Case3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of cross-validation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.loadtxt('rotation_update_pj.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff0c9f7d850>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qer9SnOS6PUgrsNP1Ur+8JL+ZknYikX4+PPLEZty6e/xwEeudWKMq2zKR16MB/V/XjonDqenm1+VaD7lsX/GWnmiP/uXu/nEtPTR6C3ae12w7CaD0Ea2gcIlJOiatt4VIFhDpqix33vms/TWgkm1pOOX1e9NW/KUF6CeeQNfNkYrUgYFTf2sfHvrr+1HCp95UVr7S4D9T+3JTkK5Y2Ev+2xi/LiFdjwmKnGR5ZFQFRTo+5zwGjUQl29Jw24svUK1F5K+tiQeUyZeBsuO9X+8bKwPAaa8sE64PH3/HBf1f9knlsHBe4r9hPOF06D2vB0t8rMwkzP27DsnQo4o6L1O/oSNDjUYuEWZEBcdc+pKF0JtAF2LO1guqpuujANd8i0bADZfvnjp/OkJJPN600vvGmmB9/lnBnAzXy5KYEJqy8TRyUfDbJkYc52BGVrItESTzroEQxg1BQj41BocmH1M3SCCz3OTpWuEA4OEX7HeD+LQAtTIMdwtc99bJdW+BcJJFBnoLpQ75F5UEmaolN8p+1TV1k61JlfGl6ojSPZOqhzHPtFFUM3JJeOhvyBiPa3BT9bZuNlEaUZgSCtI4mZS5SbaHaDotH+l3jBnqCfCY87KAyaQCNylqOQl54c7Upz84lPlchQrrTjOsgt+XBJjUfeYRGPGoyrYk0BpApFqfk3PWKfgZ5NaVuZPUgytTUY9lH8exp7EkcxdJf3c9d1+caOKIH2zCPq1wKPhsOr3nGr53eai1msbZytVtmvzOGJVsS4IZITVrfJ2UbzYkpCMD1zIifKW5Y0beclG/Cfjdr9hfNCc7plquKtFiLZtqufrk+xQ6ZtqEa0Tzr8yi8ClQxJs2cDNNXR6zBLBmjEq2JaFZjZG0PPKWdgPhoHCua0wWOZhQyG68dHey/fA9hchkT4EnucdYc6tUeBlTF+4kD9QlWul8Ug/t/DhK8tHnx00isc73PFBbkGwCPHz3ftfDOm+SpH0TMTNLUUkfifz8K3d3L17AI16yXzqrxnnJ8k6rfpYXfZ+ARJHHj1OSDECk17NzUr+Nx5O5kKcOf8axXF8/r1iXD9yteyYr23pbkFRl2wQwawCMC4Z02v2RMid1dM9jPX6LwLciedQLy61MvFkWTEhtpuVlWPbrqoHQvyw/Ljsnv2cv0aZQG9Z5lEwkBEwyp/LrI6sL8N0q2TYBmjVO1IAt3OKJx247VA/0mG7f/Yr9ocBNMwn7p9+Y+nqP+YV9imhjxifJoMcISSvY3VRNfR+EsXV32XPmx03DjfB9mOYhGH5ekbkxr5JtE8CsaROLYSXCaBvEX++35dG0DF5VvucSRzyy6Mzb1ofr3ronDMQzbYslP4e19+8gHwny6171plTgaaOSZV9OH4CuQuuTSQ0U1Dl5Pqhk2wQwI0YYAYoBw64FumGS3+i7+QpwoPC11wWaI/EevicS7/rXT0e8XmizEX5dCGclj1bts/nEhHGZJCAT/bZC1jxy4gXCcfrytHr7D0I+5MSsUMm2ZJxxzl5RBVcipKyC2E3jCyagQSCPduaL0XRlyhUruqfAxONywsEXWkoHE/J51SRD9suqoGe+X7iG/p0GuZrl/hy0zybmss/fHBWutiBZInz/MV8ZTdZFAH0TKiNzo4V1tZiwH+Ecvd0ZFk8K0yNfVAj9K1z7tim/6orQyRiOltOopnquzvB8SNWYcjL23La0uIulS9G/VV+STt1j4fBZopJtSXjcs/Z2VIg4K5iBVOzC9IpMcZjySKy8eZfJjvdkyPuyPeYXsqikBDe0KarzqI/r5t/l10h+oQjX22YzUToK651BfMagQzr57fQwKJ20iFAkqhm5PKhCyirN1W1FeyY1F72thaRAlVRCF1JPnHAMA6f/0r54P3YjFEdzs0y0eL2sfJYIocjn86vXAwm0Kany1yFObuL1BU58fv21KT3VraugTv5wc0Ql2xLw+LOVnybBA99ChE1KOKK8nEnAQRdWf7hXJKBIkoRA2vxLFGc80TTGtdRw0UgGqbpBat3z+qhq+GhIQIUMQug9CVwgvoCC+5VlSvLAinDZ/kA4HSuhuG9eqGRbMB7/s27ckdBbRv68BIphcrdDKV1GOCOTORXUKyFQDkXMItHsGKLp65WqBjjd558hzCvgqy1sRjQCuEEMkrD/6MTARcKZktKVzENShMvfX349pbTj2pluFJVsC0buh8SyE2aFVnVWmaZJqSMfrsxKD6nCmpPDH6PzcEREK23n+7wKMVyJt1GJyQoDdFSVEPunqXo6F910Ff6acF7te6FNSE04//7UO0mabY275gxQybZg5MN+K0FICBcKhRRWb24B6BQ+oEyg/B69x+XrGEO0aaDUyJvE3pyMH4oec9ITTaa+Gku4XK6gTFiVHqoV1DEJPHnnHC6sZFsgnvhTV6RiRM5JL7aGYFVYjSNc0lN5XADN+yXZcX1Eo6wx+rgo4MQW9SFBkcPnWZuVRt3HB05UhTiZdRBOnrmUl+R9F8gZDqIJ73QGqGRbEL7/J65IzReDJCrGap9H4r/pbUnrELUQeZuEaXy9aYJ1Ez8AinDwZquauxs6aqmVrY9whXv1pmm1y58l+GtcJuIMUck2ZzzpR90E74FoJIomJtO4P3BeeDrEyr7mIehC6n6ZORn8wXHQPuWROjKl51KkZlG03IwElJnsPyiKcMiirWWl6mZadzgtHluVbevgiT91hStMbVSLou/EAPloItyxyfxkgPraQrrYqIF/8kKhCac2We3PzcL1NDSeFlMX1KBsiHKYN/FSJnQgnhAu7OP4URlrHob1fpLmjZ/nhUq2WcEXoOI+9xf24X3NikA0QjCrOkTLB7FJjoklJNQd+YCALl8SnPAFtWhSHemj9xTS1F8UdnjfTalbyItXOyBGBzU5GPFL4Y/XXxdvDgLq/WaZKm33HTtjTIy/ENFJRPQxIvpnIrqBiF4s6fcnog8T0c3y+22STkT0BiI6QETXE9Hp832E5eOJP3VFcPoDAYpmVNah0RPGdxLV1/B920qDk4bzKBYWua+bkJDS6xZ6RIcs5aqKfP86X0ZBIfKWLXklemgbqvYVqymgjoG6Zn6fnDxUWFQvitAL3sxY7jNME+wcAbiAmR8G4HEAXkhEDwNwCYCPMPOpAD4i2wDwIwBOleV8AG+aea43GazMEcZGzRFmSI0lQs45UQEBNzQAKZLlE6pTefaVQEJFKD1sWyBWRryElIq0QLJ/3d1a8gKt0EeKxJT06qZ/OSNezzXGmcLF3t7qfbEmmQ+OLNuMZOYvAPiCrP8XEd0I4AQATwfwg3LY1QD+FsDFkv7HMr/2x4nofkR0vFxnaux802t7X/TtL3xp5/hl4Yyf2wsaEnyEjcRUChXFrUTRfMExbnL1SKYCyToKp9b9LJodhfIriCak9398LbBlN/yCOs5FMCmG/6lram7Yxwt5iqakNieh033VACN5lsQvyzLUaxGXTMSkkWfP75ywLp+NiHYCeDSATwB4oCLQvwF4oKyfAOAOddqdkjY12Xa8+QrQmCf3Uy3d/oLlk65dIdWqPR2VmCxADWRwVdeaP8yKaZCNohVJlpt9gWSJqYoeU7X861tkwHJUQSG/bx4FG83cnGAlwuXqUTqGFFmDD6YWkt7cHd+N1Tn6WnkwJN8uvRJCx5dLgycAL6Ax8tRkI6L7APhLAC9h5v8kZeszMxOtL7dEdD6cmYmTTz452Xfw+RfGyd3DCfkFnPqVXlKY1TMveEi3b3/RkZP1ES9xw3ibbWIOqeG/40Cpyhex7nOtzT9Puo6CJUSjTtq4MHUgS4F0rixnOtCiSzg5PlQhTPrLajXtyVPghG8R408lxMikbilDUfkCSTeqPPoDFT4QGQnniKnIRkRDOKK9nZn/SpL/3ZuHRHQ8gLsl/S4AJ6nTT5S0BMx8JYArATeUXb4/n5kzmTiQxrwkdvsoxIVLDyTXfKMo5DrM0tNeJSTbDtDImYQm9NviTNm8eenTUv+payL69e5xU/lVvlCXVM2Th4BOc3iJoobWHf7SGeHWHSwpEYQB8hdTZmMYr8QrnVbAwn2LKtc5SKlaRrRO+dkMZCMnYVcBuJGZdS/D9wI4F8Br5Pc9Kv1FRPROAGcA+Op6/bUSbn/BS1Ml6zOjBAyOCjeOdAzs/N29qpOljJdhYyGTC7r0bYBpCTxyJqJpAW7Tcylx9DkQLr93L7n8s2UkLEbZdB6RKYH/9a/MqmuVpIgBGPExgSLhpkZOeETedMxJIVwyXknBrAytT0rEz4nXVzY80RZIMo9plO0JAJ4N4LNE9GlJezkcyd5NROcBOAjgZ2Tf+wE8DcABAN8A8NxZZdar3Y4rr0h36LevXl5oDDWpkCQNY+WsQitwX0jYsiPaiBzRWhRI5k9Ix+IPF9XE0SaiSiuqGdLz80JWUrZQWIPJ5q8hpVlfy2I84fR1dT4mIZixypxUwZLEdCyp23qIniN5n5nKhWM2coPpME008n+j/5U+pXA8A3jhBvM1FgfPvxA73nJ59iXzzgrSkrHedyilq+T8+4oSagEyBG7YzUBjKXT/16ZbgCJa0v0lMWvUL9L9STCk9JcokUv9BgKyewbfp4xawMB9ZIy3QYHxhJtguk2KXIbvYk6k8H7S0bi48B45v57OU56BSSTabD7bZsTBX7wIO952WXcHy+dRVIUNOzIA/T6AUkU5LUKTIEwJS65wgmANXA/kgqkYLsspIYvH5cQr5aNw3eS5oLbzX194gdgeERKTaAELeaZW9rUYT7jCcyZQpntCMC2muvtQpm65heA/GNEE9TKYPffYD0Gmagv227Ys2QDg4PMuBgDsuPo1LsH/RRnujyidL0PAxEP90TvKV1K0wh+JDQEDBvuOkYW/uzcPfZ1bGJyH1TiK2X362j2W4AteCL4kO7Lf3Bzzz9PG0wLh/DkWgB4ZWdKnCpSUFB4xLZCGumajr4/LSReOKaCzKydeyWzMSTZnU3JLk82DTFa6OOu6IcWFVMi5ywx0Cn7vF09I5NY51FmxP1nO9SMcg8mZm4ZADZL6rJAF5Vf4LI0t0x1Vi/aVNr0CETXZ5L346CQRYLzSacLBEyMj3KQ8yTsqElNdxFdo54O46kCI3s4/IqT3Tatwhb/lokzJo2Iou9uf/TKYAcMMGNQwaGBlYWDAQMPJxOhxDjQOLztpvtMw0DiicMPpsR7EYSx+OwB44NZ5wDHN37dh2AFgBww7YPDADSkeFjk/HDNk8NAdq4/rnaS9yRbJS5iZs2FY/yxNvFe4hr++z0eTtmrxpnMpUJM0/dImdwnaOggX6C55+8jipfoUbpw4aQUL+V0M0YCjRNkA4NZnvhwPefdvug2W5lAAuJU2iiMjzZYozlvNcMGClkLLimRC+QzMmToi/qESXyL5Y0q671bj/8D6M9dTWGNTpni3bqSRkH/xi1/6sMggOnpGHPHTLMVseYUTSzJWbYAjBwrEi/5Y+R2G49QxwWzUz6DzPm4733ckpKk+2/px4GdeidP+6tfDNjO5r7Ql2KEF+6lvPdGYHBnXDNBK4fOtv/WL9+dZFXDRf9hM8Trw+434ZPmINd50zIjOPpDS549p81Ffr9SCJl8PHwSp8mgRBsaR5peOcMSJXxcCGvmz6fzZaFr3HuN3cXyMdD32+TsSJFHL0mUmuQpzwFFFNgDYNhx10qx8NVkVWmaCtYS2NWhHDWzr9pH+AwghYR1h0QJoJeAyaYinoFJR/XwjZfYlIQ8rdtQQmZylCtrbNAucFFyXVPDbRMnJf2jaEPV3nCK3QcSpEHNU4iRy6gljfNO1tGOsHtNyUiyi2PXH36cP+pvUUd1xNmnI4PhMbRBHJdmIGIbid9HIS/S/RG7esJE1GFmD1VGDtbYJBAQiGdmSI2tLsKT6sOS9iz3U1zwPNXMY78MX/OyPmyvGOLOIfE+CqDS6rHC+onuPW4rNtzyZW8APMeeJ5sjmTU7WU3rHli0GyTeDAVfFoT4m8aPgW9hMN+9bnznvSc6F/VNFSovXnC/RgKOQbJ8863fC+ll/92IMjIUBY2BaDIzFgCwMWVg2GLHBatvgUDvE4XaAtbbBmjVorUFrCaO2gWWnfqORARnAGgavNt1RnVRBBoRoOmLpj2Xqkm5alAio7KXegiaFPEbuOCGdL7wkIxgTAWTcAv+btaeMM6NmZiV7H8wTLv66ezryOh9QOo7mJp9SzSRKO458UPty1dXXLpy7KBx1ZNP4wA+8HgBw3jXPwTYzwr2aVWwzIwypRUMWa9zgG+0Kvt5uw9dGK/jGaAWrdoDDowFWrVO7oHxNg9GI0RqD1jDsyMTW6/4r3qrwgUH0/zRsPJ5tT3OyPgLm6ePULyFAVMES6YIfJlFaMo6I1CA0R3MRVoIZxnvm1RX6XSREE5MyTAgiPmAI7QeTNv4mvcoV8eI9Oc1DeO5CXWWvdZBtzpl4RzXZPK76vj8K62/7/BNwb3MYK9RilRt8pf0WfLW9F746uje+1m7D10fb8M12iEPtICreoMHhtsHqqMHqaIDRqEHbEqw18JWvbJ2piVZKgBCNfMHwlbQG7hg/lBuyCvf1YpIPk5AxRkQD6YAy6aQHAwau4bW1SGfRKd1f+2JZvz5w7B1h/HsBwUgrEE8w2yDr59dPvLANdInn33vnnczfXOzDMUE2jed91z/i0/96Er6FRvg6D/Cl9t74YnsffLG5jyNdc298sx3ia60n3RCr7QCH2gEODwZYbUfO3Bw1aNkRzlrn39nWwIaSAIDS3mMsPiCTkBKIv7PAJOJJnoKaANHnY1E1K76ViQrIQw6kcYRTaqzKbmIu5r8WoXcEtwRjWOoBKfh+ru6PhHBqUWRkUd9Ob3VNrlzZJr0b/yhz5uExRzYAeNTJdyTb/3D7gzGkEbbTKrbTCP9ltuNezRq+2Q7xTbuCw+0A32yHWLXOv1trG6zaBq01RR/PRz1D8E3SAiENgUnaaSi/zUfM1u3LAdP5HrrgKVVLJm03LuMub9npjFAnqev78jaaPjiSd6J1KunNUoIdKJXUhArjuGTq5iM0SsUSVYNKn+Z95H73nHFMki3H9++8pZh+6ed+DIf5ML7RruCwHeD3Tn9H55in/f2vYmQN1oR8I+tI1FoTiGcZsLLPB1ssATYM+JOWjlB5rgt77hcV0hOy5sfl6Hz92f8rX99nJzclMx8N7C8X1dAHY4K6+S5JbdwfCKIH5OkMdKQUzXeBCr4ed/2zEolCCLacvuPq1+DguZd0988AlWxj8Irvfd/EY46/13/isG0wsk7tRrbBiIV0TBixCSQsBlsaBjfG1eNJK41g5hXGoUz8uwLR/HrSU33aL3fhuKSjJRDG1w9BFndU7NWgCcuxI6gOkJCl1P/LyN0NlHB3XBZAmZKZ2vX5azG76fP6d9RHxBmhkm0D+I3P/Ti+c3uDQ3aIw3aAkW3cLzul86TzRFyzDQ43A6y2DQ6bgZDOoDVNqNPjlqTHAmIB8GAgmzNzfGUtMJZoveeOUQfXsBqZr+Za1iCQMJZiTyjm6OsRs5qSWKmi3M+TJY9SJiF+KhOt/2EL+3X7Mk24OaGSbQM4ceVLWOUBDtshDvlfO8QaN4F8a2ywagdYs42LcDYuwjkwFqutU7m1gXWkGzXOn2ulxYonXSiMTq14nCc/jnt9PkqpfHWOCY5lx1xL6g1dQtjPFqraIRKVWpKRx/y5SAhRijZ2VEtb3wUCFp8/EEq/jMVEKCvZjhAfvu00fOfAYA0NDtkVHOIhvm63CdEi6Tzx/OIjnNuaFXxzNMShZoDV0QCHTYORYefPGRMIF0jnC6N0GUowqazk+6XQEVNZ3fqIlq+HqCvSj0Ao1AAaH/DhJBvBdxOzMjVLMZlcOp8JQbm7fxK8+bgek/sIUMl2hDjzlH8J6/9w+4PxdbsN9zVDHGJPtAHWuMEhHiYE9NUKK6bFihlhaFbwDWIQMdaIYYz4c2hcvZO0yE/GxVcV4wA6H+digKRESE84ZAU0L3TT+jL5uYEE2bneciO4hgCGU1NSXa9YcZ5lLR4fSVk+WOdTvTTtr81R5CrZ1olb7jgeDQE7T4wDht3fHMJ2GuEQDxzZjCPbKjeBdN7c/Eazgnu1a64li9kOI+04DTEOqUJJBLQtASTBk9Z3QZFCwuQqxvt8jLzw+vX8Ky6E08ECLqnDNL5MgSy9hxrVJVU3ks7OT8zCSUQY42smKl40J+UkYux422VhFIBZopJtnXjwSZFkX/5/J2KNLe5rGA2vYcgW23mEQ7yGFkbMyAFaENZ4gEPWmZrbzBoaso5k8se2qjA7lWsANAAsGCZYjyxjg/gWKIF4QPSbfMFVBEvExdcD9phdFDqKqi99plYJSqQuKWR2H0+40JxM5a23J0R+v55rd/KnVTxXbR0kwZQqfgSoZNsAvu077yymf/pfT8IaG7REsGzQgtDC+WL/abdj2LpuQC27urg11nVyFCvGBwDQoJWunGCE0bHCJPClchHIlkX6smMJcp2woQMO0o1I7QsE7EE+zguPmxVGE05/IEqqWvo9Qj4kZnOi2hnh5oBKtjnAt1D57L+eiDZr3bqd1mBhcIhXcN/mUBK5HHEDC4oqNwK4ITiGsYtUiqD1lrVczXz0TxEwQUaeWL9OGckQCThNWdSEmPb4nu2c4InZm6Pvxai8TCTcnFDJNkc8/OSu8l1zcAe20xq20yq2mRVsN2vYZoZYbUauUtwYjBrfCoVgpJkXGQaYhWjlwqYVIlYmo6tySYHMyadSKHKTKM5TkBIh3Lx7uSlJFhpjF44vKenY+R2U71nEpIhjDZAcPfi+HQcBAH9y8+PQgDGkFkNqMSDr+tvJ0lqDxjCsZVj5DT49BScEPSVUES7r7pKZlJPckxgNVHPLqaBFzBN19/VVLSQ3GHfv6Ut+Z7jCaUzNXHnn3ILkqBhdayvi2ad+HEYaGBpiDI0QTvraNcYtxlgQsfScHlcyga7JqBTOxl8/fqUZATQi0Ihg1ghmtbs0YR0wa7KM3NDrtEaucrp1za/iqNCUVsZPKL8b6mKkX0H+fjIVDmmdEydszwhV2ZaIXzntowCACz/z0zBgGIlQDozFmvVVAhCysfhME/w1r2pSWexNyUA2b1Lm8w/kyqdBCJM2unWETqZ+Yvkw4aIERdi4KGOxHs/nNc97x530UdNCppKIolrNFS4/dhLmqG6VbJsAVzzyzyce891//eq4McZfC8TKt30D4LzbS3IOuqTzpqFujS8zpsbxNxEHJ5LWIoD4lmCQcXkJVQklwo0hRMccnYEQhmtO8uFmiGpGbhHc+N9fHXt9Q351SxGlaoFABaJpM5KsmITePFwFmsOyHFLLYcAcRmpKrgG0Bmd+jpwZaXzzKzErnWlJbhgEnwcm1SCZZmNCZpc4ollE1SkHf/GijeWnB5VsWwg3//Qru4l5BNKTTiuVJ5omWQvQyPttjjzNKrvlEKM5zBgciutuAZpVt3jfjfz54vvRSBNOhl3XPRlU/7ZpfboE0/hcmxTVjNxiuPXsXwvrO666PO7IFQ3RVCwubbqYltU6QK2MPemnIw7DmAM8cMRhKyYl63WCZXaNX7RZyYgRS1/ZXaqknhT+n2D2HZFSLsiUrMq2hXHwPGfudGboSRZREO2raZKNGGbEUd3WnLoNvJqtRsWL5ian5uRIrqdUzoziEAidiGUrpmRJ3aZRup5j10u0zvFzVsiqbEcLgjkZpxouLSiomq8GMK1M7tj6Tp8cur6wKBRZGWmLGdb6dBGvRnoAaZJLZ1PnYsaIJRPC/N6hbi5XGF9PXaqvy6P82n9dUMBjvajKtsVx8PwLQ8HzJmRvwMTqIEY0HU2bEs0dIySxbtuMGM0aYNYYzRrH+rY1znw3uZ5fH3m/TauqKJ2u+7OU5DdXvHHBlI0o1FTjtcwIE8lGRNuJ6JNE9BkiuoGIfl3STyGiTxDRASJ6FxGtSPo22T4g+3fO+RmOedz+gpfGqiH1m0Qks98wOWMWOCnv42BuugXhl/z6WiRYblaSCp6YnHR5hfuUpPMk6ZjQer1vWRKmUbbDAJ7MzI8E8CgAZxHR4wBcBmA/Mz8EwJcBnCfHnwfgy5K+X46rmDNu+9UL4oYmngwa1DElbUYu9RujmJzMmEpa/QqES9RN+WxxmyIJQ6CG+pUu9+kKxOs88zTYoK93pJhINnb4mmwOZWEATwbwF5J+NYCflPWnyzZk/1OI5j2wcwUA3Lo7Eo6SQglFoJRUHcKxkCuZiEORTsxKCiYoK5OREwKGqoVM5Xy9XEdR+5QuTNmFyWq1HkVbsNJN5bMRUUNEnwZwN4APA7gFwFeY2c/PdCeAE2T9BAB3AIDs/yqABxSueT4RXUtE195zzz0beoiKfnQaIav1lGSS1iEaQkEPRJRztEmZKNhaGq3MVS6plysRbpx5OU7tpiXikjAV2Zi5ZeZHATgRwGMBnLbRGzPzlcy8i5l3HXfccRu9XIXglgv34MAle5K0PsKFqGX45aSQuuNF1TLCGR+xbGMAxbdGoT6VW1NqpgIooTK8VQTrIx0rtcuJF/w9tQCbhnTrCv0z81eI6GMAHg/gfkQ0EPU6EcBdcthdAE4CcCcRDQB8K4AvzjDPFetE3lUsKhsHQkWCcUK0eBEZM8QCJPF1AzfPnQXDsJsKi427oR/IlS3LZJJwld7+Vw8pLiNwhSHxfGNn6aYTh0Zg+LaawTEZ46G4/qCh/gDLrhaYJhp5HBHdT9bvBeBMADcC+BiAZ8hh5wJ4j6y/V7Yh+z/KYwc6rJgHPv+KPbjp1btjQqdFvawElRM/Dt3jSJEvdtmRgIj32XLfTVV4u6oCXV2gqgiUaZn7cyYxL6OixW2EinLdFCxXb/ecBZXLgyRzLqXTmJHHA/gYEV0P4BoAH2bm9wG4GMAeIjoA55NdJcdfBeABkr4HwHwGTq+YCjdeujsQrTfoNmUhKxKuVb8tp5HKNVc3R4kvl5qWwXzsI52uo0uqDCi0uaTExETHZ+sQ7giff6OYaEYy8/UAHl1IvxXOf8vTDwH46ZnkrmIm+Off3o3vuXi/tNhI3Zne0a96CmAwL6X1BxDbULqFQrcbNiSThMANry7tKC0zjDYt1fnR/wJgKPSZg2FJQzAr2WdBuoyHcVI47vfPEizKfCi/BaK2IDlGcMNlTuHSaZhkFh2/7vuphYJLvWIQgyeQgIaPSBYqvlUAhYIZyUlQJZqgUOdEZXNKR2mluVK0UqV4PgxEUeEW6ODUtpHHED671/lwj3zRftc+0c+F5oMcarBHAofJMHxP686oAwz4iT4YzpxUEgIvK4YzlWvSAIptfJtJOCXTc7KJacjk95HMpOPV1R3n4yTMcpzvuAqlcD7PnmsLVreqbMcgPvN7u2EbyEJhal2/7efP9kMhOLXrV7ngx6mIZqwA56ztZU81QamNpa4U9/5a8PHItUhRwRJY4Y6vLsgVLgn/Lz4sWZXtGAU3BJZW/dYrkPg+FlJYJQ4fW//7eq2y7ZUOoMxuCmOS60ivAcNyb3aKZrzCyTze3pT11QIdpdN+nVdRIwqr/DiWZ2CTKZw6rfsAG36tY1HJdoyCGz/kOcNACEeiSEQwrVR4yXiRvmVJnP7JX6hAPEkib2S2cAEOPfgIu8nrvRkbCMVIAyyqLs6rlDcv2UQuhzniENOLhFtiPVsl2zEKlsIr8xKGvmVEzuSzpELqFq6Ut94si/3caBo5YKWUcry7BiXk6vpuTv1Iq58nnonX9iOzk9KvhHAsVFPSuwziVbIdo7ANgqIRAQYcIuuWyEUXyakcybDjRBIEsc60BFQsREMVYmJ2nUbVNqw2LxHMU8Mco6JCZuNbpXgiirrZJlU5f6kO4QhxuD3OSJYPOT7ngEkl2zEKHsRqK8CRzhA7HiSmJBAm8aBoWvqKLAolOLtBqEhXRLNecaJ56SOLQeVENdm4gE0wHTlVOYNISJnsR27ob85h0xMtTreV5nFRUclKtmMUbABowpEomgwM62fK8Y2NnfqlIf3Y+FcCIvr6us1iXpDV8QxKTcwQOXQBG19FAIa0xUSUJn1Lo5IoUzjZ7/03p7Y91QDl2M9MUMl2jOLGS3cn29/7UtfCxBDBSqDEq5wRf8fxj2ROOYlS+kIcQoBTZiAPolhEycoCKUnYPkRDKU0rXJ5AMkIyopmpyOsOjKbkVHMTbACVbBUAnFnp1A2Jf2Z8WiskIzE5M8Id+Y2VKUqF4Anr6gkoYvUwmxCmQ2ZCrJQnb7ICLpqaqVtVtopFwTbijhFgpGUGrKgcqWhl64MpkXB9BZSSLjrZvqyHgfflkuCJIpPvzhOip+zmZC2GFL0P53cJAZPeNl7dfMRkAe0lK9kq8NDf2A8awA0THtRNwv6+OkCCJz6C6QlHykzTSIaug/LpcmJKAQ/kMz46qOQmXN9t++CI++Woev40UTVPOLJiTloATaZunN5qnuOR1OZaFbjpVbthB3BLI79D98uy2IFvxuUXUr8k+1XltHT+jIU/jUoCyPwwt6QDDiGOf6JG+srHL9GjOZcGNHLXovQ++brOz5xQla0CAMAD5TdZdFXOm5ESrWwNYAwpQkg9VjIMHccCDYk8Zi1O9LZXQh+dZLl+blZSrmaATKlFQOvzisR/g/hrveqmTck5oSpbBQAo1eKOykV1A+yQ0A4pbit1Y2ncnHfhSXhB6A2oEHMclkGrmto2Ss068xOEIflSVYtKOUHdZNn5ptfO5R1XslUAAA5ctMcRZABww+ABy7oyLTOz0iaEQzQlC4TrkE5DFXQACeHcb0o4SkjGag4BJCakJ1HS700pLYK6xfWQnzmgmpEVATxQ0cAWYGIYitu5WUm+gbIoDod9sY1lbIHiCMTUNSXTTEACFbH+zdXliZkYSOtmGPXtOklq531zslD/p3sJeAXriUyG9TnFSKqyVQTcuucCp2pGzMEGsE2/WRmWxqkcyzlpb/AYLBk7FIMGp+tRpTiQIjUbVY9xrZJ58CNTr042/P6qbBWLADdO0ahVKtJ6uQHQxl4Cvm2jU7HYEsMj1MUxpPKuXE0Qb47k/LwRs7u8UzxATENyCkdcUCnEdU1Azo4NgRKo9DmgKltFgtt+9YIw8aFXOW6cienSlIIpNfO9vYOSBYWLE9/73t5MVA6STFK8PNCiWp0UDy+RRpOvZ3tefW8q2So6uP1XLlCkygjWxMBJIFVOuI4pSdGUVKNjhfq3dZRtf3wcqCiaqEWOZKomFxkbKJlX88hqRlaUYaK5ltt3TH5woC5cnMI3yYimWjTvpLEvEWAkkghAnRJuFYglBA2KGMimx0hBuvRBEUmbjH6dgLmZkZVsFUVw48f0KBCOoEbjij0Ivufi/QCEcDLmCDdytt/W6uHVzVd2a/csJ5oadg9ExSBMMC3DNfKHkuznHwDCQiq2K9kqijj4/AvXfc4Nl+0O6494yX4Afh4A6RkexvOXXy9njDhJhkcwEyPpvJrFfVrdsuN7Ip8Jl7hf3eaBSraKueD61zniPfJF+8FNNCl1I+a010Cca6BoNmZ+X5dkPmCS+YVAlz1axWQ7Ubc5SVsNkFTMFW6MStWUS49N6cesTEZkRkoukx2XB2GSur2MkCEIg6Jc9c9dNx9tq8pWMXdwoxVNDzTErn6O4Eq5NyX71Cvz1RJzUlU7JPMBZNUFYZWViPkqQqpmZMUWhw+SBMKRb2LlTLkw46g36zSpMpXTBCuREqXACSLpEvKxGmYd84tCelQzsmLu+Oy+3Z0GzKwaL9sBwQ6BdoWkV0G2T1eeJ+uq4XOhqVjisxVMyWAyajMSmFs0spKtYiH47N7dqueA6qYzBNoh0A6FaIFwlPQ0COsZyTptMjXhsoBJqmpxteO7AXjwFftm/g6qGVmxMIRBhYxrV2kNxUkVs9YbeXCD3Vh6nVYogVSFgEjaU1wykRHOuWvRnAy+2xyev5KtYmEIgwrJCMVxCANK2yd6jCEOm8I2ysf69CJ8XZtx0ZFN0RCZiBoi+hQRvU+2TyGiTxDRASJ6FxGtSPo22T4g+3fOJ+sVWw03/tbubBwTGeukb1EmZGJKBnMyVifoagDkflvWHrPXf5sz1uOzvRhu4nqPywDsZ+aHAPgygPMk/TwAX5b0/XJcRQUA7XdNQTZZOoQrECz325KoJHUXAAnxFjHJxlRkI6ITAfwogLfKNgF4MoC/kEOuBvCTsv502Ybsf4ocX1GBm169OxCIc+KVFhUECSTrDNHgyea6AVlZ3Ow46eI7vrHhlHzEcyfdtMr2OgAXAWH6ggcA+Aozj2T7TgAnyPoJAO4AANn/VTk+ARGdT0TXEtG199xzz5HlvmJL4vOv3O0GFhqyI15ByQKRhkjNx7DO7hq+392AEzOys2QBFefnaSKiv3nXjDCRbET0YwDuZubrZnljZr6SmXcx867jjjtulpeu2AK4+df2pKQR8nEgYdwOaXJc0sdOKWTRpCz0DOjz4ZJmXXMg3DTK9gQAP0FEtwN4J5z5+HoA9yMiH808EcBdsn4XgJMAQPZ/K4AvzjDPFUcJDlyyx5mG+XB5hSXpGe79tgKZSj6aJljamDk9J3b5mQ8mko2ZX8bMJzLzTgBnA/goM58D4GMAniGHnQvgPbL+XtmG7P8o87wbwlRsVdxy0Z7oi01aTPYbVE4tJh3OAaafeMniyQiVNmNspJ7tYgDvJKJLAXwKwFWSfhWAPyGiAwC+BEfQiope3HrBHgDAg/bvjYmFOrewq2TqkTqHEYbLS+YAJ8RBfqhTvx2vM6dACW0G0dm1axdfe+21y85GxSbCKW/Y27svbcnPnR29A/p40gFdMofrqUsR49Y9F/Tmg4iuY+ZdvQdkqC1IKjYlbvtVV8h3vrEwFHhva5BUzQBkw9vFcf3HCZeuCpglakPkik2N21/40rJfVloaVuF+doMW+bC+jkYW6tkWUedWla1i00OPh7LjyivGHsth1GQdVpShzCGECwMOqQFJ9Hjkc0IlW8WWwsHzHfF2vPXymKgJ4wMcOem0CalMS5Dnl+5zk9W5zQjVjKzYkjj4CxcFUzE3HYO/ZWIrEX2MNi3zXgKd7RmiKlvFlsXB510c1nf84WUh4hjGOPCVZqJ0ZNNgvzctQ5WAxhx8tqpsFUcFDj734qhuXtl8sAOQ1iM9Kkc6eKJblMzWf6vKVnHU4OBznNLt+KPLorLJSMyxaUiiaQ5h7jh1seqzVVRMxsHnXOyUTteX6fXcl/PNtQq9BHa8eXz0cz2oylZx1OLgc5VPd5WPXnL06+BUzkUkUxljZX7OClXZKo4JHDzvoqIfF3sD5J1M475ZoZKt4phBQjgfSAHK45OExs6zC5JUslUcUzh4ntTP5aQrLf64GaGSreKYw8HnXZySaUz/tkq2iooN4uDzLo6k88TTi0rf8YezGSCuRiMrjm1Iw+Qiaj1bRcXscPA5F6c+XMGXIzObIElVtopjHgfPvQQAsOPq1wAAOqOczigiWclWUSHoVbAZmZPVjKyoENz+7JcV69uoKltFxexRJFZVtoqK2eO2c15eULbZXLuSraIiw23Pejlue9bL3VxyQrQHvfO3NnzdSraKih7c+syXx8rtGaD6bBUVEzCruu2qbBUVYzDLRiSVbBUVY3DL2b8GIp5J+L+SraJiAg787CsAAA9516Ubuk712SoqpoAn3EZQla2iYkGoZKuoWBAq2SoqFoRKtoqKBaGSraJiQZiKbER0OxF9log+TUTXStr9iejDRHSz/H6bpBMRvYGIDhDR9UR0+jwfoKJiq2A9yvZDzPwoNYfwJQA+wsynAviIbAPAjwA4VZbzAbxpVpmtqNjK2IgZ+XQAV8v61QB+UqX/MTt8HMD9iOj4DdynouKowLRkYwAfIqLriOh8SXsgM39B1v8NwANl/QQAd6hz75S0BER0PhFdS0TX3nPPPUeQ9YqKrYVpW5A8kZnvIqLvAPBhIvoXvZOZmdbZeIyZrwRwJQDs2rVrfhMZV1RsEkylbMx8l/zeDeCvATwWwL9781B+75bD7wJwkjr9REmrqDimMZFsRPQtRHRfvw7gqQA+B+C9AM6Vw84F8B5Zfy+An5eo5OMAfFWZmxUVxyymMSMfCOCvyfUPHwB4BzN/gIiuAfBuIjoPwEEAPyPHvx/A0wAcAPANAM+dea4rKrYgiDszdy8hE0T/BeCmZedjSnw7gP9YdiamwFbJJ7B18prncwczHzftyZuli81Nqv5uU4OIrt0Ked0q+QS2Tl43ms/aXKuiYkGoZKuoWBA2C9muXHYG1oGtktetkk9g6+R1Q/ncFAGSiopjAZtF2SoqjnosnWxEdBYR3SRdci6ZfMZc8/I2IrqbiD6n0jZlVyIiOomIPkZE/0xENxDRizdjfoloOxF9kog+I/n8dUk/hYg+Ifl5FxGtSPo22T4g+3cuIp8qvw0RfYqI3jfzfDLz0hYADYBbADwIwAqAzwB42BLz8yQApwP4nEq7HMAlsn4JgMtk/WkA/gZuHM/HAfjEgvN6PIDTZf2+AD4P4GGbLb9yv/vI+hDAJ+T+7wZwtqS/GcAvy/oLALxZ1s8G8K4Fv9c9AN4B4H2yPbN8LqVQqwd7PIAPqu2XAXjZkvO0MyPbTQCOl/Xj4eoEAeAPADyzdNyS8v0eAGdu5vwCuDeAfwJwBlzl8CAvBwA+CODxsj6Q42hB+TsRrm/mkwG8Tz4UM8vnss3IqbrjLBkb6kq0CIgJ82g41dh0+RXT7NNwjdU/DGfNfIWZR4W8hHzK/q8CeMAi8gngdQAuAmBl+wGzzOeyybalwO4ztqnCt0R0HwB/CeAlzPyfet9myS8zt8z8KDjleCyA05aboy6I6McA3M3M183rHssm21bojrNpuxIR0RCOaG9n5r+S5E2bX2b+CoCPwZlj9yMi31xQ5yXkU/Z/K4AvLiB7TwDwE0R0O4B3wpmSr59lPpdNtmsAnCoRnxU4R/O9S85Tjk3ZlYhcN4yrANzIzPs2a36J6Dgiup+s3wvOr7wRjnTP6Mmnz/8zAHxUFHquYOaXMfOJzLwTrhx+lJnPmWk+l+XQK6f0aXCRtFsA/NqS8/JnAL4AYA3OPj8Pzg7/CICbAfwvAPeXYwnAGyXfnwWwa8F5fSKciXg9gE/L8rTNll8AjwDwKcnn5wC8StIfBOCTcF2x/hzANknfLtsHZP+DllAOfhAxGjmzfNYWJBUVC8KyzciKimMGlWwVFQtCJVtFxYJQyVZRsSBUslVULAiVbBUVC0IlW0XFglDJVlGxIPx/beKAvuUQWCcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(t, origin='lower')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.model_selection import cross_validate, StratifiedKFold\n",
    "\n",
    "from mpstool.cv_metrics import brier_score, zero_one_score, balanced_linear_score\n",
    "\n",
    "from geone.img import readImageGslib, readPointSetGslib\n",
    "from geone.img import Img\n",
    "from geone.imgplot import drawImage2D\n",
    "from geone.deesseinterface import DeesseEstimator\n",
    "\n",
    "from geone import imgplot\n",
    "from geone.img import writePointSetGslib, readPointSetGslib, pointSetToImage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUTPUT_DIR = 'output/'\n",
    "DATA_DIR = 'data_roussillon/'\n",
    "SAMPLES_DIR = DATA_DIR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter selection (Roussillon)\n",
    "\n",
    "The datasets with 600 points. Need to run `2-results.py` which performs cross-validation and writes results to the csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_distanceThreshold</th>\n",
       "      <th>param_maxScanFraction</th>\n",
       "      <th>param_nneighboringNode</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_brier</th>\n",
       "      <th>split1_test_brier</th>\n",
       "      <th>...</th>\n",
       "      <th>std_test_brier</th>\n",
       "      <th>rank_test_brier</th>\n",
       "      <th>split0_test_skill_brier</th>\n",
       "      <th>split1_test_skill_brier</th>\n",
       "      <th>split2_test_skill_brier</th>\n",
       "      <th>split3_test_skill_brier</th>\n",
       "      <th>split4_test_skill_brier</th>\n",
       "      <th>mean_test_skill_brier</th>\n",
       "      <th>std_test_skill_brier</th>\n",
       "      <th>rank_test_skill_brier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000993</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>129.224922</td>\n",
       "      <td>0.462563</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>{'distanceThreshold': [0.12501, 0.1], 'maxScan...</td>\n",
       "      <td>-0.396292</td>\n",
       "      <td>-0.453594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049748</td>\n",
       "      <td>21</td>\n",
       "      <td>0.165015</td>\n",
       "      <td>0.044280</td>\n",
       "      <td>0.173004</td>\n",
       "      <td>0.330193</td>\n",
       "      <td>0.329941</td>\n",
       "      <td>0.208487</td>\n",
       "      <td>0.109249</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000976</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>126.292234</td>\n",
