bias_wise_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y7fbAPoWFhXC5XDh8+HC3r+V2u+FyuYIeRERESigOvEOHDiEpKQlmsxm//vWvsWnTJuTn58PpdMJkMiE1NTVof7vdDqfTCQBwOp1BYdfZ3tnWnaVLlyIlJSXwyM3NVVo2ERFpnOLVEn70ox/h4MGDaGpqwhtvvIHi4mJ8+OGHkagtYP78+SgtLQ1873K5GHrxoGHnd5/hhbi8eeBUE7w+GbWNHdDrOGC4v/GePRv4DI+XNykWKA48k8mEYcOGAQDGjh2LvXv34l/+5V9wzz33wOPxoLGxMegsr66uDllZWQCArKws7NmzJ+j5Okdxdu4TitlshtkceuADxbDPH+hxeaBn3z7B5YH6sZb33+fyQBRTev1ntSzLcLvdGDt2LIxGIyorKwNtx48fx6lTp+BwOAAADocDhw4dQn19fWCf7du3w2azIT8/v7elEBERdUvRGd78+fNx++23Iy8vD83NzVi/fj0++OADvPvuu0hJScGcOXNQWlqK9PR02Gw2PPzww3A4HJg4cSIAYOrUqcjPz8fs2bOxfPlyOJ1OLFiwACUlJTyDIyKiiFIUePX19bjvvvtQW1uLlJQUjB49Gu+++y5uu+02AMCLL74InU6HmTNnwu12o7CwECtXrgz01+v12Lx5Mx566CE4HA5YrVYUFxdj8eLFffuuiIiILqIo8FavXt1ju8ViQVlZGcrKyrrdZ8iQIXjnnXeUvCwREVGvcWgcERFpAgOPiIg0gYFHRESawMAjIiJNYOAREZEmKJ5phegHCzG7yve9UTIOAPDarhqultAPcXYVijU8wyMiIk1g4BERkSYw8IiISBP4GR5FzhfPAr4mwJACXPGbLs1rP6pBq9uH6vpWDE5LUKFAiqT2/fshezzQmUxIGDtW7XKIGHgUQWde/W55oBCBt/mzusDyQAy8/qfj2LHA8kAMPIoFvKRJRESawMAjIiJNYOAREZEmMPCIiEgTGHjUK7IQapdARPSDcJQm9YpOkrDlszo0tHadGmyGxw8rgFaPHxt31XRp9/jkKFRIRHQBA496reHcCdQ31HXZLts8gA6Q/R7Un9nTtV1kR6M8IiIADDzqC7Ib8LV03d55uVOIbtojWxYR0fcx8ChiTnuHI8HfinbZGrI9N1VCUmJKyMuhFP+M2dmQOzqgs1jULoUIAAOPIuidljk9tv9kpB5FE/O5PFA/lTRlitolEAXhKE0iItIEBh7FrUSTvte3RfC2CiLt4CVNilsWo67H2yIuJd1qQtE19ghURkSxiIFHEfNT24tI1LnQJtuwwfVYl/bXP/Njw+cH4PbJGDc0NezXaWj18DPAGOTavBmivR1SQgJsP/mJ2uUQMfAoctL0dUjWN6LZ3x6yvaFNoMXTjkSTPsqVUTT4m5ouLA/k4R8jFBv4GR4REWkCz/BIPdK3X4UfaD6ovH/65QCy+rAgIurPGHgUA7qZieVSZG/fl0JE/RYvaRIRkSYw8IiISBMYeEREpAkMPCIi0gQGHhERaQJHaVLEVLUVwSS54RHmkO3XDzViWM4gfPbll1wbrx9KuO46CK8XktGodilEABh4FEGH3Df22H5NjgFF43Pgbv4S9fVRKoqixjJypNolEAXhJU0iItIERYG3dOlS/PjHP0ZycjIyMzMxffp0HD9+PGifjo4OlJSUICMjA0lJSZg5cybq6uqC9jl16hSKioqQmJiIzMxMPPHEE/D5fL1/N0RERN1QFHgffvghSkpKsHv3bmzfvh1erxdTp05Fa2trYJ/HHnsMb7/9NjZs2IAPP/wQZ8+exYwZMwLtfr8fRUVF8Hg82LVrF9atW4e1a9di4cKFffeuKCZYpSYk6b6BVWoK2d7iFqh3udHGuYX7JbmtDXJLC+S2NrVLIQKg8DO8rVu3Bn2/du1aZGZmYv/+/bjpppvQ1NSE1atXY/369ZgyZQoAYM2aNRg5ciR2796NiRMnYtu2bThy5Ajee+892O12jBkzBkuWLMGTTz6Jp59+GiaTqe/eHalqVurSb1dLSMWfv1nWpf21/R14edd+JBol3JqtQoEUUU2bNl1YLcFqRdqsWWqXQ9S7z/Cami785Z6eng4A2L9/P7xeLwoKCgL7jBgxAnl5eaiqqgIAVFVVYdSoUbDbv1t4s7CwEC6XC4cPHw75Om63Gy6XK+hBRESkRNiBJ8syHn30UUyaNAlXX301AMDpdMJkMiE1NTVoX7vdDqfTGdjn+2HX2d7ZFsrSpUuRkpISeOTm5oZbNhERaVTYgVdSUoLPP/8cFRUVfVlPSPPnz0dTU1PgUVNTE/HXJCKi/iWs+/DmzZuHzZs3Y+fOnRg8eHBge1ZWFjweDxobG4PO8urq6pCVlRXYZ8+ePUHP1zmKs3Ofi5nNZpjNoW9eJiIi+iEUneEJITBv3jxs2rQJO3bswGWXXRbUPnbsWBiNRlRWVga2HT9+HKdOnYLD4QAAOBwOHDp0CPXfu9N4+/btsNlsyM/P7817ISIi6paiM7ySkhKsX78e//Vf/4Xk5OTAZ24pKSlISEhASkoK5syZg9LSUqSnp8Nms+Hhhx+Gw+HAxIkTAQBTp05Ffn4+Zs+ejeXLl8PpdGLBggUoKSnhWRwREUWMosB7+eWXAQC33HJL0PY1a9bg/vvvBwC8+OKL0Ol0mDlzJtxuNwoLC7Fy5crAvnq9Hps3b8ZDDz0Eh8MBq9WK4uJiLF68uHfvhCgOCSEgSZJq/Ym0RFHgCXHpGX4tFgvKyspQVlbW7T5DhgzBO++8o+SlifolSZJw4qszaO9Qfvd9gsWE4UMHRaAqov6Jk0cTqay9w4O29g61yyDq9xh4FDEbXI9CBxlyN2Oj7hljxg1XjcTmPZ/Cw7kE+h1bURGELEPScY56ig0MPIqYb/yhbzPplJ6ow2UDE5FqAeoZeP2O/qIJKIjUxj+9iIhIExh4WidktSsgIooKXtLUOkkHnF4LeELPY9qjFAeQcXO3zSPMe2CEB16YcMw9vkv7kTofxGd1+OI8kKz81SnGuaurIXw+SAYDzMOGqV0OEQOPcCHsOk4r72cNvc5dp5sSNwaWBwoVeB9+4cWWI1/E9fJAvA+ue22ffBJYHoiBR7GAgUead+psPRpdrZfe8SK8D44ovjDwSPPcbm9Y98EZDXqe4RHFEQYeUZgMen2vZkpJtVmRl5MZgcqIKBQGHlEvhTtTSoLZFIFqiKg7vC2BiIg0gYFHRESawMAjIiJNYOAREZEmcNAKRUyrbAv6ejGrSYLFaARkdzTLoijRJSRA/vYrUSxg4FHElDf9vx7b7xtnQdH4a/Fa5Ueor49SURQ1KTNmqF3CJQkhQ5LCv9DV2/4UXQw8ItIsSdLB5fsf+EXP0+SFopdSYDPcGIGqKFIYeESkaX7RBB8alHcUfV8LRRbPxSl+Sfo+eRqDgf8bEGkBz/AoYgqs5bDoWtEhW/Fe66wu7e8e92DP2eNwnpdwZWIYL9D52UlHLdBco7y/0Q4gF3pd3wQnBWvduROy2w2d2QzrTTepXQ4RA48i53LTocDyQAixGMGX5/1oOXseiUaEF3idhAfwtSjvJ6f24kXpUjw1NYHlgaxqF0MEXtIkIiKNYOAREYVBggVCyL16jt72J2V4SZOIKAw6mHhbQ5xh4FHc88syOjxexf18fn8EqiGlDFlZUe3X13hbQ/xg4FHcEwKQhfLfHmF0oT6ks1ohhIwBv/xl2M/BmU5ICQYeEalCslggSTqcq34N7oavFPdPzL4OablFYV9SNEo5SDJcp7gfxS8GHhGpytteB0/zKcX9TGlDAIR/SVEvQk9qTv0XrwUQEZEm8AyPIuaY+8ew6NrQIYe+q3xkpgED09JRU1cX5cr6B6NBDyEEJEkK+zl6278n5iuugHC7IZnNEXl+IqUYeBQxO9tm9th+yzAjisYPw2uVTi4PFAaDXg9JknDiqzNo7/Ao7p9gMWH40EERqOyCxIkTI/bcROFg4BHFufYOD9raO9Qugyjm8TM8IiIVcKaW6OMZHhGRCjhTS/Qx8ChiHkhdBKuuCa1yCtY0PtOlffUnHSj7+BMYdRJuylShQIqoxtdfh9zWBl1iIlLvuUftcmIWZ2qJHl7SpIgxSm6YdR0wSu6Q7R6/QJvHDy9n+OqXhM8HeL0XvhLFAMWBt3PnTkybNg05OTmQJAlvvvlmULsQAgsXLkR2djYSEhJQUFCAEydOBO3T0NCAWbNmwWazITU1FXPmzEFLSxjrmREREf1AigOvtbUV11xzDcrKykK2L1++HH/84x+xatUqfPLJJ7BarSgsLERHx3ejyGbNmoXDhw9j+/bt2Lx5M3bu3IkHH3ww/HdBRER0CYo/w7v99ttx++23h2wTQmDFihVYsGAB7rzzTgDAv//7v8Nut+PNN9/Evffei6NHj2Lr1q3Yu3cvxo0bBwB46aWX8I//+I94/vnnkZOT04u3Q0REFFqffoZ38uRJOJ1OFBQUBLalpKRgwoQJqKqqAgBUVVUhNTU1EHYAUFBQAJ1Oh08++STk87rdbrhcrqAHERGREn0aeE6nEwBgt9uDttvt9kCb0+lEZmbwkDyDwYD09PTAPhdbunQpUlJSAo/c3Ny+LJuIiDQgLkZpzp8/H01NTYFHTU2N2iUREVGc6dPAy/p2BeK6iyYDrqurC7RlZWWh/qKJE30+HxoaGgL7XMxsNsNmswU9iIiIlOjTwLvsssuQlZWFysrKwDaXy4VPPvkEDocDAOBwONDY2Ij9+/cH9tmxYwdkWcaECRP6shwiIqIAxaM0W1paUF1dHfj+5MmTOHjwINLT05GXl4dHH30Uv/vd7zB8+HBcdtll+O1vf4ucnBxMnz4dADBy5Ej8wz/8A+bOnYtVq1bB6/Vi3rx5uPfeezlCs595r+UXMEhe+IQxZPvUK00YfflQfHz4GKB8sn+KcdYbbgD8fkCvV7sUIgBhBN6+ffswefLkwPelpaUAgOLiYqxduxb//M//jNbWVjz44INobGzEDTfcgK1bt8JisQT6lJeXY968ebj11luh0+kwc+ZM/PGPf+yDt0Ox5Evv6B7brxigxy0jBqDmDLg8UD9kGjJE7RKIgigOvFtuuQVCdD+JmyRJWLx4MRYvXtztPunp6Vi/fr3SlyYiIgpbXIzSJCIi6i2ulkARk6n/O/SSH36hR72/6+UtZ7OMw2eaca5VheKozxi6GV3tPXMGwu+HpNfDOKjryur6lJRIl0YUhIEX74QMSLF5oj7d9jKS9Y1o9qfiz98s69K+6ZAbr+07hESjhFuzVSiQek0IGQN++cuQbUcGD4b3zBkYBw1C/unT3T+JMfSgJqK+xsCLd5IOOL0W8ISepaZH1nzAfkefl0TaIUk6nKt+De6Gr7q0+T3Nga9n9izp0m7Nm4DUrKmQOIqTooSB1x94nEBHD39Bd8dkv/Q+RJfg9TXA03yqy3YhfIGvodrNnisjXhvR9zHwiDSuxfcpvOKs4n5GKQdJhut4hkZxg4FHpHGyaIEPDYr76QWn+KP4EpujHYiIiPoYA4+IiDSBlzRJ84xGAxITLJfe8SJmU/8YTq9DEgxID6sfUTxh4JFmWS0WyEIgOzMd2ZnKf+EDgCwEjIb4HLRhNOghhECS8ToA14X1HEII6AwMPooPDDzSLIvJCJ0kYeOeGtSca1bcf1BGEu6ekAdDnI5SNOj1kCQJx04eRnP714r7JycMxIjLroJOnxCB6oj6HgOPImZN49OQICAghWyfM96CqWNHY/37u1Bf51X8/H65+0nMlfi62Y0z37Qp7mfqJzOEtLW3oqW9SXE/PRJ7bE969W5AAN388xNFHQOPIsYrev5czGSQYDUbYNBduDSoWN/kHUWIlGhSuwSiIAw80jydpINBr/x/BZ2Opy5E8YSBR5olSRc+e7NYjMhITVbcPzHhwhmMwcC7e4jiAQOPImas5T2YdO3wyAnY31HQpX3vKS/qPadwuF6CKgvFSBd+/P3tZ9Bx7qTi7n7dIAC50Ovic9BKpLnfOATR6oFkNcF89yi1y+l3JFgghAypF6ul9LZ/vGHgUfiTSBt6jqmxCe8FlgcKFXj7TvvwwRenkWjUwRHeXQF9Qvg9kH0tivvJ/o4IVNN/eN44BHGuDdKARAZeBOhggiTp4PL9D/wijEFHUgpshhsjUFnsYuBpmd4KWQjoBj/Qu+eR+GNEpBa/aAprLlQtDvribyot01mgkyRs+WQPGr6pU9x96OArcePVPwIkXtIjotjHwCM0uJpR/43yvxDT05Xfu0bUZwz89UXK8CeGiOJS5zp