lung_cancer_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of lung cancer subtype with a nested cross-validation pipeline\n",
    "\n",
    "This jupyter notebook aims to illustrate our nested cross-validation python implementation with a simple example. We study the classification of Non-small cell lung cancer histological subtypes using transcriptomic data.   \n",
    "\n",
    "**Note** : To run this jupyter notebook, you first need to extract the data set \"data.csv\" from the zip file \"data.zip\" (same directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Load the data set\n",
    "\n",
    "* Data set extracted from [\"Defining the Biological Basis of Radiomic Phenotypes in Lung Cancer\" Grossman et al. 2017](https://elifesciences.org/articles/23421).<p>  \n",
    "    \n",
    "* Expression of **21,766 unique genes** for **224 patients** with Non-small cell lung cancer (NSCLC).<p>  \n",
    "    \n",
    "* Gene expression values were measured on a custom Rosetta/Merck Affymetrix 2.0 microarray chipset and normalized with the robust multi-array average (RMA) algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "224  samples with histological information\n",
      "21766  unique genes\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geneid.3643</th>\n",
       "      <th>geneid.84263</th>\n",
       "      <th>geneid.7171</th>\n",
       "      <th>geneid.2934</th>\n",
       "      <th>geneid.11052</th>\n",
       "      <th>geneid.1241</th>\n",
       "      <th>geneid.6453</th>\n",
       "      <th>geneid.57541</th>\n",
       "      <th>geneid.9349</th>\n",
       "      <th>geneid.11165</th>\n",
       "      <th>...</th>\n",
       "      <th>geneid.1572</th>\n",
       "      <th>geneid.8551</th>\n",
       "      <th>geneid.26784</th>\n",
       "      <th>geneid.26783</th>\n",
       "      <th>geneid.26782</th>\n",
       "      <th>geneid.26779</th>\n",
       "      <th>geneid.26778</th>\n",
       "      <th>geneid.26777</th>\n",
       "      <th>geneid.100132941</th>\n",
       "      <th>histology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RadioGenomic-001</th>\n",
       "      <td>4.871974</td>\n",
       "      <td>8.202717</td>\n",
       "      <td>9.819270</td>\n",
       "      <td>8.492876</td>\n",
       "      <td>7.712078</td>\n",
       "      <td>7.063770</td>\n",
       "      <td>10.247928</td>\n",
       "      <td>8.614199</td>\n",
       "      <td>5.649873</td>\n",
       "      <td>7.049535</td>\n",
       "      <td>...</td>\n",
       "      <td>3.608932</td>\n",
       "      <td>6.584940</td>\n",
       "      <td>6.109479</td>\n",
       "      <td>4.685262</td>\n",
       "      <td>5.636591</td>\n",
       "      <td>3.165858</td>\n",
       "      <td>9.095403</td>\n",
       "      <td>3.477838</td>\n",
       "      <td>5.435395</td>\n",
       "      <td>adeno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RadioGenomic-002</th>\n",
       "      <td>4.884625</td>\n",
       "      <td>7.930854</td>\n",
       "      <td>9.777377</td>\n",
       "      <td>8.011972</td>\n",
       "      <td>7.862847</td>\n",
       "      <td>8.735264</td>\n",
       "      <td>10.122395</td>\n",
       "      <td>9.285171</td>\n",
       "      <td>8.348704</td>\n",
       "      <td>7.146661</td>\n",
       "      <td>...</td>\n",
       "      <td>3.527583</td>\n",
       "      <td>6.884115</td>\n",
       "      <td>6.724831</td>\n",
       "      <td>5.413868</td>\n",
       "      <td>6.310839</td>\n",
       "      <td>3.306311</td>\n",
       "      <td>9.579478</td>\n",
       "      <td>3.445292</td>\n",
       "      <td>5.280185</td>\n",
       "      <td>squamous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RadioGenomic-003</th>\n",
       "      <td>6.544434</td>\n",
       "      <td>7.952210</td>\n",
       "      <td>10.185486</td>\n",
       "      <td>9.233013</td>\n",
       "      <td>6.840010</td>\n",
       "      <td>9.234796</td>\n",
       "      <td>10.790635</td>\n",
       "      <td>7.844318</td>\n",
       "      <td>3.943773</td>\n",
       "      <td>7.151620</td>\n",
       "      <td>...</td>\n",
       "      <td>4.327221</td>\n",
       "      <td>7.774516</td>\n",
       "      <td>6.727119</td>\n",
       "      <td>5.079287</td>\n",
       "      <td>5.938707</td>\n",
       "      <td>3.677257</td>\n",
       "      <td>9.817884</td>\n",
       "      <td>3.578696</td>\n",
       "      <td>5.879146</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RadioGenomic-004</th>\n",
       "      <td>7.951348</td>\n",
       "      <td>7.607591</td>\n",
       "      <td>9.666986</td>\n",
       "      <td>7.831439</td>\n",
       "      <td>7.081398</td>\n",
       "      <td>8.202348</td>\n",
       "      <td>10.632971</td>\n",
       "      <td>8.918782</td>\n",
       "      <td>4.857194</td>\n",
       "      <td>9.226158</td>\n",
       "      <td>...</td>\n",
       "      <td>3.571539</td>\n",
       "      <td>5.680616</td>\n",
       "      <td>5.882869</td>\n",
       "      <td>4.717828</td>\n",
       "      <td>6.143484</td>\n",
       "      <td>2.819706</td>\n",
       "      <td>8.918029</td>\n",
       "      <td>3.776916</td>\n",
       "      <td>4.909247</td>\n",
       "      <td>adeno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RadioGenomic-006</th>\n",
       "      <td>5.888098</td>\n",
       "      <td>7.357627</td>\n",
       "      <td>10.091727</td>\n",
       "      <td>7.951360</td>\n",
