{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ffc55deb-dc28-4d36-be12-c375ca24e5d3",
   "metadata": {},
   "source": [
    "# Visualizing Experiments\n",
    "\n",
    "In [part one](https://capitalone.github.io/rubicon-ml/quick-look/logging-experiments.html) of the quick look,\n",
    "we logged some experiments and in [part two](https://capitalone.github.io/rubicon-ml/quick-look/sharing-experiments.html)\n",
    "we learned how to share them with collaborators. This final part of the\n",
    "quick look will show how users can leverage ``rubicon_ml``'s visualizations to inspect logged experiments.\n",
    "\n",
    "We'll start by reading in our shared catalog detailing the hyperparameter search experiments from the\n",
    "first part of the quick look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61971f2b-ee91-4066-afbf-e7e040689267",
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake\n",
    "import rubicon_ml\n",
    "\n",
    "catalog = intake.open_catalog(\"./penguin_catalog.yml\")\n",
    "\n",
    "for source in catalog:\n",
    "    catalog[source].discover()\n",
    "    \n",
    "experiments = [catalog[source].read() for source in catalog]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459e5a4e-9475-480a-9c71-653bd6d031ae",
   "metadata": {},
   "source": [
    "From here, using the ``rubicon_ml`` visualizations is quite simple. Just import the visualization you want \n",
    "to use from the ``viz`` module and pass in the experiments you want to visualize. To see all currently available\n",
    "visualizations, check out the [\"Visualization\" section](https://capitalone.github.io/rubicon-ml/visualizations.html)\n",
    "in our docs.\n",
    "\n",
    "Each instance of a visualization class exposes ``show`` and ``serve`` methods. ``show`` is used to display a\n",
    "visualization in-line in a Jupyter notebook. ``show`` is non-blocking, so other cells can be executed while the\n",
    "visualization is displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa0b1c12-36c1-476c-8fa0-46b8f05cab54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash is running on http://127.0.0.1:8050/\n",
      "\n",
      " * Serving Flask app 'rubicon_ml.viz.base' (lazy loading)\n",
      " * Environment: production\n",
      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
      " * Debug mode: off\n"
     ]
    }
   ],
   "source": [
    "from rubicon_ml.viz import ExperimentsTable\n",
    "\n",
    "ExperimentsTable(experiments=experiments).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192ecf10-a397-4b80-8f83-2fad933ad0ce",
   "metadata": {},
   "source": [
    "![experiments-table](visualizing-experiments-table.png \"experiments-table\")\n",
    "\n",
    "``serve`` only serves the visualization on the provided port without displaying it in-line. This is generally more useful for \n",
    "displaying visualizations from non-Jupyter runtimes. Under the hood, ``show`` simply calls ``serve`` on a background thread.\n",
    "Therefore, ``serve`` used directly is a blocking call and will not return until the server is shut down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5afddf6f-094c-438a-975d-8b9c4a873364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash is running on http://127.0.0.1:8051/\n",
      "\n",
      " * Serving Flask app 'rubicon_ml.viz.base' (lazy loading)\n",
      " * Environment: production\n",
      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
      " * Debug mode: off\n"
     ]
    }
   ],
   "source": [
    "from rubicon_ml.viz import Dashboard\n",
    "\n",
    "Dashboard(experiments=experiments).serve(run_server_kwargs={\"port\": 8051})"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}