       "      <td>4.236635</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>{'distanceThreshold': [0.12501, 0.1], 'maxScan...</td>\n",
       "      <td>-0.411146</td>\n",
       "      <td>-0.450948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047465</td>\n",
       "      <td>22</td>\n",
       "      <td>0.133717</td>\n",
       "      <td>0.049855</td>\n",
       "      <td>0.209306</td>\n",
       "      <td>0.321237</td>\n",
       "      <td>0.313057</td>\n",
       "      <td>0.205434</td>\n",
       "      <td>0.104265</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>129.498730</td>\n",
       "      <td>0.648454</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>{'distanceThreshold': [0.12501, 0.1], 'maxScan...</td>\n",
       "      <td>-0.396156</td>\n",
       "      <td>-0.445260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043132</td>\n",
       "      <td>24</td>\n",
       "      <td>0.165300</td>\n",
       "      <td>0.061838</td>\n",
       "      <td>0.149871</td>\n",
       "      <td>0.308010</td>\n",
       "      <td>0.305741</td>\n",
       "      <td>0.198152</td>\n",
       "      <td>0.095536</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000901</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>124.915347</td>\n",
       "      <td>1.118605</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>{'distanceThreshold': [0.12501, 0.1], 'maxScan...</td>\n",
       "      <td>-0.407844</td>\n",
       "      <td>-0.467865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045956</td>\n",
       "      <td>28</td>\n",
       "      <td>0.140675</td>\n",
       "      <td>0.014211</td>\n",
       "      <td>0.097569</td>\n",
       "      <td>0.275324</td>\n",
       "      <td>0.273259</td>\n",
       "      <td>0.160208</td>\n",
       "      <td>0.101639</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000933</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>108.698390</td>\n",
       "      <td>0.544352</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>{'distanceThreshold': [0.25001, 0.1], 'maxScan...</td>\n",
       "      <td>-0.487073</td>\n",
       "      <td>-0.603667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050528</td>\n",
       "      <td>41</td>\n",
       "      <td>-0.026261</td>\n",
       "      <td>-0.271923</td>\n",
       "      <td>-0.182244</td>\n",
       "      <td>-0.044130</td>\n",
       "      <td>0.034688</td>\n",
       "      <td>-0.097974</td>\n",
       "      <td>0.112251</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.000993      0.000024       129.224922        0.462563   \n",
       "1       0.000976      0.000056       126.292234        4.236635   \n",
       "2       0.001001      0.000013       129.498730        0.648454   \n",
       "3       0.000901      0.000046       124.915347        1.118605   \n",
       "4       0.000933      0.000070       108.698390        0.544352   \n",
       "\n",
       "  param_distanceThreshold  param_maxScanFraction param_nneighboringNode  \\\n",
       "0          [0.12501, 0.1]                    0.1                 [8, 1]   \n",
       "1          [0.12501, 0.1]                    0.2                 [8, 1]   \n",
       "2          [0.12501, 0.1]                    0.4                 [8, 1]   \n",
       "3          [0.12501, 0.1]                    0.8                 [8, 1]   \n",
       "4          [0.25001, 0.1]                    0.1                 [8, 1]   \n",
       "\n",
       "                                              params  split0_test_brier  \\\n",
       "0  {'distanceThreshold': [0.12501, 0.1], 'maxScan...          -0.396292   \n",
       "1  {'distanceThreshold': [0.12501, 0.1], 'maxScan...          -0.411146   \n",
       "2  {'distanceThreshold': [0.12501, 0.1], 'maxScan...          -0.396156   \n",
       "3  {'distanceThreshold': [0.12501, 0.1], 'maxScan...          -0.407844   \n",
       "4  {'distanceThreshold': [0.25001, 0.1], 'maxScan...          -0.487073   \n",
       "\n",
       "   split1_test_brier  ...  std_test_brier  rank_test_brier  \\\n",
       "0          -0.453594  ...        0.049748               21   \n",
       "1          -0.450948  ...        0.047465               22   \n",
       "2          -0.445260  ...        0.043132               24   \n",
       "3          -0.467865  ...        0.045956               28   \n",
       "4          -0.603667  ...        0.050528               41   \n",
       "\n",
       "   split0_test_skill_brier  split1_test_skill_brier  split2_test_skill_brier  \\\n",
       "0                 0.165015                 0.044280                 0.173004   \n",
       "1                 0.133717                 0.049855                 0.209306   \n",
       "2                 0.165300                 0.061838                 0.149871   \n",
       "3                 0.140675                 0.014211                 0.097569   \n",
       "4                -0.026261                -0.271923                -0.182244   \n",
       "\n",
       "   split3_test_skill_brier  split4_test_skill_brier  mean_test_skill_brier  \\\n",
       "0                 0.330193                 0.329941               0.208487   \n",
       "1                 0.321237                 0.313057               0.205434   \n",
       "2                 0.308010                 0.305741               0.198152   \n",
       "3                 0.275324                 0.273259               0.160208   \n",
       "4                -0.044130                 0.034688              -0.097974   \n",
       "\n",
       "   std_test_skill_brier  rank_test_skill_brier  \n",
       "0              0.109249                     21  \n",
       "1              0.104265                     22  \n",
       "2              0.095536                     24  \n",
       "3              0.101639                     28  \n",
       "4              0.112251                     41  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_roussillon = pd.read_csv('df_roussillon.csv', index_col=0)\n",
    "df_roussillon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>param_distanceThreshold</th>\n",
       "      <th>param_maxScanFraction</th>\n",
       "      <th>param_nneighboringNode</th>\n",
       "      <th>mean_test_brier</th>\n",
       "      <th>std_test_brier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>182.169288</td>\n",
       "      <td>[0.06251, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.312848</td>\n",
       "      <td>0.046924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>178.729551</td>\n",
       "      <td>[0.06251, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.322479</td>\n",
       "      <td>0.032288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>294.807481</td>\n",
       "      <td>[0.06251, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.324254</td>\n",
       "      <td>0.034422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>191.765087</td>\n",
       "      <td>[0.03126, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.329104</td>\n",
       "      <td>0.034577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>172.108105</td>\n",
       "      <td>[0.06251, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.329350</td>\n",
       "      <td>0.033295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>179.712859</td>\n",
       "      <td>[0.03126, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.331606</td>\n",
       "      <td>0.032176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>379.288599</td>\n",
       "      <td>[0.03126, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.334727</td>\n",
       "      <td>0.028549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>303.787415</td>\n",
       "      <td>[0.03126, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.335873</td>\n",
       "      <td>0.028401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>184.619265</td>\n",
       "      <td>[0.06251, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.342706</td>\n",
       "      <td>0.036357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>175.666733</td>\n",
       "      <td>[0.06251, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.345396</td>\n",
       "      <td>0.033509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>166.648695</td>\n",
       "      <td>[0.06251, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.348100</td>\n",
       "      <td>0.024212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>200.547318</td>\n",
       "      <td>[0.06251, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.348108</td>\n",
       "      <td>0.031771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>153.660981</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.350577</td>\n",
       "      <td>0.049387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>148.764899</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.357394</td>\n",
       "      <td>0.042474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>157.138941</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.359087</td>\n",
       "      <td>0.040144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>166.542440</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.362927</td>\n",
       "      <td>0.047393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>148.335323</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.366960</td>\n",
       "      <td>0.037957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>138.964573</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.368385</td>\n",
       "      <td>0.044824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>142.032750</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.369960</td>\n",
       "      <td>0.033228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>143.954340</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.372442</td>\n",
       "      <td>0.044110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>129.224922</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.377581</td>\n",
