notebooks/lmsys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['text.latex.preamble'] = r'\\usepackage{amsmath,amsfonts,geometry}'\n",
    "mpl.rcParams['axes.formatter.use_mathtext'] = True\n",
    "plt.rcParams.update({\n",
    "    'font.family':'serif',\n",
    "    \"font.serif\": [\"Computer Modern Roman\"],\n",
    "    \"text.usetex\": True,\n",
    "    \"font.size\": 40,\n",
    "    \"figure.figsize\":(13,8),\n",
    "    \"lines.markersize\": 20\n",
    "})\n",
    "plt.rcParams['axes.autolimit_mode'] = 'round_numbers'\n",
    "plt.rcParams['axes.xmargin'] = 0\n",
    "plt.rcParams['axes.ymargin'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_labels={'meta-llama/Llama-3.1-8B-Instruct':'8B',\n",
    "              'meta-llama/Llama-3.1-8q-Instruct':'bnb-8bit',\n",
    "                'meta-llama/Llama-3.1-4q-Instruct':'bnb-4bit',\n",
    "              'hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4':'AWQ-INT4',\n",
    "              'meta-llama/Llama-3.2-3B-Instruct':'3B',\n",
    "              'meta-llama/Llama-3.2-1B-Instruct':'1B'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read pairwise comparison data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_full=['meta-llama/Llama-3.1-8B-Instruct',\n",
    "             'meta-llama/Llama-3.1-8q-Instruct',\n",
    "             'meta-llama/Llama-3.1-4q-Instruct',\n",
    "             'hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4',\n",
    "             'meta-llama/Llama-3.2-3B-Instruct',\n",
    "             'meta-llama/Llama-3.2-1B-Instruct']\n",
    "output_dir=f'../outputs/LMSYS/'\n",
    "\n",
    "clean_different=pd.read_json(f'{output_dir}independent_comparisons.json')\n",
    "clean_shared=pd.read_json(f'{output_dir}coupled_comparisons.json')\n",
    "\n",
    "# for queries that were asked in reverse model order, switch winner/models/seeds to match original order\n",
    "def swap_order(row):\n",
    "    if models_full.index(row['model_a']) > models_full.index(row['model_b']):\n",
    "        row['winner'] = 'A' if row['winner'] == 'B' else 'B' if row['winner'] == 'A' else row['winner']\n",
    "        row['seed_a'], row['seed_b'] = row['seed_b'], row['seed_a']\n",
    "        row['model_a'], row['model_b'] = row['model_b'], row['model_a']\n",
    "    return row\n",
    "\n",
    "clean_different_set=clean_different.apply(swap_order,axis=1)\n",
    "clean_shared_set=clean_shared.apply(swap_order,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ties per pair of models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute win-rates and rank-sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code to compute rank-sets based on [Prediction-Powered Ranking of Large Language Models](https://github.com/Networks-Learning/prediction-powered-ranking)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize_dataset(models, dataset):\n",
    "    n = len(dataset['winner'])\n",
    "    \n",
    "    winner1_llm = np.zeros(shape=(n,1))\n",
    "    winner2_llm = np.zeros(shape=(n,1))\n",
    "    model_a_matrix = np.zeros(shape=(len(models),n))\n",
    "    model_b_matrix = np.zeros(shape=(len(models),n))\n",
    "    \n",
    "    for i in range(n):        \n",
    "        if dataset['winner'][i] == 'A':\n",
    "            winner1_llm[i] = 1\n",
    "        elif dataset['winner'][i] == 'B':\n",
    "            winner2_llm[i] = 1\n",
    "        \n",
    "        model_a_matrix[models.index(dataset['model_a'][i])][i] = 1\n",
    "        model_b_matrix[models.index(dataset['model_b'][i])][i] = 1\n",
    "    \n",
