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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Groupe de travail\n",
    "\n",
    "Le but de ce notebook est de tester l'idée de réduction des random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import scikit-learn"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mean \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
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    "from sklearn.datasets import load_boston, load_breast_cancer, fetch_california_housing\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.linear_model import OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors.kde import KernelDensity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables globales"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "NB_TREES = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load jeu de donnée"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 113,
   "metadata": {},
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading Cal. housing from https://ndownloader.figshare.com/files/5976036 to /home/l_bouscarrat/scikit_learn_data\n"
     ]
    }
   ],
   "source": [
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    "X, y = fetch_california_housing(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_forest(X_train, y_train, nb_trees, random_seed):\n",
    "    '''\n",
    "    Function that will train a random forest with nb_tress\n",
    "    :param X_train: list of inputs\n",
    "    :param y_train: list of results\n",
    "    :param nb_trees: int, number of trees in the forest\n",
    "    :param random_seed: int, seed for the random_states\n",
    "    :return: a RandomForestRegressor\n",
    "    '''\n",
    "    \n",
    "     # Entraînement de la forêt aléatoire\n",
    "    \n",
    "    regressor = RandomForestRegressor(n_estimators=nb_trees, random_state = random_seed)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    return regressor\n",
    "\n",
    "\n",
    "def extract_subforest(random_forest, X_train, y_train, nb_trees_extracted):\n",
    "    '''\n",
    "    Function use to get the weight list of a subforest of size nb_trees_extracted for random_forest\n",
    "    using OMP.\n",
    "    :param random_forest: a RandomForestRegressor\n",
    "    :param X_train: list of inputs\n",
    "    :param y_train: list of results\n",
    "    :param nb_trees_extracted: int, number of trees extracted \n",
    "    :return: a list of int, weight of each tree\n",
    "    '''\n",
    "    \n",
    "    # Accès à la la liste des arbres\n",
    "\n",
    "    tree_list = random_forest.estimators_\n",
    "    \n",
    "    # Création de la matrice des prédictions de chaque arbre\n",
    "    \n",
    "    # L'implémentation de scikit-learn est un peu différente que celle vue en réunion, D est de même taille que X \n",
