ResBS64_training1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8253b39-8c67-49f1-96a6-fee731e689cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n",
      "Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.\n",
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/mathiane/miniconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - nb_conda\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    argon2-cffi-20.1.0         |   py38h27cfd23_1          46 KB\n",
      "    ca-certificates-2021.5.25  |       h06a4308_1         112 KB\n",
      "    conda-4.10.1               |   py38h06a4308_1         2.9 MB\n",
      "    importlib-metadata-3.10.0  |   py38h06a4308_0          33 KB\n",
      "    ipython-7.22.0             |   py38hb070fc8_0         982 KB\n",
      "    jupyter_core-4.7.1         |   py38h06a4308_0          68 KB\n",
      "    markupsafe-2.0.1           |   py38h27cfd23_0          22 KB\n",
      "    nb_conda-2.2.1             |           py38_1          33 KB\n",
      "    nb_conda_kernels-2.3.1     |   py38h06a4308_0          28 KB\n",
      "    notebook-6.4.0             |   py38h06a4308_0         4.1 MB\n",
      "    pygments-2.9.0             |     pyhd3eb1b0_0         721 KB\n",
      "    terminado-0.9.4            |   py38h06a4308_0          25 KB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         9.0 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  argon2-cffi        pkgs/main/linux-64::argon2-cffi-20.1.0-py38h27cfd23_1\n",
      "  async_generator    pkgs/main/noarch::async_generator-1.10-pyhd3eb1b0_0\n",
      "  attrs              pkgs/main/noarch::attrs-21.2.0-pyhd3eb1b0_0\n",
      "  backcall           pkgs/main/noarch::backcall-0.2.0-pyhd3eb1b0_0\n",
      "  bleach             pkgs/main/noarch::bleach-3.3.0-pyhd3eb1b0_0\n",
      "  decorator          pkgs/main/noarch::decorator-5.0.9-pyhd3eb1b0_0\n",
      "  defusedxml         pkgs/main/noarch::defusedxml-0.7.1-pyhd3eb1b0_0\n",
      "  entrypoints        pkgs/main/linux-64::entrypoints-0.3-py38_0\n",
      "  importlib-metadata pkgs/main/linux-64::importlib-metadata-3.10.0-py38h06a4308_0\n",
      "  importlib_metadata pkgs/main/noarch::importlib_metadata-3.10.0-hd3eb1b0_0\n",
      "  ipykernel          pkgs/main/linux-64::ipykernel-5.3.4-py38h5ca1d4c_0\n",
      "  ipython            pkgs/main/linux-64::ipython-7.22.0-py38hb070fc8_0\n",
      "  ipython_genutils   pkgs/main/noarch::ipython_genutils-0.2.0-pyhd3eb1b0_1\n",
      "  jedi               pkgs/main/linux-64::jedi-0.17.0-py38_0\n",
      "  jinja2             pkgs/main/noarch::jinja2-3.0.0-pyhd3eb1b0_0\n",
      "  jsonschema         pkgs/main/noarch::jsonschema-3.2.0-py_2\n",
      "  jupyter_client     pkgs/main/noarch::jupyter_client-6.1.12-pyhd3eb1b0_0\n",
      "  jupyter_core       pkgs/main/linux-64::jupyter_core-4.7.1-py38h06a4308_0\n",
      "  jupyterlab_pygmen~ pkgs/main/noarch::jupyterlab_pygments-0.1.2-py_0\n",
      "  libsodium          pkgs/main/linux-64::libsodium-1.0.18-h7b6447c_0\n",
      "  markupsafe         pkgs/main/linux-64::markupsafe-2.0.1-py38h27cfd23_0\n",
      "  mistune            pkgs/main/linux-64::mistune-0.8.4-py38h7b6447c_1000\n",
      "  nb_conda           pkgs/main/linux-64::nb_conda-2.2.1-py38_1\n",
      "  nb_conda_kernels   pkgs/main/linux-64::nb_conda_kernels-2.3.1-py38h06a4308_0\n",
      "  nbclient           pkgs/main/noarch::nbclient-0.5.3-pyhd3eb1b0_0\n",
      "  nbconvert          pkgs/main/linux-64::nbconvert-6.0.7-py38_0\n",
      "  nbformat           pkgs/main/noarch::nbformat-5.1.3-pyhd3eb1b0_0\n",
      "  nest-asyncio       pkgs/main/noarch::nest-asyncio-1.5.1-pyhd3eb1b0_0\n",
      "  notebook           pkgs/main/linux-64::notebook-6.4.0-py38h06a4308_0\n",
      "  packaging          pkgs/main/noarch::packaging-20.9-pyhd3eb1b0_0\n",
      "  pandoc             pkgs/main/linux-64::pandoc-2.12-h06a4308_0\n",
      "  pandocfilters      pkgs/main/linux-64::pandocfilters-1.4.3-py38h06a4308_1\n",
      "  parso              pkgs/main/noarch::parso-0.8.2-pyhd3eb1b0_0\n",
      "  pexpect            pkgs/main/noarch::pexpect-4.8.0-pyhd3eb1b0_3\n",
      "  pickleshare        pkgs/main/noarch::pickleshare-0.7.5-pyhd3eb1b0_1003\n",
      "  prometheus_client  pkgs/main/noarch::prometheus_client-0.10.1-pyhd3eb1b0_0\n",
      "  prompt-toolkit     pkgs/main/noarch::prompt-toolkit-3.0.17-pyh06a4308_0\n",
      "  ptyprocess         pkgs/main/noarch::ptyprocess-0.7.0-pyhd3eb1b0_2\n",
      "  pygments           pkgs/main/noarch::pygments-2.9.0-pyhd3eb1b0_0\n",
      "  pyparsing          pkgs/main/noarch::pyparsing-2.4.7-pyhd3eb1b0_0\n",
      "  pyrsistent         pkgs/main/linux-64::pyrsistent-0.17.3-py38h7b6447c_0\n",
      "  python-dateutil    pkgs/main/noarch::python-dateutil-2.8.1-pyhd3eb1b0_0\n",
      "  pyzmq              pkgs/main/linux-64::pyzmq-20.0.0-py38h2531618_1\n",
      "  send2trash         pkgs/main/noarch::send2trash-1.5.0-pyhd3eb1b0_1\n",
      "  terminado          pkgs/main/linux-64::terminado-0.9.4-py38h06a4308_0\n",
      "  testpath           pkgs/main/noarch::testpath-0.4.4-pyhd3eb1b0_0\n",
      "  tornado            pkgs/main/linux-64::tornado-6.1-py38h27cfd23_0\n",
      "  traitlets          pkgs/main/noarch::traitlets-5.0.5-pyhd3eb1b0_0\n",
      "  wcwidth            pkgs/main/noarch::wcwidth-0.2.5-py_0\n",
      "  webencodings       pkgs/main/linux-64::webencodings-0.5.1-py38_1\n",
      "  zeromq             pkgs/main/linux-64::zeromq-4.3.4-h2531618_0\n",
      "  zipp               pkgs/main/noarch::zipp-3.4.1-pyhd3eb1b0_0\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  ca-certificates                              2020.10.14-0 --> 2021.5.25-h06a4308_1\n",
      "  certifi                          2020.11.8-py38h06a4308_0 --> 2020.12.5-py38h06a4308_0\n",
      "  conda                                4.9.2-py38h06a4308_0 --> 4.10.1-py38h06a4308_1\n",
      "  openssl                                 1.1.1h-h7b6447c_0 --> 1.1.1k-h27cfd23_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "nb_conda-2.2.1       | 33 KB     | ##################################### | 100% \n",
      "importlib-metadata-3 | 33 KB     | ##################################### | 100% \n",
      "terminado-0.9.4      | 25 KB     | ##################################### | 100% \n",
      "argon2-cffi-20.1.0   | 46 KB     | ##################################### | 100% \n",
      "nb_conda_kernels-2.3 | 28 KB     | ##################################### | 100% \n",
      "markupsafe-2.0.1     | 22 KB     | ##################################### | 100% \n",
      "ca-certificates-2021 | 112 KB    | ##################################### | 100% \n",
      "conda-4.10.1         | 2.9 MB    | ##################################### | 100% \n",
      "ipython-7.22.0       | 982 KB    | ##################################### | 100% \n",
      "jupyter_core-4.7.1   | 68 KB     | ##################################### | 100% \n",
      "pygments-2.9.0       | 721 KB    | ##################################### | 100% \n",
      "notebook-6.4.0       | 4.1 MB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: / Enabling nb_conda_kernels...\n",
      "CONDA_PREFIX: /home/mathiane/miniconda3\n",
      "Status: enabled\n",
      "\n",
      "| Config option `kernel_spec_manager_class` not recognized by `EnableNBExtensionApp`.\n",
      "Enabling notebook extension nb_conda/main...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n",
      "Enabling tree extension nb_conda/tree...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n",
      "Config option `kernel_spec_manager_class` not recognized by `EnableServerExtensionApp`.\n",
      "Enabling: nb_conda\n",
      "- Writing config: /home/mathiane/miniconda3/etc/jupyter\n",
      "    - Validating...\n",
      "      nb_conda 2.2.1 \u001b[32mOK\u001b[0m\n",
      "\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "! conda install -y nb_conda\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edff72d3-d8a0-4fe2-b8e4-0e1c4eb3c3f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     active environment : base\n",
      "    active env location : /home/mathiane/miniconda3\n"
     ]
    }
   ],
   "source": [
    "!conda info | grep 'active env'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6952b8ff-5fef-4518-8dc4-b3d604244ca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting environment_kernels\n",
      "  Downloading environment_kernels-1.1.1.tar.gz (28 kB)\n",
      "Building wheels for collected packages: environment-kernels\n",