       "      <td>6.990130</td>\n",
       "      <td>7.073570</td>\n",
       "      <td>9.687328</td>\n",
       "      <td>8.537944</td>\n",
       "      <td>6.253872</td>\n",
       "      <td>7.541714</td>\n",
       "      <td>...</td>\n",
       "      <td>3.435329</td>\n",
       "      <td>7.009447</td>\n",
       "      <td>5.809794</td>\n",
       "      <td>4.576371</td>\n",
       "      <td>5.523023</td>\n",
       "      <td>3.164927</td>\n",
       "      <td>8.552383</td>\n",
       "      <td>3.376662</td>\n",
       "      <td>5.805264</td>\n",
       "      <td>adeno</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21767 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  geneid.3643  geneid.84263  geneid.7171  geneid.2934  \\\n",
       "RadioGenomic-001     4.871974      8.202717     9.819270     8.492876   \n",
       "RadioGenomic-002     4.884625      7.930854     9.777377     8.011972   \n",
       "RadioGenomic-003     6.544434      7.952210    10.185486     9.233013   \n",
       "RadioGenomic-004     7.951348      7.607591     9.666986     7.831439   \n",
       "RadioGenomic-006     5.888098      7.357627    10.091727     7.951360   \n",
       "\n",
       "                  geneid.11052  geneid.1241  geneid.6453  geneid.57541  \\\n",
       "RadioGenomic-001      7.712078     7.063770    10.247928      8.614199   \n",
       "RadioGenomic-002      7.862847     8.735264    10.122395      9.285171   \n",
       "RadioGenomic-003      6.840010     9.234796    10.790635      7.844318   \n",
       "RadioGenomic-004      7.081398     8.202348    10.632971      8.918782   \n",
       "RadioGenomic-006      6.990130     7.073570     9.687328      8.537944   \n",
       "\n",
       "                  geneid.9349  geneid.11165  ...  geneid.1572  geneid.8551  \\\n",
       "RadioGenomic-001     5.649873      7.049535  ...     3.608932     6.584940   \n",
       "RadioGenomic-002     8.348704      7.146661  ...     3.527583     6.884115   \n",
       "RadioGenomic-003     3.943773      7.151620  ...     4.327221     7.774516   \n",
       "RadioGenomic-004     4.857194      9.226158  ...     3.571539     5.680616   \n",
       "RadioGenomic-006     6.253872      7.541714  ...     3.435329     7.009447   \n",
       "\n",
       "                  geneid.26784  geneid.26783  geneid.26782  geneid.26779  \\\n",
       "RadioGenomic-001      6.109479      4.685262      5.636591      3.165858   \n",
       "RadioGenomic-002      6.724831      5.413868      6.310839      3.306311   \n",
       "RadioGenomic-003      6.727119      5.079287      5.938707      3.677257   \n",
       "RadioGenomic-004      5.882869      4.717828      6.143484      2.819706   \n",
       "RadioGenomic-006      5.809794      4.576371      5.523023      3.164927   \n",
       "\n",
       "                  geneid.26778  geneid.26777  geneid.100132941  histology  \n",
       "RadioGenomic-001      9.095403      3.477838          5.435395      adeno  \n",
       "RadioGenomic-002      9.579478      3.445292          5.280185   squamous  \n",
       "RadioGenomic-003      9.817884      3.578696          5.879146      other  \n",
       "RadioGenomic-004      8.918029      3.776916          4.909247      adeno  \n",
       "RadioGenomic-006      8.552383      3.376662          5.805264      adeno  \n",
       "\n",
       "[5 rows x 21767 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data.csv\" , index_col=0)\n",
    "print(df.shape[0] , \" samples with histological information\")\n",
    "print(df.shape[1] - 1, \" unique genes\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adeno       129\n",
       "squamous     61\n",
       "other        34\n",
       "Name: histology, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10 , 6))\n",
    "df['histology'].value_counts().plot.bar(rot = 0 , color = ['blue' , 'red' , 'green'], fontsize = 12)\n",
    "df['histology'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we tackle the binary classification problem \"adeno vs (squamous + other)\" defining the binary target y (0 : adeno , 1 : squamours or other)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[: , :-1].values\n",
    "y = 1*(df.iloc[: , -1] != 'adeno').values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Define the pipeline\n",
    "   \n",
    "Defining complex pipelines with several operations that will be fitted through the different folds is an easy task thanks to the [Pipeline](https://scikit-learn.org/stable/modules/compose.html) tool of scikit-learn. You can integrate any estimator that is compatible with the sklearn API (even custom-made !).   \n",
    "    \n",
    "One of the main advantage of nested_cross_val is that once your pipeline has been created, you do not have to worry about it anymore. It will be fitted through the different folds of a nested cross-validation scheme without any data leakage.   \n",
    "\n",
    "Here, to simply illustrate our nested cross-validation implementation, we use a pipeline composed by a PCA step and a logistic regression for the classification of the NSCLC subtype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='diagram')\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity (and a later analysis of feature importances), we would like to fix the number of PCA components. To choose an appropriate number of components we can first look at the PCA decomposition of the full data set.   \n",
    "\n",
