result-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66bc3ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d8e04952",
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_flattened_experiment_results(results):\n",
    "    df = pd.DataFrame(results)\n",
    "    grp = df.groupby(['al_algorithm','trained_samples']).agg(['mean', 'median', 'var']).reset_index()\n",
    "    grp.columns = [first if second == '' else first + '_' + second for first, second in grp.columns.values]\n",
    "    return grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81b89e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_plot(results, title=\"Experiments\"):\n",
    "#     results = results.sort_values('al_algorithm', ascending=False)\n",
    "    fig, ax =plt.subplots(figsize=(15,10))\n",
    "    \n",
    "    sns.lineplot(data=results, hue='al_algorithm', style='al_algorithm', x = \"trained_samples\",  y = \"_f1\", ci='sd',  ax = ax,\n",
    "                 marker='o', \n",
    "                  palette=['goldenrod', 'indigo', 'darkgreen']\n",
    "                )\n",
    "#     ax.lines[0].set_linestyle(\"dotted\")\n",
    "#     ax.lines[2].set_linestyle(\"dotted\")\n",
    "    ax.legend( title = \"F1-Score \" + title)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8923efe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_result(src:str):\n",
    "    from datetime import datetime, timedelta\n",
    "    res = []\n",
    "    data = pd.read_json(src).drop(columns=['duration'])\n",
    "    for a in data.columns:\n",
    "        for rand_id, rand_row in enumerate(data[a]):\n",
    "            for fold_id in range(len(rand_row)):\n",
    "                l = len(rand_row[fold_id][list(rand_row[fold_id])[0]])\n",
    "                res_row = [{c : None for c in rand_row[fold_id] } for i in range(l)]\n",
    "\n",
    "                for col in rand_row[fold_id]:\n",
    "                     i = 0\n",
    "                     for k in rand_row[fold_id][col]:\n",
    "                         if col.startswith('duration'):\n",
    "                             res_row[i][col] = timedelta(milliseconds=rand_row[fold_id][col][k])\n",
    "#                              res_row[i][col+'_timestamp'] = rand_row[fold_id][col][k]\n",
    "                             res_row[i][col+'_seconds'] = res_row[i][col].total_seconds()\n",
    "#                              res_row[i][col+'_milliseconds'] = res_row[i][col].total_seconds()*1000\n",
    "                         else:\n",
    "                             res_row[i][col] = rand_row[fold_id][col][k]\n",
    "\n",
    "                         res_row[i]['fold_id'] = fold_id\n",
    "                         res_row[i]['random_id'] = rand_id\n",
    "                         res_row[i]['al_algorithm'] = a\n",
    "                         i = i + 1\n",
    "                for r in res_row:\n",
    "                    res.append(r)\n",
    "    return res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "66e00b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame(load_result('results/full_combined_runtimes_optimized.json'))\n",
    "\n",
    "print_plot(data[data['trained_samples']<10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c0085eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_plot(data[data['trained_samples']<1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "951cc938",
   "metadata": {},
   "source": [
    "## Comparison Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "471bb772",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pivot_from_data(data):\n",
    "    sub = data[['al_algorithm', 'random_id', 'fold_id', 'trained_samples', '_f1']]\n",
    "    sgrp = sub.groupby(['al_algorithm', 'trained_samples']).agg(['mean']).reset_index()\n",
    "    sgrp.columns = [first if second == '' else first + '_' + second for first, second in sgrp.columns.values]\n",
    "    sgrp = sgrp.drop(['random_id_mean', 'fold_id_mean'], axis=1)\n",
    "    pvt = sgrp.pivot(index='trained_samples', columns='al_algorithm')\n",
    "\n",
    "    # pvt.columns = pvt.columns.get_level_values(0)\n",
    "    pvt.columns = ['_'.join([c.replace('_mean','').strip('_') for c in col]) for col in pvt.columns.values]\n",
    "    pvt[\"Difference-SBERT-KMeans-to-RandomSampling\"] = pvt['f1_SBERT-KMeans'] - pvt['f1_Random-Sampling']\n",
    "    pvt[\"Difference-BERTopic-Clustering-Distance-to-RandomSampling\"] = pvt['f1_BERTopic-Clustering'] - pvt['f1_Random-Sampling']\n",
    "    pvt[\"Difference-SBERT-KMeans-to-BERTopic-Clustering\"] = pvt['f1_SBERT-KMeans'] - pvt['f1_BERTopic-Clustering']\n",
    "    cols = pvt.columns.tolist()\n",
    "    cols = list([cols[1]]) + list([cols[2]]) +  list([cols[0]]) + cols[-3:]\n",
    "    pvt = pvt[cols]\n",
    "    return pvt\n",
    "def get_max_arg_by_column(col, data):\n",
    "    arg = np.argmax(pvt[col])\n",
    "    return arg, data.iloc[arg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6d9bd337",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1_Random-Sampling</th>\n",
       "      <th>f1_SBERT-KMeans</th>\n",
       "      <th>f1_BERTopic-Clustering</th>\n",
       "      <th>Difference-SBERT-KMeans-to-RandomSampling</th>\n",
       "      <th>Difference-BERTopic-Clustering-Distance-to-RandomSampling</th>\n",
       "      <th>Difference-SBERT-KMeans-to-BERTopic-Clustering</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trained_samples</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.721044</td>\n",
       "      <td>0.737707</td>\n",
       "      <td>0.739272</td>\n",
       "      <td>0.016662</td>\n",
       "      <td>0.018228</td>\n",
       "      <td>-0.001565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.786343</td>\n",
       "      <td>0.806655</td>\n",
       "      <td>0.805274</td>\n",
       "      <td>0.020312</td>\n",
       "      <td>0.018931</td>\n",
       "      <td>0.001381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0.820844</td>\n",
       "      <td>0.840919</td>\n",
       "      <td>0.835584</td>\n",
       "      <td>0.020076</td>\n",
       "      <td>0.014741</td>\n",
       "      <td>0.005335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.835781</td>\n",
       "      <td>0.850974</td>\n",
       "      <td>0.848670</td>\n",
       "      <td>0.015193</td>\n",
       "      <td>0.012889</td>\n",
       "      <td>0.002304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.847555</td>\n",
       "      <td>0.856220</td>\n",
       "      <td>0.855811</td>\n",
       "      <td>0.008665</td>\n",
       "      <td>0.008256</td>\n",
       "      <td>0.000408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>0.853124</td>\n",
       "      <td>0.862127</td>\n",
       "      <td>0.856714</td>\n",
       "      <td>0.009003</td>\n",
       "      <td>0.003591</td>\n",
       "      <td>0.005413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>0.856975</td>\n",
       "      <td>0.864235</td>\n",
       "      <td>0.862227</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>0.005252</td>\n",
       "      <td>0.002008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>0.861487</td>\n",
       "      <td>0.866168</td>\n",
       "      <td>0.862228</td>\n",
