Pushing the docs for revision for branch: master, commit bd4e00b1eb5c1fee4603a248be23ff72be9d855c

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "print(__doc__)\n\nfrom time import time\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import metrics\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import load_digits\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import scale\n\nnp.random.seed(42)\n\ndigits = load_digits()\ndata = scale(digits.data)\n\nn_samples, n_features = data.shape\nn_digits = len(np.unique(digits.target))\nlabels = digits.target\n\nsample_size = 300\n\nprint(\"n_digits: %d, \\t n_samples %d, \\t n_features %d\"\n      % (n_digits, n_samples, n_features))\n\n\nprint(79 * '_')\nprint('% 9s' % 'init'\n      '    time  inertia    homo   compl  v-meas     ARI AMI  silhouette')\n\n\ndef bench_k_means(estimator, name, data):\n    t0 = time()\n    estimator.fit(data)\n    print('% 9s   %.2fs    %i   %.3f   %.3f   %.3f   %.3f   %.3f    %.3f'\n          % (name, (time() - t0), estimator.inertia_,\n             metrics.homogeneity_score(labels, estimator.labels_),\n             metrics.completeness_score(labels, estimator.labels_),\n             metrics.v_measure_score(labels, estimator.labels_),\n             metrics.adjusted_rand_score(labels, estimator.labels_),\n             metrics.adjusted_mutual_info_score(labels,  estimator.labels_),\n             metrics.silhouette_score(data, estimator.labels_,\n                                      metric='euclidean',\n                                      sample_size=sample_size)))\n\nbench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),\n              name=\"k-means++\", data=data)\n\nbench_k_means(KMeans(init='random', n_clusters=n_digits, n_init=10),\n              name=\"random\", data=data)\n\n# in this case the seeding of the centers is deterministic, hence we run the\n# kmeans algorithm only once with n_init=1\npca = PCA(n_components=n_digits).fit(data)\nbench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),\n              name=\"PCA-based\",\n              data=data)\nprint(79 * '_')"
+        "print(__doc__)\n\nfrom time import time\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import metrics\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import load_digits\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import scale\n\nnp.random.seed(42)\n\ndigits = load_digits()\ndata = scale(digits.data)\n\nn_samples, n_features = data.shape\nn_digits = len(np.unique(digits.target))\nlabels = digits.target\n\nsample_size = 300\n\nprint(\"n_digits: %d, \\t n_samples %d, \\t n_features %d\"\n      % (n_digits, n_samples, n_features))\n\n\nprint(82 * '_')\nprint('init\\t\\ttime\\tinertia\\thomo\\tcompl\\tv-meas\\tARI\\tAMI\\tsilhouette')\n\n\ndef bench_k_means(estimator, name, data):\n    t0 = time()\n    estimator.fit(data)\n    print('%-9s\\t%.2fs\\t%i\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f'\n          % (name, (time() - t0), estimator.inertia_,\n             metrics.homogeneity_score(labels, estimator.labels_),\n             metrics.completeness_score(labels, estimator.labels_),\n             metrics.v_measure_score(labels, estimator.labels_),\n             metrics.adjusted_rand_score(labels, estimator.labels_),\n             metrics.adjusted_mutual_info_score(labels,  estimator.labels_),\n             metrics.silhouette_score(data, estimator.labels_,\n                                      metric='euclidean',\n                                      sample_size=sample_size)))\n\nbench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),\n              name=\"k-means++\", data=data)\n\nbench_k_means(KMeans(init='random', n_clusters=n_digits, n_init=10),\n              name=\"random\", data=data)\n\n# in this case the seeding of the centers is deterministic, hence we run the\n# kmeans algorithm only once with n_init=1\npca = PCA(n_components=n_digits).fit(data)\nbench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),\n              name=\"PCA-based\",\n              data=data)\nprint(82 * '_')"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -52,15 +52,14 @@
       % (n_digits, n_samples, n_features))
 
 
-print(79 * '_')
-print('% 9s' % 'init'
-      '    time  inertia    homo   compl  v-meas     ARI AMI  silhouette')
+print(82 * '_')
+print('init\t\ttime\tinertia\thomo\tcompl\tv-meas\tARI\tAMI\tsilhouette')
 
 
 def bench_k_means(estimator, name, data):
     t0 = time()
     estimator.fit(data)
-    print('% 9s   %.2fs    %i   %.3f   %.3f   %.3f   %.3f   %.3f    %.3f'
+    print('%-9s\t%.2fs\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
           % (name, (time() - t0), estimator.inertia_,
              metrics.homogeneity_score(labels, estimator.labels_),
              metrics.completeness_score(labels, estimator.labels_),
@@ -83,7 +82,7 @@ def bench_k_means(estimator, name, data):
 bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),
               name="PCA-based",
               data=data)
-print(79 * '_')
+print(82 * '_')
 
 ###############################################################################
 # Visualize the results on PCA-reduced data