@@ -726,6 +726,59 @@

     "learner.extra_data"

    ]

   },

+  {

+   "cell_type": "markdown",

+   "metadata": {},

+   "source": [

+    "# `Scikit-Optimize`"

+   ]

+  },

+  {

+   "cell_type": "markdown",

+   "metadata": {},

+   "source": [

+    "We have wrapped the `Optimizer` class from [`scikit-optimize`](https://github.com/scikit-optimize/scikit-optimize), to show how existing libraries can be integrated with `adaptive`.\n",

+    "\n",

+    "The `SKOptLearner` attempts to \"optimize\" the given function `g` (i.e. find the global minimum of `g` in the window of interest).\n",

+    "\n",

+    "Here we use the same example as in the `scikit-optimize` [tutorial](https://github.com/scikit-optimize/scikit-optimize/blob/master/examples/ask-and-tell.ipynb). Although `SKOptLearner` can optimize functions of arbitrary dimensionality, we can only plot the learner if a 1D function is being learned."

+   ]

+  },

+  {

+   "cell_type": "code",

+   "execution_count": null,

+   "metadata": {

+    "collapsed": true

+   },

+   "outputs": [],

+   "source": [

+    "def g(x, noise_level=0.1):\n",

+    "    return (np.sin(5 * x) * (1 - np.tanh(x ** 2))\n",

+    "            + np.random.randn() * noise_level)"

+   ]

+  },

+  {

+   "cell_type": "code",

+   "execution_count": null,

+   "metadata": {},

+   "outputs": [],

+   "source": [

+    "learner = adaptive.SKOptLearner(g, dimensions=[(-2., 2.)],\n",

+    "                                base_estimator=\"ET\",\n",

+    "                                acq_optimizer=\"sampling\")\n",

+    "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 1)\n",

+    "runner.live_info()"

+   ]

+  },

+  {

+   "cell_type": "code",

+   "execution_count": null,

+   "metadata": {},

+   "outputs": [],

+   "source": [

+    "runner.live_plot()"

+   ]

+  },

   {

    "cell_type": "markdown",

    "metadata": {