8ntqz6Kg9orx/thnI6+uqKJYx8Igorgm3B3JLaxj93BGohmKZdsajEhGRpvEMjyjO+eRv4JbrlfcTQwAMguxxw3funOL+sl3Fe0mIwsDAI4p7PsgI5/Kc/8IXWQBen/LufTR5N1G0MPAoYup9eWiW09Auh562y56kQ15GIlwtrihX1re0voBsd/TDB0AM7ICUqvzYEEUCA48i5s3m/9tj+4zRZhSNH40123bi9NkoFdWHJN2F/316s4AshBy3C8heSuKSqWqXQBSEgUcUrm9nmGmv+Svavv5UcfcEWx4Sh/0qbheQpfimxbk4GXhEvSR3nIOv9SvF/fz9ZAFZvTENpmTlN7TpjWkRqIZ+KC3OxcnAI6KwmIxmCCGQlj0VadnhXb4UQkBnSOrjykgJLc3FycCjiJmevBIJuma0y8khP8/b+Dc3thz/G1wtOvwoQYUCqVcMBiMkScKRYwfgqv+yS7tp/jJIjS6IVBs8S5/q0m7LvBz5I66FTs9/fIoOBh5FTKbhVGB5oFDqWmS0nG9BolEC+DsvbrW1NaPZ1fU+vozj1dCdb4Q/IzVkuyFpYDTKIwpg4FGv+WUZHR5vl+3ie19DtcfjJREiil8MPILP7w8dSJfg//bGYyEAWYRKr+8iL3R7/6AzD4DBOlR5P0t23xdDFCXxOMqTgac2IQMqD+vtPrAu1bHva4knVosZshBIyJuJhLyZYT2HLASga4Nb/rvivp1Tg8U7jvKMT/E4ypOBpzZJB5xeC3icyvta8wH7HX1eEv0wFqMROknCW7s/RV39V4r72wfk4I7rJ8Kgl3o3NVic4ijP/iGeRnmqFnhlZWX4/e9/D6fTiWuuuQYvvfQSxo8fr1Y56vI4gY7TyvuZ7H1fCyl23tUCZ8N5xf30xgu/qE1GM5ISUhT3t5jie8quS43yvBSO8iSlVAm8119/HaWlpVi1ahUmTJiAFStWoLCwEMePH0dmZqYaJcW3cIPPoPyXLPWdzqnJBtlzMcieG96TCBlGg6kPq4q+7kZ5XkrnKM9wL4kazFztQWtUCbwXXngBc+fOxQMPPAAAWLVqFbZs2YJXX30VTz3V9X4d6obeClkI6AY/0Kun6fzFS+ESEEL5oB9IF6YUa695Hc1f71Lc3Wq7EtZhJUhKTEKHT3tniH11SdSA8G6P0IGXUuNN1H/TeTwe7N+/H/Pnzw9s0+l0KCgoQFVVVcg+brcb7u+tTtzUdOEDUpcrBmbZ74tBJ/JQQLIp7+e1A83N2HP8CzS3Kj8W9nQ7rh6ag0RjAtKSMhT3N0oSXC4XEg3GkP1b23WAJNAqdCHb/XXt8HXI8AspIq8fvf4mpCcrvzKRaDDB5XKho8MMjwhjxKacCX9TIzJsmciwhXdlxNXUCFuKHXpJ+XyeZnMyXC4XkpIGQD/o6i7t7ToDBC78QTUgRPul+l+K1ZqO5uZmnGuoh6ejWXF/S0IK0lIzIEkjocdIxf1lAE1tTfD6L4cslF9l6ZCSAYMLHl8ehAb7y5IZLkPvf4d35oD4AQPvoh54586dg9/vh90efIDsdjuOHTsWss/SpUvxzDPPdNmemxvmZSCKiocD/1ULoOdRjNsiXAup6OtzwHU3qF0F9XPNzc1ISen5SkdcXMuaP38+SktLA9/LsoyGhgZkZGRAkiQVK4ssl8uF3Nxc1NTUwGYL4wxQ43j8eofHr3d4/Hrnhx4/IQSam5uRk5NzyeeMeuANGDAAer0edXV1Qdvr6uqQlZUVso/ZbIbZbA7alpqaGqkSY47NZuP/ML3A49c7PH69w+PXOz/k+F3qzK5T1O94NplMGDt2LCorKwPbZFlGZWUlHA5HtMshIiKNUOWSZmlpKYqLizFu3DiMHz8eK1asQGtra2DUJhERUV9TJfDuuecefP3111i4cCGcTifGjBmDrVu3dhnIonVmsxmLFi3qcjmXfhgev97h8esdHr/eicTxk8QPGctJREQU59SdtZiIiChKGHhERKQJDDwiItIEBh4REWkCA09lZWVlGDp0KCwWCyZMmIA9e/b0uP+GDRswYsQIWCwWjBo1Cu+8806UKo1NSo7f4cOHMXPmTAwdOhSSJGHFihXRKzRGKTl+r7zyCm688UakpaUhLS0NBQUFl/x57e+UHL+NGzdi3LhxSE1NhdVqxZgxY/Daa69FsdrYo/T3X6eKigpIkoTp06cre0FBqqmoqBAmk0m8+uqr4vDhw2Lu3LkiNTVV1NXVhdz/448/Fnq9XixfvlwcOXJELFiwQBiNRnHo0KEoVx4blB6/PXv2iMcff1z85S9/EVlZWeLFF1+MbsExRunx+8UvfiHKysrEgQMHxNGjR8X9998vUlJSxOnTp6NceWxQevzef/99sXHjRnHkyBFRXV0tVqxYIfR6vdi6dWuUK48NSo9fp5MnT4pBgwaJG2+8Udx5552KXpOBp6Lx48eLkpKSwPd+v1/k5OSIpUuXhtz/Zz/7mSgqKgraNmHCBPGrX/0qonXGKqXH7/uGDBmi+cDrzfETQgifzyeSk5PFunXrIlViTOvt8RNCiGuvvVYsWLAgEuXFvHCOn8/nE9dff734t3/7N1FcXKw48HhJUyWdyyQVFBQEtl1qmaSqqqqg/QGgsLCw2/37s3COH32nL45fW1sbvF4v0tO1t5Bqb4+fEAKVlZU4fvw4brrppkiWGpPCPX6LFy9GZmYm5syZE9brxsVqCf1ROMskOZ3OkPs7nc6I1Rmrwjl+9J2+OH5PPvkkcnJyuvwRpgXhHr+mpiYMGjQIbrcber0eK1euxG233RbpcmNOOMfvo48+wurVq3Hw4MGwX5eBR0SKLVu2DBUVFfjggw9gscT3yunRlJycjIMHD6KlpQWVlZUoLS3F5ZdfjltuuUXt0mJac3MzZs+ejVdeeQUDBgwI+3kYeCoJZ5mkrKwsRfv3Z+EcP/pOb47f888/j2XLluG9997D6NGjI1lmzAr3+Ol0OgwbNgwAMGbMGBw9ehRLly7VXOApPX5ffPEFvvrqK0ybNi2wTZZlAIDBYMDx48dxxRVXXPJ1+RmeSsJZJsnhcATtDwDbt2/X5LJKXGaqd8I9fsuXL8eSJUuwdetWjBs3LhqlxqS++vmTZRlutzsSJcY0pcdvxIgROHToEA4ePBh43HHHHZg8eTIOHjyI3NzcH/