    "This is not, in fact, a trivial question. We chose a number of 20 components as it seemed to be a reasonable compromise between a limited number of components and a large fraction of the variance explained. Obviously for further analyses this choice will need to be tackled more cautiously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'explained variance')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UccZtLzJqQDH3X9rpMRMiIiLSzZjZG+5e3rpcQxUToH9xPvdfehgDehVEHYqIiIikMS0hlSCjBvSkV0EutQ1N6q8mIiIiu0WJWoI98MoHfO3Bt3jkjRVRhyIiIiJpRolagl181AiOHNmPax+dy5wVVVGHIyIiImlEiVqC5WRn8ZvPTaS0OJ8v3f8GazfXRR2SiIiIpAklaknQrzif3114CBu31XPt1DlRhyMiIiJpQqM+k2T/st785vyDGVnaI+pQREREJE2oRi2JThg/kJGlxbg7i1ZtjjocERERSXFK1CJw9wtLOOO2F5n94caoQxEREZEUpkQtAuccMowBvfK5/IE3WLO5NupwREREJEUpUYtAnx553HVhOdU1DXz5gTepb2yOOiQRERFJQUrUIjJ+SC9+dvaBzPpgIz/4x/yowxEREZEUpFGfEfrkgUNYtm4rE4b2jjoUERERSUFK1CJ21fGjtz/fWtdIj3zdEhEREQmo6TNFPPjah5zwi+dYvUmDC0RERCSgRC1FHDS8hOqaBi5/4A3qGpuiDkdERERSgBK1FLHvoF7ccs6BzP6wiusfn4+7Rx2SiIiIREwdolLIqQcM5svH7cNvn32ff85bxaaaBoaUFDJl8ljOmlgWdXgiIiKSZErUUsyo0mKyDKprGgBYWVXDtVPnAihZExER6WbU9Jlifv7UuzS3avWsaWji5hmLoglIREREIqNELcVUVNV0qlxEREQylxK1FDOkpLBT5SIiIpK5lKilmCmTx1KYm71DWWFuNlMmj40oIhEREYmKBhOkmJYBAzfPWMTKqhoM+OGZ+2kggYiISDekGrUUdNbEMl665hPcdeEhOFDWpyjqkERERCQCStRS2FGj+pOTZcxevjHqUERERCQCavpMYcX5Obx8zScY0Ksg6lBEREQkAqpRS3FK0kRERLovJWopblNtA1f+5U2mz62MOhQRERFJMiVqKa44L4dXlqxnxvxVUYciIiIiSaZELcVlZRmTRpfywnvraG69tpSIiIhkNCVqaWDSmFI2bK1nXkV11KGIiIhIEilRSwPHjO4PwHOL1kYciYiIiCSTErU00L84nzMOHEJJj7yoQxEREZEk0jxqaeK28ydGHYKIiIgkmWrU0khDUzNV2+qjDkNERESSRIlamnB3Jv3sGX76r3eiDkVERESSRIlamjAzJgztzfPvrsNd03SIiIh0B0rU0sikMaWsrKrh/bVbow5FREREkkCJWhqZNLoUgOfe1TQdIiIi3YEStTQyrG8RI0t78LwSNRERkW5B03Okme+cOo7ehblRhyEiIiJJkLQaNTM72cwWmdliM7umje37mtlMM6szs2915tju5PhxAykf0TfqMERERCQJkpKomVk2cDtwCjAeON/MxrfabQNwFXDLbhzbrcx8fz3/WbA66jBEREQkwZJVo3YYsNjdl7h7PfAgcGbsDu6+xt1fBxo6e2x389tnF2s+NRERkW4gWYlaGbA85vWKsCzRx2akSaNLeW/NFiqqaqIORURERBIoWYmatVEW76ytcR9rZpeZ2Swzm7V2beaOjDx2bDBNh0Z/ioiIZLZkJWorgGExr4cCFV19rLvf5e7l7l5eWlq6W4Gmg9EDihnUq4Dn31OiJiIiksmSlai9Dow2s73NLA84D5iWhGMzkplx7JhS5q6s1nJSIiIiGSwp86i5e6OZXQnMALKBe919vpldHm6/08wGAbOAXkCzmV0NjHf3TW0dm4y4U9l1p42jOD8Hs7ZahkVERCQTWKbWyJSXl/usWbOiDkNERERkl8zsDXcvb12uJaTS2B9eWspX/zo76jBEREQkQZSopbHqmgb+MaeCjVvrow5FREREEkCJWho7dkwp7vDC4nVRhyIiIiIJoEQtjU0YWkJJUa7mUxMREclQStTSWHaWcfSo/rzw3lpN0yEiIpKBkjI9hyTOqfsPJj87i631TRTn63aKiIhkEn2zp7nTJgzmtAmDow5DREREEkBNnxnA3bVAu4iISAZSopYBbv3vYo67+Vm21TdGHYqIiIh0ISVqGeCQvfpQ39TMK0vWRx2KiIiIdCElahmgfEQfCnOzef5dzacmIiKSSZSoZYCC3GyOGNmX5zSfmoiISEZRopYhJo0pZem6rXy4flvUoYiIiEgX0fQcGeLk/QcxoGcB/Yrzog5FREREuogStQwxuHchp00ojDoMERER6UJq+swgKzZu4+7nl1Df2Bx1KCIiItIFlKhlkPkVm7hx+kLe/HBj1KGIiIhIF1CilkGO2qcfOVnG8xr9KSIikhGUqGWQngW5HLxXH03TISIikiGUqGWYY8eUMr9iE2s310UdioiIiOwhJWoZZtLoUnKyjPkV1VGHIiIiIntI03NkmP2G9OKt60+iOF+3VkREJN2pRi3DZGWZkjQREZEMoUQtAy1atZlz7nyZeSvV/CkiIpLOlKhloH7Feby+bKNGf4qIiKQ5JWoZqH9xPvsN6cVzi5SoiYiIpDMlahnq2DGlvPnhRjbVNkQdioiIiOwmJWoZatKYUhqbnZcXr486FBEREdlNStQy1MHD+3DCuAEaASoiIpLG9C2eofJysrjnokOjDkNERET2gGrUMtyGrfVU16ifmoiISDpSopbBVm+q5ZAfPcXUN1dEHYqIiIjshrgSNTPLN7MbzWyJmVWHZSeZ2ZWJDU/2xMBeBYzo10PzqYmIiKSpeGvUfgnsD1wAeFg2H7giEUFJ1zl2TCmvLFlPbUNT1KGIiIhIJ8WbqH0K+Jy7zwSaAdx9JVCWqMCka0wa05/ahmZeX7Yh6lBERESkk+JN1OppNULUzEoBTdKV4o4Y2Y+87CyeV/OniIhI2ol3eo6HgD+Z2dcBzGww8CvgwQTFJV2kKC+HW8+fyH5DekUdioiIiHRSvDVq1wHLgLlACfAeUAH8ICFRSZc6ef9BDOtbFHUYIiIi0klxJWruXu/uV7t7MTAQ6OnuX3f3usSGJ12hsamZh99Ywcz31VItIiKSTuKdnuMLZjYBwN3Xurub2YFmdmFiw5OukJ1l3DzjHR545YOoQxEREZFOiLfp84fA8lZly4EfdW04kghmxqTRpby4eB2NTc1RhyMiIiJxijdR6wVsalVWTdBfTdLApDGlVNc08PaK6qhDERERkTjFm6gtAD7TquxTwMKuDUcS5ZhR/ckyNE2HiIhIGok3Ufs2cI+ZPWJmPzOzqcDvgW/GeyEzO9nMFpnZYjO7po3tZma3htvnmNnBMdu+bmbzzWyemf3VzAriva4E+vTIY8LQEpas2xp1KCIiIhKneEd9vkiwhNTrQA/gNWB/d38pnuPNLBu4HTgFGA+cb2bjW+12CjA6fFwG3BEeWwZcBZS7+/5ANnBePNeVHf31/x3BbedPjDoMERERiVO8E97i7h8CN+3mdQ4DFrv7EgAzexA4k6BJtcWZwH3u7sArZlYSTqzbEmehmTUARQRzuEknFeZlRx2CiIiIdEJciZqZ9QW+BRwEFMduc/dJcZyijB1Hja4ADo9jnzJ3n2VmtwAfAjXAv9393/HELTv79sNz6JGfw/+d0bpCU0RERFJNvDVqfwHygb8D23bjOtZGmcezj5n1Iaht2xuoAh4ys8+7+wM7XcTsMoJmU4YPH74bYWa+LXWNPPvuGr53+jjM2nrLRUREJFXEm6gdBZTuwUoEK4BhMa+HsnPzZXv7nAAsdfe1AOFAhqOAnRI1d78LuAugvLy8dSIowKQx/XlybiWLVm9m30Fa/1NERCSVxTvqcw5B4rS7XgdGm9neZpZHMBhgWqt9pgFfCEd/HgFUu3slQZPnEWZWZEEV0PFoWpDdNmlMKaBpOkRERNJBvDVqTwP/MrM/AKtiN7j7vbs62N0bzexKYAbBqM173X2+mV0ebr8TmA6cCiwmaF69JNz2qpk9DLwJNAKzCWvNpPMG9y5kzMBinnt3LZdN2ifqcERERKQDFgyy3MVOZs+0s8nd/RNdG1LXKC8v91mzZkUdRkq698WlVG2r5xsnjY06FBEREQHM7A13L29dHleNmrt/vOtDkqj07ZHH719cym1PL2ZISSFTJo/lrIllUYclIiIircQ9j1qLsJ/Y9uGC7q5VvtPIY7NXcu3UudQ0NAGwsqqGa6fOBVCyJiIikmLiGkxgZmVm9qiZrSfoJ9YQ85A0cvOMRduTtBY1DU3cPGNRRBGJiIhIe+Id9XknUE8w4nILcDDBKM3LExSXJEhFVU2nykVERCQ6nZlHbbi7bzUzd/e3zexS4GXg7sSFJ11tSEkhK9tIyoaUFEYQjYiIiHQk3hq1JoImT4AqMysFthIs+yRpZMrksRTm7rjmZ2FuNlMmawSoiIhIqom3Ru1VgjnOHiWYC+1vBOtuav6LNNMyYODmGYu216xdfcIoDSQQERFJQfHOo1YCZLn7BjMrJFigvRj4Vbh6QMrRPGq7tmFrPUvXbWHisD5kZWndTxERkajs6TxqVTHPa4Afdl1oEpW+PfLo26Nv1GGIiIhIO9pN1MzsO+5+Y/j8B+3t5+7/l4jAJDkqqmq449n3ueioEYwaUBx1OCIiIhKjoxq12EXYhyU6EIlGTpbxwKsf0K84j6tPGBN1OCIiIhKj3UTN3a8AMLMs4H7gJXevS1ZgkhwDehVw6F59eXJOpRI1ERGRFLPL6TnCJaIeV5KWuU6bMJj31mzh3dWbow5FREREYsQ7j9rzZnZEQiORyJyy/yDM4Mk5KTmAV0REpNuKdx61D4B/mtnjwHJg+5weGkyQ/gb0KuCk8QPJ1hQdIiIiKSXeRK0QeCx8PrSD/SRN/e7CnaZuERERkYjFO4/aJYkORKLn7qzfWk//4vyoQxERERHir1EDwMx6Av2B7W1k7r6kq4OSaHztwbdYWLmJp75xbNShiIiICHEOJjCz8WY2G6gGFoeP98KHZIjyEX00+lNERCSFxDvq87fAM0BfYBPQB/gdcFGC4pIInKzRnyIiIikl3kTtQODb4Zqf5u7VwBS05mdGGdCzgMNG9GX6XCVqIiIiqSDeRK0WyA2frzOz4eGx/RISlUTmdE1+KyIikjLiHUzwAnAu8EfgYeCfQB3wdGLCkqicesBgSnvmM7xvUdShiIiIdHvxTs9xbszL64B5QE/gvkQEJdHpV5zPyfsPjjoMERERIf5Rnwe1PHf3Znd/wN3vcPetCYtMIrNhaz23/vc93l+7JepQREREurV4+6g9ZWYLzOy7ZjYyoRFJ5Bqbm/nlf97l8bcqog5FRESkW4s3URtEMMpzX+AtM5tpZl81swGJC02i0jL688k5Fbj7rg8QERGRhIgrUXP3Jnd/0t0/DwwEfg2cTbBAu2Sg0ycM5v21W3l3tZo/RUREohJvjRoAZlYAnA58FignGA0qGWjy9slv1fwpIiISlXgHE5xqZg8Aa4BvAs8B+7j7CYkMTqIzoGcBx4zqz6baxqhDERER6bbinUftFuAvwPXu/n4C45EU8qdLDiMry6IOQ0REpNuKdx618YkORFJPS5JW29BEQW52xNGIiIh0P53qoybdzw3T5nP6bS9q9KeIiEgElKhJh/YZUMziNVs0+lNERCQCStSkQyfvN4gsjf4UERGJhBI16VBpz3wO37sfT86tVPOniIhIkrU7mMDM7gd2+c3s7l/o0ogk5Zw2YTDffWwei1ZvZt9BvaIOR0REpNvoaNTn4pjn/YGLgCeAD4DhwBnAnxIXmqSKU/YfhAODexVGHYqIiEi30m6i5u7fb3luZjOA09z9hZiyY4DvJTY8SQX9ivO58Ii9og5DRESk24m3j9oRwCutyl4FjuzacCRVba1r5MHXPmTpuq1RhyIiItJtxJuozQZ+bGaFAOG/NwJvJSguSTE1DU1c9+hcpr65IupQREREuo14E7WLgaOBajNbDVQDxxD0W5NuoH9xPkeM7MeTczT6U0REJFniStTcfZm7HwXsA3wSGOXuR7n70oRGJynltAmDWbJuK++s2hx1KCIiIt1C3POomVk/4DjgWHf/0MyGmNnQThx/spktMrPFZnZNG9vNzG4Nt88xs4NjtpWY2cNm9o6ZLTQz9Y2LwOTtk99WRh2KiIhItxBXomZmxwKLgAv4aKTnaOCOOI/PBm4HTgHGA+ebWeuF3k8JzzkauKzVuX8N/Mvd9wUOBBbGc13pWv2L8zlyn34sXa8BBSIiIsnQ0TxqsX4FfNbd/2tmG8OyV4HD4jz+MGCxuy8BMLMHgTOBBTH7nAnc50EHqFfCWrTBwFZgEkE/Ody9HqiP87rSxX5/0aEU5GZHHYaIiEi3EG/T5wh3/2/4vKUneT3xJ3plwPKY1yvCsnj2GQmsBf5gZrPN7B4z69HWRczsMjObZWaz1q5dG2do0hktSVpzswYUiIiIJFq8idoCM5vcquwEYG6cx1sbZa2/6dvbJwc4GLjD3ScS1LDt1McNwN3vcvdydy8vLS2NMzTprF//5z1Ou+1Fjf4UERFJsHgTtW8CfzazPwGFZvY74I/AlDiPXwEMi3k9FKiIc58VwAp3fzUsf5ggcZOI9O+Zx8LKTRr9KSIikmDxTs/xCkEn/vnAvcBS4DB3fz3O67wOjDazvc0sDzgPmNZqn2nAF8LRn0cA1e5e6e6rgOVmNjbc73h27NsmSXayRn+KiIgkRbx9zHD3lcDPduci7t5oZlcCM4Bs4F53n29ml4fb7wSmA6cSLAa/Dbgk5hRfJajRywOWtNomSdYvHP05fW4l3zxpDGZttVqLiIjInoorUTOzvsC3gIOA4tht7j4pnnO4+3SCZCy27M6Y5w58pZ1j3wLK47mOJMdpBwzhukfnsrByM+OH9Io6HBERkYwUb43aX4B84O8EtV3SzU3ebyCrNtXSt0de1KGIiIhkrHgTtaOAUnevS2Qwkj76FefzjRPHRB2GiIhIRot31OccglGYItvVNzbz34Wr+UArFYiIiCREvDVqTwP/MrM/AKtiN7j7vV0elaSFLXWNXHb/G1x+7EimTN436nBEREQyTryJ2scI5jM7sVW5E0zXId1Q3x55HLVPP56cU8m3Thqr0Z8iIiJdLK5Ezd0/nuhAJD2desBgrp06lwWVm9hvSO+owxEREcko7fZRs5jqETPLau+RnDAlVU3ebxDZWabJb0VERBKgoxq1aqBlgqxG2l6b0wkmsJVuqqX5880PN0YdioiISMbpKFHbL+b53okORNLXredNpHdhbtRhiIiIZJx2EzV3Xx7z/IPkhCPpqI8mvRUREUmIuNf6NLNPAscC/QmaPQFw9y8kIC5JM/fNXMYTb1fw9y8dqdGfIiIiXSSuwQBmdj3wu3D/c4D1wGSgKmGRSVrJzc7i9WUbWVC5KepQREREMka8oza/CJzo7l8H6sN/zwBGJCowSS8a/SkiItL14k3UStx9Xvi83sxy3f01gqZQke2jP6fPrcS99QBhERER2R3xJmrvm1nLKNB5wBVmdiGgORlku9MOGMyy9duYX6HmTxERka4Q72CC7wL9wufXAn8GioEvJyIoSU+T9xvE2yuqKcrT1HoiIiJdwTK1maq8vNxnzZoVdRgiIiIiu2Rmb7h7eevydmvUzGxkPCd29yV7EphkFndnzopq+hXnMbRPUdThiIiIpLWOmj4XEywR1dGkWFpCSnZQXdPAp+94mcsmjeTbJ+8bdTgiIiJprd3BBO6e5e7Z4b/tPZSkyQ5KivI4elR/npyj0Z8iIiJ7Kt5RnwCYWZmZHWpmQxIVkKS/wb3z+XDDNkZeO52jb3qax2avjDokERGRtBTvygTDzewF4APgSeBDM3vRzPZKaHSSdh6bvZLH36oAgnbxlVU1XDt1rpI1ERGR3RBvjdqfgDeA3u4+ACgBXg/LRba7ecYiahuadyiraWji5hmLIopIREQkfcU7j9ohwEnu3gDg7lvM7NsEa36KbFdRVdOpchEREWlfvDVqrwCHtSorB2Z2bTiS7oaUFHaqXERERNoX9xJSwHQz+4uZ/dTM/gJMBxab2Q9aHokLU9LFlMljKczdcTBwbrYxZfLYiCISERFJX/E2fRYAU8PnA4A64FGgEBgWlmsuBuGsiWVA0FetoqqGvJwscOeY0f0jjkxERCT9aAkpSajFa7Zw8q+e5+xDhnLTZyZEHY6IiEhKam8JqXin5/h8G2VmZtd2RXCSuUYNKObio0bwt1nLmbuiOupwRERE0kq8fdSuN7O/mVkf2L4O6IvAqQmLTDLGVSeMpl+PPP4+a3nUoYiIiKSVeBO1g4BNwFwz+yHwGvAP4NgExSUZpFdBLo9ccRTf/+R+UYciIiKSVuJK1Nx9K3AdsAH4DvAEcJO7N3d4oEhor349yMoy1m2pY1t9Y9ThiIiIpIV4+6idBrwNPANMAMYAL5jZ3gmMTTLM+i11fPyWZ/ntM+9HHYqIiEhaiLfp807gInf/mrvPAz4GzAA0rFLi1q84n+P3HcBdLyzhw/Xbog5HREQk5cWbqE1w96daXrh7s7v/EDgxMWFJprrmlHHkZBk3Tl8QdSgiIiIpL94+ahvN7EQzu9fMngAws3KCxdlF4jaodwFf+fgoZsxfzUuL10UdjoiISEqLt4/aV4E7gHeBSWFxDfCjBMUlGezSY/ZmeN8iXn5fiZqIiEhH4l1C6mrgeHdfZmbfDsveAbSAo3RaQW42/7jqGHoV5EYdioiISEqLt49aT6BlttKWNadygfouj0i6hZYkbdGqzWzYql8jERGRtsSbqD0PXNOq7CqC6TpEdsv6LXWc8ZsX+fm/F0UdioiISEqKN1H7KvApM1sG9DSzRcA5wDcSFZhkvn7F+XzusOH89bUPWVCxKepwREREUk68oz4rgUOBc4HPARcBh7v7qgTGJt3A108YQ+/CXG54Yj7uvusDREREupF4a9TwwGvu/pC7v9LZ5aPM7GQzW2Rmi82sdTMqFrg13D7HzA5utT3bzGab2T86c11Jbb2LcvnmSWN5bekGps9V3i8iIhIr7kRtT5hZNnA7cAowHjjfzMa32u0UYHT4uIxgOpBYXwMWJjhUicD5hw3noGElrNpUG3UoIiIiKSUpiRpwGLDY3Ze4ez3wIHBmq33OBO4La+5eAUrMbDCAmQ0FTgPuSVK8kkTZWcYjVxzFpcdo6VgREZFYyUrUyvhoeg+AFWFZvPv8CvhfoFPNrZI+srMMgGcWrWFlVU3E0YiIiKSGZCVq1kZZ657jbe5jZqcDa9z9jV1exOwyM5tlZrPWrl27O3FKhNZvqeOKB97gx9PVwi0iIgLJS9RWAMNiXg8FKuLc52jgk+HUIA8CnzCzB9q6iLvf5e7l7l5eWlraVbFLkvQrzueKY0fx5JxKXlmyPupwREREIpesRO11YLSZ7W1mecB5wLRW+0wDvhCO/jwCqHb3Sne/1t2HuvuI8Lin3f3zSYpbkuxLx46krKSQG6bNp7FJLd0iItK9JSVRc/dG4EpgBsHIzb+7+3wzu9zMLg93mw4sARYDdwNfTkZskloKcrP5zmnjeGfVZv76+vJdHyAiIpLB4l2UfY+5+3SCZCy27M6Y5w58ZRfneBZ4NgHhSQo5Zf9BnHbAYHrmJ+3XU0REJCXpm1BSjplx+wUH73pHERGRDJesPmoindbY1Mz9M5exaNXmqEMRERGJhBI1SVmbaxu55d/v8n2tAyoiIt2UEjVJWX165PGNE8fw8vvrmTFf64CKiEj3o0RNUtoFhw9n7MCe/OjJhdQ2NEUdjoiISFIpUZOUlpOdxfVnjGfFxhrufn5J1OGIiIgklRI1SXlHjerPl44dySF79Yk6FBERkaTS9BySFq49ZVzUIYiIiCSdatQkbWyta+TGJxfw+rINUYciIiKSFErUJG2YwT/mVHLDtPk0NWu6DhERyXxK1CRtFOXlcO2p45hfsYm/z9I6oCIikvnUR03SyhkTBvPAzA/40T8WcOt/32NVdS1DSgqZMnksZ00sizo8ERGRLqUaNUkrZsbHxvRna30TldW1OLCyqoZrp87lsdkrow5PRESkSylRk7Tz4Gs7N3vWNDRx84xFEUQjIiKSOErUJO1UVNV0qlxERCRdKVGTtDOkpLBT5SIiIulKiZqknSmTx1KYm71T+Sn7D4ogGhERkcRRoiZp56yJZfzk0wdQVlKIAYN7FzCsTyF/fHkZT7xdEXV4IiIiXUbTc0haOmti2Q7TcWyubeDSP87i2qlzOWZUf/r0yIswOhERka6hRE0yQs+CXP70xcN4d/VmJWkiIpIx1PQpGaMwL5sDh5UAcN/MZdzx7PvRBiQiIrKHVKMmGcfdefODjTz2VgXb6hv5xoljMLOowxIREek0JWqSccyMn597EPk52dz29GK21Tfx3dPGKVkTEZG0o0RNMlJ2lvGTTx9AYV42v39xKdvqm/jxp/ZXsiYiImlFiZpkrKws4/ozxtMjP5s+RXlK0kREJO0oUZOMZmZMmbzv9tcLKzcxsrQH+Tk7T5grIiKSajTqU7qN9VvqOPfOmVx23xvUNjRFHY6IiMguKVGTbqNfcT7fOW0cz7+3lovufY0tdY1RhyQiItIhJWrSrZx32HB+9dmDmPXBRj5/z6tUb2uIOiQREZF2KVGTbufMg8r47QUHs6BiE/e8uCTqcERERNqlwQTSLU3ebxAPX3Ek4wb3ijoUERGRdqlGTbqtCUNLyM3OYu3mOi7+w2ss37At6pBERER2oERNur3Vm2p584ONnPu7mSxZuyXqcERERLZToibd3v5lvXnwsiOpb2zm3N+9wjurNkUdkoiICADm7lHHkBDl5eU+a9asqMOQNLJ4zRY+f8+r1DY28T8f25u/vrqciqoahpQUMmXyWM6aWBZ1iCIikqHM7A13L29drho1kdCoAcU8dPmRDO5VwG+eXszKqhocWFlVw7VT5/LY7JVRhygiIt2MRn2KxBjWt4hNtY3UNjTvUF7T0MT3n5jPkJJC9h3ck14FuXt0ncdmr+TmGYtUYyciIh1SoibSSkVVTZvlG7c1cO7vZvLHSw7luLEDeGbRGn74xAIG9MpnQM8CBvTMZ0CvfM6aWMaAngVsrWuksdnpVZCzw4Lwj81eybVT51ITLmPVUmMHKFkTEZEdKFETaWVISSEr20jWBvTM55ZzDuSAst4AFOfnsO/gnqzZVMdby6tYs7mW2oZmjhs7gAE9C3j4jRVcP20+BblZlPb8KJmbvbxqe5LWoqahiZtnLFKiJiIiO1CiJtLKlMljd6jxAijMzea6U8cxaUzp9rJDR/Tl0BF9t792dzbXNVKUm719+3dOHceazbWs2VzHmk11vLt6M6ura9u8bns1eSIi0n0pURNppaVWq7N9yMxsh75r44f0YvyQnVc+OPqmp9ussQO4Ydp8bvjkfnsQvYiIZBIlaiJtOGtiWcKaIduqscvPyeLg4SXkZgd92dyd+1/5gBPHD2Rw78KExCEiIqlPiZpIksVTY/femi383+PzuWHafI4ZXcq55UM5cfxA8nOyowpbREQikLQJb83sZODXQDZwj7vf1Gq7hdtPBbYBF7v7m2Y2DLgPGAQ0A3e5+693dT1NeCvp7oP1W3n4jRU88sYKKqpr6V2YywOXHs4BQ3tHHZqIiHSx9ia8TUqNmpllA7cDJwIrgNfNbJq7L4jZ7RRgdPg4HLgj/LcR+GaYtPUE3jCzp1odK5Jx9urXg2+eNJarTxjDS4vXMe3tCkYPLAbgkTdWsKm2gbMOKqNPj7yIIxURkURJVtPnYcBid18CYGYPAmcCscnWmcB9HlTxvWJmJWY22N0rgUoAd99sZguBslbHimSs7Cxj0pjSHUacPr1oDU/OqeQn09/hhPEDOKd8GJNGl5KdZR2cSURE0k2yErUyYHnM6xUEtWW72qeMMEkDMLMRwETg1YREKZImbv/cwVz58U08NGsFj85ewfS5q/jUxDJ++dmDAK18ICKSKZKVqLX13/zWneM63MfMioFHgKvdfVObFzG7DLgMYPjw4bsXqUiaGDe4F/93xniuOWVfnn5nNf2K8wH4w0tL+eE/FtAcfnq08oGISPpK1qLsK4BhMa+HAhXx7mNmuQRJ2p/dfWp7F3H3u9y93N3LS0tL29tNJKPk5WRx8v6Dt0++e/szi7cnaS1qGpr46b/eiSA6ERHZE8lK1F4HRpvZ3maWB5wHTGu1zzTgCxY4Aqh298pwNOjvgYXu/oskxSuSttZvqW+zfFW4IsIrS9bz0uJ11Dc2t7mfiIikjqQ0fbp7o5ldCcwgmJ7jXnefb2aXh9vvBKYTTM2xmGB6jkvCw48GLgTmmtlbYdl17j49GbGLpJv21iodUhJMnPvbZ9/n+XfXUpyfwzGj+vOJfQdw3L6lDOhZkOxQRURkF5I2j1qyaR416a4em72yzbVKf/LpAzhrYhnb6ht5afF6nn5nDc+8s4ZVm2o5dEQfHrr8KAAWr9nCyP49yNIIUhGRpIl0HjURSZ5drXxQlJfDieMHcuL4gbg7Cys3b0/qNtU2cPKvnqekKJdjxwzgE/sO4GNj+u+whqmIiCSPatREZLua+iZmzF/F0++s4bl311Jd00BOlvHzcw/kzIPKaG52zODxtyo0/YeISBdSjZqI7FJhXvb2Bekbm5qZvbyKp99ZwwFlwbJV0+dV8n+Pz6O6ppGmcGippv8QEUkcJWoi0qac7CwOHdF3+7QfAH2L8tha17Q9SWtR09DEzTMW0a84j4qqGob2KWJYnyIGlxSQm52sweUiIplHiZqIxO2oUf3bndajoqqGh2atYNrbH02RmGWwT2kxT33jWAD+ObeSzXWNDO1TyLA+RQzq3XYip5UVREQCStREpFM6mv7j5+ceyJTJY1m+cRsrNtawYmMNjU0fJXZ/eGkZry3bsP11lsGR+/Tjz/9zBAB/f305by7fyNQ3V25PCNW0KiLdmRI1EemUKZPHtjn9x5TJY8nNzmJY3yKG9S1q89gH/udwVlXXsmJ7IreN4oKP/gzd9sx7LN+wcxLY0rTa7E7/4nz27t+DISWFWoReRDKeRn2KSKclqmmyvrGZsd/9504LAUOwGHB2ltEY9o/Ly85ieL8iPn/4cC4+em/cnZlL1rN3/x4M7FnQ4TxwaloVkVSjUZ8i0mVaRoZ2tbycrA6aVguY+uWjWbpuK8vWbWXp+q0sXbuVorzgz9jqTXV87u5XASjIzWJEvx7s3b8HFxy+F8eMDvrWVdc08OJ7a7nu0XnbawTVtCoiqUyJmoiklPabVvdlYK8CBvYq4IiR/XY6rqQolwcuPZyl64NEbtm6rSxavZkN24K1T+dVVPPp376MwU41di2L1h8wtDf9e+TTqzCHYJnh3adaOxHpCmr6FJGUk4gkZ1V1Lf+cV8n3n1iwy33zsrPoV5xHv+I8fnHuQYwZ2JN5K6uZ+f56+vfMo1+PfPoX59O/OI9+xfk79ZXb1TJeIiKtqelTRNJGIppWB/Uu4JKj9+aeF5a22bQ6sFc+154yjnVb6li3pZ71W+pYt6WOwtxsAF5duoEbpy/c6bjnp3yc4f2K+Pus5Ux9cwX9i/N5ZtGaHZI0aBkQ8Y4SNRHpFCVqItKttNe0eu0p4zpMor549AjOKR/Kus11rN9az7rNdazbWs+AXvkAZJnR1OwsqNjE1rqmNs+xsqqWE37xHIN7FzCkdyFDSgq56vhRmBnrt9RRmJe9vc/drqhpVaR7UKImIt3Krhatb4+Z0asgl14FuYws3Xn72YcM5exDhgJw9E1Pt1lr1yM/m1GlxVRW1/DOqs0AfO2E0QBc9+hcZsxfTe/CXAb3LmBw7wLGDurFNafsC8CiVZvJz8liUO8C/jVv1Q7JpgZEiGQuJWoi0u0katRqi/Zq7W48a8c+arGTAX/u8L04cFgJlVW1VFbXUFFVS2Nz9UfnfPht5qwIXmcZtFrFi5qGJm6YNp+C3Cx6FeZSUphHac98Snvm79bPoBo7kdSgwQQiIgnQ1YnO68s2sGzdViqra/nFU+/GdcypBwzitxccAsDxP38WM6OkMJeSolx6F+bxsdH9t8f0xNsV9CrMpXdhLq8v3cDPn1pEbcNHiaQGQ4gklgYTiIgkUVfX2h06oi+HjugLwN9eX97ugIg/XHwYVTX1VG9roF/xR7VpR4zsx8Zt9VRta6CiqpaFlZvp3zOPsyijrrGJr/51dofXr2lo4ruPBfPPtazVOqSkkLycnddqFZGuoxo1EZE009XTfzQ3O0vWbaW6pp7qmga++Mf4/nZ+7/TxXHrM3lRU1XDLjEUM7Vu0PYkb2qeQwb0LyMluO5FT06rIjlSjJiKSIXZ3QER7srKMUQOKt78u62B1iIcuP4rlG4K1Wg8a1huAtZvreGXJeirfWkns//3vuOBgTjlgMPNWVnPfzGUM7VPEsL6FLF23lbueW0JtY9C0qsEQIu1TjZqIiOxgd2vs6hubqayuYcXGGpZv2MaxY0sZ3LuQ/yxYzbWPzmXt5roOr1uYm81nDx1Gn6I8+vTI5ayJZfQqyGXD1npqGproU5RLYW52XKtGqMZO0k17NWpK1EREZCeJSHRqG5pYsbGGE37xXLv79MzPYXNdIwCvXXc8A3oV8Mun3uXX/30PCNaD7VuUR0lRLg9fcRTF+TnMmL+KuSuqKSnKpU9RHotWbeJPMz+grlGDISR9qOlTRETilogpTApysxk1oLjdptWykkJeuuYTNDQ1U7WtgX498gCYvN8gBvcuYOO2Bqq21bNhaz1VNQ0UhatGvLZ0A394aelOU5bEqmlo4lsPvc1fXv2QkqJcyvoUcv0Z+wEw8/31bKlrpE9RLn165NGnKI/ehbk7LQ0WSzV2kixK1EREJKnam2duyuSxAORmZ+0w/9v4Ib0YP6RXu+f73unj+c6p49hc28jGbfV8/JZnaStna2x2srLgg/XbWLflo2bY259ZzIuL1+2w7+gBxTz1jWMB+O5jc6msqg2TuFwqq2v49/zV1DcFV1lZVcM1j8yhsamZs8uHdfr9aI+SQQElaiIikmRdPRgCggERvYty6V2Uy5AOauwevOzIncpvOedA1myuZeO2BjZurWfjtvrta7wCNDQ6ldW1LKjcxIat9Ts0qbaobWzmWw/P4fpp8ykpyuPE8QO54ZNBjd1P//UO2WaUFOVSUhQke3v167F9AEdTs+9Ue9e6n2AiBlwoEUwPStRERCTpErk6xK5q7Fob1LuAQb0L2j3fT8+esMPrva95ss0aO4DzDhvOxm31DO1TuL3s8dkrWbWpdoem2fMOHcZNn5lAc7Mz9rv/pCgvmz498sIJifN4e0XVDvFD0Hx7/bR5bNxWzyF79WHC0BK21jXy5JxKsrKM7KxgzdnsLGP/Ib0Z0b8HW+oaeX3ZBrLD8pbt76zaxE+mv7NDInjN1Dm4O586eGi770VnJDoR7C6JpgYTiIhIxknkl3h7a7m29LFrS3Ozs7mukapw0uGeBTmMLC2mvrGZ259ZHJTXNGzvh9eyXFh7pkwey1c+PooP129j0s3P7LT9+5/cj4uOGsHCyk2c8usXdtpeUpRL1baGNs/dpyiXn597IJ/YdyDzVlZz63/fo2dBLj0LcuhVkEPPglxOnTCYspJC1m+p48MN2+hV2LI9l/ycLB5/q6JL5/prravnEmzvGslMBDXqU0REpAskI0loLxkc3LuA6Vd9jILcbArzsmlsag5q65qhyZ2mZqfZndLifPr0yKOmvomFqzbR3Bxsa3KnuRku/P2r7dYKfv6I4Vxw+F6MG9yLme+v54Zp89lS18im2ga21DXiDn+77AgOH9mPx2av5Oq/vbXD8bnZRklRXpvTseTnZHHMqP785DMHMKBnAf9ZsJrpcyvJyTays7LIyTJyso1vnjSW4vwcXlq8jteXbSAnK9iemx3UCN79whIqqmp3On+/Hnncdv5EjhrVH4CKqhq21jWSl5NFbnbwyMvJondhbofvfzLucWsa9SkiItIFEtHHrrX2mm+/ffK+9AlHwwLkZGcxtE9Ru+cpzMvm4OF9dirvqB/fj846YPvrI/fpx4yvT9r+urnZ2VrfSEHYh+/Iffpx78XlbK5tZFNtI5trG9hc28idz77fZjx1jUFi2VJHVFldw2vLNtDU7DQ0OU3NzTQ2O1d9YjTkw0uL1/HbNs7V3njc9VvruegPr/HejacC8PN/v8sjb67YYZ+Solze+r+TAPjKn9/kPwtXk5edRW5OFnnZWQztU0hldW2bTc83z1iU9OZV1aiJiIikoEQ2vSW6xmh3mofb0xzWBDY2OY3NzTQ1O6fd+gIr26hRKy3O547PH0x5uC7u28ur+HDDNhqammloaqa+sZnsrCw+d/hwAB6dvYJ3Vm2modG379OrMJe7n1/SZo2jAUtvOq1T8cdLNWoiIiJpJJEDLhJdK9jZAR0dycoysjCCSrzs8Pz7tnn+75w2bnuSBnDgsBIOHFbS7rk/NbHtgRNPzqlsZxm1wjb2TiwlaiIiIt1QOieC6ZRo7ik1fYqIiIi0kiqjPlWjJiIiItJKImscOyMr6gBEREREpG1K1ERERERSlBI1ERERkRSlRE1EREQkRSlRExEREUlRStREREREUpQSNREREZEUpURNREREJEUpURMRERFJUUrURERERFJUxq71aWZrga3AuqhjkYTqj+5xJtP9zXy6x5lP9zg+e7l7aevCjE3UAMxsVlsLnErm0D3ObLq/mU/3OPPpHu8ZNX2KiIiIpCglaiIiIiIpKtMTtbuiDkASTvc4s+n+Zj7d48yne7wHMrqPmoiIiEg6y/QaNREREZG0lZGJmpmdbGaLzGyxmV0TdTzS9cxsmZnNNbO3zGxW1PHInjOze81sjZnNiynra2ZPmdl74b99ooxR9kw79/gGM1sZfpbfMrNTo4xRdp+ZDTOzZ8xsoZnNN7OvheX6HO+BjEvUzCwbuB04BRgPnG9m46ONShLk4+5+kIZ9Z4w/Aie3KrsG+K+7jwb+G76W9PVHdr7HAL8MP8sHufv0JMckXacR+Ka7jwOOAL4Sfv/qc7wHMi5RAw4DFrv7EnevBx4Ezow4JhHZBXd/HtjQqvhM4E/h8z8BZyUzJula7dxjyRDuXunub4bPNwMLgTL0Od4jmZiolQHLY16vCMskszjwbzN7w8wuizoYSZiB7l4JwZcAMCDieCQxrjSzOWHTqJrFMoCZjQAmAq+iz/EeycREzdoo09DWzHO0ux9M0MT9FTObFHVAIrJb7gD2AQ4CKoGfRxqN7DEzKwYeAa52901Rx5PuMjFRWwEMi3k9FKiIKBZJEHevCP9dAzxK0OQtmWe1mQ0GCP9dE3E80sXcfbW7N7l7M3A3+iynNTPLJUjS/uzuU8NifY73QCYmaq8Do81sbzPLA84DpkUck3QhM+thZj1bngMnAfM6PkrS1DTgovD5RcDjEcYiCdDyBR76FPospy0zM+D3wEJ3/0XMJn2O90BGTngbDu/+FZAN3OvuN0YbkXQlMxtJUIsGkAP8Rfc4/ZnZX4HjgP7AauB64DHg78Bw4EPgHHdXZ/Q01c49Po6g2dOBZcCXWvozSXoxs2OAF4C5QHNYfB1BPzV9jndTRiZqIiIiIpkgE5s+RURERDKCEjURERGRFKVETURERCRFKVETERERSVFK1ERERERSlBI1ERERkRSlRE1EksLMlplZjZltMbPVZvaHcKmZlu2Tzex5M9tsZmvN7Dkz+2SrcxxnZm5m/5v8nyA9hO/RiqjjEJGuoURNRJLpDHcvBg4GDgW+C2BmZwMPAfcRLPs2EPg/4IxWx18EbOCjWc5FRDKaEjURSTp3Xwn8E9g/XHbmF8AP3f0ed69292Z3f87d/1/LMWZWBJwNfIVgmbjyjq5hZmea2VtmtsnM3jezk8PyIWY2zcw2mNliM4u9xg1m9pCZPRDW7M01szFmdq2ZrTGz5WZ2Usz+z5rZT8zsNTOrNrPHzaxvzPZPmtl8M6sK9x0Xs22ZmX3LzOaEx/7NzApitp8exl9lZi+b2YRdHRsuqfZPYEhYc7kl/HkPM7NZ4Xux2sxil/cRkRSmRE1Eks7MhgGnArOBscAw4OFdHPYZYAtBzdsM4AsdnP8wgtq5KUAJMIlgeSKAvwIrgCEEid+Pzez4mMPPAO4H+oTxzSD4W1kG/AD4XavLfQH4Yni+RuDWMIYx4bWuBkqB6cAT4RrELc4FTgb2BiYAF4fHHgzcC3wJ6Bdec5qZ5Xd0rLtvBU4BKty9OHxUAL8Gfu3uvYB9CJbzEZE0oERNRJLpMTOrAl4EngN+TJCIAOxqfceLgL+5exPwF+B8M8ttZ99LCdb5fSqsnVvp7u+ECeIxwLfdvdbd3wLuAS6MOfYFd5/h7o0ESWEpcJO7NwAPAiPMrCRm//vdfV6YJH0PONfMsoHPAk+GMTQAtwCFwFExx97q7hXhuodPEKx5CfD/gN+5+6vu3uTufwLqgCPiOLYtDcAoM+vv7lvc/ZUO9hWRFKJETUSS6Sx3L3H3vdz9y+5eA6wPtw1u76Awwfo48Oew6HGgADitnUOGAe+3UT4E2ODum2PKPiCoLWuxOuZ5DbAuTA5bXgMUx+yzvNW5cgkWHR8SvgbA3ZvDfWOvtSrm+baY8+4FfDNs9qwKk9th4Tl3dWxbLgXGAO+Y2etmdnoH+4pIClGiJiJRW0SQwHymg30uJPh79YSZrQKWECRq7TV/Lido4mutAuhrZj1jyoYDKzsbdIxhrc7VAKwLr7VXy4awL96wOK+1HLgxTGpbHkXu/tc4jvWdCtzfc/fzgQHAT4GHw/5sIpLilKiJSKTc3YFvAN8zs0vMrJeZZZnZMWZ2V7jbF4DvEzTvtTw+A5xmZv12Piu/By4xs+PDc5WZ2b7uvhx4GfhJ2Pl+AkFt05/bOEe8Pm9m48PBDj8AHg5r4P4exnd82ET7TYLmy5fjOOfdwOVmdrgFepjZaa0SzPasBvqZWe+WAjP7vJmVhrV6VWFxU1sHi0hqUaImIpFz94cJ+nR9kaAmajXwI+BxMzsCGAHc7u6rYh7TgMXA+W2c7zXgEuCXQDVBf7iW2q3zw/NVAI8C17v7U3sQ/v3AHwmaIguAq8IYFgGfB24jqGE7g2B6kvpdndDdZxH0U/sNsJHg57w4nmDc/R2CQQxLwmbTIQSDDuab2RaCgQXnuXtt/D+iiETFgv/MiohIZ5nZs8AD7n5P1LGISGZSjZqIiIhIilKiJiIiIpKi1PQpIiIikqJUoyYiIiKSopSoiYiIiKQoJWoiIiIiKUqJmoiIiEiKUqImIiIikqKUqImIiIikqP8PnFZwM2Iy8t0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_full = PCA(n_components = 0.6 , whiten = True).fit(X)\n",
    "\n",
    "fig , ax = plt.subplots(figsize = (10 , 6))\n",
    "ax.plot(range(1 , len(pca_full.explained_variance_ratio_) +1) , pca_full.explained_variance_ratio_ , linestyle='--', marker='o')\n",
    "ax.set_title(\"PCA decomposition of the full data set\" , fontsize = 14)\n",
    "ax.set_xlabel(\"PCA components\" , fontsize = 12)\n",
    "ax.set_ylabel(\"explained variance\" , fontsize = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b11057a2-aefc-4a89-9362-f563d705c021\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b11057a2-aefc-4a89-9362-f563d705c021\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('PCA', PCA(n_components=20, whiten=True)),\n",
       "                ('logistic',\n",
       "                 LogisticRegression(max_iter=2000, penalty='elasticnet',\n",
       "                                    solver='saga'))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"94107f21-ed7c-449b-ac15-10bdee29ff41\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"94107f21-ed7c-449b-ac15-10bdee29ff41\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=20, whiten=True)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"879c854c-4f8c-4e99-9530-efe3ca519a32\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"879c854c-4f8c-4e99-9530-efe3ca519a32\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=2000, penalty='elasticnet', solver='saga')</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('PCA', PCA(n_components=20, whiten=True)),\n",
       "                ('logistic',\n",
       "                 LogisticRegression(max_iter=2000, penalty='elasticnet',\n",
       "                                    solver='saga'))])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline(steps=[('PCA' , PCA(n_components = 20 , whiten = True)),\n",
    "                       ('logistic',LogisticRegression(solver='saga' ,penalty='elasticnet' , max_iter = 2000))\n",
    "                      ]\n",
    "                )\n",
    "\n",
    "pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note :** Here we decided not to scale the genes before the PCA step since since we believe that the differences of variability between genes carry some relevant information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Nested-cross-validation scheme    \n",
    "\n",