    "    # et chaque élément est composé de d signaux, d'où la création suivante de D où on créé une liste pour chaque\n",
    "    # élément comprenant les valeurs prédites par chaque arbre\n",
    "\n",
    "    D = [[tree.predict([elem])[0] for tree in tree_list] for elem in X_train]\n",
    "    \n",
    "    # OMP\n",
    "    \n",
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    "    omp = OrthogonalMatchingPursuit(n_nonzero_coefs=nb_trees_extracted, fit_intercept = False, normalize = False)\n",
    "    omp.fit(D, y_train)\n",
    "    \n",
    "    weights = omp.coef_\n",
    "    \n",
    "    return weights\n",
    "\n",
    "\n",
    "def compute_results(weights, random_forest, X_train, X_dev, X_test, y_train, y_dev, y_test, \n",
    "                    nb_trees, nb_trees_extracted, random_seed):\n",
    "    '''\n",
    "    Compute the score of the different techniques\n",
    "    :param weights: weights given by the OMP\n",
    "    :param random_forest: a RandomForestRegressor\n",
    "    :param X_train: list of inputs\n",
    "    :param X_dev: list of inputs\n",
    "    :param X_test: list of inputs\n",
    "    :param y_train: list of results\n",
    "    :param y_dev: list of results\n",
    "    :param y_test: list of results\n",
    "    :param nb_trees: int, number of trees in the main forest\n",
    "    :param nb_trees_extracted: int, number of trees extracted from the main forest\n",
    "    :param random_seed: int, seed for the random_states\n",
    "    :return: 4 results of 4 different methods, in order: results of the main forest, \n",
    "    results of the weighted results of the extracted trees, results of the mean results \n",
    "    of the extracted trees, results of a random_forest train with nb_trees_extracted directly\n",
    "    '''\n",
    "    \n",
    "    # Calcul des différents résultats\n",
    "    \n",
    "    res_base_forest = mean_squared_error(random_forest.predict(X_test), y_test)\n",
    "    \n",
    "    # Résultat de la forêt extraite avec l'OMP, où chaque arbre est multiplié par son poids\n",
    "    \n",
    "    y_pred = [sum([random_forest.estimators_[i].predict([elem])[0] * weights[i] for i in range(nb_trees)]) \n",
    "              for elem in X_test]\n",
    "    res_extract_weight = mean_squared_error(y_pred, y_test)\n",
    "    \n",
    "    # Résultat de la forêt extraite avec l'OMP, où chaque arbre est multiplié par son poids\n",
    "    \n",
    "    y_pred = [sum([random_forest.estimators_[i].predict([elem])[0] * weights[i] for i in range(nb_trees)])/sum(weights) \n",
    "              for elem in X_test]\n",
    "    res_extract_weight_norm = mean_squared_error(y_pred, y_test)\n",
    "    \n",
    "    # Résultat de la forêt extraite avec l'OMP, où on prends la moyenne des arbres extraits\n",
    "    \n",