       "      <td>0.004681</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>0.003940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>0.858684</td>\n",
       "      <td>0.871793</td>\n",
       "      <td>0.863705</td>\n",
       "      <td>0.013109</td>\n",
       "      <td>0.005021</td>\n",
       "      <td>0.008088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0.862624</td>\n",
       "      <td>0.872014</td>\n",
       "      <td>0.868531</td>\n",
       "      <td>0.009390</td>\n",
       "      <td>0.005907</td>\n",
       "      <td>0.003483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>0.864141</td>\n",
       "      <td>0.873839</td>\n",
       "      <td>0.871911</td>\n",
       "      <td>0.009698</td>\n",
       "      <td>0.007770</td>\n",
       "      <td>0.001928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>0.864864</td>\n",
       "      <td>0.874860</td>\n",
       "      <td>0.872173</td>\n",
       "      <td>0.009996</td>\n",
       "      <td>0.007309</td>\n",
       "      <td>0.002687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>0.867544</td>\n",
       "      <td>0.876458</td>\n",
       "      <td>0.876585</td>\n",
       "      <td>0.008914</td>\n",
       "      <td>0.009042</td>\n",
       "      <td>-0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>0.867830</td>\n",
       "      <td>0.877308</td>\n",
       "      <td>0.878460</td>\n",
       "      <td>0.009477</td>\n",
       "      <td>0.010629</td>\n",
       "      <td>-0.001152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>0.873247</td>\n",
       "      <td>0.877551</td>\n",
       "      <td>0.878386</td>\n",
       "      <td>0.004304</td>\n",
       "      <td>0.005138</td>\n",
       "      <td>-0.000835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>0.875185</td>\n",
       "      <td>0.878585</td>\n",
       "      <td>0.878760</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.003574</td>\n",
       "      <td>-0.000175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>0.870006</td>\n",
       "      <td>0.878285</td>\n",
       "      <td>0.880399</td>\n",
       "      <td>0.008279</td>\n",
       "      <td>0.010392</td>\n",
       "      <td>-0.002113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1800</th>\n",
       "      <td>0.871226</td>\n",
       "      <td>0.882122</td>\n",
       "      <td>0.879833</td>\n",
       "      <td>0.010896</td>\n",
       "      <td>0.008607</td>\n",
       "      <td>0.002289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>0.873726</td>\n",
       "      <td>0.881733</td>\n",
       "      <td>0.882580</td>\n",
       "      <td>0.008007</td>\n",
       "      <td>0.008854</td>\n",
       "      <td>-0.000847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.875699</td>\n",
       "      <td>0.882445</td>\n",
       "      <td>0.881387</td>\n",
       "      <td>0.006746</td>\n",
       "      <td>0.005688</td>\n",
       "      <td>0.001058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>0.876529</td>\n",
       "      <td>0.882065</td>\n",
       "      <td>0.880132</td>\n",
       "      <td>0.005536</td>\n",
       "      <td>0.003603</td>\n",
       "      <td>0.001933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>0.876520</td>\n",
       "      <td>0.882778</td>\n",
       "      <td>0.880659</td>\n",
       "      <td>0.006258</td>\n",
       "      <td>0.004139</td>\n",
       "      <td>0.002119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>0.878462</td>\n",
       "      <td>0.884205</td>\n",
       "      <td>0.881194</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.003011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>0.878922</td>\n",
       "      <td>0.884521</td>\n",
       "      <td>0.880744</td>\n",
       "      <td>0.005599</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.003777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>0.878840</td>\n",
       "      <td>0.883630</td>\n",
       "      <td>0.883163</td>\n",
       "      <td>0.004790</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.000467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2600</th>\n",
       "      <td>0.879509</td>\n",
       "      <td>0.886240</td>\n",
       "      <td>0.884722</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.005213</td>\n",
       "      <td>0.001518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2700</th>\n",
       "      <td>0.881190</td>\n",
       "      <td>0.885452</td>\n",
       "      <td>0.885994</td>\n",
       "      <td>0.004261</td>\n",
       "      <td>0.004804</td>\n",
       "      <td>-0.000543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2800</th>\n",
       "      <td>0.880036</td>\n",
       "      <td>0.886284</td>\n",
       "      <td>0.883711</td>\n",
       "      <td>0.006247</td>\n",
       "      <td>0.003675</td>\n",
       "      <td>0.002573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>0.878862</td>\n",
       "      <td>0.886011</td>\n",
       "      <td>0.884713</td>\n",
       "      <td>0.007149</td>\n",
       "      <td>0.005851</td>\n",
       "      <td>0.001298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>0.878737</td>\n",
       "      <td>0.887653</td>\n",
       "      <td>0.886582</td>\n",
       "      <td>0.008916</td>\n",
       "      <td>0.007846</td>\n",
       "      <td>0.001070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3100</th>\n",
       "      <td>0.879623</td>\n",
       "      <td>0.888128</td>\n",
       "      <td>0.886560</td>\n",
       "      <td>0.008505</td>\n",
       "      <td>0.006937</td>\n",
       "      <td>0.001568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3200</th>\n",
       "      <td>0.884762</td>\n",
       "      <td>0.886641</td>\n",
       "      <td>0.888710</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>0.003948</td>\n",
       "      <td>-0.002069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3300</th>\n",
       "      <td>0.878949</td>\n",
       "      <td>0.890634</td>\n",
       "      <td>0.890475</td>\n",
       "      <td>0.011685</td>\n",
       "      <td>0.011526</td>\n",
       "      <td>0.000159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3400</th>\n",
       "      <td>0.883801</td>\n",
       "      <td>0.889322</td>\n",
       "      <td>0.886253</td>\n",
       "      <td>0.005521</td>\n",
       "      <td>0.002453</td>\n",
       "      <td>0.003068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3500</th>\n",
       "      <td>0.881965</td>\n",
       "      <td>0.890735</td>\n",
       "      <td>0.888134</td>\n",
       "      <td>0.008769</td>\n",
       "      <td>0.006169</td>\n",
       "      <td>0.002601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3600</th>\n",
       "      <td>0.883462</td>\n",
       "      <td>0.891234</td>\n",
       "      <td>0.888791</td>\n",
       "      <td>0.007772</td>\n",
       "      <td>0.005329</td>\n",
       "      <td>0.002443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3700</th>\n",
       "      <td>0.888412</td>\n",
       "      <td>0.891801</td>\n",
       "      <td>0.889583</td>\n",