       "      <td>0.049748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126.292234</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.379073</td>\n",
       "      <td>0.047465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>173.144793</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.382367</td>\n",
       "      <td>0.038330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>129.498730</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.382554</td>\n",
       "      <td>0.043132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>224.995641</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.385904</td>\n",
       "      <td>0.037625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>173.537677</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.387869</td>\n",
       "      <td>0.037976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>184.516076</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.391569</td>\n",
       "      <td>0.033112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124.915347</td>\n",
       "      <td>[0.12501, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.400656</td>\n",
       "      <td>0.045956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>139.913899</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.435233</td>\n",
       "      <td>0.055577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>134.462968</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.442785</td>\n",
       "      <td>0.052232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>128.964159</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.446444</td>\n",
       "      <td>0.047339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>123.821917</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.449944</td>\n",
       "      <td>0.046715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>138.845776</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.452956</td>\n",
       "      <td>0.060992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>144.769345</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.453115</td>\n",
       "      <td>0.061264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>133.612724</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.453208</td>\n",
       "      <td>0.058009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>128.693879</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.455029</td>\n",
       "      <td>0.048649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>154.765577</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.469546</td>\n",
       "      <td>0.057718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>185.178676</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.473910</td>\n",
       "      <td>0.060682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>160.404141</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.475631</td>\n",
       "      <td>0.060200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>157.583515</td>\n",
       "      <td>[0.18751, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.476631</td>\n",
       "      <td>0.061618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.698390</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.523931</td>\n",
       "      <td>0.050528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>105.861030</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.527288</td>\n",
       "      <td>0.041771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>124.993697</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.528775</td>\n",
       "      <td>0.065622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>130.972167</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.530942</td>\n",
       "      <td>0.064215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>115.933869</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.531215</td>\n",
       "      <td>0.055246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>121.209646</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.532308</td>\n",
       "      <td>0.064158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>117.182107</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.533085</td>\n",
       "      <td>0.057509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>121.255215</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.533573</td>\n",
       "      <td>0.065633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>104.609649</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.533606</td>\n",
       "      <td>0.048151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>109.625747</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.536517</td>\n",
       "      <td>0.062803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>111.953485</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.537027</td>\n",
       "      <td>0.065784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>142.548926</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.2</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.541694</td>\n",
       "      <td>0.062064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>139.871689</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.542852</td>\n",
       "      <td>0.058557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>146.866032</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.545202</td>\n",
       "      <td>0.062126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>144.654182</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.546458</td>\n",
       "      <td>0.054695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>103.107917</td>\n",
       "      <td>[0.25001, 0.1]</td>\n",
       "      <td>0.8</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.549852</td>\n",
       "      <td>0.047386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_score_time param_distanceThreshold  param_maxScanFraction  \\\n",
       "20       182.169288          [0.06251, 0.1]                    0.1   \n",
       "21       178.729551          [0.06251, 0.1]                    0.2   \n",
       "23       294.807481          [0.06251, 0.1]                    0.8   \n",
       "36       191.765087          [0.03126, 0.1]                    0.1   \n",
       "22       172.108105          [0.06251, 0.1]                    0.4   \n",
       "37       179.712859          [0.03126, 0.1]                    0.2   \n",
       "39       379.288599          [0.03126, 0.1]                    0.8   \n",
       "38       303.787415          [0.03126, 0.1]                    0.4   \n",
       "40       184.619265          [0.06251, 0.1]                    0.1   \n",
       "41       175.666733          [0.06251, 0.1]                    0.2   \n",
       "43       166.648695          [0.06251, 0.1]                    0.8   \n",
       "42       200.547318          [0.06251, 0.1]                    0.4   \n",
       "8        153.660981          [0.12501, 0.1]                    0.1   \n",
       "9        148.764899          [0.12501, 0.1]                    0.2   \n",
       "25       157.138941          [0.12501, 0.1]                    0.2   \n",
       "24       166.542440          [0.12501, 0.1]                    0.1   \n",
       "26       148.335323          [0.12501, 0.1]                    0.4   \n",
       "11       138.964573          [0.12501, 0.1]                    0.8   \n",
       "27       142.032750          [0.12501, 0.1]                    0.8   \n",
       "10       143.954340          [0.12501, 0.1]                    0.4   \n",
       "0        129.224922          [0.12501, 0.1]                    0.1   \n",
       "1        126.292234          [0.12501, 0.1]                    0.2   \n",
       "44       173.144793          [0.12501, 0.1]                    0.1   \n",
       "2        129.498730          [0.12501, 0.1]                    0.4   \n",
       "46       224.995641          [0.12501, 0.1]                    0.4   \n",
       "45       173.537677          [0.12501, 0.1]                    0.2   \n",
       "47       184.516076          [0.12501, 0.1]                    0.8   \n",
       "3        124.915347          [0.12501, 0.1]                    0.8   \n",
       "12       139.913899          [0.18751, 0.1]                    0.1   \n",
       "13       134.462968          [0.18751, 0.1]                    0.2   \n",
       "14       128.964159          [0.18751, 0.1]                    0.4   \n",
       "15       123.821917          [0.18751, 0.1]                    0.8   \n",
       "29       138.845776          [0.18751, 0.1]                    0.2   \n",
       "28       144.769345          [0.18751, 0.1]                    0.1   \n",
       "30       133.612724          [0.18751, 0.1]                    0.4   \n",
       "31       128.693879          [0.18751, 0.1]                    0.8   \n",
       "50       154.765577          [0.18751, 0.1]                    0.4   \n",
       "51       185.178676          [0.18751, 0.1]                    0.8   \n",
       "48       160.404141          [0.18751, 0.1]                    0.1   \n",
       "49       157.583515          [0.18751, 0.1]                    0.2   \n",
       "4        108.698390          [0.25001, 0.1]                    0.1   \n",
       "5        105.861030          [0.25001, 0.1]                    0.2   \n",
       "33       124.993697          [0.25001, 0.1]                    0.2   \n",
       "32       130.972167          [0.25001, 0.1]                    0.1   \n",
       "17       115.933869          [0.25001, 0.1]                    0.2   \n",
       "16       121.209646          [0.25001, 0.1]                    0.1   \n",
       "35       117.182107          [0.25001, 0.1]                    0.8   \n",
       "34       121.255215          [0.25001, 0.1]                    0.4   \n",
       "6        104.609649          [0.25001, 0.1]                    0.4   \n",