    "    summarized = {'winner1_predicted':winner1_llm,\n",
    "                  'winner2_predicted':winner2_llm,\n",
    "                  'model_a_matrix': model_a_matrix,\n",
    "                  'model_b_matrix': model_b_matrix}\n",
    "        \n",
    "    return summarized\n",
    "\n",
    "def sample_avg(k, Dn):\n",
    "    n = np.shape(Dn['model_a_matrix'])[1]\n",
    "    M1, M2 = Dn['model_a_matrix'], Dn['model_b_matrix']\n",
    "    w1, w2 = Dn['winner1_predicted'], Dn['winner2_predicted']\n",
    "    \n",
    "    a = np.matmul(np.ones((k,1)), np.transpose(np.matmul(\n",
    "        M1+M2,np.ones((n,1))))) *np.identity(k)\n",
    "    for i in range(k):\n",
    "        a[i][i] = 1/a[i][i]\n",
    "    a = np.matmul(a,np.matmul(M1,w1) + np.matmul(M2,w2))\n",
    "    \n",
    "    A = np.matmul(np.ones((k,1)), np.transpose(\n",
    "        w1 - np.matmul(np.transpose(M1) ,a)))*M1\n",
    "    A += np.matmul(np.ones((k,1)), \n",
    "                   np.transpose(w2 - np.matmul(np.transpose(M2), a)))*M2\n",
    "    \n",
    "    Sigma = (np.matmul(A, np.transpose(A)))/(n**2)\n",
    "    \n",
    "    return a, Sigma\n",
    "\n",
    "import scipy.stats as stats\n",
    "\n",
    "def find_ranksets(k, thetahat, Sigma, alpha=0.05):\n",
    "    lhat = np.ones((k,1))\n",
    "    uhat = np.ones((k,1))*k\n",
    "    \n",
    "    assert alpha>0 and alpha<1, 'alpha must be between 0 and 1'\n",
    "    \n",
    "    for m1 in range(k):\n",
    "        for m2 in range(k):\n",
    "            if m1 == m2:\n",
    "                continue\n",
    "            d = abs(thetahat[m1] - thetahat[m2])/np.sqrt(2) - \\\n",
    "            np.sqrt((Sigma[m1][m1] + Sigma[m2][m2] - 2*Sigma[m1][m2])*\\\n",
    "                    (stats.chi2.ppf(1-alpha,k)/(2)))\n",
    "            if d > 0 and thetahat[m1] < thetahat[m2]:\n",
    "                lhat[m1] += 1\n",
    "            elif d > 0 and thetahat[m1] > thetahat[m2]:\n",
    "                uhat[m1] -= 1\n",
    "    \n",
    "    return [[int(lhat[m][0]), int(uhat[m][0])] for m in range(k)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairwise win-rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_pairwise_winrates(df,models):\n",
    "    winrates=dict()\n",
    "    ci=dict()\n",
    "    for m1 in range(len(models)):\n",
    "        for m2 in range(m1+1,len(models)):\n",
    "            wins_A=len(df[(df['model_a'] == models[m1]) & (df['model_b'] == models[m2]) & (df['winner'] == 'A')])\n",
    "            wins_B=len(df[(df['model_a'] == models[m1]) & (df['model_b'] == models[m2]) & (df['winner'] == 'B')])\n",
    "            winrates[(models[m1],models[m2])]=wins_A/len(df[(df['model_a'] == models[m1]) & (df['model_b'] == models[m2])])\n",
    "            winrates[(models[m2],models[m1])]=wins_B/len(df[(df['model_a'] == models[m1]) & (df['model_b'] == models[m2])])\n",
    "        \n",
    "            ci[(models[m1],models[m2])]=1.96*np.sqrt(winrates[(models[m1],models[m2])]*(1-winrates[(models[m1],models[m2])])/len(df[(df['model_a'] == models[m1]) & (df['model_b'] == models[m2])]))\n",
    "            ci[(models[m2],models[m1])]=1.96*np.sqrt(winrates[(models[m2],models[m1])]*(1-winrates[(models[m2],models[m1])])/len(df[(df['model_a'] == models[m1]) & (df['model_b'] == models[m2])]))\n",
    "    return winrates,ci\n",
    "\n",
    "p_w_s,ci_s=compute_pairwise_winrates(clean_shared_set,models_full)\n",