bCYQ6woT5QUVEhzGazWLt2rThy5Ih48MEHRWpqqnA6nUIIIWbPni2eeuqpwP4ff/yxMBgM4vnnnxdHjx4VixYt0vxtCUqOn9vtFgcOHBAHDhwQ2dnZ4vHHHxcHDhwQJ06cUOstqErp8Vu2bJkwmUzijTfeELW1tYFHc3OzWm9BVUqP33PPPSe2bdsmvvjiC3HkyBHx/PPPC4PBIF555RW13oKqlB6/i4UzSpOBp7KXXnpJ5OXlCZPJJMaPHy92794daLv55ptFcXFx0P7/+Z//Ka688kphMpnEVVddJbZs2RLlimOLkuN38uRJAaDL4+abb45+4TFCyfEbMmRIyOO3aNGi6BceI5Qcv9/85jdi2LBhwmKxiLS0NOFwOERFRYUKVccOpb//vi+cwOPyQEREpAn8DI+IiDSBgUdERJrAwCMiIk1g4BERkSYw8IiISBMYeEREpAkMPCIi0gQGHhERaQIDj4iINIGBR0REmsDAIyIiTWDgERGRJvx/kXGBlcEZffMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = \"Age\"\n",
    "# Generate random data for demonstration\n",
    "data1 = np.random.normal(0.22, 0.01, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data2 = np.random.normal(0.26, 0.05, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data3 = np.random.normal(0.13, 0.04, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "data4 = np.random.normal(0.06, 0.03, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data5 = np.random.normal(0.07, 0.02, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "\n",
    "bins = np.arange(-0.05, 0.4, 0.02)\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "\n",
    "# Create the histogram using Seaborn\n",
    "sns.histplot(data1, bins=bins, kde=False, edgecolor='white', color='#d00000', alpha=0.6, label='Baseline')\n",
    "sns.histplot(data2, bins=bins, kde=False, edgecolor='white', color='#dcf763', alpha=0.6, label='Sequential')\n",
    "sns.histplot(data3, bins=bins, kde=False, edgecolor='white', color='#a2aebb', alpha=0.6, label='Voting')\n",
    "sns.histplot(data4, bins=bins, kde=False, edgecolor='white', color='#ffba08', alpha=0.6, label='Debating')\n",
    "sns.histplot(data5, bins=bins, kde=False, edgecolor='white', color='#3f88c5', alpha=0.6, label='Committee')\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(domain)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "# Add a vertical line at x=0.1\n",
    "plt.axvline(x=0.22, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.26, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.13, color='#a2aebb', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.06, color='#ffba08', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.07, color='#3f88c5', linestyle='--', linewidth=2)\n",
    "\n",
    "# Show the legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(f'./images/{domain}.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = \"Gender\"\n",
    "# Generate random data for demonstration\n",
    "data1 = np.random.normal(0.04, 0.014, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data2 = np.random.normal(0.07, 0.05, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data3 = np.random.normal(0.04, 0.04, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "data4 = np.random.normal(0.02, 0.03, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data5 = np.random.normal(0.03, 0.02, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "\n",
    "bins = np.arange(-0.05, 0.4, 0.02)\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "\n",
    "# Create the histogram using Seaborn\n",
