{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fait du poireautage¹ de données comme un pro\n",
    "\n",
    "\n",
    "\n",
    "----\n",
    "\n",
    "1: Traduction approximative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![leek](https://media.giphy.com/media/d8KkDupA6b3o9UQlbv/source.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import cross_val_score, StratifiedShuffleSplit\n",
    "from sklearn.metrics import cohen_kappa_score, make_scorer\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 100\n",
    "n_features = 3072\n",
    "n_classes = 5\n",
    "n_features_selection = 927"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples, n_features, \n",
    "                           n_classes=n_classes,\n",
    "                           n_informative=10,\n",
    "                           n_redundant=0,\n",
    "                           n_repeated=0,\n",
    "                           class_sep=3,\n",
    "                           flip_y=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 3072),)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = StratifiedShuffleSplit(n_splits=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Stratified Shuffle](https://scikit-learn.org/stable/_images/sphx_glr_plot_cv_indices_0091.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "302160b95f4b4f58aba21f5ca8e42c74",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=25.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "𝛋 = 0.19 ± 0.19\n"
     ]
    }
   ],
   "source": [
    "def benchmark(X):\n",
    "    s = []\n",
    "    for i in tqdm(range(25)):\n",
    "        s += [cross_val_score(RandomForestClassifier(), X, y, cv=kf, n_jobs=-1, scoring=make_scorer(cohen_kappa_score))]\n",
    "    s = np.array(s)\n",
    "    print('𝛋 = {:.2f} ± {:.2f}'.format(s.mean(), s.std()))\n",
    "\n",
    "benchmark(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_best_features(X):\n",
    "    clf = RandomForestClassifier(random_state=0)\n",
    "    clf.fit(X, y)\n",
    "\n",
    "    ibest = np.argsort(clf.feature_importances_)[-n_features_selection:]\n",
    "\n",
    "    return X[:,ibest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 927)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_best_features(X).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28ec83d68b1849e4abc56f240708fa7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=25.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "𝛋 = 0.52 ± 0.19\n"
     ]
    }
   ],
   "source": [
    "benchmark(select_best_features(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who cares about data quality anyway"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now same but full random X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xr = np.random.random((n_samples, n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 3072)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a1163d81cf34c919ba23b3b41ab74a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=25.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "𝛋 = 0.05 ± 0.14\n"
     ]
    }
   ],
   "source": [
    "benchmark(Xr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d1b80a130ed4e6fade6173e4752c1d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=25.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "𝛋 = 0.18 ± 0.17\n"
     ]
    }
   ],
   "source": [
    "benchmark(select_best_features(Xr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://media1.tenor.com/images/766adcaf4097f8c68aa069ad0fbaeb79/tenor.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <https://www.nature.com/articles/s41598-019-56185-5>\n",
    "- <https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-019-56185-5/MediaObjects/41598_2019_56185_MOESM1_ESM.pdf>\n",
    "- Part 7.10.2 (p. 264) of <https://web.stanford.edu/~hastie/ElemStatLearn/>\n",
    "- <https://towardsdatascience.com/data-leakage-in-machine-learning-10bdd3eec742>\n",
    "- <https://www.reddit.com/r/MachineLearning/comments/jviymn/d_i_keep_running_across_studies_designed_this_way/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESPECTEZ LES GESTES BARRIÈRES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **If train/val then test is your god**\n",
    "2. Feature selection should be done in CV\n",
    "3. _For nihilists_: get more training data.\n",
    "4. _Get better data._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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