{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2352e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from utils.compute_tools import make_normalizer, load_normalizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a3a5a5e",
   "metadata": {},
   "source": [
    "## 1. normalization with max-min from your own results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e95b280a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read results from csv file\n",
    "results = pd.read_csv(f\"examples/results_and_parameters.csv\")\n",
    "\n",
    "benchmark_name=\"your_benchmark_name\" #set your desired benchmark_name\n",
    "metric = \"test metric\" #column containing test metrics in csv file\n",
    "make_normalizer(\n",
    "    results, \n",
    "    metrics=(metric,), \n",
    "    benchmark_name=benchmark_name\n",
    "    )\n",
    "\n",
    "#normalize results\n",
    "normalizer = load_normalizer(benchmark_name=benchmark_name)\n",
    "new_metric = normalizer.normalize_data_frame(df=results, metric=metric)\n",
    "\n",
    "#save normalized values to file\n",
    "results.to_csv(\"examples/normalized_results_and_parameters.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3f1e10",
   "metadata": {},
   "source": [
    "## 2. normalization with max-min from leaderboard base results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07a21745",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read results from csv file\n",
    "results = pd.read_csv(f\"examples/results_and_parameters.csv\")\n",
    "\n",
    "metric = \"test metric\" #column containing test metrics in csv file\n",
    "make_normalizer(\n",
    "    results, \n",
    "    metrics=(metric,), \n",
    "    # benchmark_name=benchmark_name\n",
    "    )\n",
    "\n",
    "#normalize results\n",
    "normalizer = load_normalizer() #leave benchmark name blank to use default leaderboard normalization values\n",
    "new_metric = normalizer.normalize_data_frame(df=results, metric=metric)\n",
    "\n",
    "#save normalized values to file\n",
    "results.to_csv(\"examples/normalized_results_and_parameters.csv\")"
   ]
  }
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