    "sns.histplot(data1, bins=bins, kde=False, edgecolor='white', color='#d00000', alpha=0.6, label='Baseline')\n",
    "sns.histplot(data2, bins=bins, kde=False, edgecolor='white', color='#dcf763', alpha=0.6, label='Sequential')\n",
    "sns.histplot(data3, bins=bins, kde=False, edgecolor='white', color='#a2aebb', alpha=0.6, label='Voting')\n",
    "sns.histplot(data4, bins=bins, kde=False, edgecolor='white', color='#ffba08', alpha=0.6, label='Debating')\n",
    "sns.histplot(data5, bins=bins, kde=False, edgecolor='white', color='#3f88c5', alpha=0.6, label='Committee')\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(domain)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "# Add a vertical line at x=0.1\n",
    "plt.axvline(x=0.04, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.07, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.04, color='#a2aebb', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.02, color='#ffba08', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.03, color='#3f88c5', linestyle='--', linewidth=2)\n",
    "\n",
    "# Show the legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(f'./images/{domain}.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = \"Disability\"\n",
    "# Generate random data for demonstration\n",
    "data1 = np.random.normal(0.05, 0.015, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data2 = np.random.normal(0.06, 0.05, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data3 = np.random.normal(0.07, 0.03, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "data4 = np.random.normal(0.027, 0.03, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data5 = np.random.normal(0.04, 0.01, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "\n",
    "bins = np.arange(-0.05, 0.4, 0.02)\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "\n",
    "# Create the histogram using Seaborn\n",
    "sns.histplot(data1, bins=bins, kde=False, edgecolor='white', color='#d00000', alpha=0.6, label='Baseline')\n",
    "sns.histplot(data2, bins=bins, kde=False, edgecolor='white', color='#dcf763', alpha=0.6, label='Sequential')\n",
    "sns.histplot(data3, bins=bins, kde=False, edgecolor='white', color='#a2aebb', alpha=0.6, label='Voting')\n",
    "sns.histplot(data4, bins=bins, kde=False, edgecolor='white', color='#ffba08', alpha=0.6, label='Debating')\n",
    "sns.histplot(data5, bins=bins, kde=False, edgecolor='white', color='#3f88c5', alpha=0.6, label='Committee')\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(domain)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "# Add a vertical line at x=0.1\n",
    "plt.axvline(x=0.05, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.06, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.07, color='#a2aebb', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.027, color='#ffba08', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.04, color='#3f88c5', linestyle='--', linewidth=2)\n",
    "\n",
    "# Show the legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(f'./images/{domain}.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = \"Nationality\"\n",
    "# Generate random data for demonstration\n",