      "  Building wheel for environment-kernels (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for environment-kernels: filename=environment_kernels-1.1.1-py3-none-any.whl size=30796 sha256=a33cd38f123ac118e51927705f1ac7bc556f059feb505b6a56cc1280c304045a\n",
      "  Stored in directory: /home/mathiane/.cache/pip/wheels/d4/c6/ff/26eeec379303ffaf71ba975be8cc13e646c4ea601fb2c5bde6\n",
      "Successfully built environment-kernels\n",
      "Installing collected packages: environment-kernels\n",
      "Successfully installed environment-kernels-1.1.1\n"
     ]
    }
   ],
   "source": [
    "! pip install environment_kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e17c8289-9c82-4611-ae3a-7bfc24773643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: conda install [-h] [--revision REVISION] [-n ENVIRONMENT | -p PATH]\n",
      "                     [-c CHANNEL] [--use-local] [--override-channels]\n",
      "                     [--repodata-fn REPODATA_FNS] [--strict-channel-priority]\n",
      "                     [--no-channel-priority] [--no-deps | --only-deps]\n",
      "                     [--no-pin] [--copy] [-C] [-k] [--offline] [-d] [--json]\n",
      "                     [-q] [-v] [-y] [--download-only] [--show-channel-urls]\n",
      "                     [--file FILE] [--force-reinstall]\n",
      "                     [--freeze-installed | --update-deps | -S | --update-all | --update-specs]\n",
      "                     [-m] [--clobber] [--dev]\n",
      "                     [package_spec [package_spec ...]]\n",
      "conda install: error: argument -c/--channel: expected one argument\n"
     ]
    }
   ],
   "source": [
    "! conda install -c -y anaconda ipykernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48fa339c-411d-4380-95ed-a3fa7b70ac6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.\n",
      "To initialize your shell, run\n",
      "\n",
      "    $ conda init <SHELL_NAME>\n",
      "\n",
      "Currently supported shells are:\n",
      "  - bash\n",
      "  - fish\n",
      "  - tcsh\n",
      "  - xonsh\n",
      "  - zsh\n",
      "  - powershell\n",
      "\n",
      "See 'conda init --help' for more information and options.\n",
      "\n",
      "IMPORTANT: You may need to close and restart your shell after running 'conda init'.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! conda activate ImgProcess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2481981f-b57f-4587-980b-ccd5eb5fb5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#python -m ipykernel install --user --name ex --display-name \"Python (ex)\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9eb72cdd-7ee3-40cb-aef7-01fa0b0eaaf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efd57572-cbf2-4885-bee1-9c3d65641037",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_convergence = pd.read_csv('Res_64BS/convergence.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d7a77d14-9689-48d1-8333-177b3e711205",
   "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>epoch</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>loss</td>\n",
       "      <td>1.237357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>loss</td>\n",
       "      <td>0.876148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>loss</td>\n",
       "      <td>0.651634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>loss</td>\n",
       "      <td>0.624793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>loss</td>\n",
       "      <td>0.633192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epoch metric     value\n",
       "0      1   loss  1.237357\n",
       "1      2   loss  0.876148\n",
       "2      3   loss  0.651634\n",
       "3      4   loss  0.624793\n",
       "4      5   loss  0.633192"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_convergence.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed0d3afd-04f7-4b9d-94ce-1482d0ff9064",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_loss = df_convergence[df_convergence['metric'] == 'loss' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93efb9f5-3d4e-457b-afe5-cbaa02bd785d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe0600c048>"
      ]
     },
     "execution_count": 5,
     "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": [
    "sns.lineplot(x=df_loss['epoch'], y=df_loss['value'], hue=None, size=None, style=None, data=df_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5cce97f0-1188-4a53-9b8a-04e8d1318e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_error = df_convergence[df_convergence['metric'] == 'error' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d13b431-cf19-4911-93eb-61ed6a90e91d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe03f57cc0>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.lineplot(x=df_error['epoch'], y=df_error['value'], hue=None, size=None, style=None, data=df_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53ebd483-0945-43fe-a0ae-a398e7fd2f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_error = df_convergence[df_convergence['metric'] == 'fpr' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9eed0956-d6b9-4973-925e-49442e44eaa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe03ecb588>"
      ]
     },
     "execution_count": 8,
     "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": [
    "sns.lineplot(x=df_error['epoch'], y=df_error['value'], hue=None, size=None, style=None, data=df_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c4cdd31-b714-4e57-bcca-c41be978b461",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_error = df_convergence[df_convergence['metric'] == 'fnr' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "27da6359-2316-416c-a67c-7968b4db3823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe03f576d8>"
      ]
     },
     "execution_count": 10,
     "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": [
    "sns.lineplot(x=df_error['epoch'], y=df_error['value'], hue=None, size=None, style=None, data=df_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4593d578-2972-4511-b802-c1e88113525a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_FPR_FNR = df_convergence[(df_convergence['metric'] == 'fpr') | (df_convergence['metric'] == 'fnr') ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "643d3086-6a5e-42b8-bb8e-141f12e617cc",
   "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>epoch</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>fpr</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>fnr</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch metric     value\n",
       "11     10    fpr  1.000000\n",
       "12     10    fnr  0.058824"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FPR_FNR.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1599eb3-4108-4ee2-a9e2-eee57278e5a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe03e4c4a8>"
      ]
     },
     "execution_count": 13,
     "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": [
    "sns.lineplot(x=df_FPR_FNR['epoch'], y=df_FPR_FNR['value'], hue='metric', size=None, style=None, data=df_FPR_FNR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59cdbdc7-1d6b-4fb3-b075-650d2d7318bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Show prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce23eb14-5eb4-4026-b63f-bd3c8737bee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_proba_sample = pd.read_csv('Res_64BS/probability.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5bcd4802-819c-4eda-a0f4-8f2de78d9291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(311, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proba_sample[df_proba_sample.index == 'TNE0075'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bd16f671-fc4a-436f-b707-4f1897b569fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the 5 tiles per slides with the highest_proba to be atypical\n",
    "def tiles_highest_atypical_scores(df_proba_sample):\n",
    "    sample = set(df_proba_sample.index)\n",
    "    dict_sample_tiles_scores = {}\n",
    "    for s in sample:\n",
    "        print('SAMPLE  ', s)\n",
    "        dict_sample_tiles_scores[s] = []\n",
    "        sub_df = df_proba_sample[df_proba_sample.index == s]\n",
    "        sub_df = sub_df.sort_values(by=['probability'], ascending = False)\n",
    "        top_tiles =  sub_df.iloc[:5, 0]\n",
    "        top_scores  =  sub_df.iloc[:5, -1]\n",
    "        dict_sample_tiles_scores[s].append({'TopTiles' : top_tiles })\n",
    "        dict_sample_tiles_scores[s].append({'TopScores' : top_scores })\n",
    "        dict_sample_tiles_scores[s].append({'Targets' : sub_df.iloc[0, 1] })\n",