       "      <td>0.003390</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.002218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3800</th>\n",
       "      <td>0.888703</td>\n",
       "      <td>0.890660</td>\n",
       "      <td>0.890459</td>\n",
       "      <td>0.001957</td>\n",
       "      <td>0.001756</td>\n",
       "      <td>0.000201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3900</th>\n",
       "      <td>0.888607</td>\n",
       "      <td>0.891723</td>\n",
       "      <td>0.888834</td>\n",
       "      <td>0.003116</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.002889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4000</th>\n",
       "      <td>0.889437</td>\n",
       "      <td>0.891450</td>\n",
       "      <td>0.889506</td>\n",
       "      <td>0.002013</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.001944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4100</th>\n",
       "      <td>0.888493</td>\n",
       "      <td>0.891561</td>\n",
       "      <td>0.890890</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.002397</td>\n",
       "      <td>0.000671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4200</th>\n",
       "      <td>0.887267</td>\n",
       "      <td>0.890216</td>\n",
       "      <td>0.890856</td>\n",
       "      <td>0.002949</td>\n",
       "      <td>0.003589</td>\n",
       "      <td>-0.000640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4300</th>\n",
       "      <td>0.887656</td>\n",
       "      <td>0.892468</td>\n",
       "      <td>0.889256</td>\n",
       "      <td>0.004811</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.003211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4400</th>\n",
       "      <td>0.888418</td>\n",
       "      <td>0.890903</td>\n",
       "      <td>0.890525</td>\n",
       "      <td>0.002485</td>\n",
       "      <td>0.002107</td>\n",
       "      <td>0.000378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4500</th>\n",
       "      <td>0.887616</td>\n",
       "      <td>0.891296</td>\n",
       "      <td>0.891889</td>\n",
       "      <td>0.003681</td>\n",
       "      <td>0.004274</td>\n",
       "      <td>-0.000593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4600</th>\n",
       "      <td>0.888105</td>\n",
       "      <td>0.892462</td>\n",
       "      <td>0.890358</td>\n",
       "      <td>0.004358</td>\n",
       "      <td>0.002253</td>\n",
       "      <td>0.002104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4700</th>\n",
       "      <td>0.889855</td>\n",
       "      <td>0.892562</td>\n",
       "      <td>0.891038</td>\n",
       "      <td>0.002706</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.001524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4800</th>\n",
       "      <td>0.887708</td>\n",
       "      <td>0.894238</td>\n",
       "      <td>0.887419</td>\n",
       "      <td>0.006531</td>\n",
       "      <td>-0.000289</td>\n",
       "      <td>0.006819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>0.886316</td>\n",
       "      <td>0.892719</td>\n",
       "      <td>0.885430</td>\n",
       "      <td>0.006403</td>\n",
       "      <td>-0.000886</td>\n",
       "      <td>0.007289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0.892755</td>\n",
       "      <td>0.894808</td>\n",
       "      <td>0.888538</td>\n",
       "      <td>0.002054</td>\n",
       "      <td>-0.004217</td>\n",
       "      <td>0.006270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>0.889372</td>\n",
       "      <td>0.893689</td>\n",
       "      <td>0.888694</td>\n",
       "      <td>0.004318</td>\n",
       "      <td>-0.000677</td>\n",
       "      <td>0.004995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5200</th>\n",
       "      <td>0.891075</td>\n",
       "      <td>0.893995</td>\n",
       "      <td>0.888971</td>\n",
       "      <td>0.002920</td>\n",
       "      <td>-0.002105</td>\n",
       "      <td>0.005024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5300</th>\n",
       "      <td>0.890360</td>\n",
       "      <td>0.893439</td>\n",
       "      <td>0.886216</td>\n",
       "      <td>0.003079</td>\n",
       "      <td>-0.004144</td>\n",
       "      <td>0.007223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5400</th>\n",
       "      <td>0.892458</td>\n",
       "      <td>0.893279</td>\n",
       "      <td>0.886744</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>-0.005714</td>\n",
       "      <td>0.006535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5500</th>\n",
       "      <td>0.891810</td>\n",
       "      <td>0.891973</td>\n",
       "      <td>0.884324</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>-0.007487</td>\n",
       "      <td>0.007649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5600</th>\n",
       "      <td>0.890761</td>\n",
       "      <td>0.892861</td>\n",
       "      <td>0.883849</td>\n",
       "      <td>0.002101</td>\n",
       "      <td>-0.006911</td>\n",
       "      <td>0.009012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5700</th>\n",
       "      <td>0.890429</td>\n",
       "      <td>0.893031</td>\n",
       "      <td>0.884115</td>\n",
       "      <td>0.002602</td>\n",
       "      <td>-0.006314</td>\n",
       "      <td>0.008916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5800</th>\n",
       "      <td>0.889865</td>\n",
       "      <td>0.892275</td>\n",
       "      <td>0.885375</td>\n",
       "      <td>0.002410</td>\n",
       "      <td>-0.004490</td>\n",
       "      <td>0.006901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5900</th>\n",
       "      <td>0.891870</td>\n",
       "      <td>0.892873</td>\n",
       "      <td>0.885292</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>-0.006577</td>\n",
       "      <td>0.007580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>0.895240</td>\n",
       "      <td>0.892539</td>\n",
       "      <td>0.887042</td>\n",
       "      <td>-0.002701</td>\n",
       "      <td>-0.008199</td>\n",
       "      <td>0.005498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6100</th>\n",
       "      <td>0.893258</td>\n",
       "      <td>0.893621</td>\n",
       "      <td>0.884331</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>-0.008927</td>\n",
       "      <td>0.009290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>0.892265</td>\n",
       "      <td>0.892338</td>\n",
       "      <td>0.886315</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>-0.005950</td>\n",
       "      <td>0.006023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6300</th>\n",
       "      <td>0.893304</td>\n",
       "      <td>0.894460</td>\n",
       "      <td>0.889146</td>\n",
       "      <td>0.001156</td>\n",
       "      <td>-0.004158</td>\n",
       "      <td>0.005315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6400</th>\n",
       "      <td>0.892784</td>\n",
       "      <td>0.889592</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>-0.003192</td>\n",
       "      <td>-0.006199</td>\n",
       "      <td>0.003007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6500</th>\n",