    "p_w_d,ci_d=compute_pairwise_winrates(clean_different_set,models_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ties per pair of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 23.544411389715258, 8.442694663167105, 8.388520971302428, 1.9928461931527848, 2.3424878836833605], [23.544411389715258, 0, 11.326097215667767, 14.729064039408867, 5.224274406332454, -2.6548672566371683], [8.442694663167105, 11.326097215667767, 0, 5.865657521286661, 3.3157894736842106, -0.14803849000740193], [8.388520971302428, 14.729064039408867, 5.865657521286661, 0, -1.2833675564681726, 6.031746031746032], [1.9928461931527848, 5.224274406332454, 3.3157894736842106, -1.2833675564681726, 0, 7.341269841269841], [2.3424878836833605, -2.6548672566371683, -0.14803849000740193, 6.031746031746032, 7.341269841269841, 0]]\n"
     ]
    }
   ],
   "source": [
    "# Count number of ties per pair of models\n",
    "def compute_ties(df,models):\n",
    "    ties=[[0 for _ in range(len(models))] for _ in range(len(models))]\n",
    "    for m1 in range(len(models)):\n",
    "        for m2 in range(m1,len(models)):\n",
    "            if m1==m2:\n",
    "                ties[m1][m2]=0\n",
    "            ties[m1][m2]=len(df[(df['model_a'] == models[m1]) & (df['model_b'] == models[m2]) & (df['winner'] == 'Tie')])\n",
    "            ties[m2][m1]=len(df[(df['model_a'] == models[m1]) & (df['model_b'] == models[m2]) & (df['winner'] == 'Tie')])\n",
    "    return ties\n",
    "\n",
    "ties_different=compute_ties(clean_different_set,models_full)\n",
    "ties_shared=compute_ties(clean_shared_set,models_full)\n",
    "\n",
    "# compute relative increase in ties under shared noise compared to different noise\n",
    "ties_diff=[[0 for j in range(len(models_full))] for i in range(len(models_full))]\n",
    "for i in range(len(models_full)):\n",
    "    for j in range(len(models_full)):\n",
    "        if i==j:\n",
    "            continue\n",
    "        ties_diff[i][j]=100*(ties_shared[i][j]-ties_different[i][j])/ties_different[i][j]\n",
    "print(ties_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures 3,6 and Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hypothesis_test_pairwise(dfs,dfd,models,m1,m2):\n",
    "    count_s=len(dfs[(dfs['model_a']==models[m1]) & (dfs['model_b']==models[m2])])\n",
    "    count_s+=len(dfs[(dfs['model_a']==models[m2]) & (dfs['model_b']==models[m1])])\n",
    "\n",
    "    count_d=len(dfd[(dfd['model_a']==models[m1]) & (dfd['model_b']==models[m2])])\n",
    "    count_d+=len(dfd[(dfd['model_a']==models[m2]) & (dfd['model_b']==models[m1])])\n",
    "\n",
    "    win_s=len(dfs[(dfs['model_a'] == models[m1]) & (dfs['model_b'] == models[m2]) & (dfs['winner'] == 'A')])\n",
    "    win_s+=len(dfs[(dfs['model_a'] == models[m2]) & (dfs['model_b'] == models[m1]) & (dfs['winner'] == 'B')])\n",
    "\n",
    "    win_d=len(dfd[(dfd['model_a'] == models[m1]) & (dfd['model_b'] == models[m2]) & (dfd['winner'] == 'A')])\n",
    "    win_d+=len(dfd[(dfd['model_a'] == models[m2]) & (dfd['model_b'] == models[m1]) & (dfd['winner'] == 'B')])\n",
    "\n",
    "    p_s=win_s/count_s\n",
    "    p_d=win_d/count_d\n",
    "\n",
    "    p=(win_s+win_d)/(count_s+count_d)\n",
    "    z=(p_s-p_d)/np.sqrt(p*(1-p)*(1/count_s+1/count_d))\n",
    "\n",
    "    p_value = scipy.stats.norm.sf(abs(z))*2\n",
    "    if p_value<0.0001:\n",
    "        return '****'\n",
    "    if p_value<0.001:\n",
    "        return '***'\n",
    "    if p_value<0.01:\n",
    "        return '**'\n",
    "    if p_value<0.05:\n",
    "        return '*'\n",
    "    else:\n",
    "        return 'ns'\n",
    "\n",
    "# figure 3\n",
