Pushing the docs to dev/ for branch: master, commit 290fe6210d0a59e1cea5d087d93dec0c47d8c869

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Author: Peter Prettenhofer <[email protected]>\n#         Olivier Grisel <[email protected]>\n#         Mathieu Blondel <[email protected]>\n#         Lars Buitinck\n# License: BSD 3 clause\n\nfrom __future__ import print_function\n\nimport logging\nimport numpy as np\nfrom optparse import OptionParser\nimport sys\nfrom time import time\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import HashingVectorizer\nfrom sklearn.feature_selection import SelectKBest, chi2\nfrom sklearn.linear_model import RidgeClassifier\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.linear_model import PassiveAggressiveClassifier\nfrom sklearn.naive_bayes import BernoulliNB, MultinomialNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neighbors import NearestCentroid\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.utils.extmath import density\nfrom sklearn import metrics\n\n\n# Display progress logs on stdout\nlogging.basicConfig(level=logging.INFO,\n                    format='%(asctime)s %(levelname)s %(message)s')\n\n\n# parse commandline arguments\nop = OptionParser()\nop.add_option(\"--report\",\n              action=\"store_true\", dest=\"print_report\",\n              help=\"Print a detailed classification report.\")\nop.add_option(\"--chi2_select\",\n              action=\"store\", type=\"int\", dest=\"select_chi2\",\n              help=\"Select some number of features using a chi-squared test\")\nop.add_option(\"--confusion_matrix\",\n              action=\"store_true\", dest=\"print_cm\",\n              help=\"Print the confusion matrix.\")\nop.add_option(\"--top10\",\n              action=\"store_true\", dest=\"print_top10\",\n              help=\"Print ten most discriminative terms per class\"\n                   \" for every classifier.\")\nop.add_option(\"--all_categories\",\n              action=\"store_true\", dest=\"all_categories\",\n              help=\"Whether to use all categories or not.\")\nop.add_option(\"--use_hashing\",\n              action=\"store_true\",\n              help=\"Use a hashing vectorizer.\")\nop.add_option(\"--n_features\",\n              action=\"store\", type=int, default=2 ** 16,\n              help=\"n_features when using the hashing vectorizer.\")\nop.add_option(\"--filtered\",\n              action=\"store_true\",\n              help=\"Remove newsgroup information that is easily overfit: \"\n                   \"headers, signatures, and quoting.\")\n\n(opts, args) = op.parse_args()\nif len(args) > 0:\n    op.error(\"this script takes no arguments.\")\n    sys.exit(1)\n\nprint(__doc__)\nop.print_help()\nprint()"
+        "# Author: Peter Prettenhofer <[email protected]>\n#         Olivier Grisel <[email protected]>\n#         Mathieu Blondel <[email protected]>\n#         Lars Buitinck\n# License: BSD 3 clause\n\nfrom __future__ import print_function\n\nimport logging\nimport numpy as np\nfrom optparse import OptionParser\nimport sys\nfrom time import time\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import HashingVectorizer\nfrom sklearn.feature_selection import SelectKBest, chi2\nfrom sklearn.linear_model import RidgeClassifier\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.linear_model import PassiveAggressiveClassifier\nfrom sklearn.naive_bayes import BernoulliNB, MultinomialNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neighbors import NearestCentroid\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.utils.extmath import density\nfrom sklearn import metrics\n\n\n# Display progress logs on stdout\nlogging.basicConfig(level=logging.INFO,\n                    format='%(asctime)s %(levelname)s %(message)s')\n\n\n# parse commandline arguments\nop = OptionParser()\nop.add_option(\"--report\",\n              action=\"store_true\", dest=\"print_report\",\n              help=\"Print a detailed classification report.\")\nop.add_option(\"--chi2_select\",\n              action=\"store\", type=\"int\", dest=\"select_chi2\",\n              help=\"Select some number of features using a chi-squared test\")\nop.add_option(\"--confusion_matrix\",\n              action=\"store_true\", dest=\"print_cm\",\n              help=\"Print the confusion matrix.\")\nop.add_option(\"--top10\",\n              action=\"store_true\", dest=\"print_top10\",\n              help=\"Print ten most discriminative terms per class\"\n                   \" for every classifier.\")\nop.add_option(\"--all_categories\",\n              action=\"store_true\", dest=\"all_categories\",\n              help=\"Whether to use all categories or not.\")\nop.add_option(\"--use_hashing\",\n              action=\"store_true\",\n              help=\"Use a hashing vectorizer.\")\nop.add_option(\"--n_features\",\n              action=\"store\", type=int, default=2 ** 16,\n              help=\"n_features when using the hashing vectorizer.\")\nop.add_option(\"--filtered\",\n              action=\"store_true\",\n              help=\"Remove newsgroup information that is easily overfit: \"\n                   \"headers, signatures, and quoting.\")\n\n\ndef is_interactive():\n    return not hasattr(sys.modules['__main__ '], '__file__')\n\n# work-around for Jupyter notebook and IPython console\nargv = [] if is_interactive() else sys.argv[1:]\n(opts, args) = op.parse_args(argv)\nif len(args) > 0:\n    op.error(\"this script takes no arguments.\")\n    sys.exit(1)\n\nprint(__doc__)\nop.print_help()\nprint()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -83,7 +83,13 @@
               help="Remove newsgroup information that is easily overfit: "
                    "headers, signatures, and quoting.")
 
-(opts, args) = op.parse_args()
+
+def is_interactive():
+    return not hasattr(sys.modules['__main__ '], '__file__')
+
+# work-around for Jupyter notebook and IPython console
+argv = [] if is_interactive() else sys.argv[1:]
+(opts, args) = op.parse_args(argv)
 if len(args) > 0:
     op.error("this script takes no arguments.")
     sys.exit(1)