins because the segmentation
+# is finding a large in the background.
+
+import matplotlib.pyplot as plt
+
 plt.figure(figsize=(5, 5))
 plt.imshow(rescaled_coins, cmap=plt.cm.gray)
 for l in range(n_clusters):
@@ -70,6 +89,5 @@
             plt.cm.nipy_spectral(l / float(n_clusters)),
         ],
     )
-plt.xticks(())
-plt.yticks(())
+plt.axis("off")
 plt.show()

dev/_downloads/5eeecece5c41d6edcf4555b5e7c34350/plot_coin_ward_segmentation.py

@@ -26,7 +26,97 @@
       },
       "outputs": [],
       "source": [
-        "# Author : Vincent Michel, 2010\n#          Alexandre Gramfort, 2011\n# License: BSD 3 clause\n\nimport time as time\n\nimport numpy as np\nfrom scipy.ndimage import gaussian_filter\n\nimport matplotlib.pyplot as plt\n\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.feature_extraction.image import grid_to_graph\nfrom sklearn.cluster import AgglomerativeClustering\n\n\n# #############################################################################\n# Generate data\norig_coins = coins()\n\n# Resize it to 20% of the original size to speed up the processing\n# Applying a Gaussian filter for smoothing prior to down-scaling\n# reduces aliasing artifacts.\nsmoothened_coins = gaussian_filter(orig_coins, sigma=2)\nrescaled_coins = rescale(\n    smoothened_coins, 0.2, mode=\"reflect\", anti_aliasing=False, multichannel=False\n)\n\nX = np.reshape(rescaled_coins, (-1, 1))\n\n# #############################################################################\n# Define the structure A of the data. Pixels connected to their neighbors.\nconnectivity = grid_to_graph(*rescaled_coins.shape)\n\n# #############################################################################\n# Compute clustering\nprint(\"Compute structured hierarchical clustering...\")\nst = time.time()\nn_clusters = 27  # number of regions\nward = AgglomerativeClustering(\n    n_clusters=n_clusters, linkage=\"ward\", connectivity=connectivity\n)\nward.fit(X)\nlabel = np.reshape(ward.labels_, rescaled_coins.shape)\nprint(\"Elapsed time: \", time.time() - st)\nprint(\"Number of pixels: \", label.size)\nprint(\"Number of clusters: \", np.unique(label).size)\n\n# #############################################################################\n# Plot the results on an image\nplt.figure(figsize=(5, 5))\nplt.imshow(rescaled_coins, cmap=plt.cm.gray)\nfor l in range(n_clusters):\n    plt.contour(\n        label == l,\n        colors=[\n            plt.cm.nipy_spectral(l / float(n_clusters)),\n        ],\n    )\nplt.xticks(())\nplt.yticks(())\nplt.show()"
+        "# Author : Vincent Michel, 2010\n#          Alexandre Gramfort, 2011\n# License: BSD 3 clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generate data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skimage.data import coins\n\norig_coins = coins()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Resize it to 20% of the original size to speed up the processing\nApplying a Gaussian filter for smoothing prior to down-scaling\nreduces aliasing artifacts.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom scipy.ndimage import gaussian_filter\nfrom skimage.transform import rescale\n\nsmoothened_coins = gaussian_filter(orig_coins, sigma=2)\nrescaled_coins = rescale(\n    smoothened_coins,\n    0.2,\n    mode=\"reflect\",\n    anti_aliasing=False,\n)\n\nX = np.reshape(rescaled_coins, (-1, 1))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Define structure of the data\n\nPixels are connected to their neighbors.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.feature_extraction.image import grid_to_graph\n\nconnectivity = grid_to_graph(*rescaled_coins.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compute clustering\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import time as time\n\nfrom sklearn.cluster import AgglomerativeClustering\n\nprint(\"Compute structured hierarchical clustering...\")\nst = time.time()\nn_clusters = 27  # number of regions\nward = AgglomerativeClustering(\n    n_clusters=n_clusters, linkage=\"ward\", connectivity=connectivity\n)\nward.fit(X)\nlabel = np.reshape(ward.labels_, rescaled_coins.shape)\nprint(f\"Elapsed time: {time.time() - st:.3f}s\")\nprint(f\"Number of pixels: {label.size}\")\nprint(f\"Number of clusters: {np.unique(label).size}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the results on an image\n\nAgglomerative clustering is able to segment each coin however, we have had to\nuse a ``n_cluster`` larger than the number of coins because the segmentation\nis finding a large in the background.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nplt.figure(figsize=(5, 5))\nplt.imshow(rescaled_coins, cmap=plt.cm.gray)\nfor l in range(n_clusters):\n    plt.contour(\n        label == l,\n        colors=[\n            plt.cm.nipy_spectral(l / float(n_clusters)),\n        ],\n    )\nplt.axis(\"off\")\nplt.show()"
       ]
     }
   ],