       "      <td>0.893754</td>\n",
       "      <td>0.893535</td>\n",
       "      <td>0.884926</td>\n",
       "      <td>-0.000219</td>\n",
       "      <td>-0.008829</td>\n",
       "      <td>0.008610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6600</th>\n",
       "      <td>0.894690</td>\n",
       "      <td>0.891770</td>\n",
       "      <td>0.887866</td>\n",
       "      <td>-0.002921</td>\n",
       "      <td>-0.006824</td>\n",
       "      <td>0.003903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6700</th>\n",
       "      <td>0.894148</td>\n",
       "      <td>0.888561</td>\n",
       "      <td>0.882424</td>\n",
       "      <td>-0.005587</td>\n",
       "      <td>-0.011724</td>\n",
       "      <td>0.006137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6800</th>\n",
       "      <td>0.892679</td>\n",
       "      <td>0.886299</td>\n",
       "      <td>0.885163</td>\n",
       "      <td>-0.006380</td>\n",
       "      <td>-0.007515</td>\n",
       "      <td>0.001135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6900</th>\n",
       "      <td>0.893793</td>\n",
       "      <td>0.888934</td>\n",
       "      <td>0.879317</td>\n",
       "      <td>-0.004859</td>\n",
       "      <td>-0.014476</td>\n",
       "      <td>0.009617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7000</th>\n",
       "      <td>0.894651</td>\n",
       "      <td>0.886609</td>\n",
       "      <td>0.884917</td>\n",
       "      <td>-0.008043</td>\n",
       "      <td>-0.009735</td>\n",
       "      <td>0.001692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7100</th>\n",
       "      <td>0.893522</td>\n",
       "      <td>0.888870</td>\n",
       "      <td>0.886919</td>\n",
       "      <td>-0.004651</td>\n",
       "      <td>-0.006603</td>\n",
       "      <td>0.001952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7200</th>\n",
       "      <td>0.893453</td>\n",
       "      <td>0.885137</td>\n",
       "      <td>0.885995</td>\n",
       "      <td>-0.008316</td>\n",
       "      <td>-0.007458</td>\n",
       "      <td>-0.000858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7300</th>\n",
       "      <td>0.894673</td>\n",
       "      <td>0.883682</td>\n",
       "      <td>0.880550</td>\n",
       "      <td>-0.010991</td>\n",
       "      <td>-0.014123</td>\n",
       "      <td>0.003132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7400</th>\n",
       "      <td>0.898558</td>\n",
       "      <td>0.886538</td>\n",
       "      <td>0.882530</td>\n",
       "      <td>-0.012020</td>\n",
       "      <td>-0.016028</td>\n",
       "      <td>0.004008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7500</th>\n",
       "      <td>0.895945</td>\n",
       "      <td>0.889151</td>\n",
       "      <td>0.880134</td>\n",
       "      <td>-0.006794</td>\n",
       "      <td>-0.015811</td>\n",
       "      <td>0.009016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7600</th>\n",
       "      <td>0.895736</td>\n",
       "      <td>0.884462</td>\n",
       "      <td>0.881355</td>\n",
       "      <td>-0.011274</td>\n",
       "      <td>-0.014381</td>\n",
       "      <td>0.003107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7700</th>\n",
       "      <td>0.899206</td>\n",
       "      <td>0.881621</td>\n",
       "      <td>0.881340</td>\n",
       "      <td>-0.017584</td>\n",
       "      <td>-0.017866</td>\n",
       "      <td>0.000281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7800</th>\n",
       "      <td>0.897071</td>\n",
       "      <td>0.881166</td>\n",
       "      <td>0.880083</td>\n",
       "      <td>-0.015905</td>\n",
       "      <td>-0.016988</td>\n",
       "      <td>0.001083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7900</th>\n",
       "      <td>0.896890</td>\n",
       "      <td>0.875329</td>\n",
       "      <td>0.882564</td>\n",
       "      <td>-0.021561</td>\n",
       "      <td>-0.014326</td>\n",
       "      <td>-0.007234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 f1_Random-Sampling  f1_SBERT-KMeans  f1_BERTopic-Clustering  \\\n",
       "trained_samples                                                                \n",
       "100                        0.721044         0.737707                0.739272   \n",
       "200                        0.786343         0.806655                0.805274   \n",
       "300                        0.820844         0.840919                0.835584   \n",
       "400                        0.835781         0.850974                0.848670   \n",
       "500                        0.847555         0.856220                0.855811   \n",
       "600                        0.853124         0.862127                0.856714   \n",
       "700                        0.856975         0.864235                0.862227   \n",
       "800                        0.861487         0.866168                0.862228   \n",
       "900                        0.858684         0.871793                0.863705   \n",
       "1000                       0.862624         0.872014                0.868531   \n",
       "1100                       0.864141         0.873839                0.871911   \n",
       "1200                       0.864864         0.874860                0.872173   \n",
       "1300                       0.867544         0.876458                0.876585   \n",
       "1400                       0.867830         0.877308                0.878460   \n",
       "1500                       0.873247         0.877551                0.878386   \n",
       "1600                       0.875185         0.878585                0.878760   \n",
       "1700                       0.870006         0.878285                0.880399   \n",
       "1800                       0.871226         0.882122                0.879833   \n",
       "1900                       0.873726         0.881733                0.882580   \n",
       "2000                       0.875699         0.882445                0.881387   \n",
       "2100                       0.876529         0.882065                0.880132   \n",
       "2200                       0.876520         0.882778                0.880659   \n",
       "2300                       0.878462         0.884205                0.881194   \n",
       "2400                       0.878922         0.884521                0.880744   \n",
       "2500                       0.878840         0.883630                0.883163   \n",
       "2600                       0.879509         0.886240                0.884722   \n",
       "2700                       0.881190         0.885452                0.885994   \n",
       "2800                       0.880036         0.886284                0.883711   \n",
       "2900                       0.878862         0.886011                0.884713   \n",
       "3000                       0.878737         0.887653                0.886582   \n",
       "3100                       0.879623         0.888128                0.886560   \n",
       "3200                       0.884762         0.886641                0.888710   \n",
       "3300                       0.878949         0.890634                0.890475   \n",