       "19       109.625747          [0.25001, 0.1]                    0.8   \n",
       "18       111.953485          [0.25001, 0.1]                    0.4   \n",
       "53       142.548926          [0.25001, 0.1]                    0.2   \n",
       "54       139.871689          [0.25001, 0.1]                    0.4   \n",
       "52       146.866032          [0.25001, 0.1]                    0.1   \n",
       "55       144.654182          [0.25001, 0.1]                    0.8   \n",
       "7        103.107917          [0.25001, 0.1]                    0.8   \n",
       "\n",
       "   param_nneighboringNode  mean_test_brier  std_test_brier  \n",
       "20                [32, 1]        -0.312848        0.046924  \n",
       "21                [32, 1]        -0.322479        0.032288  \n",
       "23                [32, 1]        -0.324254        0.034422  \n",
       "36                [64, 1]        -0.329104        0.034577  \n",
       "22                [32, 1]        -0.329350        0.033295  \n",
       "37                [64, 1]        -0.331606        0.032176  \n",
       "39                [64, 1]        -0.334727        0.028549  \n",
       "38                [64, 1]        -0.335873        0.028401  \n",
       "40                [64, 1]        -0.342706        0.036357  \n",
       "41                [64, 1]        -0.345396        0.033509  \n",
       "43                [64, 1]        -0.348100        0.024212  \n",
       "42                [64, 1]        -0.348108        0.031771  \n",
       "8                 [16, 1]        -0.350577        0.049387  \n",
       "9                 [16, 1]        -0.357394        0.042474  \n",
       "25                [32, 1]        -0.359087        0.040144  \n",
       "24                [32, 1]        -0.362927        0.047393  \n",
       "26                [32, 1]        -0.366960        0.037957  \n",
       "11                [16, 1]        -0.368385        0.044824  \n",
       "27                [32, 1]        -0.369960        0.033228  \n",
       "10                [16, 1]        -0.372442        0.044110  \n",
       "0                  [8, 1]        -0.377581        0.049748  \n",
       "1                  [8, 1]        -0.379073        0.047465  \n",
       "44                [64, 1]        -0.382367        0.038330  \n",
       "2                  [8, 1]        -0.382554        0.043132  \n",
       "46                [64, 1]        -0.385904        0.037625  \n",
       "45                [64, 1]        -0.387869        0.037976  \n",
       "47                [64, 1]        -0.391569        0.033112  \n",
       "3                  [8, 1]        -0.400656        0.045956  \n",
       "12                [16, 1]        -0.435233        0.055577  \n",
       "13                [16, 1]        -0.442785        0.052232  \n",
       "14                [16, 1]        -0.446444        0.047339  \n",
       "15                [16, 1]        -0.449944        0.046715  \n",
       "29                [32, 1]        -0.452956        0.060992  \n",
       "28                [32, 1]        -0.453115        0.061264  \n",
       "30                [32, 1]        -0.453208        0.058009  \n",
       "31                [32, 1]        -0.455029        0.048649  \n",
       "50                [64, 1]        -0.469546        0.057718  \n",
       "51                [64, 1]        -0.473910        0.060682  \n",
       "48                [64, 1]        -0.475631        0.060200  \n",
       "49                [64, 1]        -0.476631        0.061618  \n",
       "4                  [8, 1]        -0.523931        0.050528  \n",
       "5                  [8, 1]        -0.527288        0.041771  \n",
       "33                [32, 1]        -0.528775        0.065622  \n",
       "32                [32, 1]        -0.530942        0.064215  \n",
       "17                [16, 1]        -0.531215        0.055246  \n",
       "16                [16, 1]        -0.532308        0.064158  \n",
       "35                [32, 1]        -0.533085        0.057509  \n",
       "34                [32, 1]        -0.533573        0.065633  \n",
       "6                  [8, 1]        -0.533606        0.048151  \n",
       "19                [16, 1]        -0.536517        0.062803  \n",
       "18                [16, 1]        -0.537027        0.065784  \n",
       "53                [64, 1]        -0.541694        0.062064  \n",
       "54                [64, 1]        -0.542852        0.058557  \n",
       "52                [64, 1]        -0.545202        0.062126  \n",
       "55                [64, 1]        -0.546458        0.054695  \n",
       "7                  [8, 1]        -0.549852        0.047386  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted = df_roussillon.sort_values('rank_test_brier', ascending=True)\n",
    "df_sorted[['mean_score_time', 'param_distanceThreshold', 'param_maxScanFraction', 'param_nneighboringNode', 'mean_test_brier', 'std_test_brier']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_entries(df_original):\n",
    "    new_columns = {\n",
    "    \"param_distanceThreshold\" : \"t\",\n",
    "    \"param_maxScanFraction\" : \"f\",\n",
    "    \"param_nneighboringNode\" : \"n\",\n",
    "    \"mean_score_time\" : \"time\",\n",
    "    \"mean_test_brier\" : \"score\",\n",
    "    \"std_test_brier\" : \"score std\"\n",
    "    }\n",
    "\n",
    "    df = df_original.rename(columns=new_columns)\n",
    "    df['time'] = df['time'].apply(lambda x: x/40).round(1)\n",
    "    df['t'] = df['t'].apply(lambda x: eval(x)[0])\n",
    "    df['n'] = df['n'].apply(lambda x: eval(x)[0])\n",
    "    df['score'] = df['score'].round(2)\n",
    "    df[\"score std\"] = df[\"score std\"].round(2)\n",
    "    \n",
    "    columns = ['n', 't', 'f', 'time', 'score', 'score std']\n",
    "    return df[columns].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_entries(df_sorted).to_latex('figures/roussillon-table-ds.tex', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dsbc_roussillon = pd.read_csv('df_dsbc_roussillon.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>param_distanceThreshold</th>\n",
       "      <th>param_maxScanFraction</th>\n",
       "      <th>param_nneighboringNode</th>\n",
       "      <th>mean_test_brier</th>\n",
       "      <th>std_test_brier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>170.815525</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.020</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.287840</td>\n",
       "      <td>0.036715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>170.174281</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.292675</td>\n",
       "      <td>0.033997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>169.880360</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.294056</td>\n",
       "      <td>0.028436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>165.139261</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.200</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.294860</td>\n",
       "      <td>0.025568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>167.183764</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.080</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.294935</td>\n",
       "      <td>0.029452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>170.241511</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.010</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.297571</td>\n",
       "      <td>0.038998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>181.118044</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.080</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.297837</td>\n",
       "      <td>0.031636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>181.388542</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.298404</td>\n",
       "      <td>0.031788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>168.204787</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.100</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.298802</td>\n",
       "      <td>0.034168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>180.554313</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.300129</td>\n",
       "      <td>0.039439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>158.837191</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.020</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.301094</td>\n",
       "      <td>0.031496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>158.041348</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.600</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.301298</td>\n",
       "      <td>0.020769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>175.318592</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.200</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.301485</td>\n",
       "      <td>0.030849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>177.555068</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.008</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.301944</td>\n",
       "      <td>0.030814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>156.738076</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.302460</td>\n",
       "      <td>0.036495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>182.265508</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.020</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.303660</td>\n",
       "      <td>0.042695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>160.882223</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.400</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.304050</td>\n",
       "      <td>0.023648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>157.028821</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.305175</td>\n",