    "def plot_pairwise_winrates_barplot(p_w_s,ci_s,p_w_d,ci_d,models_full,model):\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    labels=[model_labels[m] for m in models_full if m != model]\n",
    "    winrates_shared={model_labels[m]:p_w_s[(model,m)] for m in models_full if m != model}\n",
    "    ci_shared={model_labels[m]:ci_s[(model,m)] for m in models_full if m != model}\n",
    "    winrates_different={model_labels[m]:p_w_d[(model,m)] for m in models_full if m != model}\n",
    "    ci_different={model_labels[m]:ci_d[(model,m)] for m in models_full if m != model}\n",
    "    x=np.arange(len(labels))\n",
    "    ax.bar(x-0.15, winrates_shared.values(), width=0.3, label='Coupled', color='teal',alpha=0.8)\n",
    "    ax.bar(x+0.15, winrates_different.values(), width=0.3, label='Independent', color='palevioletred',alpha=0.8)\n",
    "    \n",
    "    ax.errorbar(x-0.15, winrates_shared.values(), yerr=ci_shared.values(), fmt='none', ecolor='black', capsize=5)\n",
    "    ax.errorbar(x+0.15, winrates_different.values(), yerr=ci_different.values(), fmt='none', ecolor='black', capsize=5)\n",
    "\n",
    "    for m in range(len(models_full)):\n",
    "        if models_full[m]==model:\n",
    "            continue\n",
    "        annot=hypothesis_test_pairwise(clean_shared_set,clean_different_set,models_full,models_full.index(model),m)\n",
    "        if annot!='ns':\n",
    "            y_star=max(winrates_shared[model_labels[models_full[m]]]+ci_shared[model_labels[models_full[m]]],winrates_different[model_labels[models_full[m]]]+ci_different[model_labels[models_full[m]]])\n",
    "            x_star=m\n",
    "            if models_full.index(model) <m:\n",
    "                x_star-=1\n",
    "            ax.plot([x_star-0.2, x_star-0.2, x_star+0.2, x_star+0.2], [y_star+0.02, y_star+0.04, y_star+0.04, y_star+0.02], lw=1.5, c='k')\n",
    "            ax.text(x_star-0.05*len(annot),y_star+0.03,annot,fontsize=30)\n",
    "\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xlim(-0.5,len(labels)-0.5)\n",
    "    ax.set_xticklabels(labels, rotation=35,ha='right',rotation_mode='anchor')\n",
    "    ax.set_ylabel(f'Win-rate')\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "    ax.legend(frameon=False,ncol=2,loc='upper center',bbox_to_anchor=(0.5,1.05))\n",
    "    folder_path = '../figures/LMSYS/'\n",
    "    if not os.path.exists(folder_path):\n",
    "        os.makedirs(folder_path)\n",
    "    fig.savefig(f'../figures/LMSYS/pairwise_winrates_{model_labels[model]}.pdf',bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "# figure 6\n",
    "def plot_pairwise_winrates_all(p_w_s,ci_s,p_w_d,ci_d,models):\n",
    "    fig,ax=plt.subplots(3,2,figsize=(20,20),sharey=True)\n",
    "    # fig.tight_layout()\n",
    "    fig.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, wspace=0.2, hspace=1)\n",
    "    ax=ax.flatten()\n",
    "    for i in range(len(models)):\n",
    "        labels=[model_labels[m] for m in models if m != models[i]]\n",
    "        x=np.arange(len(labels))\n",
    "\n",
    "        winrates_shared={model_labels[m]:p_w_s[(models[i],m)] for m in models if m != models[i]}\n",
    "        ci_shared={model_labels[m]:ci_s[(models[i],m)] for m in models if m != models[i]}\n",
    "        winrates_different={model_labels[m]:p_w_d[(models[i],m)] for m in models if m != models[i]}\n",
    "        ci_different={model_labels[m]:ci_d[(models[i],m)] for m in models if m != models[i]}\n",
    "\n",
    "        ax[i].bar(x-0.15, winrates_shared.values(), width=0.3, label='Coupled', color='teal',alpha=0.8)\n",
    "        ax[i].bar(x+0.15, winrates_different.values(), width=0.3, label='Independent', color='palevioletred',alpha=0.8)\n",