    "    return dict_sample_tiles_scores\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9223b701-1add-4661-bc51-3f113e8902c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAMPLE   TNE1430\n",
      "SAMPLE   TNE2142\n",
      "SAMPLE   TNE1099\n",
      "SAMPLE   TNE1422\n",
      "SAMPLE   TNE0974\n",
      "SAMPLE   TNE0810\n",
      "SAMPLE   TNE0952\n",
      "SAMPLE   TNE0985\n",
      "SAMPLE   TNE1095\n",
      "SAMPLE   TNE1080\n",
      "SAMPLE   TNE2141\n",
      "SAMPLE   TNE0288\n",
      "SAMPLE   TNE1017\n",
      "SAMPLE   TNE0520\n",
      "SAMPLE   TNE1410\n",
      "SAMPLE   TNE0522\n",
      "SAMPLE   TNE1086\n",
      "SAMPLE   TNE0521\n",
      "SAMPLE   TNE1338\n",
      "SAMPLE   TNE0286\n",
      "SAMPLE   TNE1456\n",
      "SAMPLE   TNE1414\n",
      "SAMPLE   TNE1365\n",
      "SAMPLE   TNE0287\n",
      "SAMPLE   TNE1024\n",
      "SAMPLE   TNE1450\n",
      "SAMPLE   TNE1411\n",
      "SAMPLE   TNE0944\n",
      "SAMPLE   TNE0289\n",
      "SAMPLE   TNE1413\n",
      "SAMPLE   TNE0002\n",
      "SAMPLE   TNE0408\n",
      "SAMPLE   TNE0519\n",
      "SAMPLE   TNE0075\n"
     ]
    }
   ],
   "source": [
    "dict_sample_tiles_scores_ATYPICAL  = tiles_highest_atypical_scores(df_proba_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "93b68eef-a26f-4ffe-86e5-0ed43e6e9c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'TNE1430': [{'TopTiles': TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1430    0.997237\n",
       "   TNE1430    0.997018\n",
       "   TNE1430    0.996806\n",
       "   TNE1430    0.996736\n",
       "   TNE1430    0.996233\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE2142': [{'TopTiles': TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE2142    0.996736\n",
       "   TNE2142    0.996233\n",
       "   TNE2142    0.993960\n",
       "   TNE2142    0.992867\n",
       "   TNE2142    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1099': [{'TopTiles': TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1099    0.996736\n",
       "   TNE1099    0.996233\n",
       "   TNE1099    0.993960\n",
       "   TNE1099    0.992867\n",
       "   TNE1099    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1422': [{'TopTiles': TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1422    0.996233\n",
       "   TNE1422    0.993960\n",
       "   TNE1422    0.992867\n",
       "   TNE1422    0.992012\n",
       "   TNE1422    0.991828\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0974': [{'TopTiles': TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0974    0.996233\n",
       "   TNE0974    0.993960\n",
       "   TNE0974    0.992867\n",
       "   TNE0974    0.992012\n",
       "   TNE0974    0.991828\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0810': [{'TopTiles': TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0810    0.993960\n",
       "   TNE0810    0.992867\n",
       "   TNE0810    0.990759\n",
       "   TNE0810    0.989946\n",
       "   TNE0810    0.985411\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0952': [{'TopTiles': TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0952    0.993960\n",
       "   TNE0952    0.992867\n",
       "   TNE0952    0.989946\n",
       "   TNE0952    0.985411\n",
       "   TNE0952    0.983833\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0985': [{'TopTiles': TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0985    0.996736\n",
       "   TNE0985    0.996233\n",
       "   TNE0985    0.993960\n",
       "   TNE0985    0.992867\n",
       "   TNE0985    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1095': [{'TopTiles': TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1095    0.996233\n",
       "   TNE1095    0.993960\n",
       "   TNE1095    0.992867\n",
       "   TNE1095    0.991828\n",
       "   TNE1095    0.990759\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1080': [{'TopTiles': TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1080    0.996736\n",
       "   TNE1080    0.996233\n",
       "   TNE1080    0.993960\n",
       "   TNE1080    0.992867\n",
       "   TNE1080    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE2141': [{'TopTiles': TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE2141    0.996233\n",
       "   TNE2141    0.993960\n",
       "   TNE2141    0.992867\n",
       "   TNE2141    0.992012\n",
       "   TNE2141    0.991828\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0288': [{'TopTiles': TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0288    0.997237\n",
       "   TNE0288    0.997018\n",
       "   TNE0288    0.996869\n",
       "   TNE0288    0.996806\n",
       "   TNE0288    0.996736\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1017': [{'TopTiles': TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1017    0.996233\n",
       "   TNE1017    0.993960\n",
       "   TNE1017    0.992867\n",
       "   TNE1017    0.992012\n",
       "   TNE1017    0.991828\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0520': [{'TopTiles': TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0520    0.996736\n",
       "   TNE0520    0.996233\n",
       "   TNE0520    0.993960\n",
       "   TNE0520    0.992867\n",
       "   TNE0520    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1410': [{'TopTiles': TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1410    0.996233\n",
       "   TNE1410    0.993960\n",
       "   TNE1410    0.992867\n",
       "   TNE1410    0.992012\n",
       "   TNE1410    0.991828\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0522': [{'TopTiles': TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0522    0.996736\n",
       "   TNE0522    0.996233\n",
       "   TNE0522    0.993960\n",
       "   TNE0522    0.992867\n",
       "   TNE0522    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1086': [{'TopTiles': TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1086    0.993960\n",
       "   TNE1086    0.950080\n",
       "   TNE1086    0.799582\n",
       "   TNE1086    0.792495\n",
       "   TNE1086    0.774569\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0521': [{'TopTiles': TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0521    0.950080\n",
       "   TNE0521    0.760178\n",
       "   TNE0521    0.752086\n",
       "   TNE0521    0.683222\n",
       "   TNE0521    0.682953\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1338': [{'TopTiles': TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1338    0.993960\n",
       "   TNE1338    0.992867\n",
       "   TNE1338    0.989946\n",
       "   TNE1338    0.985411\n",
       "   TNE1338    0.981788\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0286': [{'TopTiles': TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0286    0.997237\n",
       "   TNE0286    0.997018\n",
       "   TNE0286    0.996869\n",
       "   TNE0286    0.996806\n",
       "   TNE0286    0.996736\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1456': [{'TopTiles': TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1456    0.996736\n",
       "   TNE1456    0.996233\n",
       "   TNE1456    0.993960\n",
       "   TNE1456    0.992867\n",
       "   TNE1456    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1414': [{'TopTiles': TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1414    0.993960\n",
       "   TNE1414    0.992867\n",
       "   TNE1414    0.990759\n",
       "   TNE1414    0.989946\n",
       "   TNE1414    0.985411\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1365': [{'TopTiles': TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1365    0.996736\n",
       "   TNE1365    0.996233\n",
       "   TNE1365    0.993960\n",
       "   TNE1365    0.992867\n",
       "   TNE1365    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0287': [{'TopTiles': TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0287    0.997237\n",
       "   TNE0287    0.997018\n",
       "   TNE0287    0.996869\n",
       "   TNE0287    0.996806\n",
       "   TNE0287    0.996736\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1024': [{'TopTiles': TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1024    0.993960\n",
       "   TNE1024    0.992867\n",
       "   TNE1024    0.989946\n",
       "   TNE1024    0.985411\n",
       "   TNE1024    0.981788\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1450': [{'TopTiles': TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1450    0.996233\n",
       "   TNE1450    0.993960\n",
       "   TNE1450    0.992867\n",
       "   TNE1450    0.991828\n",