       "      <td>0.028412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>159.851040</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.010</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.306925</td>\n",
       "      <td>0.030586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>174.225206</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.400</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.307125</td>\n",
       "      <td>0.033011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>172.587358</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.006</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.308925</td>\n",
       "      <td>0.038526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>179.262564</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.100</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.310004</td>\n",
       "      <td>0.034500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>154.697596</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.080</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.310365</td>\n",
       "      <td>0.033956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>154.198860</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.100</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.312402</td>\n",
       "      <td>0.031864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>161.872601</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.008</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.312842</td>\n",
       "      <td>0.031123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>158.547811</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.006</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.313040</td>\n",
       "      <td>0.032348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>184.167873</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.006</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.313131</td>\n",
       "      <td>0.031906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>190.429099</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.600</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.314113</td>\n",
       "      <td>0.029926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>171.836242</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.004</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.314456</td>\n",
       "      <td>0.038861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>185.644413</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.008</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.315425</td>\n",
       "      <td>0.043329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>184.506969</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.010</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.315448</td>\n",
       "      <td>0.038716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>160.848449</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.004</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.316442</td>\n",
       "      <td>0.036023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>150.565777</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.200</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.320354</td>\n",
       "      <td>0.029031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>186.248812</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.004</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.320846</td>\n",
       "      <td>0.037119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>201.285045</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.100</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.322273</td>\n",
       "      <td>0.033776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>143.381268</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.600</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.324392</td>\n",
       "      <td>0.034339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>193.915991</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.326581</td>\n",
       "      <td>0.035242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>196.466847</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.080</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.327869</td>\n",
       "      <td>0.035335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>145.664968</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.400</td>\n",
       "      <td>[8, 1]</td>\n",
       "      <td>-0.328840</td>\n",
       "      <td>0.029735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>194.248749</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.329323</td>\n",
       "      <td>0.033037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>179.209399</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>[16, 1]</td>\n",
       "      <td>-0.334598</td>\n",
       "      <td>0.039660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>193.707791</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.200</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.335871</td>\n",
       "      <td>0.031147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>196.050711</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.020</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.336187</td>\n",
       "      <td>0.041792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>196.998267</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.010</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.337985</td>\n",
       "      <td>0.041320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>196.377844</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.008</td>\n",
       "      <td>[64, 1]</td>\n",
       "      <td>-0.338815</td>\n",
       "      <td>0.042884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>190.496307</td>\n",
       "      <td>[1e-05, 0.1]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>[32, 1]</td>\n",
       "      <td>-0.341419</td>\n",
       "      <td>0.037172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_score_time param_distanceThreshold  param_maxScanFraction  \\\n",
       "26       170.815525            [1e-05, 0.1]                  0.020   \n",
       "34       170.174281            [1e-05, 0.1]                  0.060   \n",
       "30       169.880360            [1e-05, 0.1]                  0.040   \n",
       "46       165.139261            [1e-05, 0.1]                  0.200   \n",
       "38       167.183764            [1e-05, 0.1]                  0.080   \n",
       "22       170.241511            [1e-05, 0.1]                  0.010   \n",
       "37       181.118044            [1e-05, 0.1]                  0.080   \n",
       "29       181.388542            [1e-05, 0.1]                  0.040   \n",
       "42       168.204787            [1e-05, 0.1]                  0.100   \n",
       "33       180.554313            [1e-05, 0.1]                  0.060   \n",
       "27       158.837191            [1e-05, 0.1]                  0.020   \n",
       "54       158.041348            [1e-05, 0.1]                  0.600   \n",
       "45       175.318592            [1e-05, 0.1]                  0.200   \n",
       "18       177.555068            [1e-05, 0.1]                  0.008   \n",
       "35       156.738076            [1e-05, 0.1]                  0.060   \n",
       "25       182.265508            [1e-05, 0.1]                  0.020   \n",
       "50       160.882223            [1e-05, 0.1]                  0.400   \n",
       "31       157.028821            [1e-05, 0.1]                  0.040   \n",
       "23       159.851040            [1e-05, 0.1]                  0.010   \n",
       "49       174.225206            [1e-05, 0.1]                  0.400   \n",
       "14       172.587358            [1e-05, 0.1]                  0.006   \n",
       "41       179.262564            [1e-05, 0.1]                  0.100   \n",
       "39       154.697596            [1e-05, 0.1]                  0.080   \n",
       "43       154.198860            [1e-05, 0.1]                  0.100   \n",
       "19       161.872601            [1e-05, 0.1]                  0.008   \n",
       "15       158.547811            [1e-05, 0.1]                  0.006   \n",
       "13       184.167873            [1e-05, 0.1]                  0.006   \n",
       "53       190.429099            [1e-05, 0.1]                  0.600   \n",
       "10       171.836242            [1e-05, 0.1]                  0.004   \n",
       "17       185.644413            [1e-05, 0.1]                  0.008   \n",
       "21       184.506969            [1e-05, 0.1]                  0.010   \n",
       "11       160.848449            [1e-05, 0.1]                  0.004   \n",
       "47       150.565777            [1e-05, 0.1]                  0.200   \n",
       "9        186.248812            [1e-05, 0.1]                  0.004   \n",
       "40       201.285045            [1e-05, 0.1]                  0.100   \n",
       "55       143.381268            [1e-05, 0.1]                  0.600   \n",
       "28       193.915991            [1e-05, 0.1]                  0.040   \n",
       "36       196.466847            [1e-05, 0.1]                  0.080   \n",
       "51       145.664968            [1e-05, 0.1]                  0.400   \n",
       "32       194.248749            [1e-05, 0.1]                  0.060   \n",
       "6        179.209399            [1e-05, 0.1]                  0.002   \n",
       "44       193.707791            [1e-05, 0.1]                  0.200   \n",
       "24       196.050711            [1e-05, 0.1]                  0.020   \n",
       "20       196.998267            [1e-05, 0.1]                  0.010   \n",
       "16       196.377844            [1e-05, 0.1]                  0.008   \n",
       "5        190.496307            [1e-05, 0.1]                  0.002   \n",
       "\n",
       "   param_nneighboringNode  mean_test_brier  std_test_brier  \n",
       "26                [16, 1]        -0.287840        0.036715  \n",