       "3400                       0.883801         0.889322                0.886253   \n",
       "3500                       0.881965         0.890735                0.888134   \n",
       "3600                       0.883462         0.891234                0.888791   \n",
       "3700                       0.888412         0.891801                0.889583   \n",
       "3800                       0.888703         0.890660                0.890459   \n",
       "3900                       0.888607         0.891723                0.888834   \n",
       "4000                       0.889437         0.891450                0.889506   \n",
       "4100                       0.888493         0.891561                0.890890   \n",
       "4200                       0.887267         0.890216                0.890856   \n",
       "4300                       0.887656         0.892468                0.889256   \n",
       "4400                       0.888418         0.890903                0.890525   \n",
       "4500                       0.887616         0.891296                0.891889   \n",
       "4600                       0.888105         0.892462                0.890358   \n",
       "4700                       0.889855         0.892562                0.891038   \n",
       "4800                       0.887708         0.894238                0.887419   \n",
       "4900                       0.886316         0.892719                0.885430   \n",
       "5000                       0.892755         0.894808                0.888538   \n",
       "5100                       0.889372         0.893689                0.888694   \n",
       "5200                       0.891075         0.893995                0.888971   \n",
       "5300                       0.890360         0.893439                0.886216   \n",
       "5400                       0.892458         0.893279                0.886744   \n",
       "5500                       0.891810         0.891973                0.884324   \n",
       "5600                       0.890761         0.892861                0.883849   \n",
       "5700                       0.890429         0.893031                0.884115   \n",
       "5800                       0.889865         0.892275                0.885375   \n",
       "5900                       0.891870         0.892873                0.885292   \n",
       "6000                       0.895240         0.892539                0.887042   \n",
       "6100                       0.893258         0.893621                0.884331   \n",
       "6200                       0.892265         0.892338                0.886315   \n",
       "6300                       0.893304         0.894460                0.889146   \n",
       "6400                       0.892784         0.889592                0.886584   \n",
       "6500                       0.893754         0.893535                0.884926   \n",
       "6600                       0.894690         0.891770                0.887866   \n",
       "6700                       0.894148         0.888561                0.882424   \n",
       "6800                       0.892679         0.886299                0.885163   \n",
       "6900                       0.893793         0.888934                0.879317   \n",
       "7000                       0.894651         0.886609                0.884917   \n",
       "7100                       0.893522         0.888870                0.886919   \n",
       "7200                       0.893453         0.885137                0.885995   \n",
       "7300                       0.894673         0.883682                0.880550   \n",
       "7400                       0.898558         0.886538                0.882530   \n",
       "7500                       0.895945         0.889151                0.880134   \n",
       "7600                       0.895736         0.884462                0.881355   \n",
       "7700                       0.899206         0.881621                0.881340   \n",
       "7800                       0.897071         0.881166                0.880083   \n",
       "7900                       0.896890         0.875329                0.882564   \n",
       "\n",
       "                 Difference-SBERT-KMeans-to-RandomSampling  \\\n",
       "trained_samples                                              \n",
       "100                                               0.016662   \n",
       "200                                               0.020312   \n",
       "300                                               0.020076   \n",
       "400                                               0.015193   \n",
       "500                                               0.008665   \n",
       "600                                               0.009003   \n",
       "700                                               0.007260   \n",
       "800                                               0.004681   \n",
       "900                                               0.013109   \n",
       "1000                                              0.009390   \n",
       "1100                                              0.009698   \n",
       "1200                                              0.009996   \n",
       "1300                                              0.008914   \n",
       "1400                                              0.009477   \n",
       "1500                                              0.004304   \n",
       "1600                                              0.003400   \n",
       "1700                                              0.008279   \n",
       "1800                                              0.010896   \n",
       "1900                                              0.008007   \n",
       "2000                                              0.006746   \n",
       "2100                                              0.005536   \n",
       "2200                                              0.006258   \n",
       "2300                                              0.005744   \n",
       "2400                                              0.005599   \n",
       "2500                                              0.004790   \n",
       "2600                                              0.006731   \n",
       "2700                                              0.004261   \n",
       "2800                                              0.006247   \n",
       "2900                                              0.007149   \n",
       "3000                                              0.008916   \n",
       "3100                                              0.008505   \n",
       "3200                                              0.001880   \n",
       "3300                                              0.011685   \n",
       "3400                                              0.005521   \n",
       "3500                                              0.008769   \n",
       "3600                                              0.007772   \n",
       "3700                                              0.003390   \n",
       "3800                                              0.001957   \n",
       "3900                                              0.003116   \n",