    "    y_pred = [mean([random_forest.estimators_[i].predict([elem])[0] for i in range(nb_trees) if abs(weights[i]) >= 0.01])\n",
    "              for elem in X_test]\n",
    "    res_extract_mean = mean_squared_error(y_pred, y_test)\n",
    "    \n",
    "    # Résultat d'une forêt avec le même nombre d'arbre que le nombre d'arbre extrait\n",
    "\n",
    "    small_forest = train_forest(np.concatenate((X_train, X_dev)), np.concatenate((y_train, y_dev)), nb_trees_extracted, random_seed)\n",
    "    res_small_forest = mean_squared_error(small_forest.predict(X_test), y_test)\n",
    "    \n",
    "    return res_base_forest, res_extract_weight, res_extract_weight_norm, res_extract_mean, res_small_forest, weights\n",
    "\n",
    "\n",
    "def extract_and_get_results(random_forest, X_train, X_dev, X_test, y_train, y_dev, y_test, nb_trees, \n",
    "                            nb_trees_extracted, random_seed):\n",
    "    '''\n",
    "    Extract the subforest and returns the resuts of the different methods\n",
    "    :param X_train: list of inputs\n",
    "    :param X_dev: list of inputs\n",
    "    :param X_test: list of inputs\n",
    "    :param y_train: list of results\n",
    "    :param y_dev: list of results\n",
    "    :param y_test: list of results\n",
    "    :param nb_trees: int, number of trees in the main forest\n",
    "    :param nb_trees_extracted: int, number of trees extracted from the main forest\n",
    "    :param random_seed: int, seed for the random_states\n",
    "    :return: 4 results of 4 different methods, in order: results of the main forest, \n",
    "    results of the weighted results of the extracted trees, results of the mean results \n",
    "    of the extracted trees, results of a random_forest train with nb_trees_extracted directly\n",
    "    '''\n",
    "    \n",
    "    weights = extract_subforest(random_forest, X_dev, y_dev, nb_trees_extracted)\n",
    "    \n",
    "    res_base_forest, res_extract_weight, res_extract_weight_norm, res_extract_mean, res_small_forest = \\\n",
    "        compute_results(weights, random_forest, X_train, X_dev, X_test, y_train, y_dev, y_test, \n",
    "                        nb_trees, nb_trees_extracted, random_seed)\n",
    "    \n",
    "    return res_base_forest, res_extract_weight, res_extract_weight_norm, res_extract_mean, res_small_forest, weights\n",
    "    \n",
    "    \n",
    "\n",
    "def train_extract_subforest(X_train, X_test, y_train, y_test, nb_trees, nb_trees_extracted, random_seed):\n",
    "    '''\n",
    "    Function that takes data, number of trees and a random seed. Train a forest with nb_trees, extract\n",
    "    with OMP nb_trees_extracted and compare the results of the different method\n",
    "    :param X_train: list of inputs\n",
    "    :param X_test: list of inputs\n",
    "    :param y_train: list of results\n",
    "    :param y_test: list of results\n",