       "34                [16, 1]        -0.292675        0.033997  \n",
       "30                [16, 1]        -0.294056        0.028436  \n",
       "46                [16, 1]        -0.294860        0.025568  \n",
       "38                [16, 1]        -0.294935        0.029452  \n",
       "22                [16, 1]        -0.297571        0.038998  \n",
       "37                [32, 1]        -0.297837        0.031636  \n",
       "29                [32, 1]        -0.298404        0.031788  \n",
       "42                [16, 1]        -0.298802        0.034168  \n",
       "33                [32, 1]        -0.300129        0.039439  \n",
       "27                 [8, 1]        -0.301094        0.031496  \n",
       "54                [16, 1]        -0.301298        0.020769  \n",
       "45                [32, 1]        -0.301485        0.030849  \n",
       "18                [16, 1]        -0.301944        0.030814  \n",
       "35                 [8, 1]        -0.302460        0.036495  \n",
       "25                [32, 1]        -0.303660        0.042695  \n",
       "50                [16, 1]        -0.304050        0.023648  \n",
       "31                 [8, 1]        -0.305175        0.028412  \n",
       "23                 [8, 1]        -0.306925        0.030586  \n",
       "49                [32, 1]        -0.307125        0.033011  \n",
       "14                [16, 1]        -0.308925        0.038526  \n",
       "41                [32, 1]        -0.310004        0.034500  \n",
       "39                 [8, 1]        -0.310365        0.033956  \n",
       "43                 [8, 1]        -0.312402        0.031864  \n",
       "19                 [8, 1]        -0.312842        0.031123  \n",
       "15                 [8, 1]        -0.313040        0.032348  \n",
       "13                [32, 1]        -0.313131        0.031906  \n",
       "53                [32, 1]        -0.314113        0.029926  \n",
       "10                [16, 1]        -0.314456        0.038861  \n",
       "17                [32, 1]        -0.315425        0.043329  \n",
       "21                [32, 1]        -0.315448        0.038716  \n",
       "11                 [8, 1]        -0.316442        0.036023  \n",
       "47                 [8, 1]        -0.320354        0.029031  \n",
       "9                 [32, 1]        -0.320846        0.037119  \n",
       "40                [64, 1]        -0.322273        0.033776  \n",
       "55                 [8, 1]        -0.324392        0.034339  \n",
       "28                [64, 1]        -0.326581        0.035242  \n",
       "36                [64, 1]        -0.327869        0.035335  \n",
       "51                 [8, 1]        -0.328840        0.029735  \n",
       "32                [64, 1]        -0.329323        0.033037  \n",
       "6                 [16, 1]        -0.334598        0.039660  \n",
       "44                [64, 1]        -0.335871        0.031147  \n",
       "24                [64, 1]        -0.336187        0.041792  \n",
       "20                [64, 1]        -0.337985        0.041320  \n",
       "16                [64, 1]        -0.338815        0.042884  \n",
       "5                 [32, 1]        -0.341419        0.037172  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dsbc_sorted = df_dsbc_roussillon.sort_values('rank_test_brier', ascending=True)\n",
    "df_dsbc_sorted[['mean_score_time', 'param_distanceThreshold', 'param_maxScanFraction', 'param_nneighboringNode', 'mean_test_brier', 'std_test_brier']].head(-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_entries(df_dsbc_sorted).drop(columns=['t']).to_latex('figures/roussillon-table-dsbc.tex', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff0cfc0c2e0>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_roussillon['mean_score_time'], df_roussillon['mean_test_brier'])\n",
    "plt.scatter(df_dsbc_roussillon['mean_score_time'], df_dsbc_roussillon['mean_test_brier'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3.54330709,3.54330709))\n",
    "ax.scatter(x=df_roussillon['mean_score_time']/40, y=-df_roussillon['mean_test_brier'])\n",
    "ax.scatter(x=df_dsbc_roussillon['mean_score_time']/40, y=-df_dsbc_roussillon['mean_test_brier'], marker='x')\n",
    "ax.set(xlim=[0,10], ylim=[0.25,0.6])\n",
    "ax.set_xticks([0,10])\n",
    "ax.set_yticks([0.25,0.6])\n",
    "ax.set_xticklabels(['0 s','10 s'], fontsize=14)\n",
    "ax.set_yticklabels(['0.25','0.6'], fontsize=14)\n",
    "ax.legend(['DS', 'DSBC'], fontsize=14)\n",
    "ax.set_title('A', fontsize=14)\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'figures/roussillon-scatter.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3.54330709,3.54330709))\n",
    "ax.hist((-df_roussillon['mean_test_brier'])*df_roussillon['mean_score_time']/40, bins=np.linspace(0,4,10), alpha=0.5)\n",
    "ax.hist((-df_dsbc_roussillon['mean_test_brier'])*df_dsbc_roussillon['mean_score_time']/40, bins=np.linspace(0,4,10), alpha=0.5)\n",
    "ax.set(xlim=[0,4], ylim=[0, 40])\n",
    "ax.set_xticks([0,4])\n",
    "ax.set_yticks([0, 40])\n",
    "ax.set_xticklabels(['0 s','4 s'], fontsize=14)\n",
    "ax.set_yticklabels(['0','40'], fontsize=14)\n",
    "ax.legend(['DS', 'DSBC'], fontsize=14)\n",
    "ax.set_title('B', fontsize=14)\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'figures/roussillon-histogram.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trend = readImageGslib('./data_roussillon/trend.gslib')\n",
    "plt.figure(figsize=(3.54330709,3.54330709))\n",
    "imgplot.drawImage2D(trend,\n",
    "                cmap='cividis',\n",
    "                excludedVal=-9999999,\n",
    "                cticks=[0,1],\n",
    "                vmin=0, vmax=1,\n",
    "                cticklabels_fontsize=14,\n",
    "                xticklabels_fontsize=14,\n",
    "                yticklabels_fontsize=14,\n",
    "                xticks=[0,407],\n",
    "                yticks=[0,504],\n",
    "                title='A',\n",
    "                title_fontsize=14\n",
    "                \n",
    "               )\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'figures/roussillon-ti-A.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orientation = readImageGslib('./data_roussillon/orientation.gslib')\n",
    "orientation.val = ((orientation.val + 2000) * (trend.val != -9999999) -2000)\n",
    "plt.figure(figsize=(3.54330709,3.54330709))\n",
    "imgplot.drawImage2D(orientation,\n",
    "                cmap='twilight_shifted',\n",
    "                excludedVal=-2000,\n",
    "                cticks=[-180,0,180],\n",
    "                cticklabels=['$-180^\\circ$','$0^\\circ$', '$180^\\circ$'],\n",
    "                vmin=-180, vmax=180,\n",
    "                cticklabels_fontsize=14,\n",
    "                xticklabels_fontsize=14,\n",
    "                yticklabels_fontsize=14,\n",
    "                xticks=[0,407],\n",
    "                yticks=[0,504],\n",
    "                title='B',\n",
    "                title_fontsize=14\n",
    "                \n",
    "               )\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'figures/roussillon-ti-B.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ti = readImageGslib('./data_roussillon/trueTI.gslib')\n",
    "plt.figure(figsize=(3.54330709,3.54330709))\n",
    "imgplot.drawImage2D(ti,\n",
    "                categ=True,\n",
    "                cmap=plt.cm.get_cmap('Oranges', 4),\n",
    "                cticklabels=['af', 'fp', 's', 'rb'],\n",
    "                cticklabels_fontsize=14,\n",
    "                xticklabels_fontsize=14,\n",
    "                yticklabels_fontsize=14,\n",
    "                xticks=[0,100],\n",
    "                yticks=[0,150],\n",
    "                title='C',\n",
    "                title_fontsize=14\n",
    "                \n",
    "               )\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'figures/roussillon-ti-C.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANUAAAD3CAYAAACKJBG+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAALK0lEQVR4nO3dXaxl5xjA8f+z9plWo6hk6KAahDSohLYSRZsGpb6uXIi7uilK4yvpBS5KhESkIW4YkYwUIfFZElXiM/VRJoShPkJ9lXZMG6VNZzpz9uNirXPOnj3nnL1nPHuvPXv+v2QlZ631rr3ePft95n3Xu5+1dmQmkuo0fVdAWjYGlVTMoJKKGVRSMYNKKmZQScUMKqmYQbWgImJPRGS3HI6I/RHxnYh4Y0Ts6Lt+2ppBtdi+BTwGeALwYuCrwLuBH0TEQ3usl7ZhUC22Q5l5Z2bekZm/yMzrgcuAC4Br+62atmJQnWQycx9wE/CqvuuizRlUJ6ffAE/quxLanEF1cgrATOgFZVCdnJ4G/KnvSmhzBtVJJiLOB64APt93XbS5lb4roG2dHhG7aP/zexTwQuAdwF7gg31WTFszqBbbi4B/AqvAv4F9wHXA7sx8sL9qaTvhnb9SLa+ppGJTBVVEXBoRN0bEHV0u2pVj+0fz1NaWH4+VOT0iPhIRByLi/u71zil8L1KpSe1+K9P2VGfSjuffDDywRZm1PLW15WVj+z9EmwXwGuAS4OHA1yJiMGUdpHmbpt0f47ivqSLiPuBNmblnZNseYGdmvmKLYx4B/At4bWZ+utv2eOAvwEsz8xvHVQlpzjZr91upnP17fkTsp52l+h7wzszc3+27ENgB3LxWODP/FhG3Ac8FjgmqiLgKuKpdGVzIaQ9b29MuEaOlu/UYOX6jG45uPUb2weh6jq2P7D+m7Ggdj6pBW42u5KRyR7/m0QWOemfdAbHF/vX1beuZo8XXj9m6Xnn0edbPsfHvtPE6uWWZo/az9hmMls+x8tnVa+2Y7P5u1//y90McuOfIUW9/M1e84Kw8cPeRScXY+8v7fw0cHNm0OzN3Tzxwgqqgugn4InA77W0K7wW+HREXZuYhYBfttPCBsePu6vYdo3tzuwHi9LOScy5rdwx2QKxA091StPZ3M7oNTm/gjKb9MHYEnNYkgy7KdjQwiGSlW18ZJE2wvt4EDLrya8cMmrXtXZkGBk2sN8xBs7FtbT2iXW/Wj4n2NQaxfp6mifVjoonuPLFRvlvWyjTNRl0AmsFm9cpj1pvR99Hk2LZj19fKtK+RDAZHr680w7EyQwaDpGmG3WsMWRm0Zdr11fa4wepG+WZ1vfzKYJXB4AhNM2SlOdKVWWVlcJimW3/OK/cd0042c+Duw/z0pvMnlmsee+vBzLxoqhc9DiVBlZmfHVn9VUTspR3avZw22KT56vGroplMqWfmP4C/A0/pNt0JDICdY0XP7vZJdRJymBOXWZlJUEXETuBxtNkA0KbVHAYuHylzDvBU4IezqINObZnNxGVWphr+RcSZwJO71QY4NyKeCdzTLdcBX6ANoicA7wf2A18CyMx7I+ITwAe6yYy7geuBX9JOxUtlkigJmu3afWb+davjpj3zRcDPu+UM2uck/Bx4D+0ExDOArwC/Bz4J/A64ODP/O/Iab6ENss8BtwD3Aa/MzNUp6yBNKVgdrkxcprBdu9/SVK+cmd9lbCZ3zEumeI1DwDXdIs1OUtJTTdHuN2WWupZOApnHHQtlDCotoWB12F/2m0GlpTTL2b1JDCotnUyHf1KxMKikakOvqaQ6zv5J1TIYGlRSrWE6/JPKOPyTyjn7J9VKGA798lcqZU8lFUqc/ZPKOfyTKpn7J9VySl0q5zWVVM6eSipmUEmFvElRmoHVoUElFTL3TyrllLpULXFKXapmTyUVM6ikQkkwdPZPKpS9/pCiQaXl5PBPKpTAqkEl1bKnkooZVFIlJyqkWk6pSzPg8E8q1uPoz6DS8snE4Z9UzeGfVMzZP6mY91NJhdLb6aVifvkr1XP2TypmTyUVStrrqr4YVFpK9lRSJTMqpFoJDO2ppFpeU0nFhsP+zm1QafmkGRVSqfYHCvo7v0GlpWRCrVTMnkqqZEKtVCvxy1+pnA9+kYo5/JMK+TQlaQYc/knFHP5JxQwqqZBT6tIMeE0lVTKjQqrl71NJM+DwTyrm8E+qlN5OL5Xyzl9pBnyaklQsHf5JtRz+ScWcUpcKpRkVUj2n1KViDv+kYg7/pEJpRoVUz+GfVMzhn1TI2+mlGXD4JxVz+CdVShj2+EvaBpWWTuLwTyrn8E8qZlBJxQwqqZK3fki1fPCLNAPDHqPKoNJSsqeSihlUUqHMJB3+SbUMKqmYwz+pmLN/UjV7KqmOWepSNe+nkuo5USEVyx4HgAaVlo4JtdIMGFRSJdOUpHr2VFKhBHqcUTeotJwc/knFHP5JlXzwi1TPoJIKtV/+ek0llXL2Tyrm8E8q5v1UUqWeM2oNKi0dMyqkGfCaSipmUEmF2uGf11RSHdOUpHoGlVTMoJKKGVRSIZ9QK1VLGA77O71BpSXk05SkUg7/pBlwokIqZkKtVMyeSiqUpilJ9Rz+ScXsqaRiTqlLhdKMCqmewz+pmMM/qVDi8E8qZ08lFfOaSqpkRoVUyyfUSjNgTyUVc6JCKpTAqlPqUiEnKqR6Dv+kUsFwGL2d3aDS0vFpStIM+D2VVMygkgplGlRSOafUpUIJrKazf1IpeyqpmNdUUjG/p5IKZcKqGRVSrR6T1A0qLR/v/JWKJXDE+6mkWkO8ppLqmKYk1UrgsEEl1WnTlPo7v0GlpZPAEYNKqpPAIRNqpUI+TUmahf6iyqDScrKnkorlam+nNqi0hPq9qDKotKT6S/4zqLR8MmF4pLfTG1RaUvZUUqGENKikWs7+SZXsqaR6BpVUKNPhn1Sux56qmfcJI+LqiLg9Ig5GxN6IuGTeddApIIeTlymcSHuda1BFxKuBDwPvA54F/BD4ekScO896aNnllMv2TrS9zrunehuwJzM/npm3ZeY1wD+BN8y5HlpqXUbFpGWyE2qvcwuqiDgNuBC4eWzXzcBz51UPnQKSbrJiwrKN/6e9znOiYicwAO4a234X8KLxwhFxFXBVt3qIP3553/Gc7GC3aKmcN1WpB+/9Bn+6cecUJR8SET8bWd+dmbu7v4+rvY5a2Nm/7s3tBoiIn2XmRT1XST0bC4AtZeYVs67LduZ5TXUAWAXOHtt+NnDnHOshTeOE2+vcgiozHwT2ApeP7bqcdlZFWhj/T3ud9/DveuCGiLgVuAV4PfBY4KMTjts9Yb9ODfNuByfUXiPnfNtxRFwNXAs8BtgHvDUzvz/XSkhTOpH2Ovegkpbd3NOUpGVnUEnFFjqoTL5dbhFxaUTcGBF3RERGxJVj+yMirouIf0TEAxHx3Yh4+liZR0bEDRFxb7fcEBFnzfN9jFvYoDL59pRwJu3F/5uBBzbZfy3wduAa4NnAfuCbEfGwkTKfAS4AruiWC4AbZljnyTJzIRfgJ8DHx7b9AXh/33VzmcnnfR9w5ch60CavvnNk2xnAf4HXdetPpc30e95Imed3287r670sZE9l8q2AJwK7GGkDmfkA8H022sDFtME4+mXsLcD99NhOFjKo2D6Zcdf8q6MerH3O27WBXcC/suuiALq/99NjO1nUoJJOWosaVCbfau1z3q4N3Ak8KiLWfzax+/vR9NhOFjKo0uRbwe20gbHeBiLiIcAlbLSBH9HOIF48ctzFwEPpsZ0s7P1UnHjyrU4SEXEm8ORutQHOjYhnAvdk5l8j4kPAOyLit8DvgXfRTkx8BiAzb4uIm4CPdTe1AnwM+Fpm/m5+72RM31OpE6ZZrwb+DByi7bku7btOLqWf72Vs/kSWPd3+AK6jnVo/CHwPOH/sNR4JfAr4T7d8Cjirz/dlQq1UbCGvqaSTmUElFTOopGIGlVTMoJKKGVRSMYNKKmZQScX+BwSe3AHm7HTyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.54330709,3.54330709))\n",
    "imgplot.drawImage2D(ti,\n",
    "                categ=False,\n",
    "                cmap='cividis',\n",
    "                cticks=[0,1],\n",
    "                vmax=1,\n",
    "                cticklabels_fontsize=14,\n",
    "                xticklabels_fontsize=14,\n",
    "                yticklabels_fontsize=14,\n",
    "                xticks=[0,100],\n",
    "                yticks=[0,150],\n",
    "                title='D',\n",
    "                title_fontsize=14,\n",
    "                iv=1 \n",
    "               )\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'figures/roussillon-ti-D.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[276.5, 306.5, 343.5, ..., 333.5, 218.5, 270.5],\n",
       "       [157.5, 299.5, 234.5, ..., 126.5, 162.5, 284.5],\n",
       "       [  0.5,   0.5,   0.5, ...,   0.5,   0.5,   0.5],\n",
       "       [  1. ,   3. ,   1. , ...,   1. ,   1. ,   1. ]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observations.val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "pcm = ax.scatter(observations.val[0,:],\n",
    "           observations.val[1,:],\n",
    "           c=observations.val[3,:],\n",
    "           cmap=plt.cm.get_cmap('Oranges', 4),\n",
    "           s=5,)\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "ax.set(xlim=[0,407], ylim=[0,504], xticks=[0,407], yticks=[0,504])\n",
    "ax.tick_params(labelsize=14)\n",
    "#ax.set_title('observations', fontsize=14)\n",
    "cbar = fig.colorbar(pcm, ax=ax, ticks=np.arange(4)*3/4 + 3/8)\n",
    "cbar.ax.tick_params(labelsize=14)\n",
    "cbar.ax.set_yticklabels(['af', 'fp', 's', 'rb'])\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'figures/roussillon-observations.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "from geone.deesseinterface import DeesseClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = 'data_roussillon/'\n",
    "ti_true = readImageGslib(DATA_DIR+'trueTI.gslib')\n",
    "mask = readImageGslib(DATA_DIR+'mask.gslib')\n",
    "trend = readImageGslib(DATA_DIR+'trend.gslib')\n",
    "im_angle = readImageGslib(DATA_DIR+'orientation.gslib')\n",
    "nx, ny, nz = mask.nx, mask.ny, mask.nz      # number of cells\n",
    "sx, sy, sz = mask.sx, mask.sy, mask.sz      # cell unit\n",
    "ox, oy, oz = mask.ox, mask.oy, mask.oz      # origin (corner of the \"first\" grid cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd = readPointSetGslib('data_roussillon/roussillon_observations_600.gslib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "deesse_input = geone.deesseinterface.DeesseInput(\n",
    "    nx=nx, ny=ny, nz=nz,\n",
    "    sx=sx, sy=sy, sz=sz,\n",
    "    ox=ox, oy=oy, oz=oz,\n",
    "    nv=2, varname=['Facies', 'trend'],\n",
    "    nTI=1, TI=ti_true,\n",
    "    mask=mask.val,\n",
    "    rotationUsage=1,            # use rotation without tolerance\n",
    "    rotationAzimuthLocal=True,  #    rotation according to azimuth: local\n",
    "    rotationAzimuth=im_angle.val[0,:,:,:],      #    rotation azimuth: map of values\n",
    "    dataImage=trend,\n",
    "    dataPointSet=hd,\n",
    "    outputVarFlag=[True, False],\n",
    "    distanceType=[0,1],\n",
    "    nneighboringNode=[32,1],\n",
    "    distanceThreshold=[0.06251, 0.1],\n",
    "    maxScanFraction=0.1,\n",
    "    npostProcessingPathMax=1,\n",
    "    seed=20191201,\n",
    "    nrealization=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_example = geone.deesseinterface.deesseRun(deesse_input,\n",
    "                                             verbose=0)['sim'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsbc_input = geone.deesseinterface.DeesseInput(\n",
    "    nx=nx, ny=ny, nz=nz,\n",
    "    sx=sx, sy=sy, sz=sz,\n",
    "    ox=ox, oy=oy, oz=oz,\n",
    "    nv=2, varname=['Facies', 'trend'],\n",
    "    nTI=1, TI=ti_true,\n",
    "    mask=mask.val,\n",
    "    rotationUsage=1,            # use rotation without tolerance\n",
    "    rotationAzimuthLocal=True,  #    rotation according to azimuth: local\n",
    "    rotationAzimuth=im_angle.val[0,:,:,:],      #    rotation azimuth: map of values\n",
    "    dataImage=trend,\n",
    "    dataPointSet=hd,\n",
    "    outputVarFlag=[True, False],\n",
    "    distanceType=[0,1],\n",
    "    nneighboringNode=[16,1],\n",
    "    distanceThreshold=[1e-5, 0.1],\n",
    "    maxScanFraction=0.02,\n",
    "    npostProcessingPathMax=1,\n",
    "    seed=20191201,\n",
    "    nrealization=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsbc_example = geone.deesseinterface.deesseRun(dsbc_input,\n",
    "                                             verbose=0)['sim'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_example(example, title):\n",
    "    plt.figure(figsize=(3.54330709,3.54330709))\n",
    "    imgplot.drawImage2D(example,\n",
    "                categ=True,\n",
    "                cmap=plt.cm.get_cmap('Oranges', 4),\n",
    "                cticklabels=['af', 'fp', 's', 'rb'],\n",
    "                cticklabels_fontsize=14,\n",
    "                xticklabels_fontsize=14,\n",
    "                yticklabels_fontsize=14,\n",
    "                xticks=[0,407],\n",
    "                yticks=[0,504],\n",
    "                title=title,\n",
    "                title_fontsize=14\n",
    "                \n",
    "               )\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'figures/roussillon-ex-{title}.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_example(ds_example, 'A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_example(dsbc_example, 'B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def best_results_for_each_TI(nsamples, score, score_name):\n",
    "    df = pd.read_csv(OUTPUT_DIR+'roussillon_observations_{}.csv'.format(nsamples))\n",
    "    info = ['param_TI',\n",
    "        'param_distanceThreshold',\n",
    "        'param_maxScanFraction',\n",
    "        'param_nneighboringNode',\n",
    "        ]\n",
    "    ref = reference_score(observation_filename=SAMPLES_DIR+'roussillon_observations_{}.gslib'.format(nsamples),\n",
    "                          score=score, varname='Facies_real00000')\n",
    "    df['ref_score'] = ref\n",
    "    df1 = df[df['param_TI'] == 'data/trueTI.gslib'].sort_values('mean_test_'+score_name,ascending=False).head(1)\n",
    "    df2 = df[df['param_TI'] == 'data/analogTI.gslib'].sort_values('mean_test_'+score_name,ascending=False).head(1)\n",
    "    return df1.append(df2, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "info = ['param_TI',\n",
    "        'param_distanceThreshold',\n",
    "        'param_maxScanFraction',\n",
    "        'param_nneighboringNode',\n",
    "        'mean_test_score',\n",
    "        'score_method',\n",
    "        'nsamples',\n",
    "        'ref_score'\n",
    "       ]\n",
    "\n",
    "df_best_roussillon = pd.DataFrame()\n",
    "for nsamples in [50, 150, 600]:\n",
    "    for score in [(brier_score, 'brier'), (zero_one_score, 'zero_one'), (balanced_linear_score, 'linear')]:\n",
    "        df = best_results_for_each_TI(nsamples, score[0], score[1])\n",
    "        df['mean_test_score'] = df['mean_test_{}'.format(score[1])]\n",
    "        df['score_method'] = score[1]\n",
    "        df['nsamples'] = nsamples\n",
    "        df_best_roussillon = df_best_roussillon.append(df[info],ignore_index=True)\n",
    "df_best_roussillon  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_columns = {\n",
    "    \"param_TI\" : \"TI\",\n",
    "    \"param_distanceThreshold\" : \"t\",\n",
    "    \"param_maxScanFraction\" : \"f\",\n",
    "    \"param_nneighboringNode\" : \"n\",\n",
    "    \"mean_test_score\" : \"score\",\n",
    "    \"ref_score\" : \"reference\",   \n",
    "    \"score_method\" : \"function\",\n",
    "    \"nsamples\" : \"wells\",\n",
    "}\n",
    "\n",
    "df_renamed = df_original.rename(columns=new_columns)\n",
    "df_renamed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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