    "Using the parameters \"params\", \"cv_inner\", \"cv_outer\", the user can shape the nested cross-validation scheme to its needs. Scikit-learn proposes a wide range of [cross-validation strategies](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators) but you can also use a custom-made CV splitters which yield (train, test) splits as arrays of indices.   \n",
    "\n",
    "Here we use a simple strategy whose main characteristics are :\n",
    "\n",
    "* 10 fold outer cross-validation with 5 fold inner cross-validation for hyperparameter tuning (good bias-variance trade-off + keep computational time relatively low).<p>  \n",
    "    \n",
    "* Stratified cross-validation (ensure that each fold has the same proportion of observations for both classes).<p>  \n",
    "\n",
    "* Gridsearch strategy (predefined grid of possible values for hyperparameters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nested_cross_val.base import NestedCV\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A brief word about dask   \n",
    "\n",
    "We added a Dask implementation in our nested_cross_validation (library = 'dask') since it provides elegant solutions to save some precious computational time, optimizing memory and redundant operations through smart parallelization. For simple estimators Dask is not necessarily needed and scikit-learn (library = 'sklearn') will work perfectly. However, for complex pipelines with costly operations we think that Dask can help reducing the computational time significantly.   \n",
    "\n",
    "Our goal, here, is not to give a detailed tutorial on dask but we still want to draw the reader attention on the \"scheduler\" parameter. It defines the parallel hardware that will execute the different tasks (you will find more details [here](https://docs.dask.org/en/latest/scheduling.html)). You have three options :   \n",
    "\n",
    "* You can use the default value (scheduler = None), it will create a default single-machine scheduler with multiple threads. <p>\n",
    "\n",
    "* You can define a dask.distributed scheduler on your local machine (you will find more details [here](https://docs.dask.org/en/latest/setup/single-distributed.html)). In particular, it provides a very nice interactive dashboard with live information that will help you better understand how the different tasks are executed (we recomend [this tutorial](https://docs.dask.org/en/latest/diagnostics-distributed.html) for more details).<p>\n",
    "    \n",
    "* You can also run Dask on a distributed cluster. There are a variety of ways to set this up depending on your cluster. Unfortunatly we do not know enough about it to guide you through (see [here](https://docs.dask.org/en/latest/setup.html) for some explanations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>inproc://192.168.1.133/11640/1</li>\n",
       "  <li><b>Dashboard: </b><a href='http://192.168.1.133:8787/status' target='_blank'>http://192.168.1.133:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>1</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>16.91 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'inproc://192.168.1.133/11640/1' processes=1 threads=8, memory=16.91 GB>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "client = Client(processes=False)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'logistic__C': np.logspace(-2, 2, 20) , 'logistic__l1_ratio' : np.arange(0 , 1.1 , 0.1)}\n",
    "\n",
    "cv_out = StratifiedKFold(n_splits = 10, shuffle = True , random_state = 0)\n",
    "\n",
    "ncv = NestedCV(estimator = pipe , params = param_grid , cv_inner = 5  , cv_outer = cv_out  , \n",
    "               scoring_inner = 'roc_auc' , scoring_outer = {'roc_auc' : 'roc_auc' , 'average_precision' : 'average_precision'} ,\n",
    "               library = 'dask' , scheduler = client)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner training out fold 0 done ! running time (min): 00:12.62\n",
      "inner training out fold 1 done ! running time (min): 00:14.72\n",
      "inner training out fold 2 done ! running time (min): 00:15.21\n",
      "inner training out fold 3 done ! running time (min): 00:14.41\n",
      "inner training out fold 4 done ! running time (min): 00:16.21\n",
      "inner training out fold 5 done ! running time (min): 00:14.82\n",
      "inner training out fold 6 done ! running time (min): 00:15.44\n",
      "inner training out fold 7 done ! running time (min): 00:15.99\n",
      "inner training out fold 8 done ! running time (min): 00:16.25\n",
      "inner training out fold 9 done ! running time (min): 00:16.90\n",
      "Total fitting time (min): 02:35.55\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "ncv.fit(X , y)\n",
    "end = time.time()\n",
    "minutes, seconds = divmod(end - start, 60)\n",
    "print(\"Total fitting time (min): \" + \"{:0>2}:{:05.2f}\".format(int(minutes),seconds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Plot scores and hyperparameters\n",
    "\n",
    "In complement to our nested cross-validation implementation, we provide some plotting tools to visualize the results. Obviously, these are just some suggestions and the user is free to use any custom-made plotting tool having directly access to the scores, the attributes of the estimators and the predictions with methods such as NestedCV.get_predictions(), NestedCV.get_attributes() or the attribute NestedCV.outer_results_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nested_cross_val.tools.plotting import plot_roc , plot_PR , plot_score , plot_hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , axes = plt.subplots(1 , 2 , figsize = (20, 6))\n",
