{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2cd3303a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ccc0bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/data/yzhouc01/FILIP-MS/experiments/20251110_filip-global/result_MassSpecGym_retrieval_candidates_formula.pkl\", \"rb\") as f:\n",
    "    result = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e517777",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank_fine</th>\n",
       "      <th>rank_global</th>\n",
       "      <th>rank_sum</th>\n",
       "      <th>rank_weighted</th>\n",
       "      <th>rank_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R@1</th>\n",
       "      <td>0.214571</td>\n",
       "      <td>0.163306</td>\n",
       "      <td>0.192869</td>\n",
       "      <td>0.191274</td>\n",
       "      <td>0.192869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R@5</th>\n",
       "      <td>0.483140</td>\n",
       "      <td>0.403566</td>\n",
       "      <td>0.447425</td>\n",
       "      <td>0.444862</td>\n",
       "      <td>0.447425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R@20</th>\n",
       "      <td>0.747095</td>\n",
       "      <td>0.694350</td>\n",
       "      <td>0.728355</td>\n",
       "      <td>0.726361</td>\n",
       "      <td>0.728355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rank_fine  rank_global  rank_sum  rank_weighted  rank_avg\n",
       "R@1    0.214571     0.163306  0.192869       0.191274  0.192869\n",
       "R@5    0.483140     0.403566  0.447425       0.444862  0.447425\n",
       "R@20   0.747095     0.694350  0.728355       0.726361  0.728355"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for i in [1, 5, 20]:\n",
    "    curr_d = {}\n",
    "    for c in result.columns.tolist():\n",
    "        if c.startswith('rank'):\n",
    "            curr_d[c] = result[result[c] <= i].shape[0] / result.shape[0]\n",
    "    data.append(curr_d)\n",
    "\n",
    "data_df = pd.DataFrame(data, index=['R@1', 'R@5', 'R@20'])\n",
    "data_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10493857",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank_fine</th>\n",
       "      <th>rank_global</th>\n",
       "      <th>rank_sum</th>\n",
       "      <th>rank_weighted</th>\n",
       "      <th>rank_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R@1</th>\n",
       "      <td>0.420882</td>\n",
       "      <td>0.369731</td>\n",
       "      <td>0.412907</td>\n",
       "      <td>0.411939</td>\n",
       "      <td>0.412907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R@5</th>\n",
       "      <td>0.744475</td>\n",
       "      <td>0.707052</td>\n",
       "      <td>0.738893</td>\n",
       "      <td>0.737412</td>\n",
       "      <td>0.738893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R@20</th>\n",
       "      <td>0.927660</td>\n",
       "      <td>0.916325</td>\n",
       "      <td>0.926407</td>\n",
       "      <td>0.926122</td>\n",
       "      <td>0.926407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rank_fine  rank_global  rank_sum  rank_weighted  rank_avg\n",
       "R@1    0.420882     0.369731  0.412907       0.411939  0.412907\n",
       "R@5    0.744475     0.707052  0.738893       0.737412  0.738893\n",
       "R@20   0.927660     0.916325  0.926407       0.926122  0.926407"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for i in [1, 5, 20]:\n",
    "    curr_d = {}\n",
    "    for c in result.columns.tolist():\n",
    "        if c.startswith('rank'):\n",
    "            curr_d[c] = result[result[c] <= i].shape[0] / result.shape[0]\n",
    "    data.append(curr_d)\n",
    "\n",
    "data_df = pd.DataFrame(data, index=['R@1', 'R@5', 'R@20'])\n",
    "data_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e4201db",
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  }
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