       "4000                                              0.002013   \n",
       "4100                                              0.003068   \n",
       "4200                                              0.002949   \n",
       "4300                                              0.004811   \n",
       "4400                                              0.002485   \n",
       "4500                                              0.003681   \n",
       "4600                                              0.004358   \n",
       "4700                                              0.002706   \n",
       "4800                                              0.006531   \n",
       "4900                                              0.006403   \n",
       "5000                                              0.002054   \n",
       "5100                                              0.004318   \n",
       "5200                                              0.002920   \n",
       "5300                                              0.003079   \n",
       "5400                                              0.000821   \n",
       "5500                                              0.000162   \n",
       "5600                                              0.002101   \n",
       "5700                                              0.002602   \n",
       "5800                                              0.002410   \n",
       "5900                                              0.001003   \n",
       "6000                                             -0.002701   \n",
       "6100                                              0.000363   \n",
       "6200                                              0.000073   \n",
       "6300                                              0.001156   \n",
       "6400                                             -0.003192   \n",
       "6500                                             -0.000219   \n",
       "6600                                             -0.002921   \n",
       "6700                                             -0.005587   \n",
       "6800                                             -0.006380   \n",
       "6900                                             -0.004859   \n",
       "7000                                             -0.008043   \n",
       "7100                                             -0.004651   \n",
       "7200                                             -0.008316   \n",
       "7300                                             -0.010991   \n",
       "7400                                             -0.012020   \n",
       "7500                                             -0.006794   \n",
       "7600                                             -0.011274   \n",
       "7700                                             -0.017584   \n",
       "7800                                             -0.015905   \n",
       "7900                                             -0.021561   \n",
       "\n",
       "                 Difference-BERTopic-Clustering-Distance-to-RandomSampling  \\\n",
       "trained_samples                                                              \n",
       "100                                                       0.018228           \n",
       "200                                                       0.018931           \n",
       "300                                                       0.014741           \n",
       "400                                                       0.012889           \n",
       "500                                                       0.008256           \n",
       "600                                                       0.003591           \n",
       "700                                                       0.005252           \n",
       "800                                                       0.000741           \n",
       "900                                                       0.005021           \n",
       "1000                                                      0.005907           \n",
       "1100                                                      0.007770           \n",
       "1200                                                      0.007309           \n",
       "1300                                                      0.009042           \n",
       "1400                                                      0.010629           \n",
       "1500                                                      0.005138           \n",
       "1600                                                      0.003574           \n",
       "1700                                                      0.010392           \n",
       "1800                                                      0.008607           \n",
       "1900                                                      0.008854           \n",
       "2000                                                      0.005688           \n",
       "2100                                                      0.003603           \n",
       "2200                                                      0.004139           \n",
       "2300                                                      0.002732           \n",
       "2400                                                      0.001822           \n",
       "2500                                                      0.004323           \n",
       "2600                                                      0.005213           \n",
       "2700                                                      0.004804           \n",
       "2800                                                      0.003675           \n",
       "2900                                                      0.005851           \n",
       "3000                                                      0.007846           \n",
       "3100                                                      0.006937           \n",
       "3200                                                      0.003948           \n",
       "3300                                                      0.011526           \n",
       "3400                                                      0.002453           \n",
       "3500                                                      0.006169           \n",
       "3600                                                      0.005329           \n",
       "3700                                                      0.001171           \n",
       "3800                                                      0.001756           \n",
       "3900                                                      0.000227           \n",
       "4000                                                      0.000069           \n",
       "4100                                                      0.002397           \n",
       "4200                                                      0.003589           \n",
       "4300                                                      0.001600           \n",
       "4400                                                      0.002107           \n",
       "4500                                                      0.004274           \n",
       "4600                                                      0.002253           \n",