    "data1 = np.random.normal(0.19, 0.02, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data2 = np.random.normal(0.11, 0.05, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data3 = np.random.normal(0.09, 0.03, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "data4 = np.random.normal(0.04, 0.04, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data5 = np.random.normal(0.036, 0.02, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "\n",
    "bins = np.arange(-0.05, 0.4, 0.02)\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "\n",
    "# Create the histogram using Seaborn\n",
    "sns.histplot(data1, bins=bins, kde=False, edgecolor='white', color='#d00000', alpha=0.6, label='Baseline')\n",
    "sns.histplot(data2, bins=bins, kde=False, edgecolor='white', color='#dcf763', alpha=0.6, label='Sequential')\n",
    "sns.histplot(data3, bins=bins, kde=False, edgecolor='white', color='#a2aebb', alpha=0.6, label='Voting')\n",
    "sns.histplot(data4, bins=bins, kde=False, edgecolor='white', color='#ffba08', alpha=0.6, label='Debating')\n",
    "sns.histplot(data5, bins=bins, kde=False, edgecolor='white', color='#3f88c5', alpha=0.6, label='Committee')\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(domain)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "# Add a vertical line at x=0.1\n",
    "plt.axvline(x=0.19, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.11, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.09, color='#a2aebb', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.04, color='#ffba08', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.036, color='#3f88c5', linestyle='--', linewidth=2)\n",
    "\n",
    "# Show the legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(f'./images/{domain}.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = \"Race\"\n",
    "# Generate random data for demonstration\n",
    "data1 = np.random.normal(0.08, 0.018, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data2 = np.random.normal(0.07, 0.046, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data3 = np.random.normal(0.07, 0.05, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "data4 = np.random.normal(0.02, 0.04, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data5 = np.random.normal(0.025, 0.02, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "\n",
    "bins = np.arange(-0.05, 0.4, 0.02)\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "\n",
    "# Create the histogram using Seaborn\n",
    "sns.histplot(data1, bins=bins, kde=False, edgecolor='white', color='#d00000', alpha=0.6, label='Baseline')\n",
    "sns.histplot(data2, bins=bins, kde=False, edgecolor='white', color='#dcf763', alpha=0.6, label='Sequential')\n",
    "sns.histplot(data3, bins=bins, kde=False, edgecolor='white', color='#a2aebb', alpha=0.6, label='Voting')\n",
    "sns.histplot(data4, bins=bins, kde=False, edgecolor='white', color='#ffba08', alpha=0.6, label='Debating')\n",
    "sns.histplot(data5, bins=bins, kde=False, edgecolor='white', color='#3f88c5', alpha=0.6, label='Committee')\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(domain)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "# Add a vertical line at x=0.1\n",
