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   "source": [
    "# Metric Correlation Plot\n",
    "\n",
    "The metric correlation plot is used to compare how various input parameters\n",
    "effect a selected output metric across a number of experiments. Users can\n",
    "dynamically choose between available metrics to anchor the visualization on\n",
    "and rearrange and highlight the plot as desired.\n",
    "\n",
    "The plot itself is a Plotly parallel coordinates plot. More information can\n",
    "be found in [the Plotly documentation](https://plotly.com/python/parallel-coordinates-plot/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0b35758-483e-463a-b955-13a3f8ab5129",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "from rubicon_ml import Rubicon\n",
    "from rubicon_ml.viz import MetricCorrelationPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd871177-8049-44fa-ac2d-f7c592fecc23",
   "metadata": {},
   "source": [
    "First, we'll create a few experiments and log some parameters and metrics to them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a4bcd49-999f-48ff-9dea-955f1ea1881d",
   "metadata": {},
   "outputs": [],
   "source": [
    "rubicon = Rubicon(persistence=\"memory\", auto_git_enabled=True)\n",
    "project = rubicon.get_or_create_project(\"metric correlation plot\")\n",
    "\n",
    "for i in range(0, 100):\n",
    "    experiment = project.log_experiment()\n",
    "\n",
    "    experiment.log_parameter(\n",
    "        name=\"is_standardized\",\n",
    "        value=random.choice([True, False]),\n",
    "    )\n",
    "    experiment.log_parameter(name=\"n_estimators\", value=random.randrange(2, 10, 2))\n",
    "    experiment.log_parameter(\n",
    "        name=\"sample\",\n",
    "        value=random.choice([\"A\", \"B\", \"C\", \"D\", \"E\"]),\n",
    "    )\n",
    "\n",
    "    experiment.log_metric(name=\"accuracy\", value=random.random())\n",
    "    experiment.log_metric(name=\"confidence\", value=random.random())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1210c856-736b-4819-b424-7f1cec6baaf1",
   "metadata": {},
   "source": [
    "Now, we can instantiate the `MetricCorrelationPlot` object with the experiments we just\n",
    "logged and view the plot right in the notebook with `show`. The Dash application\n",
    "itself will be running on http://127.0.0.1:8050/ when running locally. Use the\n",
    "`serve` command to launch the server directly without rendering the widget in the\n",
    "current Python interpreter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c1bbd4e-7783-467b-8831-3a3dbfa932c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash is running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "MetricCorrelationPlot(\n",
    "    experiments=project.experiments(),\n",
    "    selected_metric=\"accuracy\",\n",
    ").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6b2019-47c2-48d5-ae38-397b7b107784",
   "metadata": {},
   "source": [
    "![metric-correlation-plot](metric-correlation-plot.png \"metric correlation plot\")"
   ]
  }
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