       "4700                                                      0.001182           \n",
       "4800                                                     -0.000289           \n",
       "4900                                                     -0.000886           \n",
       "5000                                                     -0.004217           \n",
       "5100                                                     -0.000677           \n",
       "5200                                                     -0.002105           \n",
       "5300                                                     -0.004144           \n",
       "5400                                                     -0.005714           \n",
       "5500                                                     -0.007487           \n",
       "5600                                                     -0.006911           \n",
       "5700                                                     -0.006314           \n",
       "5800                                                     -0.004490           \n",
       "5900                                                     -0.006577           \n",
       "6000                                                     -0.008199           \n",
       "6100                                                     -0.008927           \n",
       "6200                                                     -0.005950           \n",
       "6300                                                     -0.004158           \n",
       "6400                                                     -0.006199           \n",
       "6500                                                     -0.008829           \n",
       "6600                                                     -0.006824           \n",
       "6700                                                     -0.011724           \n",
       "6800                                                     -0.007515           \n",
       "6900                                                     -0.014476           \n",
       "7000                                                     -0.009735           \n",
       "7100                                                     -0.006603           \n",
       "7200                                                     -0.007458           \n",
       "7300                                                     -0.014123           \n",
       "7400                                                     -0.016028           \n",
       "7500                                                     -0.015811           \n",
       "7600                                                     -0.014381           \n",
       "7700                                                     -0.017866           \n",
       "7800                                                     -0.016988           \n",
       "7900                                                     -0.014326           \n",
       "\n",
       "                 Difference-SBERT-KMeans-to-BERTopic-Clustering  \n",
       "trained_samples                                                  \n",
       "100                                                   -0.001565  \n",
       "200                                                    0.001381  \n",
       "300                                                    0.005335  \n",
       "400                                                    0.002304  \n",
       "500                                                    0.000408  \n",
       "600                                                    0.005413  \n",
       "700                                                    0.002008  \n",
       "800                                                    0.003940  \n",
       "900                                                    0.008088  \n",
       "1000                                                   0.003483  \n",
       "1100                                                   0.001928  \n",
       "1200                                                   0.002687  \n",
       "1300                                                  -0.000127  \n",
       "1400                                                  -0.001152  \n",
       "1500                                                  -0.000835  \n",
       "1600                                                  -0.000175  \n",
       "1700                                                  -0.002113  \n",
       "1800                                                   0.002289  \n",
       "1900                                                  -0.000847  \n",
       "2000                                                   0.001058  \n",
       "2100                                                   0.001933  \n",
       "2200                                                   0.002119  \n",
       "2300                                                   0.003011  \n",
       "2400                                                   0.003777  \n",
       "2500                                                   0.000467  \n",
       "2600                                                   0.001518  \n",
       "2700                                                  -0.000543  \n",
       "2800                                                   0.002573  \n",
       "2900                                                   0.001298  \n",
       "3000                                                   0.001070  \n",
       "3100                                                   0.001568  \n",
       "3200                                                  -0.002069  \n",
       "3300                                                   0.000159  \n",
       "3400                                                   0.003068  \n",
       "3500                                                   0.002601  \n",
       "3600                                                   0.002443  \n",
       "3700                                                   0.002218  \n",
       "3800                                                   0.000201  \n",
       "3900                                                   0.002889  \n",
       "4000                                                   0.001944  \n",
       "4100                                                   0.000671  \n",
       "4200                                                  -0.000640  \n",
       "4300                                                   0.003211  \n",
       "4400                                                   0.000378  \n",
       "4500                                                  -0.000593  \n",
       "4600                                                   0.002104  \n",
       "4700                                                   0.001524  \n",
       "4800                                                   0.006819  \n",
       "4900                                                   0.007289  \n",
       "5000                                                   0.006270  \n",
       "5100                                                   0.004995  \n",
       "5200                                                   0.005024  \n",
       "5300                                                   0.007223  \n",