    "    :param nb_trees: int, number of trees in the main forest\n",
    "    :param nb_trees_extracted: int, number of trees extracted from the main forest\n",
    "    :param random_seed: int, seed for the random_states\n",
    "    :return: 4 results of 4 different methods, in order: results of the main forest, \n",
    "    results of the weighted results of the extracted trees, results of the mean results \n",
    "    of the extracted trees, results of a random_forest train with nb_trees_extracted directly\n",
    "    '''\n",
    "    \n",
    "    random_forest = train_forest(X_train, y_train, nb_trees, random_seed)\n",
    "    \n",
    "    weight = extract_subforest(random_forest, X_train, y_train, nb_trees_extracted)\n",
    "    \n",
    "    res_base_forest, res_extract_weight, res_extract_mean, res_small_forest = \\\n",
    "        compute_results(weight, random_forest, X_train, X_test, y_train, y_test,\n",
    "                        nb_trees, nb_trees_extracted, random_seed)\n",
    "    \n",
    "    \n",
    "    return res_base_forest, res_extract_weight, res_extract_mean, res_small_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "over\n",
      "over\n",
      "over\n",
      "over\n",
      "over\n",
      "over\n",
      "over\n"
     ]
    }
   ],
   "source": [
    "results_global = []\n",
    "results_dev_global = []\n",
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    "results_without_dev_global = []\n",
    "\n",
    "nb_trees = 100\n",
    "random_seeds = list(range(10))\n",
    "\n",
    "for random_seed in random_seeds:\n",
    "    \n",
    "    # Séparation train_test avec random_state\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = random_seed)\n",
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    "    X_train, X_dev, y_train, y_dev = train_test_split(X_train, y_train, test_size = 0.2, random_state = random_seed)\n",
    "\n",
    "    random_forest = train_forest(X_train, y_train, NB_TREES, random_seed)\n",
    "    \n",
    "    results = []\n",
    "    results_dev = []\n",
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    "    results_without_dev = []\n",
    "\n",
    "    for nb_trees_extracted in [int(NB_TREES/k) for k in [2, 5, 10, 20, 50, 100]]:\n",
    "        \n",
    "        weights = extract_subforest(random_forest, X_dev, y_dev, nb_trees_extracted)\n",
    "\n",
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    "        weights_train = extract_subforest(random_forest, X_train, y_train, nb_trees_extracted)\n",
    "\n",
    "        results.append(compute_results(weights, random_forest, X_train, X_dev, X_test, y_train, y_dev, y_test, \n",
    "                        nb_trees, nb_trees_extracted, random_seed))\n",
    "        \n",
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    "        results_without_dev.append(compute_results(weights_train, random_forest, X_train, X_train, \n",
