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   "cell_type": "markdown",
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   "source": [
    "# OPTaaS: Surrogate Visualization with Holoviews and Plotly\n",
    "\n",
    "If you have the holoviews and plotly libraries installed it is also possible to get an interactive 3d view of\n",
    "the surrogate predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from mindfoundry.optaas.client.goal import Goal\n",
    "from mindfoundry.optaas.client.task import Task\n",
    "from mindfoundry.optaas.client.parameter import FloatParameter, CategoricalParameter\n",
    "from mindfoundry.optaas.client.viz import SurrogateViz\n",
    "\n",
    "## Connect to OPTaaS using your API Key\n",
    "from mindfoundry.optaas.client.client import OPTaaSClient\n",
    "client = OPTaaSClient('https://optaas.mindfoundry.ai', '<Your OPTaaS API key>')\n",
    "\n",
    "## Start the holoviews/plotly engine\n",
    "import holoviews as hv\n",
    "hv.extension('plotly')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Create a simple task optimizing a function with 3 continuous and one categorical parameter"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
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   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "task = client.create_task(\n",
    "    title='Test',\n",
    "    parameters=[\n",
    "        FloatParameter('x', minimum=-2., maximum=2.),\n",
    "        FloatParameter('y', minimum=-2., maximum=2.),\n",
    "        FloatParameter('z', minimum=-2., maximum=2.),\n",
    "        CategoricalParameter('c', values=[\"a\", \"b\"]),\n",
    "    ],\n",
    "    goal=Goal.min,\n",
    ")\n",
    "\n",
    "\n",
    "## Define the scoring function\n",
    "def scoring_function(x, y, z, c):\n",
    "    rate = {\"a\": 1.5, \"b\": 0.75}[c] \n",
    "    score = np.sin(rate*x*3) + np.cos(rate*y*3) + np.sin(z*3) + {\"a\": -0.5, \"b\": 0}[c]\n",
    "    return score\n",
    "\n",
    "\n",
    "best_result = task.run(scoring_function, max_iterations=30)\n",
    "print(\"Best Result:\", best_result)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Make a visualizer object and visualize the Surrogate Mean and Variance in 'x' and 'z'"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "visualizer = SurrogateViz(client=client, task=task)\n",
    "visualizer.plot_surrogate_mean_and_std(\"x\",\"z\", fig_width=800, fig_height=700, z_range=(-3,3.))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## We can also make a view with the uncertainties overlaid above and below the mean"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "visualizer.plot_surrogate_with_uncertainties(\"x\",\"z\", fig_width=600, fig_height=700, z_range=(-4.,4.))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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