       "5400                                                   0.006535  \n",
       "5500                                                   0.007649  \n",
       "5600                                                   0.009012  \n",
       "5700                                                   0.008916  \n",
       "5800                                                   0.006901  \n",
       "5900                                                   0.007580  \n",
       "6000                                                   0.005498  \n",
       "6100                                                   0.009290  \n",
       "6200                                                   0.006023  \n",
       "6300                                                   0.005315  \n",
       "6400                                                   0.003007  \n",
       "6500                                                   0.008610  \n",
       "6600                                                   0.003903  \n",
       "6700                                                   0.006137  \n",
       "6800                                                   0.001135  \n",
       "6900                                                   0.009617  \n",
       "7000                                                   0.001692  \n",
       "7100                                                   0.001952  \n",
       "7200                                                  -0.000858  \n",
       "7300                                                   0.003132  \n",
       "7400                                                   0.004008  \n",
       "7500                                                   0.009016  \n",
       "7600                                                   0.003107  \n",
       "7700                                                   0.000281  \n",
       "7800                                                   0.001083  \n",
       "7900                                                  -0.007234  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvt = get_pivot_from_data(data)\n",
    "pd.set_option('display.max_rows', 10000)\n",
    "pvt[pvt.index < 10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d66f4b79",
   "metadata": {},
   "source": [
    "#### Max BERTTopic Clustering Distance to Random Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b228241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,\n",
       " f1_Random-Sampling                                           0.786343\n",
       " f1_SBERT-KMeans                                              0.806655\n",
       " f1_BERTopic-Clustering                                       0.805274\n",
       " Difference-SBERT-KMeans-to-RandomSampling                    0.020312\n",
       " Difference-BERTopic-Clustering-Distance-to-RandomSampling    0.018931\n",
       " Difference-SBERT-KMeans-to-BERTopic-Clustering               0.001381\n",
       " Name: 200, dtype: float64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_max_arg_by_column(\"Difference-BERTopic-Clustering-Distance-to-RandomSampling\", pvt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fd0ec2",
   "metadata": {},
   "source": [
    "#### Max SBERT-KMeans Distance to Random Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7b8fb872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,\n",
       " f1_Random-Sampling                                           0.786343\n",
       " f1_SBERT-KMeans                                              0.806655\n",
       " f1_BERTopic-Clustering                                       0.805274\n",
       " Difference-SBERT-KMeans-to-RandomSampling                    0.020312\n",
       " Difference-BERTopic-Clustering-Distance-to-RandomSampling    0.018931\n",
       " Difference-SBERT-KMeans-to-BERTopic-Clustering               0.001381\n",
       " Name: 200, dtype: float64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_max_arg_by_column(\"Difference-SBERT-KMeans-to-RandomSampling\", pvt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec454d9c",
   "metadata": {},
   "source": [
    "#### Max SBERT-KMeans Distance to BERTTopic Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6667f16f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68,\n",
       " f1_Random-Sampling                                           0.893793\n",
       " f1_SBERT-KMeans                                              0.888934\n",
       " f1_BERTopic-Clustering                                       0.879317\n",
       " Difference-SBERT-KMeans-to-RandomSampling                   -0.004859\n",
       " Difference-BERTopic-Clustering-Distance-to-RandomSampling   -0.014476\n",
       " Difference-SBERT-KMeans-to-BERTopic-Clustering               0.009617\n",
       " Name: 6900, dtype: float64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_max_arg_by_column(\"Difference-SBERT-KMeans-to-BERTopic-Clustering\", pvt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5def59c9",
   "metadata": {},
   "source": [
    "## Duration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae7d49fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>al_algorithm</th>\n",
       "      <th>duration_al_seconds_mean</th>\n",
       "      <th>duration_al_seconds_std</th>\n",
       "      <th>duration_ml_seconds_mean</th>\n",
       "      <th>duration_total_seconds_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random-Sampling</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>58.88303</td>\n",
       "      <td>58.88346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BERTopic-Clustering</td>\n",
       "      <td>14.72483</td>\n",
       "      <td>7.45658</td>\n",
       "      <td>58.45963</td>\n",
       "      <td>73.18506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SBERT-KMeans</td>\n",
       "      <td>64.20917</td>\n",
       "      <td>38.52253</td>\n",
       "      <td>58.53736</td>\n",
       "      <td>122.74720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          al_algorithm  duration_al_seconds_mean  duration_al_seconds_std  \\\n",
       "0      Random-Sampling                   0.00000                  0.00002   \n",
       "1  BERTopic-Clustering                  14.72483                  7.45658   \n",
       "2         SBERT-KMeans                  64.20917                 38.52253   \n",
       "\n",
       "   duration_ml_seconds_mean  duration_total_seconds_mean  \n",
       "0                  58.88303                     58.88346  \n",
       "1                  58.45963                     73.18506  \n",
       "2                  58.53736                    122.74720  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "grp = df.groupby(['al_algorithm']).agg(['mean', 'std'], numeric_only=False).reset_index()\n",
    "grp.columns = [first if second == '' else first + '_' + second for first, second in grp.columns.values]\n",
    "grp = grp.sort_values('duration_total_seconds_mean', ascending=True).reset_index()\n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "grp[['al_algorithm', 'duration_al_seconds_mean','duration_al_seconds_std', 'duration_ml_seconds_mean', 'duration_total_seconds_mean']]"
   ]
  }
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