    "plt.axvline(x=0.08, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.07, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.07, color='#a2aebb', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.02, color='#ffba08', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.025, color='#3f88c5', linestyle='--', linewidth=2)\n",
    "\n",
    "# Show the legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(f'./images/{domain}.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = \"Religion\"\n",
    "# Generate random data for demonstration\n",
    "data1 = np.random.normal(0.18, 0.015, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data2 = np.random.normal(0.11, 0.06, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data3 = np.random.normal(0.06, 0.04, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "data4 = np.random.normal(0.02, 0.05, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data5 = np.random.normal(0.03, 0.026, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "\n",
    "bins = np.arange(-0.05, 0.4, 0.02)\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "\n",
    "# Create the histogram using Seaborn\n",
    "sns.histplot(data1, bins=bins, kde=False, edgecolor='white', color='#d00000', alpha=0.6, label='Baseline')\n",
    "sns.histplot(data2, bins=bins, kde=False, edgecolor='white', color='#dcf763', alpha=0.6, label='Sequential')\n",
    "sns.histplot(data3, bins=bins, kde=False, edgecolor='white', color='#a2aebb', alpha=0.6, label='Voting')\n",
    "sns.histplot(data4, bins=bins, kde=False, edgecolor='white', color='#ffba08', alpha=0.6, label='Debating')\n",
    "sns.histplot(data5, bins=bins, kde=False, edgecolor='white', color='#3f88c5', alpha=0.6, label='Committee')\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(domain)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "# Add a vertical line at x=0.1\n",
    "plt.axvline(x=0.18, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.11, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.06, color='#a2aebb', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.02, color='#ffba08', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.03, color='#3f88c5', linestyle='--', linewidth=2)\n",
    "\n",
    "# Show the legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(f'./images/{domain}.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = \"SES\"\n",
    "# Generate random data for demonstration\n",
    "data1 = np.random.normal(0.053, 0.014, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data2 = np.random.normal(0.07, 0.037, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data3 = np.random.normal(0.065, 0.04, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "data4 = np.random.normal(0.013, 0.014, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data5 = np.random.normal(0.01, 0.01, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "\n",
    "bins = np.arange(-0.05, 0.4, 0.02)\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "\n",
    "# Create the histogram using Seaborn\n",
    "sns.histplot(data1, bins=bins, kde=False, edgecolor='white', color='#d00000', alpha=0.6, label='Baseline')\n",