       "   TNE1450    0.990759\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1411': [{'TopTiles': TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1411    0.993960\n",
       "   TNE1411    0.992867\n",
       "   TNE1411    0.989946\n",
       "   TNE1411    0.985411\n",
       "   TNE1411    0.983833\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0944': [{'TopTiles': TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0944    0.996736\n",
       "   TNE0944    0.996233\n",
       "   TNE0944    0.993960\n",
       "   TNE0944    0.992867\n",
       "   TNE0944    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0289': [{'TopTiles': TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0289    0.997237\n",
       "   TNE0289    0.997018\n",
       "   TNE0289    0.996869\n",
       "   TNE0289    0.996806\n",
       "   TNE0289    0.996736\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1413': [{'TopTiles': TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1413    0.996736\n",
       "   TNE1413    0.996233\n",
       "   TNE1413    0.993960\n",
       "   TNE1413    0.992867\n",
       "   TNE1413    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0002': [{'TopTiles': TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0002    0.993960\n",
       "   TNE0002    0.992867\n",
       "   TNE0002    0.989946\n",
       "   TNE0002    0.985411\n",
       "   TNE0002    0.981788\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0408': [{'TopTiles': TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0408    0.996233\n",
       "   TNE0408    0.993960\n",
       "   TNE0408    0.992867\n",
       "   TNE0408    0.991828\n",
       "   TNE0408    0.990759\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0519': [{'TopTiles': TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0519    0.996736\n",
       "   TNE0519    0.996233\n",
       "   TNE0519    0.993960\n",
       "   TNE0519    0.992867\n",
       "   TNE0519    0.992012\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0075': [{'TopTiles': TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0075    0.993960\n",
       "   TNE0075    0.992867\n",
       "   TNE0075    0.981788\n",
       "   TNE0075    0.950080\n",
       "   TNE0075    0.948586\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}]}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_sample_tiles_scores_ATYPICAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "184f83f4-cebf-41cb-8cc2-8f1d65cc9843",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the 5 tiles per slides with the highest_proba to be atypical\n",
    "def tiles_highest_typical_scores(df_proba_sample):\n",
    "    sample = set(df_proba_sample.index)\n",
    "    dict_sample_tiles_scores = {}\n",
    "    for s in sample:\n",
    "        print('SAMPLE  ', s)\n",
    "        dict_sample_tiles_scores[s] = []\n",
    "        sub_df = df_proba_sample[df_proba_sample.index == s]\n",
    "        sub_df = sub_df.sort_values(by=['probability'], ascending = True)\n",
    "        top_tiles =  sub_df.iloc[:5, 0]\n",
    "        top_scores  =  sub_df.iloc[:5, -1]\n",
    "        dict_sample_tiles_scores[s].append({'TopTiles' : top_tiles })\n",
    "        dict_sample_tiles_scores[s].append({'TopScores' : top_scores })\n",
    "        dict_sample_tiles_scores[s].append({'Targets' : sub_df.iloc[0, 1] })\n",
    "    return dict_sample_tiles_scores\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "331b12cc-663b-4d38-94e0-3a63fc1c9079",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAMPLE   TNE1430\n",
      "SAMPLE   TNE2142\n",
      "SAMPLE   TNE1099\n",
      "SAMPLE   TNE1422\n",
      "SAMPLE   TNE0974\n",
      "SAMPLE   TNE0810\n",
      "SAMPLE   TNE0952\n",
      "SAMPLE   TNE0985\n",
      "SAMPLE   TNE1095\n",
      "SAMPLE   TNE1080\n",
      "SAMPLE   TNE2141\n",
      "SAMPLE   TNE0288\n",
      "SAMPLE   TNE1017\n",
      "SAMPLE   TNE0520\n",
      "SAMPLE   TNE1410\n",
      "SAMPLE   TNE0522\n",
      "SAMPLE   TNE1086\n",
      "SAMPLE   TNE0521\n",
      "SAMPLE   TNE1338\n",
      "SAMPLE   TNE0286\n",
      "SAMPLE   TNE1456\n",
      "SAMPLE   TNE1414\n",
      "SAMPLE   TNE1365\n",
      "SAMPLE   TNE0287\n",
      "SAMPLE   TNE1024\n",
      "SAMPLE   TNE1450\n",
      "SAMPLE   TNE1411\n",
      "SAMPLE   TNE0944\n",
      "SAMPLE   TNE0289\n",
      "SAMPLE   TNE1413\n",
      "SAMPLE   TNE0002\n",
      "SAMPLE   TNE0408\n",
      "SAMPLE   TNE0519\n",
      "SAMPLE   TNE0075\n"
     ]
    }
   ],
   "source": [
    "dict_sample_tiles_scores_TYPICAL  = tiles_highest_typical_scores(df_proba_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c492985a-0852-4426-9ec2-423e386a7793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'TNE1430': [{'TopTiles': TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1430    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1430    0.222708\n",
       "   TNE1430    0.227228\n",
       "   TNE1430    0.229838\n",
       "   TNE1430    0.232375\n",
       "   TNE1430    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE2142': [{'TopTiles': TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2142    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE2142    0.222708\n",
       "   TNE2142    0.227228\n",
       "   TNE2142    0.229838\n",
       "   TNE2142    0.232375\n",
       "   TNE2142    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1099': [{'TopTiles': TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1099    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1099    0.222708\n",
       "   TNE1099    0.227228\n",
       "   TNE1099    0.229838\n",
       "   TNE1099    0.232375\n",
       "   TNE1099    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1422': [{'TopTiles': TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1422    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1422    0.222708\n",
       "   TNE1422    0.227228\n",
       "   TNE1422    0.229838\n",
       "   TNE1422    0.232375\n",
       "   TNE1422    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0974': [{'TopTiles': TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0974    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0974    0.222708\n",
       "   TNE0974    0.227228\n",
       "   TNE0974    0.229838\n",
       "   TNE0974    0.232375\n",
       "   TNE0974    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0810': [{'TopTiles': TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0810    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0810    0.229838\n",
       "   TNE0810    0.232375\n",
       "   TNE0810    0.235380\n",
       "   TNE0810    0.237028\n",
       "   TNE0810    0.237218\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0952': [{'TopTiles': TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0952    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0952    0.229838\n",
       "   TNE0952    0.232375\n",
       "   TNE0952    0.235380\n",
       "   TNE0952    0.237028\n",
       "   TNE0952    0.240450\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0985': [{'TopTiles': TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0985    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0985    0.222708\n",
       "   TNE0985    0.227228\n",
       "   TNE0985    0.229838\n",
       "   TNE0985    0.232375\n",
       "   TNE0985    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1095': [{'TopTiles': TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1095    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1095    0.222708\n",
       "   TNE1095    0.227228\n",
       "   TNE1095    0.229838\n",
       "   TNE1095    0.232375\n",
       "   TNE1095    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1080': [{'TopTiles': TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1080    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1080    0.222708\n",
       "   TNE1080    0.227228\n",
       "   TNE1080    0.229838\n",
       "   TNE1080    0.232375\n",
       "   TNE1080    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE2141': [{'TopTiles': TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE2141    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE2141    0.222708\n",
       "   TNE2141    0.227228\n",
       "   TNE2141    0.229838\n",
       "   TNE2141    0.232375\n",
       "   TNE2141    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0288': [{'TopTiles': TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0288    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0288    0.222708\n",
       "   TNE0288    0.227228\n",
       "   TNE0288    0.229838\n",
