Pushing the docs to dev/ for branch: master, commit d011aedee6b8e6f395c6b93835765ebe93373f47

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Compact estimator representations\n\n\nThis example illustrates the use of the print_changed_only global parameter.\n\nSetting print_changed_only to True will alterate the representation of\nestimators to only show the parameters that have been set to non-default\nvalues. This can be used to have more compact representations.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(__doc__)\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import set_config\n\n\nlr = LogisticRegression(penalty='l1')\nprint('Default representation:')\nprint(lr)\n# LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n#                    intercept_scaling=1, l1_ratio=None, max_iter=100,\n#                    multi_class='warn', n_jobs=None, penalty='l1',\n#                    random_state=None, solver='warn', tol=0.0001, verbose=0,\n#                    warm_start=False)\n\nset_config(print_changed_only=True)\nprint('\\nWith changed_only option:')\nprint(lr)\n# LogisticRegression(penalty='l1')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/auto_examples_python.zip

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+"""
+=================================
+Compact estimator representations
+=================================
+
+This example illustrates the use of the print_changed_only global parameter.
+
+Setting print_changed_only to True will alterate the representation of
+estimators to only show the parameters that have been set to non-default
+values. This can be used to have more compact representations.
+"""
+print(__doc__)
+
+from sklearn.linear_model import LogisticRegression
+from sklearn import set_config
+
+
+lr = LogisticRegression(penalty='l1')
+print('Default representation:')
+print(lr)
+# LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
+#                    intercept_scaling=1, l1_ratio=None, max_iter=100,
+#                    multi_class='warn', n_jobs=None, penalty='l1',
+#                    random_state=None, solver='warn', tol=0.0001, verbose=0,
+#                    warm_start=False)
+
+set_config(print_changed_only=True)
+print('\nWith changed_only option:')
+print(lr)
+# LogisticRegression(penalty='l1')