    "        \n",
    "        ax[i].errorbar(x-0.15, winrates_shared.values(), yerr=ci_shared.values(), fmt='none', ecolor='black', capsize=5)\n",
    "        ax[i].errorbar(x+0.15, winrates_different.values(), yerr=ci_different.values(), fmt='none', ecolor='black', capsize=5)\n",
    "\n",
    "        for m in range(len(models_full)):\n",
    "                if models_full[m]==models[i]:\n",
    "                    continue\n",
    "                annot=hypothesis_test_pairwise(clean_shared_set,clean_different_set,models_full,models_full.index(models[i]),m)\n",
    "                if annot!='ns':\n",
    "                    y_star=max(winrates_shared[model_labels[models_full[m]]]+ci_shared[model_labels[models_full[m]]],winrates_different[model_labels[models_full[m]]]+ci_different[model_labels[models_full[m]]])\n",
    "                    x_star=m\n",
    "                    if models_full.index(models[i]) <m:\n",
    "                        x_star-=1\n",
    "                    ax[i].plot([x_star-0.2, x_star-0.2, x_star+0.2, x_star+0.2], [y_star+0.02, y_star+0.04, y_star+0.04, y_star+0.02], lw=1.5, c='k')\n",
    "                    ax[i].text(x_star-0.055*len(annot),y_star+0.05,annot,fontsize=25)\n",
    "\n",
    "        if i%2==0:\n",
    "            ax[i].set_ylabel('Win-rate')\n",
    "            ax[i].set_ylim(0,0.76)\n",
    "            ax[i].set_yticks([0,0.25,0.5,0.75])\n",
    "        ax[i].set_title(model_labels[models[i]])\n",
    "        \n",
    "        ax[i].set_xlim(-0.5,len(labels)-0.5)\n",
    "        ax[i].set_xticks(x)\n",
    "        ax[i].set_xticklabels(labels, rotation=35,ha='right',rotation_mode='anchor')\n",
    "        ax[i].spines[['top','right']].set_visible(False)\n",
    "    ax[4].legend(ncol=2,bbox_to_anchor=(1.75,-0.5),frameon=False)\n",
    "    folder_path = '../figures/LMSYS/'\n",
    "    if not os.path.exists(folder_path):\n",
    "        os.makedirs(folder_path)\n",
    "    fig.savefig(f'../figures/LMSYS/pairwise_winrates.pdf',bbox_inches='tight')\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute pairwise win-rates and confidence intervals\n",
    "p_w_s,ci_s=compute_pairwise_winrates(clean_shared_set,models_full)\n",
    "p_w_d,ci_d=compute_pairwise_winrates(clean_different_set,models_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pairwise_winrates_barplot(p_w_s,ci_s,p_w_d,ci_d,models_full,models_full[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pairwise_winrates_all(p_w_s,ci_s,p_w_d,ci_d,models_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>LLM</th>\n",
       "      <th>Rank (coupled)</th>\n",
       "      <th>Avg. win-rate (coupled)</th>\n",
       "      <th>95% CI (coupled)</th>\n",
       "      <th>Rank (independent)</th>\n",
       "      <th>Avg win-rate (independent)</th>\n",
       "      <th>95% CI (independent)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8B</td>\n",
       "      <td>1</td>\n",
       "      <td>0.366959</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>1</td>\n",
       "      <td>0.386281</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bnb-8bit</td>\n",
       "      <td>2</td>\n",
       "      <td>0.356241</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>1</td>\n",
       "      <td>0.382527</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bnb-4bit</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333896</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>3</td>\n",