    "                                                   X_test, y_train, y_train, y_test, \n",
    "                                                   nb_trees, nb_trees_extracted, random_seed)\n",
    "                                  )\n",
    "        \n",
    "        \n",
    "        results_dev.append(compute_results(weights, random_forest, X_train, X_dev, X_dev, y_train, y_dev, y_dev, \n",
    "                           nb_trees, nb_trees_extracted, random_seed))\n",
    "\n",
    "    results_global.append(results)\n",
    "    results_dev_global.append(results_dev)\n",
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    "    results_without_dev_global.append(results_without_dev)\n",
    "    print('over')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(results_global, title_graph):\n",
    "    def plot_mean_and_CI(mean, lb, ub, x_value, color_mean=None, color_shading=None, label=None):\n",
    "        # plot the shaded range of the confidence intervals\n",
    "        plt.fill_between(x_value, ub, lb,\n",
    "                         color=color_shading, alpha=.5)\n",
    "        # plot the mean on top\n",
    "        plt.plot(x_value, mean, color_mean, label = label)\n",
    "\n",
    "    means_results = np.array(\n",
    "        [\n",
    "            [mean(\n",
    "                [results[i][k] for results in results_global] # loop over the different experiments\n",
    "            ) for i in range(len(results_global[0]))] # loop over the different number of trees extracted\n",
    "        for k in range(5)]) # loop over the different methods\n",
    "    std_results = np.array(\n",
    "        [\n",
    "            [np.std(\n",
    "                [results[i][k] for results in results_global]\n",
    "            ) for i in range(len(results_global[0]))]\n",
    "        for k in range(5)])\n",
    "\n",
    "    x_value = [int(NB_TREES/k) for k in [2, 5, 10, 20, 50, 100]]\n",
    "    # plot the data\n",
    "    fig = plt.figure(1, figsize=(15, 10))\n",
    "    plot_mean_and_CI(means_results[0], means_results[0] + std_results[0], means_results[0] - std_results[0],\n",
    "                     x_value, color_mean='k', color_shading='k', label='Results of the base forest (on train set)')\n",
    "\n",
    "    plot_mean_and_CI(means_results[1], means_results[1] + std_results[1], means_results[1] - std_results[1],\n",
    "                     x_value, color_mean='darkorange', color_shading='darkorange', \n",
    "                     label='Weighted results of the extracted trees')\n",
    "    plot_mean_and_CI(means_results[2], means_results[2] + std_results[2], means_results[2] - std_results[2], \n",
    "                     x_value, color_mean='red', color_shading='red',\n",
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    "                     label='Weighted results of the extracted trees normalized')\n",