    "plot_roc(ncv , ax = axes[0])\n",
    "plot_PR(ncv , ax = axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_score(ncv , score_name = 'average_precision' , display = 'barplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(1 , 2 , figsize = (20 , 6))\n",
    "plot_hyperparameters(ncv , name = 'logistic__C' , discrete = False , ax = ax[0])\n",
    "plot_hyperparameters(ncv , name = 'logistic__l1_ratio' , discrete = False , ax = ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Feature importance analysis\n",
    "\n",
    "In complement to our nested cross-validation implementation we also provide some tools for feature importance analysis. In particular, for pipelines with a step of feature transformation (ex : PCA) we provide a clustering tool that returns clusters of components (each one represented by a \"consensus\" component) and the associated feature importances accross the folds.   \n",
    "\n",
    "Here are the three main steps of this method for PCA components :\n",
    "\n",
    "1. Cluster the PCA components across the folds - in total 20x10 components gathered in 20 clusters using hierarchical clustering and Pearson correlation (within sample space).<p>   \n",
    "    \n",
    "2. For each fold, each component is associated with its corresponding cluster and we can then perform stability analysis (e.g How many times the cluster i has been selected across the k folds ?).<p>   \n",
    "    \n",
    "2. Each cluster is characterized by its centrotype.\n",
    "    \n",
    "**Note :** This tool is not completely satisfactory yet since it is quite rigid and does not necessarily perform well for very unstable components. We plan to improve it soon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nested_cross_val.tools.feature_importances import collect_meta_importances\n",
    "from nested_cross_val.tools.plotting import plot_feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>out fold 0</th>\n",
       "      <th>out fold 1</th>\n",
       "      <th>out fold 2</th>\n",
       "      <th>out fold 3</th>\n",
       "      <th>out fold 4</th>\n",
       "      <th>out fold 5</th>\n",
       "      <th>out fold 6</th>\n",
       "      <th>out fold 7</th>\n",
       "      <th>out fold 8</th>\n",
       "      <th>out fold 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cluster 0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.076150</td>\n",
       "      <td>-0.067217</td>\n",
       "      <td>-0.103323</td>\n",
       "      <td>-0.140207</td>\n",
       "      <td>-0.154283</td>\n",
       "      <td>-0.105887</td>\n",
       "      <td>-0.071257</td>\n",
       "      <td>-0.088192</td>\n",
       "      <td>-0.148602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster 1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082933</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.211402</td>\n",
       "      <td>-0.048552</td>\n",
       "      <td>0.024236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035165</td>\n",
       "      <td>0.051467</td>\n",
       "      <td>0.254817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster 10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.013102</td>\n",
       "      <td>-0.106506</td>\n",
       "      <td>-0.013571</td>\n",
       "      <td>-0.022527</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster 11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.017241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster 12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.033456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            out fold 0  out fold 1  out fold 2  out fold 3  out fold 4  \\\n",
       "cluster 0          0.0   -0.076150   -0.067217   -0.103323   -0.140207   \n",
       "cluster 1          0.0    0.082933    0.000000    0.211402   -0.048552   \n",
       "cluster 10         0.0    0.000000    0.000000    0.000000   -0.013102   \n",
       "cluster 11         0.0    0.000000    0.000000    0.000000   -0.017241   \n",
       "cluster 12         0.0    0.000000    0.000000    0.000000   -0.033456   \n",
       "\n",
       "            out fold 5  out fold 6  out fold 7  out fold 8  out fold 9  \n",
       "cluster 0    -0.154283   -0.105887   -0.071257   -0.088192   -0.148602  \n",
       "cluster 1     0.024236         NaN    0.035165    0.051467    0.254817  \n",
       "cluster 10   -0.106506   -0.013571   -0.022527    0.000000    0.000000  \n",
       "cluster 11    0.000000    0.000000   -0.001191    0.000000    0.000000  \n",
       "cluster 12    0.000000    0.000000    0.012701    0.000000    0.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = lambda estimator : estimator['logistic'].coef_.reshape(-1)\n",
    "components = lambda estimator : estimator['PCA'].components_\n",
    "projections = lambda estimator : estimator['PCA'].transform(X)\n",
    "\n",
    "df , stabilities , Consensus = collect_meta_importances(ncv , importances = importances , components = components, \n",
    "                                                        projections = projections , full_components = pca_full.components_ , \n",
    "                                                        full_projections = pca_full.transform(X) , clustering_space = \"samples\" ,\n",
    "                                                        plot = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(df , normalize = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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