       "      <td>0.346346</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AWQ-INT4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.316392</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>4</td>\n",
       "      <td>0.330975</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3B</td>\n",
       "      <td>5</td>\n",
       "      <td>0.278687</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>5</td>\n",
       "      <td>0.282831</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1B</td>\n",
       "      <td>6</td>\n",
       "      <td>0.164999</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>6</td>\n",
       "      <td>0.166432</td>\n",
       "      <td>0.0015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LLM  Rank (coupled)  Avg. win-rate (coupled)  95% CI (coupled)  \\\n",
       "0        8B               1                 0.366959            0.0020   \n",
       "1  bnb-8bit               2                 0.356241            0.0020   \n",
       "2  bnb-4bit               3                 0.333896            0.0020   \n",
       "3  AWQ-INT4               4                 0.316392            0.0019   \n",
       "4        3B               5                 0.278687            0.0019   \n",
       "5        1B               6                 0.164999            0.0015   \n",
       "\n",
       "   Rank (independent)  Avg win-rate (independent)  95% CI (independent)  \n",
       "0                   1                    0.386281                0.0020  \n",
       "1                   1                    0.382527                0.0020  \n",
       "2                   3                    0.346346                0.0020  \n",
       "3                   4                    0.330975                0.0019  \n",
       "4                   5                    0.282831                0.0019  \n",
       "5                   6                    0.166432                0.0015  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute win-rates and rank-sets under coupled noise\n",
    "thetahat_shared,Sigma_shared=sample_avg(len(models_full),summarize_dataset(models_full,clean_shared_set.reset_index(drop=True)))\n",
    "baseline_shared=(find_ranksets(len(models_full), thetahat_shared, Sigma_shared))\n",
    "\n",
    "# compute win-rates and rank-sets under independent noise\n",
    "thetahat_different,Sigma_different=sample_avg(len(models_full),summarize_dataset(models_full,clean_different_set.reset_index(drop=True)))\n",
    "baseline_different=(find_ranksets(len(models_full), thetahat_different, Sigma_different))\n",
    "\n",
    "# prepare data for table\n",
    "winrates_shared={models_full[m]:thetahat_shared[m][0] for m in range(len(models_full))}\n",
    "winrates_different={models_full[m]:thetahat_different[m][0] for m in range(len(models_full))}\n",
    "cid={models_full[m]:1.96*np.sqrt(Sigma_different[m][m]) for m in range(len(models_full))}\n",
    "cis={models_full[m]:1.96*np.sqrt(Sigma_different[m][m]) for m in range(len(models_full))}\n",
    "\n",
    "df=pd.DataFrame()\n",
    "df['LLM']=model_labels.values()\n",
    "df['Rank (coupled)']=np.arange(1,len(models_full)+1)\n",
    "df['Avg. win-rate (coupled)']=thetahat_shared\n",
    "df['95% CI (coupled)']=[round(1.96*np.sqrt(Sigma_shared[i][i]),4) for i in range(len(models_full))]\n",
    "df['Rank (independent)']=[1,1,3,4,5,6]\n",
    "df['Avg win-rate (independent)']=thetahat_different\n",
    "df['95% CI (independent)']=[round(1.96*np.sqrt(Sigma_different[i][i]),4) for i in range(len(models_full))]\n",
    "df"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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