       "   TNE0288    0.232375\n",
       "   TNE0288    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1017': [{'TopTiles': TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1017    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1017    0.222708\n",
       "   TNE1017    0.227228\n",
       "   TNE1017    0.229838\n",
       "   TNE1017    0.232375\n",
       "   TNE1017    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0520': [{'TopTiles': TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0520    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0520    0.222708\n",
       "   TNE0520    0.227228\n",
       "   TNE0520    0.229838\n",
       "   TNE0520    0.232375\n",
       "   TNE0520    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1410': [{'TopTiles': TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1410    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1410    0.222708\n",
       "   TNE1410    0.227228\n",
       "   TNE1410    0.229838\n",
       "   TNE1410    0.232375\n",
       "   TNE1410    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0522': [{'TopTiles': TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0522    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0522    0.222708\n",
       "   TNE0522    0.227228\n",
       "   TNE0522    0.229838\n",
       "   TNE0522    0.232375\n",
       "   TNE0522    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1086': [{'TopTiles': TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1086    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1086    0.229838\n",
       "   TNE1086    0.235380\n",
       "   TNE1086    0.251847\n",
       "   TNE1086    0.256724\n",
       "   TNE1086    0.257734\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0521': [{'TopTiles': TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0521    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0521    0.257734\n",
       "   TNE0521    0.268066\n",
       "   TNE0521    0.270435\n",
       "   TNE0521    0.282112\n",
       "   TNE0521    0.304424\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1338': [{'TopTiles': TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1338    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1338    0.229838\n",
       "   TNE1338    0.232375\n",
       "   TNE1338    0.235380\n",
       "   TNE1338    0.251847\n",
       "   TNE1338    0.256724\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0286': [{'TopTiles': TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0286    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0286    0.222708\n",
       "   TNE0286    0.227228\n",
       "   TNE0286    0.229838\n",
       "   TNE0286    0.232375\n",
       "   TNE0286    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1456': [{'TopTiles': TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1456    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1456    0.222708\n",
       "   TNE1456    0.227228\n",
       "   TNE1456    0.229838\n",
       "   TNE1456    0.232375\n",
       "   TNE1456    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1414': [{'TopTiles': TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1414    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1414    0.229838\n",
       "   TNE1414    0.232375\n",
       "   TNE1414    0.235380\n",
       "   TNE1414    0.237028\n",
       "   TNE1414    0.237218\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1365': [{'TopTiles': TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1365    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1365    0.222708\n",
       "   TNE1365    0.227228\n",
       "   TNE1365    0.229838\n",
       "   TNE1365    0.232375\n",
       "   TNE1365    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0287': [{'TopTiles': TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0287    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0287    0.222708\n",
       "   TNE0287    0.227228\n",
       "   TNE0287    0.229838\n",
       "   TNE0287    0.232375\n",
       "   TNE0287    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1024': [{'TopTiles': TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1024    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1024    0.229838\n",
       "   TNE1024    0.232375\n",
       "   TNE1024    0.235380\n",
       "   TNE1024    0.251847\n",
       "   TNE1024    0.256724\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1450': [{'TopTiles': TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1450    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1450    0.222708\n",
       "   TNE1450    0.227228\n",
       "   TNE1450    0.229838\n",
       "   TNE1450    0.232375\n",
       "   TNE1450    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE1411': [{'TopTiles': TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1411    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1411    0.229838\n",
       "   TNE1411    0.232375\n",
       "   TNE1411    0.235380\n",
       "   TNE1411    0.237028\n",
       "   TNE1411    0.250356\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0944': [{'TopTiles': TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0944    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0944    0.222708\n",
       "   TNE0944    0.227228\n",
       "   TNE0944    0.229838\n",
       "   TNE0944    0.232375\n",
       "   TNE0944    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0289': [{'TopTiles': TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0289    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0289    0.222708\n",
       "   TNE0289    0.227228\n",
       "   TNE0289    0.229838\n",
       "   TNE0289    0.232375\n",
       "   TNE0289    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE1413': [{'TopTiles': TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE1413    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE1413    0.222708\n",
       "   TNE1413    0.227228\n",
       "   TNE1413    0.229838\n",
       "   TNE1413    0.232375\n",
       "   TNE1413    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0002': [{'TopTiles': TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0002    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0002    0.229838\n",
       "   TNE0002    0.232375\n",
       "   TNE0002    0.235380\n",
       "   TNE0002    0.251847\n",
       "   TNE0002    0.256724\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0408': [{'TopTiles': TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0408    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0408    0.222708\n",
       "   TNE0408    0.227228\n",
       "   TNE0408    0.229838\n",
       "   TNE0408    0.232375\n",
       "   TNE0408    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}],\n",
       " 'TNE0519': [{'TopTiles': TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0519    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0519    0.222708\n",
       "   TNE0519    0.227228\n",
       "   TNE0519    0.229838\n",
       "   TNE0519    0.232375\n",
       "   TNE0519    0.235380\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 1}],\n",
       " 'TNE0075': [{'TopTiles': TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   TNE0075    /data/gcs/lungNENomics/work/MathianE/Tiles_512...\n",
       "   Name: Tiles, dtype: object},\n",
       "  {'TopScores': TNE0075    0.229838\n",
       "   TNE0075    0.235380\n",
       "   TNE0075    0.251847\n",
       "   TNE0075    0.256724\n",
       "   TNE0075    0.257734\n",
       "   Name: probability, dtype: float64},\n",
       "  {'Targets': 0}]}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_sample_tiles_scores_TYPICAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90df077a-df1b-4cc2-a78c-3475509dda25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "def move_best__tiles(dict_sample_tiles_scores, outputdir, best_atypical = True):\n",
    "    try:\n",
    "        os.mkdir(outputdir)\n",
    "    except:\n",
    "        print('Outputdir already created ')\n",
    "    for sample in dict_sample_tiles_scores.keys():\n",
    "        sample_info = dict_sample_tiles_scores[sample]\n",
    "        target_sample =  sample_info[2]['Targets']\n",
    "        try:\n",
    "            os.mkdir(os.path.join(outputdir,sample + '_'+ str(target_sample)))\n",
    "        except:\n",
    "            print('sample folder already created ')\n",
    "        \n",
    "        \n",
    "        if best_atypical:\n",
    "            histo_type = 'Atypical'\n",