    "sns.histplot(data2, bins=bins, kde=False, edgecolor='white', color='#dcf763', alpha=0.6, label='Sequential')\n",
    "sns.histplot(data3, bins=bins, kde=False, edgecolor='white', color='#a2aebb', alpha=0.6, label='Voting')\n",
    "sns.histplot(data4, bins=bins, kde=False, edgecolor='white', color='#ffba08', alpha=0.6, label='Debating')\n",
    "sns.histplot(data5, bins=bins, kde=False, edgecolor='white', color='#3f88c5', alpha=0.6, label='Committee')\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(domain)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "# Add a vertical line at x=0.1\n",
    "plt.axvline(x=0.053, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.07, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.065, color='#a2aebb', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.013, color='#ffba08', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.01, color='#3f88c5', linestyle='--', linewidth=2)\n",
    "\n",
    "# Show the legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(f'./images/{domain}.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = \"Sexual Orientation\"\n",
    "# Generate random data for demonstration\n",
    "data1 = np.random.normal(0.05, 0.019, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data2 = np.random.normal(0.072, 0.035, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data3 = np.random.normal(0.061, 0.055, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "data4 = np.random.normal(0.012, 0.026, 1000) + np.random.normal(0.01, 0.005, 1000)\n",
    "data5 = np.random.normal(0.03, 0.017, 1000) + np.random.normal(0.01, 0.005, 1000) \n",
    "\n",
    "bins = np.arange(-0.05, 0.4, 0.02)\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "\n",
    "# Create the histogram using Seaborn\n",
    "sns.histplot(data1, bins=bins, kde=False, edgecolor='white', color='#d00000', alpha=0.6, label='Baseline')\n",
    "sns.histplot(data2, bins=bins, kde=False, edgecolor='white', color='#dcf763', alpha=0.6, label='Sequential')\n",
    "sns.histplot(data3, bins=bins, kde=False, edgecolor='white', color='#a2aebb', alpha=0.6, label='Voting')\n",
    "sns.histplot(data4, bins=bins, kde=False, edgecolor='white', color='#ffba08', alpha=0.6, label='Debating')\n",
    "sns.histplot(data5, bins=bins, kde=False, edgecolor='white', color='#3f88c5', alpha=0.6, label='Committee')\n",
    "\n",
    "# Add a vertical line at x=0.1\n",
    "plt.axvline(x=0.05, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.072, color='#d00000', linestyle='--', linewidth=2)\n",
    "# plt.axvline(x=0.061, color='#a2aebb', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.012, color='#ffba08', linestyle='--', linewidth=2)\n",
    "plt.axvline(x=0.03, color='#3f88c5', linestyle='--', linewidth=2)\n",
    "\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(domain)\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "# Show the legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.savefig(f'./images/{domain}.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "committee",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}