    "            try:\n",
    "                os.mkdir(os.path.join(outputdir,sample + '_'+ str(target_sample), histo_type))\n",
    "            except:\n",
    "                print('sample folder already created ')\n",
    "        else:\n",
    "            histo_type = 'Typical'\n",
    "            try:\n",
    "                os.mkdir(os.path.join(outputdir,sample + '_'+ str(target_sample), histo_type))\n",
    "            except:\n",
    "                print('sample folder already created ')\n",
    "            \n",
    "        tiles = sample_info[0]['TopTiles']\n",
    "        probs = sample_info[1]['TopScores']\n",
    "        for tile, prob in zip(tiles, probs):\n",
    "            tile_names = tile.split('/')[-1].split('.')[0]\n",
    "            outputname = tile_names + '__' + str(round(prob*100)) + '.'+ tile.split('/')[-1].split('.')[1]\n",
    "            print(outputname)\n",
    "            shutil.copy(tile, os.path.join(outputdir ,sample + '_'+ str(target_sample), histo_type,outputname))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bc0677e9-f4a6-45a8-bf26-4baed18aa2fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "TNE0075_33265_36961__95.jpg\n",
      "TNE0075_92401_13861__95.jpg\n"
     ]
    }
   ],
   "source": [
    "move_best__tiles(dict_sample_tiles_scores_ATYPICAL, 'BestSortTilesVal_64BS', best_atypical = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d5c13605-2c95-4d72-af72-7b4b2ca37dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outputdir already created \n",
      "sample folder already created \n",
      "TNE1430_23101_71149__22.jpg\n",
      "TNE1430_36961_79465__23.jpg\n",
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      "sample folder already created \n",
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      "sample folder already created \n",
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      "TNE1024_16633_24025__23.jpg\n",
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      "sample folder already created \n",
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      "sample folder already created \n",
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      "TNE0519_43891_83161__23.jpg\n",
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      "sample folder already created \n",
      "TNE0075_81313_13861__23.jpg\n",
      "TNE0075_59137_24025__24.jpg\n",
      "TNE0075_7393_60061__25.jpg\n",
      "TNE0075_36961_13861__26.jpg\n",
      "TNE0075_58213_48973__26.jpg\n"
     ]
    }
   ],
   "source": [
    "move_best__tiles(dict_sample_tiles_scores_TYPICAL, 'BestSortTilesVal_64BS', best_atypical = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "02a72245-8948-4889-9a14-5fcef67e9dfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Calculation overall accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f44229e-f7cb-4e61-888d-479192d32196",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_proba_sample = pd.read_csv('Res_64BS/probability.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1588a296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .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>Sample</th>\n",
       "      <th>Tiles</th>\n",
       "      <th>target</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.308092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.337643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.268066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.346144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sample                                              Tiles  target  \\\n",
       "0  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "1  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "2  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "3  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "4  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "\n",
       "   probability  \n",
       "0     0.308092  \n",
       "1     0.354153  \n",
       "2     0.337643  \n",
       "3     0.268066  \n",
       "4     0.346144  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proba_sample.head()"
   ]
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   "cell_type": "code",
   "execution_count": 10,
   "id": "1abe0de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f33c80b2ac8>"
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     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHFxJREFUeJzt3X+03HV95/HnO8Fgwq8b4JYTJRDiohVdNtqUba1YrLUbbSuiFmFbCoJGWuxu125XrHuwR49bW7WsrameqCzQVURELLaaSi3C+gM1IkJU/IGGcy+m4RIwUEgVct/7x3wnfDOZe+/c5H7nM3Pv83HOPfnOZ74z857vzX3NZz7fz3wmMhNJUv8tKl2AJC1UBrAkFWIAS1IhBrAkFWIAS1IhBrAkFWIAS1IhBrAkFWIAS1IhB5Uu4ECsW7cuN23aVLoMSeoUvew01D3g++67r3QJkrTfhjqAJWmYGcCSVIgBLEmFGMCSVIgBLEmFGMCSVIgBLEmFGMCSVIgBLEmFGMCSVIgBLEmFDPViPAvN5OQkY2NjAKxcuZJFi3z9lIaZf8FDZGxsjPM3bOL8DZv2BLGk4WUPeMgsWz5augRJc8QesCQVYgBLUiEGsCQVYgBLUiEGsCQVYgBLUiEGsCQV4jzgIZSTk4yPjwN+Ik4aZv7lDqFdO+/j4mtu9RNx0pBrLIAj4rKIuDcittTaro6I26qfrRFxW9W+KiJ21a57X1N1zRdLR0b9VJw05JocgrgceA9wZbshM1/Z3o6IdwE7a/vflZlrGqxHkgZKYwGcmTdHxKpu10VEAGcCv9LU40vSoCs1BnwqsD0zv1drOyEivh4RN0XEqYXqkqS+KTUL4mzgqtrlbcBxmbkjIn4O+EREPCMzH+y8YUSsB9YDHHfccX0pVpKa0PcecEQcBLwMuLrdlpk/ycwd1fbXgLuAp3a7fWZuzMy1mbl2dNSTUJKGV4khiF8F7szM8XZDRIxGxOJqezVwIvCDArVJUt80OQ3tKuBLwNMiYjwiLqiuOou9hx8AngfcXk1L+xhwYWbe31RtkjQImpwFcfYU7ed1absWuLapWiRpEPlJOEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqxACWpEIMYEkqpLEAjojLIuLeiNhSa/vTiLgnIm6rfl5cu+6NEfH9iPhORPynpuqSpEHRZA/4cmBdl/ZLM3NN9fMpgIg4CTgLeEZ1m7+JiMUN1iZJxTUWwJl5M3B/j7ufDnwkM3+SmT8Evg+c0lRtkjQISowBvy4ibq+GKJZXbU8Gxmr7jFdt+4iI9RGxOSI2T0xMNF2rJDWm3wH8XuApwBpgG/Cu2d5BZm7MzLWZuXZ0dHSu65OkvulrAGfm9szcnZmTwPt5fJjhHmBlbddjqzZJmrf6GsARsaJ28QygPUPieuCsiDg4Ik4ATgS+0s/aJKnfDmrqjiPiKuA04OiIGAfeDJwWEWuABLYCrwXIzG9GxEeBbwGPARdl5u6mapOkQdBYAGfm2V2aPzjN/m8D3tZUPZI0aPwknCQVYgBLUiEGsCQVYgBLUiEGsCQVYgBLUiEGsCQVYgBLUiEGsCQVYgBLUiEGsCQVYgBLUiGNLcajuTE5OcnYWOvLQsbHx1vryEmaFwzgATc2Nsb5GzaxbPkoO7beyaErVpcuSdIccQhiCCxbPsohR61g6RFHli5F0hwygCWpEANYkgoxgCWpEANYkgoxgCWpEANYkgoxgCWpEANYkgoxgCWpEANYkgoxgCWpEANYkgoxgCWpEANYkgoxgCWpkMYCOCIui4h7I2JLre0dEXFnRNweEddFxEjVvioidkXEbdXP+5qqS5IGRZM94MuBdR1tNwDPzMyTge8Cb6xdd1dmrql+LmywLkkaCI0FcGbeDNzf0faZzHysungLcGxTjy9Jg67kGPD5wKdrl0+IiK9HxE0RcepUN4qI9RGxOSI2T0xMNF+lJDWkSABHxJuAx4APVU3bgOMy81nA64EPR8Th3W6bmRszc21mrh0dHe1PwZLUgL4HcEScB/wG8NuZmQCZ+ZPM3FFtfw24C3hqv2uTpH7qawBHxDrgfwAvycxHau2jEbG42l4NnAj8oJ+1SVK/HdTUHUfEVcBpwNERMQ68mdash4OBGyIC4JZqxsPzgLdExKPAJHBhZt7f9Y61R05OMj4+vufyypUrWbTIqd3SsGgsgDPz7C7NH5xi32uBa5uqZb7atfM+Lr5mGyPHbOeRBya47KJ1HH/88aXLktSjxgJY/bF0ZJRDjlpRugxJ+8H3q5JUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUiAEsSYUYwJJUSE8BHBG/1EubJKl3vfaA/7rHNklSjw6a7sqI+EXgOcBoRLy+dtXhwOImC5Ok+W7aAAaWAIdW+x1Wa38QeEVTRUnSQjBtAGfmTcBNEXF5Zt492zuPiMuA3wDuzcxnVm1HAlcDq4CtwJmZ+UBEBPBu4MXAI8B5mXnrbB9TkoZFr2PAB0fExoj4TET8c/unh9tdDqzraLsY+Gxmngh8troM8CLgxOpnPfDeHmuTpKE00xBE2zXA+4APALt7vfPMvDkiVnU0nw6cVm1fAXwOeEPVfmVmJnBLRIxExIrM3Nbr40nSMOk1gB/LzLnqkR5TC9V/AY6ptp8MjNX2G6/aDGBJ81KvQxCfjIjfj4gVEXFk++dAH7zq7eZsbhMR6yNic0RsnpiYONASJKmYXnvA51b//nGtLYHV+/GY29tDCxGxAri3ar8HWFnb79iqbS+ZuRHYCLB27dpZhbckDZKeesCZeUKXn/0JX4DreTzQzwX+rtb+u9HyC8BOx38lzWc99YAj4ne7tWfmlTPc7ipaJ9yOjohx4M3A24GPRsQFwN3AmdXun6I1Be37tKahvaqX2iRpWPU6BPHzte0nAi8AbgWmDeDMPHuKq17QZd8ELuqxHkkaej0FcGb+Qf1yRIwAH2mkIklaIPZ3OcqHgRPmshBJWmh6HQP+JI9PF1sMPB34aFNFSdJC0OsY8Dtr248Bd2fmeAP1SNKC0es0tJuAO2mtiLYc+GmTRUnSQtDrN2KcCXwF+C1a08a+HBEuR9mgyclJ7r77bsbHx2f5WUFJw6LXIYg3AT+fmfcCRMQo8E/Ax5oqbKEbGxvj/A2b2LVzB4euWM0hpQuSNOd6DeBF7fCt7MAv9GzcsuWj2P2V5q9eA3hTRPwjcFV1+ZW0PrkmSdpPM30n3L+jtXzkH0fEy4DnVld9CfhQ08VJ0nw2Uw/4fwNvBMjMjwMfB4iIf19d95uNVidJ89hM47jHZOYdnY1V26pGKpKkBWKmAB6Z5rqlc1mIJC00MwXw5oh4TWdjRLwa+FozJUnSwjDTGPAfAtdFxG/zeOCuBZYAZzRZmCTNd9MGcGZuB54TEc8Hnlk1/0Nm9vKV9JKkafS6HvCNwI0N1yJJC4qfZpOkQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSqk16+lnzMR8TTg6lrTauASWl9/9Bpgomr/k8z8VJ/Lk6S+6XsAZ+Z3gDUAEbEYuAe4DngVcGlmvrPfNUlSCaWHIF4A3JWZdxeuQ5L6rnQAnwVcVbv8uoi4PSIui4jl3W4QEesjYnNEbJ6YmOi2iyQNhWIBHBFLgJcA11RN7wWeQmt4Yhvwrm63y8yNmbk2M9eOjo72pVZJakLJHvCLgFurL/4kM7dn5u7MnATeD5xSsDZJalzJAD6b2vBDRKyoXXcGsKXvFUlSH/V9FgRARBwCvBB4ba35LyJiDZDA1o7r5rXJyUnGxsb2XF65cmXBaiT1S5EAzsyHgaM62s4pUcsgGBsb4/wNm1i2fJSH79/OW196cuuKLFuXpGYVCWDta9nyUQ45agWPPHAvF19zK7t3PcShK1aXLktSgwzgAbR0ZJTdS55QugxJDTOAC2qP/Y6PjzvcIC1ABnBB7bHfXTt3cOiK1RxSuiBJfWUAF7Zs+Sh2f6WFqfRHkSVpwTKAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQv5a+gMnJScbGxhgfH/cb6aUFzAAuYGxsjPM3bGLXzh0cumJ16XIkFWIAF7Js+Sh2f6WFrVgAR8RW4CFgN/BYZq6NiCOBq4FVwFbgzMx8oFSNktSk0ifhnp+ZazJzbXX5YuCzmXki8NnqsiTNS6UDuNPpwBXV9hXASwvWIkmNKhnACXwmIr4WEeurtmMyc1u1/S/AMZ03ioj1EbE5IjZPTEz0q1ZJmnMlT8I9NzPviYifAW6IiDvrV2ZmRsQ+Z6kycyOwEWDt2rWexark5GRrWhuwcuVKFi0atDc3kjoV+yvNzHuqf+8FrgNOAbZHxAqA6t97S9U3bHbtvI+Lr7mV8zdsYmxsrHQ5knpQJIAj4pCIOKy9DfwasAW4Hji32u1c4O9K1Deslo6MVtPbJA2DUkMQxwDXRUS7hg9n5qaI+Crw0Yi4ALgbOLNQfZLUuCIBnJk/AP5Dl/YdwAv6X5Ek9Z9naiSpEANYkgoxgCWpEANYkgoxgCWpEANYkgoxgCWpEBdkb1j764fANRok7c00aFj764dco0FSJ3vAfeD6DJK6sQcsSYUYwJJUiAEsSYUYwJJUiCfh+qT+lUHj4+Otb8STtKAZwH3S+sqgbYwcs50dW+/k0BWrS5ckqTCHIPpo6cgohxy1gqVHHFm6FEkDwACWpEIMYEkqxACWpEIMYEkqxFkQDWmvguaUM0lTMYAb0l4FbdfOHU45k9SVAdyg1ipodn8ldecYsCQVYgBLUiEGsCQVYgBLUiEGsCQVYgBLUiF9D+CIWBkRN0bEtyLimxHxX6v2P42IeyLiturnxf2uTZL6qcQ84MeAP8rMWyPiMOBrEXFDdd2lmfnOAjVJUt/1PYAzcxuwrdp+KCK+DTy533VIUmlFx4AjYhXwLODLVdPrIuL2iLgsIpZPcZv1EbE5IjZPTEz0qVJJmnvFAjgiDgWuBf4wMx8E3gs8BVhDq4f8rm63y8yNmbk2M9eOjo72rV5JmmtFAjginkArfD+UmR8HyMztmbk7MyeB9wOnlKhNkvqlxCyIAD4IfDsz/7LWvqK22xnAln7XJkn9VGIWxC8B5wB3RMRtVdufAGdHxBpay4dtBV5boDZJ6psSsyA+D0SXqz7V71okqSTXA55D7W/BAPwmDEkzMoDnUPtbMJYtH2XH1jv9JgxJ03ItiDm2bPkohxy1gqVHHFm6FEkDzgCWpEIMYEkqxACWpEIMYEkqxACWpEKchjYH2vN/B2nub31OMsDKlStZtMjXW2mQGMBzoD3/d9fOHRy6YjWHlC6IveckP/LABJddtI7jjz++dFmSagzgObJs+SgD0/2ttOckSxpMvieVpELsAR+AQRz7lTQ8DOAD0Dn2K0mzYQAfoEEc+5U0HAzgWXLJSUlzxQCeJZeclDRXnAWxH4ZtycmcnGR8fJy7776bycnJ0uVIqhjAC8Cunfdx8TW3cv6GTXt9Ok5SWQ5BLBBLR0Y5+OAlPe1bH+f2I8xScwzgHg3LnN/2cEPrwsz7dwvb9jg34EeYpQYZwD0axPUeumkNN2xj966HeqpzqrBtTa+T1CQDeBaGZc7v0pFRdi95Qs/7G7bNcmU6TWXBB3C3Pw7AMVDNGVemGy79fMFc8AHc7Y8DcAxUc8qV6YZHP18wF3wAQ/c/jmXLR/c6oTXoJ996UX8+7fnAP/rRjyAhc9/nWm+bqhewv72FEm/L5/vsjvn+/PqpXy+YBvA02ie0Ro7ZPi8+9db5fBYvPWzPybrdj/x4n+fabluyZMuUvYD97S2UeFs+32d3zPfnNx8ZwDXdpnAtHWm9Ej7ywL3lCptD9eezeNnIXifruj3XpSOjLHnCQY8fF/YeJx8fH2fZyP71Fjp7GXPRK56pF9iPE46dNfSTJ1SnN93/jxJTTRdsAHc72J1TuNRS7zk/fP923vrSkwG45BNb2PXg49Py6i9g+xOe9V5x+3Ge9KQnAbBo0aKe7rOzF7hy5cp9F0+KfW/XLTR7OTnbvl17SGfRokWMj49zySe2QLDnnMJMj1e/r3rb/tirI3GA9zVbTT6vmR6vff8w9Un09v+PzEne+tKTOfbYY/fsV2Kq6YIN4KkO9myncC0U9d7xxdfcuudFamk83lVoB/V0QxYzafeKH3+cm1i89DCWLFnS833We4HdFk/q9onAzuAGejo5W/9/tHjpYYwc8+RpH2eqx6vfV71tf9RfMPs966LJ5zXd483mJHpr39b/r/oxal/Xz5M9Cy6A6z3fZSPDMa930Ez3IlUfsqj3CmHfoYuZDn37cRYvG9krzHrpUdV7gct6HEbqfPvefjHovK/OHnT7j3bxspFZDVd1Gy6YaQihW48b9h3qWDqLYaH68azf7/72Vvfnec22ls6/415+T51mc4yaMnABHBHrgHcDi4EPZObb5/L+/RaL5j0+lHPTnl5hZ+9ktm/zOmekXPKJLSSPv43s/APt5ROB3dZ2rs/8mGpoqj4mPt0LSbdzCvu0dQmJ6YZyuvW468NCU9XSLbjrM2Eu+cQWlh05uufkbC/vOKZ6+z+VXoaoOt+xTFVLt3ew0/2eOmf9TFVXv/tjAxXAEbEY2AC8EBgHvhoR12fmt+bycYblE23DrN577TbFb7bHv9uMlNYsjVunHPKYaTip2/BEe+ZHZ3DX76vX2THdXgQ627oNU8w0lNOtx10fFpruuXYOldRnwtRPzvaycNNUb/+n0usQVX0Yarpauv0/mu73VH+u3eoqce5noAIYOAX4fmb+ACAiPgKcDsxpAD/ywAS7dt7P4p8+yu5dD7H4p4/y8MFL9mmb7rrZ7j9I15Wo4ZEHJvb0MurHv9t1QPf7WnrYnt/hrh9P7NVW75HO+PtdsmSvk1RT3eeuH09M/by61dLtOHS7r1pbvZa9au94Xm2dz6/rfU5x3Kezz3Od4hh11tLtcvt3OJvn1fn8gGlrmfI4TPN7qj/XfX5f0xy/JkXm4PQEI+IVwLrMfHV1+RzgP2bm62r7rAfWVxefBnyny10dDdzXcLm9GpRaBqUOGJxaBqUOsJZuBqUOmH0t92Xm9G8JGLwe8IwycyOwcbp9ImJzZq7tU0nTGpRaBqUOGJxaBqUOsJZBrgOaq2XQPqt4D1AfyT+2apOkeWfQAvirwIkRcUJELAHOAq4vXJMkNWKghiAy87GIeB3wj7SmoV2Wmd/cj7uadoiizwallkGpAwanlkGpA6ylm0GpAxqqZaBOwknSQjJoQxCStGAYwJJUyFAHcESsi4jvRMT3I+LiLte/PiK+FRG3R8RnI6KRFUl6qOPCiLgjIm6LiM9HxElN1NFLLbX9Xh4RGRGNTPPp4ZicFxET1TG5LSJe3UQdvdRS7XNm9X/lmxHx4VK1RMSltWPy3Yj4caE6jouIGyPi69Xfz4ubqKPHWo6v/n5vj4jPRcSxDdVxWUTcGxFbprg+IuKvqjpvj4hnH/CDZuZQ/tA6SXcXsBpYAnwDOKljn+cDy6rt3wOuLlTH4bXtlwCbSh2Tar/DgJuBW4C1hY7JecB7BuT/yYnA14Hl1eWfKfn7qe3/B7RORJc4JhuB36u2TwK2Fvz9XAOcW23/CvC3DdXyPODZwJYprn8x8GlaK3j8AvDlA33MYe4B7/nYcmb+FGh/bHmPzLwxMx+pLt5Ca15xiToerF08hOYWopixlspbgT8H/q1wHf3QSy2vATZk5gMAmdnU6vuzPS5nA1cVqiOBw6vtI4AfNVBHr7WcBPxztX1jl+vnRGbeDNw/zS6nA1dmyy3ASEQc0HJqwxzATwbGapfHq7apXEDr1atIHRFxUUTcBfwF8F8aqKOnWqq3TSsz8x8aqqGnOiovr97KfSwimvrqiF5qeSrw1Ij4QkTcUq3IV6oWoPW2GziBx4On33X8KfA7ETEOfIpWb7wJvdTyDeBl1fYZwGERcVRD9Uxntpkzo2EO4J5FxO8Aa4F3lKohMzdk5lOANwD/s0QNEbEI+Evgj0o8fodPAqsy82TgBuCKgrUcRGsY4jRavc73R8RIwXqg9SGkj2Xm7kKPfzZweWYeS+ut999W/39K+O/AL0fE14FfpvXp2FLHZU4NcwD39LHliPhV4E3ASzLzJ6XqqPkI8NIG6uillsOAZwKfi4ittMaxrm/gRNyMxyQzd9R+Hx8Afm6Oa+i5Flo9mesz89HM/CHwXVqBXKKWtrNoZvih1zouAD4KkJlfAp5Ia0GavteSmT/KzJdl5rNo/S2TmY2cnJzB3C+V0MRgdj9+aPVafkDrbVp78P4ZHfs8i9YA/4mF6zixtv2bwOZStXTs/zmaOQnXyzFZUds+A7il4O9nHXBFtX00rbeZR5X6/QA/C2yl+qBUoWPyaeC8avvptMaA57yeHms5GlhUbb8NeEsTx6W6/1VMfRLu19n7JNxXDvjxmnoi/fih9dbou1XIvqlqewut3i7APwHbgduqn+sL1fFu4JtVDTdOF4pN19KxbyMB3OMx+bPqmHyjOiY/W/D/SdAamvkWcAdwVsnfD63x17c3VUOPx+Qk4AvV7+c24NcK1vIK4HvVPh8ADm6ojquAbcCjtN4VXQBcCFxY+3+yoarzjrn42/GjyJJUyDCPAUvSUDOAJakQA1iSCjGAJakQA1iSCjGANe9VK2j1/GGTaqW290xx3Rerf1e1V82KiLUR8VfV9mkR8Zy5qFvz30B9JZG0vyJicfbhY7uZuU+4ZuZmYHN18TTgX4EvNl2Lhp89YA28qrd5Z0R8KCK+XS3esywitkbEn0fErcBvRcSaajGd2yPiuohYXrubc6o1drdExCnV/Z4SEV+q1rz9YkQ8rbb/yqrn/L2IeHOtln/tUt9pEfH3EbGK1sT9/1Y91qkR8cOIeEK13+H1y5IBrGHxNOBvMvPpwIPA71ftOzLz2Zn5EeBK4A3ZWuDnDuDNtdsvy8w11e0uq9ruBE7N1hoDlwD/q7b/KcDLgZNphfuMQxiZuRV4H3BpZq7JzP9H69OGv17tchbw8cx8dFbPXPOWAaxhMZaZX6i2/y/w3Gr7aoCIOAIYycybqvYraC2w3XYV7Fnz9fBqtbMjgGuqsdxLgWfU9r8hWwsG7QI+Xnu82foA8Kpq+1XA/9nP+9E8ZABrWHR+Zr59+eEDuP1bgRsz85m0Fkl6Yg+PNyvVi8aqiDgNWJyZXb/uRguTAaxhcVxE/GK1/Z+Bz9evzMydwAMRcWrVdA5wU22XVwJExHOBndX+R/D4coLndTzeCyPiyIhYSmv50C/Qm4doLftZdyXwYez9qoMBrGHxHeCiiPg2sBx4b5d9zgXeERG3A2torajV9m/Vgt7vo7XKFbS+neTPqvbOGUFfAa4FbgeurWY69OKTwBntk3BV24eqmpta31dDytXQNPCq2QV/Xw0VDJ2IeAVwemaeU7oWDRbnAUsNioi/Bl5Ea81baS/2gCWpEMeAJakQA1iSCjGAJakQA1iSCjGAJamQ/w/pghFZysDZBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df_proba_sample_c = df_proba_sample[df_proba_sample['Sample'] == 'TNE1095']\n",
    "sns.displot(data =  df_proba_sample_c , x='probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6cf78170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f33c7ef9630>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df_proba_sample_c = df_proba_sample[df_proba_sample['Sample'] == 'TNE0286']\n",
    "sns.displot(data =  df_proba_sample_c , x='probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51b5fd0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f33c7f70fd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mido6A60NhGFI/t5FhMd9LOtSRKQJSjEL/Oz9Zmw5dzt3eMqTEt39e9O9TIdaIG2iZ1Yu6xJERHaiABERkVQUIB3I3cnn8xSLxaxLEZEOpgBpE9WB9EkeD0tRdSA9jGKCBy4lDMPmFSgiex0FSJsISzHB968g3s2y7UFNiPR0a8xEROqXrB7yY+D1ZrYhWTFktzQLq41MFgruTliK2KenGzNrclUiMtO6u9hYz8yp6bzeVPvsZvWQSakF0gHiKOLLvQt0USmRDhFGfqi720z9hJE3ZHl4BUiH6LIcoS5tKyJNpADpEHEccfMmXdpWRJpHAdIBoqhE5K5L24pIUylAREQkFQVImwtKEZGr20pEmk8B0uJ2nECowXERaS0KkBZXWYl3dycQiohkQQHSBrQSr4i0IgVIG3P38tIl6t0SkQwoQNpYGMUsf+lgIp08KCIZUIC0uV3P/ahdlVdEpJEUIG0gCCOiWKEgIq1FASIiIqkoQEREJBUFSIsLgoAoqq/7qnJdEK3IKyLNoADpIHEccUvfoVqRV0SaQgHSYXJdWpFXRJpDASIiIqkoQEREJBUFiIiIpKIAaXFBEOgkQhFpSQqQNjXZQopRVCLUUiYi0gSastOmwijmpvWHEOucDxHJiFogbWzXhRRFRJpJASIiIqnoK2yLCoIg1fO0nImINItaIC0sCIJpB0kcR3y573AtZyIiDacA6UAaGxGRZlCAtKHKFF51UolIlvRVtQ0ViiE3vjSPLkJixYiIZEQtkBbm7gRBMGFEqJtKRLKmAGlhYRgy9sASYg2Ii0gLUoC0uJ7uXNYliIhMSAHSoardXzofREQaRAHSocIoJvjuYsIwzLoUEelQCpAOpu4vEWkkBYiIiKSiAGlRaZYxERFpJgVIm4qiElGs6b0ikh0FiIiIpKIAaUHqvhKRdqAAaUE6h0NE2oECpAWFYcjY/Zfu0TkcQSki0hIoItJAWpGvhdR2W/V054gyrEVEZCpqgbSoHSvxqhtLRFqTAqRFhVFM9MMriVNM1Y2iEkFJ7RcRaSwFSAvrye1YiiQoRQoFEWkpmQSIma0zs8fN7FEzeyTZdoCZrTGzZ5M/5yTbzcyuMLO1ZvaYmR2bRc3NUm/XVRSViDRLS0QylGUL5A/c/Rh3X5jcPxO4392PAu5P7gO8Gzgq+TkdWNr0SpvE3RkcHGRo1QWpuq5ERJqplbqwTgKWJ7eXA++p2b7Cyx4GZpvZ/CwKbLTK9N3uLsu6FBGRKWUVIA7cZ2b/bWanJ9vmufvm5HYvMC+5vQBYX/PcDcm2nZjZ6Wb2iJk9snXr1kbV3XBagl1E2kVW54H8rrtvNLODgTVm9ovaB93dzWxaHfzuvgxYBrBw4UINDoiINFgmLRB335j8uQW4EzgO6Kt0TSV/bkl23wgcVvP0Q5Ntex13JyhFWuJERFpC0wPEzF5lZq+p3AbeBTwBrAROTXY7Fbgrub0S+HAyG+utwPaarq69ShjF3LB+PqGWKBGRFpBFF9Y84E4zq7z/re5+r5n9DLjDzE4DXgQ+kOx/D3AisBYYBT7a/JJbRy6n1WdEpDU0/Wjk7s8D/2eC7duAd06w3YEzmlBaywtLEVE8dfeVuxOqq0tEGqyVpvHKDInjiJs2zqcwnn41XxGRqShAOlSuS11dItJYCpAOpgtTiUgjKUA6WBjFjN23aI8uTCUiMhkFSIfrmaUz20WkMRQgbSQsRQRBQKhl3UWkBShAWkQQBDtd0lZEpNUpQDpYUIqIIrVWRKQxFCAiIpKKAkRERFJRgIiISCoKkA6l9bBEpNEUIBlr1NnicRyxYvOhhCUt/S4ijaEAyVgYhuRXnV/32eKVlgV15I2WfheRRlKAZCwIAnJd5WDI5/OU4omn3VaCI4ojbtk8n0hdUyKSMQVICwjCiEKhwNgDS4gnudpgGMXc1juf2F0tCxFpCQqQFtLT/fJ1q2qvg57LaV0rEWkdCpAM1DNwHpQiglKk66CLSMtSgGSgMnBeWf/KpxgRV5eViLQiBUhGZnV3USgUGFl90aTjHhXTmXklItIsCpCMhKWYsTWL6e6yKfeNo4ibNx6imVci0lIUIBmazsWedI1zEWk1CpAWUB1Un6CPKihFRLFaHiLSevS1tgWEUUzw0BIAcjZ1l5aISCtQgLSInu4c+fGAnBn7dhlBKcLcGS2GU87SEhHJgrqwMhKE5asFlq8auPMsrDCKufHFuYyMFrll83xidWGJSAtSgLSoyqC5zgERkValo1MLCkoRke9olURRCatjuq+ISDOpBdJk1VV3o9KEj1eWMKmIopLO/xCRlqQAabJCocD2VYuIY61tJSLtTQGSgcqqu0FyfY9GqV3JV0RkpilAWsiONa/2/IAfRSXCUomlL8yr+2qHIiLToQBpIWEUs2Lja2d06fbcBNcYERGZCQqQFqNpuyLSLhQgLUpLuItIq1OANNBkVx4shiXGiuFuB7fjOOL2rYdpCq+ItCwFSANVrjwYhmH16oNA3ZepnalroNe+t4jITFGANNhk1/yojHXsOtV2pruu4tgVHiLSEAqQjFVaI4ViSFiKiOOIWzbPn9GuK7VARKQRFCCtwMvjImEpwnHNxBKRtqAAaaLKoHqxVHrZVQZLUdyYpdud6kD+ZIP6IiJpKECaoFgsks/nCYKA/OrFL1sHq3bcY6ZnXUXuBN+/gkKhQKFQqA7qi4jsKQVIE4RhyNiaxYRheeru6HhA7HF55d2wvOTITI971OqpORt9skF9EZHpUoA0QGXQ2t0JwvIMq8qBO0y6qkphiRs37Fi2pJHjHuq6EpFGUIA0UBiGXP7MAYRhSDEokc/ndxok7+rKVQfOG1pHFBN9/zIKhQKlUklhIiIzQgHSYJWTAcMoZuyBzxOWdow/xFHEig0Hz+jiibWiqESUjLf0VOooxYzdt0jjICKyxxQgDRQEwU6ti56aM8srB/eZOtt8OjQOIiIzQQHSILXjDrsGiYhIJ1CANEAQBBQKBQZWLSYMY4buu4Th0fGWGHcIwogoijSwLiJ7TAHSQJXps2bG8k0LytN2a9a5KndjZXMAr13oUUQkDQVIsziMFkOWv3QwQSls+jLtxbDEyMgII+NFSqXyddinGgtRK0VEdkcB0iSVs81zXc1f56owHjBUGKfw4GVc89yBDObr607b01aKAkiksylAZtiuB804joni8iq7yzfOI4ijzGrr6c5hGMtePIRCoTDl/rVLzKehbjKRzqYA2UOVwBgZGWFkZIRCocDI6osoFApENWERRSWwbKfPBqWIyGO6crnqzLBdl3p3d/L5PMVicacTIdPSlOEyLakvnUgBsoeCIGBg5TkUi8VyeNx7ATkrL6BYLJVaYvLujsUaK60ip/jgFxgfD3dqZVQWfRxe9blqiFTOU5nsALi7A2PtUi47bWtAt5a6y0SaTwEyTbseMMMwZOkLcxkaGiK/ZjE5g0IxpPDAEq5dO5ux5EJRWSZJKYr5cu+Cnc54NzOuePZACoUCcRwzODjI1m+exfa7zy8vu7J6EcP3XkgpiqorCQdBUA2WiQ7WE302u7ZgJuvW2tMAaKXusuqy/cnnBM1rgShIJ6fPZuYpQKYw1T+6IAgISyX6+voYGSuyvTDGtc8fRBw7ua4ccRxx88ZDmj7rqqIUhoyOB3RNMHgfY+TXLGZ0dJT86h3hd9Wzc8rTjZn49y8UCmz/9jkvu9b7RCY6037Xbq1iscjg4CAjq86rBkAcx+TzeUql8hpiURRN+PdQW1/PrFxdf197eiCv5z0GVp7zsq7B3T1npg5uMx2knXTQnejvZaJ91NVYPwXIJCr/kHb9D1np5nF3BgYG6O3tJQwjhr+3lGUvHMjVzx0E1rVTl1EWM68q4jjiK1sXEMXxy1pCUalUnd5bLJUYK4Zcv+5g6MoRRjFXrz2Q0bEifXcvYmBgoPq7F4vF6sG6WCxSLBarB/zKt+7pdGsVCgXyqxfR3WXVbaOjo1x01TL6+/u56KplbN++vfr3UHtQy+fzbL3zrPL7huWQGbnnPAqFwoQtpPHxcfL5POPj49NuHdS2LHZ3IKq0SguFQrXOyu84ODhYvR5M5TOr/Ln1zrOqr5n2wL2nEx8m+l2a3bKr/VxmUuXvpRVaqZ1CATKJ8fFxBgYGiKId38ShfLDZvmpR9aATBAFxFPGNwcMxy4F1UYrCZMZVYxZJnK5KS+iWzfMJSiHFICA/HhDHzmgxJH//pVzz3AEUiiG5mmuH5LpzxFHErb2Hlg+A+TzbVy+mWCwynB/nxRdfZMOdZ5O/bxGjo6MM3L2I3t5eNm/ezLZt2ygWi9XXqhwQgyDYqVurEkKxO8WgtNPn3N3dRRAE9MwqB3B1Sfyag1oYhlyz7mCGhoZ2et0NX/sMvb291ffdtm0bAwMDDA4OcvHSGxgdHd0pACe6YmOxWKS3t5eRkZFqTQMrz6FQKFQDovKcSjdf9bOraXkFQcDAqsWMjBVZfPV1DA0N4e4MDQ1x4RevZWhoiCAIuPbFedX6JztwV/5d1gZgrUq3YSW8aj/7tK2yPZkIMTIywsDAwLQCrfLlYXR0dNJ9amuu/f0GBgYYGBiY9Hm1fy+7fhEZGBhQ62Oa2iZAzOwEM/ulma01szNn8rVrv/HEcczw8DC9vb0suX4F27dv55LHX83mzZurP8tq/qNX5Lpyu9wvH/SiqETkLRIkyTLycRyxfMN8giBk+YZDGC2GRKWI0fGAUliiFEUUxgNKpXJLIdeVo1AoMDg4yHUvzGXTpk1c8uSruHzFbXzppXnkxwKGh4cZCwO2bNnClnsuZPNd55YP4OwIhG13nU0+n9/pP3E+n+e5Oz7Lkqf35/JnDqy2EkZGRigGEQOrFxN7TLFYZGw8YGxsjG3btjEehDsOol1dbF9zKSSTF/JjAde9MJfBey+sthgGBwcZSmbHzerpJp/Plw8aK89h49fPqgbfxq/+V/Ugks/n2Xr3our92m+wbsb2VYt2apWNj4/T39/P8PAwzo5wCoKAm9bPY7QYVj+LIAgYuHsRXbmu6sXGunJdO11+GHhZsA0NDVUDMI7japjEcczIyAjDw8NgMDAwUO6aTFpbk7WYKq8xOjrKyMgI4+PjO71nHMcvazFW3quyb+3vVPt/qXJgHlp1wc7rwu0mTCr7VL407Frr8PAww8PD5S8sAwPl+oKA/m9+tvpZVD7zKBm/K5VK9Pf309/fT+xxtUt0YGCALXd+hvHxcTZt2sSmTZsmDWaZWFsEiJnlgKuAdwNHA6eY2dEz9fqVbzxbtmzh+eef59wvXMmVy28limN6e3sZHQ945itncfHS6xkeHiYMS7z44ots27btZa8VRSWCsPlnmk+bdRETYxg3rT+Y8VLIl3sXJDO2SqzYOJ+x4hhBFFGKY0a/fzWjo6NYLkf4k+vpshw5M2J3rl8/ny13X8A1z+7PTXet4qb1B3PDSwdz7e1fZ3S0yLZt28jn8yz++b48d8dniL18cBkZGaG3t5dbNs8jjGIco1Ao0HfnZ1ly/U0Uxsa5ecN8gjCmr6+PJb+cw8aNG7lo6fVc/ss5DA0NsW3VRURRTE93jlIU03/PhVy99gAAlm9cUG2lbP/OFwiC8phNFDtjDyxJxq/ictjcdzGFQoHrXjqE3t5e+vv7yefzfGn9PAqFQrU1EuNJd17AdS8dwuDgIC99/Wx6e3sZGhric5dfxXlXLqUwFtC/ajED95xPPp/Huqzaheju9PX1cc3zBxDHXv3mXBgtMnD3+dXLD1daVJXwHRkZoVgsMqsnRz6fZ3BwkEVXXFsO7S1bOOuSy1n39bMZHi5wxfJby91/X7yWrVu3svSFudWW2KZNm+jv72dkZIS+vj4uXnoDzz77LOddfjX93/xs9fettLYqdVQO7oVCgQuuuIZNX/t0tTu3WCyyefNm1q9fz0VXXcvo6ChBEND37Qu5bt08BgcHGRwcJL/q/J0uuAZUf//NmzeXWxB3LyJOvnTVtvSHhob43GVXs+jKa+nv7+fipTewYcMGXnrpJS558tVcvPSG8ljayAjbVp7N888/z/mXXcW6des4d8kX+cINKxgZLnDpdTcxNDTE+m+cy5Kn9+OJJ57g0huWc/WtX2PLli0dMd5U4OZTAAAKAklEQVTTLNl1zk/PccBad38ewMxuB04CnprJN6l8K60IwoDR0VHGw3G+suVQXvVKZ3R0lLEwoP++z5d3OvZUglJ5plVXlxO77bgdxy/fNt3baV9jiudZlxEERaCLXC45uFkXcVwkjBwzKJUiurq6iD3i5k3zee93v0gxeAOj4wFjYYAZxDF0xzGj40Fy7REjcieOwS1mrDjOxpWfY1Z3jvHwQG7e/Fr2f3WRvr4+gh9cxdBInlI8F+IS+fFx1t95DrO6c8T+SsaDcUrdOcKxMQa/cxlB6Q303XspYWkBpThmw13nM6s7RxCGDOXHGMkXuGZ4Dj3dJUqlEnGX0dvbC8BwYZzbty7gL4eWMDb+esaCgLG+PlY8/WrCKOS6dQfxgU2bCEoleu++kH1m5SiGEaXotdWujd57FpMfPZKtqy9lbPwIct3dbPjmuaxYfzCnvGl7+WAfRbhDYWyUa56fw7777MOH37CB0bExvjR8MK/Yp9waKnzvKoLSPAgCtmwvMHD3YsaDXyEolb8Zl785R9XumCue2o8PDv0nxTAiP3o4fXcvYtbbPoZTbkH09PQAEJYiglJALtfN4OBgOVTvXUwxOIL169cz/oOrGC+Wv2Ef+JpXEh3398TubH/wi7i/sTyD8BtnkXv7GdzwzGxOO2aQIIzo7e2lp6eHsQeXkHv7GZTCEpc9M5v/d/uZHPJnZ5HP59myajGzunN4/GvV/0fXPT+bfV8RVe+/YjwgGBwkeHAJPcf/Kz09PQRBwNZ7FpF768fo6elh6drZ5GYVd/q9Kv8/AWIvzxqM3RlYvZjwmI8QxTGlOGbzPRez76xurhv+Nf507iaipBVUMRYWeUXuFfT19XHDC7OJu6Lkd19A7M7Q/Us45G8Wz+RhpaNZO6Stmb0fOMHd/za5/yHgt9394zX7nA6cntx9PfDLCV7qIKC/weVOpRVqgNaoQzXs0Ap1tEIN0Bp1TLeGfnc/oVHFtKp2aYFMyd2XAct2t4+ZPeLuC5tUUsvW0Cp1qIbWqqMVamiVOlqhhnbQFmMgwEbgsJr7hybbREQkI+0SID8DjjKzI82sBzgZWJlxTSIie7W26MJy95KZfRxYDeSAG939yRQvtdsuriZphRqgNepQDTu0Qh2tUAO0Rh2tUEPLa4tBdBERaT3t0oUlIiItRgEiIiKpdFyATLXkiZl90syeMrPHzOx+M3tdRnV8zMweN7NHzewHM3lmfb011Oz3PjNzM2vItMU6PouPmNnW5LN41Mz+ttk1JPt8IPm38aSZ3TrTNdRTh5ktqfkcnjGzoQxqONzMHjCz/03+n5yYQQ2vS/5/PmZmD5rZoQ2o4UYz22JmT0zyuJnZFUmNj5nZsTNdQ9urrHvTCT+UB9ifA34F6AF+Dhy9yz5/ALwyuf0PwFcyqmO/mtt/Dtzb7BqS/V4DfA94GFiY0WfxEeCLGf+7OAr4X2BOcv/gLOrYZf9PUJ4w0uzPYhnwD8nto4F1GdTwVeDU5PYfAjc34O/j94BjgScmefxEYBVgwFuBnzTq32i7/nRaC6S65Im7B0BlyZMqd3/A3SvLfD5M+ZySLOoYrrn7Kmb+klNT1pA4H1gMjM/w+0+3jkaqp4a/A65y90EAd9+SUR21TgFuy6AGB/ZLbu8PbMqghqOB7ya3H5jg8T3m7t8DJl+6t/yeK7zsYWC2mc2f6TraWacFyAJgfc39Dcm2yZxG+RtGJnWY2Rlm9hxwMfBPza4haZIf5u53z/B7T6uOxPuSboKvmdlhEzze6Bp+Hfh1M/uhmT1sZo1YlqLuf59J1+qR7DiINrOGc4APmtkG4B7KLaFm1/Bz4C+S2+8FXmNmB85wHVOZ7vFkr9NpAVI3M/sgsBC4JKsa3P0qd/9V4FPAZ5r53mbWBXwB+Ldmvu8kvgUc4e5vAdYAyzOooZtyN9bxlL/5X2dmszOoo+Jk4GvuHmXw3qcAN7n7oZS7cW5O/r00078Dv29m/wv8PuWVJ7L4LGQ3Oi1A6lryxMz+CPg08OfuXtz18WbVUeN24D1NruE1wJuBB81sHeU+3pUNGEif8rNw9201fw/XA7/Z7Boof7tc6e6hu78APEM5UJpdR8XJzHz3Vb01nAbcAeDuPwb2pby4YNNqcPdN7v4X7v4blP+v4u4zPqFgClpCaSpZD8LM5A/lb5HPU276Vwbn3rTLPr9BeQDvqIzrOKrm9p8BjzS7hl32f5DGDKLX81nMr7n9XuDhDGo4AVie3D6IctfFgVn8nQBvANaRnOibwWexCvhIcvuNlMdAZqyWOms4COhKbl8AnDfTn0Xy2kcw+SD6n7DzIPpPG1FDO/9kXkAD/kGcSPnb43PAp5Nt51FubQB8B+gDHk1+VmZUx+XAk0kND+zu4N6oGnbZtyEBUudncWHyWfw8+SzekEENRrlL7yngceDkLD6L5P45wEWNeP86P4ujgR8mfx+PAu/KoIb3A88m+1wP7NOAGm4DNgMh5RboacDHgI/V/Ju4Kqnx8Ub9/2jnHy1lIiIiqXTaGIiIiDSJAkRERFJRgIiISCoKEBERSUUBIiIiqShAZK+QrOha90mSyQrBX5zksR8lfx5RWcnVzBaa2RXJ7ePN7O0zUbdIK2uLS9qK1MPMct6EpT/c/WXh4O6PAI8kd48H8sCPGl2LSJbUApG2kHzb/4WZ3WJmTyeLLr7SzNaZ2WIz+x/gL83smGQxxMfM7E4zm1PzMh9KrrPxhJkdl7zucWb24+TaFz8ys9fX7H9Y0nJ51szOrqklP0F9x5vZt83sCMono/1r8l7vMLMXzGxWst9+tfdF2pkCRNrJ64Gr3f2NwDDwj8n2be5+rLvfDqwAPuXlhRkfB86uef4r3f2Y5Hk3Jtt+AbzDy2sufRZYVLP/ccD7gLdQDqcpu8DcfR1wDbDE3Y9x9+9TPsv/T5JdTga+4e7htH5zkRakAJF2st7df5jc/jLwu8ntrwCY2f7AbHd/KNm+nPJFgypug+p1IPZLVtvdH/hqMpaxBHhTzf5rvLzQ4xjwjZr3m67rgY8mtz8KfCnl64i0FAWItJNd192p3C/swfPPBx5w9zdTXtRy3zreb1qS0DvCzI4Hcu4+4SVURdqNAkTayeFm9rbk9l8DP6h90N23A4Nm9o5k04eAh2p2+SsAM/tdYHuy//7sWKL7I7u83/81swPM7BWUl9v/IfUZobxcfq0VwK2o9SEdRAEi7eSXwBlm9jQwB1g6wT6nApeY2WPAMZRXeK0YTy5QdA3llVehfDXIC5Ptu85K/CnwdeAx4OvJTKt6fAt4b2UQPdl2S1JzI67xIZIJrcYrbSGZ3fTtpKup7ZjZ+4GT3P1DWdciMlN0HohIg5nZlcC7KV8DQ6RjqAUiIiKpaAxERERSUYCIiEgqChAREUlFASIiIqkoQEREJJX/D/kKzByNSzG4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data =  df_proba_sample , x='probability', hue= 'target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c0b2c7a",
   "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>Sample</th>\n",
       "      <th>Tiles</th>\n",
       "      <th>target</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.308092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.337643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.268066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TNE1095</td>\n",
       "      <td>/data/gcs/lungNENomics/work/MathianE/Tiles_512...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.346144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sample                                              Tiles  target  \\\n",
       "0  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "1  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "2  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "3  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "4  TNE1095  /data/gcs/lungNENomics/work/MathianE/Tiles_512...       0   \n",
       "\n",
       "   probability  \n",
       "0     0.308092  \n",
       "1     0.354153  \n",
       "2     0.337643  \n",
       "3     0.268066  \n",
       "4     0.346144  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proba_sample_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "773bcccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f33c6342828>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df_proba_sample_p = df_proba_sample[df_proba_sample['probability'] > .5]\n",
    "sns.displot(data =  df_proba_sample_p , x='probability', hue = 'target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "773bb662",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (ex)",
   "language": "python",
   "name": "ex"
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