    "\n",
    "    plot_mean_and_CI(means_results[3], means_results[3] + std_results[3], means_results[3] - std_results[3], \n",
    "                     x_value, color_mean='b', color_shading='b',\n",
    "                    label='Mean results of the extracted trees')\n",
    "    plot_mean_and_CI(means_results[4], means_results[4] + std_results[4], means_results[4] - std_results[4], \n",
    "                     x_value, color_mean='g', color_shading='g',\n",
    "                    label='Results of a forest train with number of trees extracted (train+dev set)')\n",
    "    plt.xlabel('Number of trees extracted')\n",
    "    plt.ylabel('MSE')\n",
    "    plt.title(title_graph)\n",
    "\n",
    "    plt.legend(loc=\"upper right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plot_results(results_global, 'Reduction of a forest with 100 trees, 10 iterations with different seed, score on train set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_results(results_dev_global, 'Reduction of a forest with 100 trees, 10 iterations with different seed, score on dev set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "metadata": {},
   "outputs": [],
   "source": [
    "plot_results(results_without_dev, \n",
    "             'Reduction of a forest with 100 trees, 10 iterations with different seed, score when there is no dev set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
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   "outputs": [
    {
     "data": {
      "image/png": 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94zlz5rjibtWqlWtP09s6/Oqrr2jXrh29e/f2Wgyz2vN2Bd2l9qjwldSbdjuupB70togUudJw+nSRfv0q9zFdy31ruW8t911Sue+DBw+Kv7+//PzzzyIiMnbsWHn//fdFRCQiIkJWr3ZUPXjsscdkuvP/qV+/fnLPPfe4pjFx4kQZN26c2O12+fTTT6VOnTqydetWsdlsEhMT45p2amqqiDiuNu/Xr59s2bKl2DK3bNmy0Ladm5srvXv3lsTERK/rMCsrS5o1ayZ79uwRu90uY8eOLbQNVZXqdCW1qiAt963lvq/0ct+tWrUiOjoacGyfycnJpKWlYbVaXcUVJ06cWGi+7vWrwFFXzBhDREQEjRs3JiIiAoCOHTuSnJxMdHQ0H330EW+++Sb5+fkcP36cHTt2uNaPN9OnT2fAgAGMGDGCL774wuM63LVrF61atXJtf7fccgtvvvlmGdZs9aIJoiQva7lvLfftmZb79m2576Lr11ORQG8xFZ2Gn59foen5+fmRn5/PwYMHmTVrFhs3bqRevXpMmjSp1G1s3rx5HDp0yFWl1ts63Lx5c5n+P6o7PQdRDWm5by33reW+iwsNDaVevXqsXbsWgPfff7/U964k6enp1KpVi9DQUE6cOMGXX35Z4vibNm1i1qxZzJ8/Hz8/x0ent3XYrl07Dh48yP79+wEqlISrA00Q1ZCW+9Zy31ru27P33nuPGTNmEBkZyebNm3n88cdLb+RFVFQUnTp1omPHjkyePLnUXwf+61//4vTp08TFxREdHc0dd9zhdR2GhITw5ptvMmzYMHr37k3Lli0rHGdVMpW1y13VYmNjpSIFyVJ+2sNVna/jX4Pe4b4Vt7Nz507at2/vgwirRkZGBrVr1yY1NZWuXbvy/fffa0XXK4zNZiMvL4+QkBD279/PwIED2bNnj1Z0vQJ5+nwzxmwSkVhP4+s5iMuclvtWWu5bVZQmiMuclvtWWu5bVZSeg1BKKeWRJgillFIeaYJQSinlkSYIpZRSHmmCqIaMMdx6662u7vz8fBo1anRJlguGwqWT//a3v1XKNHft2uUq9lZwMVIB93kkJycTHh5eKfO8FPiyGq+68miCqIZq1arFtm3bXBclffPNN64rZC8WX5WPrqwE8emnn5KQkMDPP//Mtdde65N5+IovS3OXlCB8OV91efJpgjDGDDXG7DbG7DPGPOJh+CRjTIoxZrPzcYfbMJtb/0RfxlkdXX/99SxbtgxwXKY/fvx417Bz584xefJkunTpQqdOnVxXriYnJ9OnTx9iYmKIiYlxfVCsXr2a/v37M2bMGNq1a8eECRM81ucpWjK5POWai94QZerUqYXKPYCjBk9WVhbR0dFMmDCBc+fOMWzYMKKioggPD2fRokXFYvJU3nn58uW8/PLLvP3228UKEBadBzguFPNUOtpbmWp33kpod+vWrdB9Ovr378+mTZu8vjfz5s1j7NixjBgxgsGDB5ORkcHAgQNd5dbdr45+5plnaNeuHYMGDWL8+PGukuelxeupXHvRkuDe4rPZbMyYMcNVPv6NN94A4Pjx4/Tt25fo6GjCw8NdZS7UFcJbmdcLfQD+wH6gNRAEbAE6FBlnEvAvL+0zyjM/X5T7rqJq31KrVi3ZsmWLjB49WrKysiQqKqpQyemZM2e6yh+fOXNG2rRpIxkZGXLu3DnJysoSEZE9e/ZIwTpZtWqV1K1bVw4fPiw2m026d+/uKsPsrmjJ5PKUay5aEvu+++6TuXPnuqZbUDq5oEy3iMiSJUvkjjvucHVbrdZiMXkr71y0FHjR9VegpNLR3spUu/NWQvsf//iHPP744yIicuzYMWnTpo2IeH9v5s6dK02bNnWVl87Ly5O0tDQREUlJSZFrr71W7Ha7bNy4UaKioiQzM1PS09Pld7/7nWs5yxJv0fVStCS4t/jeeOMNeeaZZ0REJDs7Wzp37iwHDhyQWbNmyV//+lcRcZTETk9P97jO1aWhOpX77grsE5EDAMaYhUACsKPEVgpw1P9JTk5mwYIF3HDDDYWGrVixgsTERNc3y+zsbH799VeuueYapk6dyubNm/H39y9UeK1r166ussrR0dEkJyfTu3fvYvN1rwF0IeWayyIiIoKHHnqIhx9+mOHDh9OnT59Cw0sr71xWnkpHl1ZG2z0GTyW0b7zxRgYNGsRTTz3FRx995JqOt/cGYNCgQdSvXx9wfDF79NFHWbNmDX5+fhw9epQTJ06wbt06EhISXBVQR4wYAZRcVrs07iXBvcW3YsUKtm7d6rqhVFpaGnv37qVLly5MnjyZvLw8Ro4c6VqP6srgywTRFDjs1n0E6OZhvNHGmL7AHuABESloE2KMSQLygedE5NOiDY0xU4ApQJmKxZVXFVX7domPj+ehhx5i9erVpKamuvqLCEuXLqVt27aFxn/yySdp3LgxW7ZswW63ExIS4hrmqTy1J+4lk8tTrrkipbmvu+46Nm3axPLly5k5cyaDBw++oOJr3ngqHV1SmWp33kpoN23alAYNGrB161YWLVrkOiTj7b3573//W2jdfvDBB6SkpLBp0yYCAwMJCwsjOzvba2nussbrift8vcUnIrz66quue1O4W7NmDcuWLePWW29lxowZxQogqsuXL89BeCqGXnTr/xwIE5FIYCXwntuwFuIoIHUz8LIx5toibRGRN0UkVkRiC24AczmZPHkyjz/+uOtGJwWGDBnCq6++6vow+fnnnwHHt74mTZrg5+fH+++/f8H3Cy5PueaWLVuyY8cOcnJySEtL4z//+Y/HaQYGBrq+hR87doyaNWtyyy238NBDD7lKexeoaHln93l4U9Yy2t5KaAPcdNNNvPDCC6SlpbneI2/vjafpXnXVVQQGBrJq1SoOHToEOEpzf/7552RnZ5ORkeE6D1XWeEsrTe4tviFDhvD666+71tuePXs4d+4chw4d4qqrruLOO+/k9ttvL/YeqcubLxPEEaC5W3czoNCdz0UkVUQK9pPfAjq7DTvmfD4ArAY6+TDWaqlZs2auO4G5e+yxx8jLyyMyMpLw8HAee+wxwFHW+b333qN79+7s2bOn2A1Uyqs85ZqbN2/uuu/xhAkTXHe6K2rKlCmucX755RfX/aOfffZZ/vKXvxQbvyLlnd3nUZKylNH2VkIbYMyYMSxcuJAbb7zR1c/be1PUhAkTSEpKIjY2lg8++MBV9rtLly7Ex8cTFRXFqFGjiI2NdZVoL0u8Rcu1F+UtvjvuuIMOHToQExPjKg+fn5/P6tWrXT9GWLp0qcftUV3GvJ2cuNAHjsNXB4BWnD9J3bHIOE3cXv8P8IPzdT0g2Pm6IbCXIie4iz58ck9qparA2bNnRUTk3Llz0rlzZ9m0aVMVR6QuF9XmJLWI5BtjpgJf4/hF07sist0Y87QzoERgmjEmHsd5htM4ftUE0B54wxhjx7GX85yI6MltdUWYMmUKO3bsIDs7m4kTJxITE1PVIakrlE/LfYvIcmB5kX6Pu72eCcz00G49EFG0v1JXgg8//LCqQ1AK0CuplVJKeaEJQimllEeaIJRSSnmkCUIppZRHmiCqIX9/f1dxtBEjRmC1Wit1+vPmzWPq1KmAoyqqezmNCzF+/HgiIyN56aWXqqR9aZKTk72eAC5pWGl69ux5IWEVM2fOHP79738Djvfq2LHzlw+FhYVx6tSpSp1fedSuXfuizMfbtlCZ26uvVGY14c2bN7N8+fLSRyzCvcT+hdAEUQ3VqFGDzZs3s23bNurXr8/s2bN9Nq/K+of77bffWL9+PVu3buWBBx64KO3LW766ogmitPlU9v0X7r77blc5i6IJ4lJW1verpG2hpO21upQz95YgRKRQOZqyqGiCqCxXfILIseXCdZ9zKjilqkPxqEePHhw9etTV/eKLL7pKMj/xxBMAXstmu3/bTEpKctURKrB+/XoSExOZMWMG0dHR7N+/n1deeYUOHToQGRnJTTfdVCye7OxsbrvtNiIiIujUqROrVq0CHGU5Tp486fEK3s8//5xu3brRqVMnfv/733PixIli0y3a3lOZb7iwkuSPPPIIa9euJTo6utg306LDylOeu+BbdVnKqp88eZLOnR0FA7Zs2YIxxlXM79prryUzM5Mnn3ySWbNmsWTJEpKSkpgwYQLR0dGuMuWvvvqqKw5PJcrnzZvHqFGjGDp0KG3atOHPf/5zsVgBlixZwqRJkwCYNGkS99xzD3FxcbRu3ZrvvvuOyZMn0759e9c4Bf70pz8RExPDwIEDSUlx/N94K0VetNy4u/JuS56217JuD97KnG/fvt11NX9kZCR79+4ttj5XrFhBjx49iImJYezYsWRkZJCWlkbbtm1d5d/Hjx/PW2+9VazcfHJyMu3bt+fee+8lJiaGw4cPc8899xAbG0vHjh1d/8MAGzdupGfPnkRFRdG1a1fS0tJ4/PHHWbRoEdHR0SxatMjrcmRlZXHTTTcRGRnJuHHjXNvKBfN2Bd2l9qjoldQ71m8QnkTG3nqzo9u93PeX06Xf3H6V+pj+Zen1vgvKVefn58uYMWPkyy+/FBGRr7/+Wu68806x2+1is9lk2LBh8t1333ktm92yZUtJSUkREZGNGzdKv379RERk7ty5ct9994mIoxz04sWLXW2bNGki2dnZIuIoB13UrFmzZNKkSSIisnPnTmnevLlkZWXJwYMHpWPHjh6X5/Tp02K320VE5K233pIHH3yw2DhF23sr812ZJcndFR1W1vLcIuffr7KWVe/QoYOkpaXJq6++KrGxsTJ//nxJTk6W7t27i0jhkt3updJFHO/pK6+8IiIis2fPlttvv73Y9OfOnSutWrUSq9UqWVlZ0qJFC/n1118LxSoisnjxYpk4caKIOLaDcePGid1ul08//VTq1KkjW7duFZvNJjExMa5y6YDMnz9fRESeeuop13bkrRR50XLj7iqyLRXdXsu6PXgrcz516lTX8uTk5EhmZmah+aWkpEifPn0kIyNDRESee+45eeqpp0REZMWKFdK9e3dZsGCBDBkyxNWmaLl5Y4xs2LDB1a9gm8rPz5d+/frJli1bJCcnR1q1aiU//vijiJwvM+/+v1rScvz973+X2267TUREtmzZIv7+/oW2mwLV5kpqVXEF30CSk5Pp3LkzgwYNAhzfZFasWOGqc5SRkcHevXvp06dPiWWzy6OghtHIkSMZOXJkseHr1q3j/vvvB6Bdu3a0bNmSPXv2ULduXa/TPHLkCOPGjeP48ePk5ubSqlWrEmMorcz3xSpJXpby3FdffXWhNmUpq96zZ0++//571qxZw6OPPspXX32FiJT5fRs1ahTgKF3+8ccfexxn4MCBrhpOHTp04NChQzRv3tzjuAVGjBiBMYaIiAgaN27sKkDYsWNHkpOTiY6Oxs/Pz7X+b7nlFkaNGlVqKXL3cuPuKrIteVKW7cFbmfMePXrw7LPPcuTIEUaNGkWbNm0KTfuHH35gx44d9OrVC4Dc3Fx69OgBOLaPxYsXc99993ksnFigZcuWdO/e3dX90Ucf8eabb5Kfn8/x48fZsWMHxhiaNGlCly5dALyuA2/LsWbNGqZNmwY4/ocjIyPLtvJKoQmiBC8PrZp63wXnINLS0hg+fDizZ89m2rRpiAgzZ87krrvuKtbGU9ls9xLcZSm/DbBs2TLWrFlDYmIizzzzDNu3bycg4PxmIl7KUZfk/vvv58EHHyQ+Pp7Vq1fz5JNPlnsa7ipakvxC5uOtPHdRZSmr3qdPH9auXcuhQ4dISEjg+eefxxhT5nuOF8yjpLLt3uIw5nyR5aLxF7Tx8/Mr1N7Pz8/rfIwxpZYi91Y0siLbUmnT97Y9iJcy5+3bt6dbt24sW7aMIUOG8PbbbzNgwIBC7QYNGsSCBQuKzddut7Nz505q1KjB6dOnvX4JcY/v4MGDzJo1i40bN1KvXj0mTZrkKvPu/t544205gDK1L68r/hxEdRYaGsorr7zCrFmzyMvLY8iQIbz77rtkZGQAcPToUU6ePOm1bHZYWBibNm0CYOnSpR7n4V4e2m63c/jwYeLi4njhhRewWq2ueRXo27cvH3zwAeAoCf3rr7963FjduZfMfu+990oct2C5y1rmuzwlyUsqhV1amWxv5bkrorTmVjgAACAASURBVG/fvsyfP582bdrg5+dH/fr1Wb58uetbanniKq/GjRuzc+dO7HY7n3zySbnb2+12102FPvzwQ3r37l3mUuRFVWRbKm19eNsevJU5P3DgAK1bt2batGnEx8ezdevWQtPr3r0733//Pfv27QMgMzPTdSOul156ifbt27NgwQLXTZWg5HLz6enp1KpVi9DQUE6cOMGXX34JOPagjh07xsaNGwE4e/Ys+fn5xZbX23K4r8tt27YVW46K0gRRzXXq1ImoqCgWLlzI4MGDufnmm+nRowcRERGMGTOGs2fPei2b/cQTTzB9+nT69OnjcRcfHPc0ePHFF+nUqRN79+7llltucZ00fOCBB7BYLIXGv/fee7HZbERERDBu3DjmzZtX6NumJ08++SRjx46lT58+NGzYsEzLXdYy3+UpSR4ZGUlAQABRUVHFTlKXNAy8l+euiLCwMMDxTw2Oe0BYLBbq1atXbNxJkyZx9913FzpJfSGee+45hg8fzoABA2jSpEm529eqVYvt27fTuXNnvv32W9f7UpZS5EVVZFty3173799fbLi37cFbmfNFixYRHh5OdHQ0u3btKnYzpEaNGjFv3jzXz267d+/Orl272LNnD2+//TZ///vf6dOnD3379uWvf/0rUHK5+aioKDp16kTHjh2ZPHmy60tBUFAQixYt4v777ycqKopBgwaRnZ1NXFwcO3bscJ2k9rYc99xzDxkZGURGRvLCCy/QtWvXUtd/WZjK2s2rarGxsVKR3/3u3PADHVb0YOz+m/no3x+wc+dO2rdv74MIlVKqann6fDPGbBLHzdmK0T0IpZRSHmmCUEop5ZEmCKWUUh5pglBKKeWRJgillFIeaYJQSinlkU8ThDFmqDFmtzFmnzHmEQ/DJxljUowxm52PO9yGTTTG7HU+Jvoyzurmciv3XXQeF1KKODk5mfDw8AuKszxmzJhBx44dmTFjhk+mb7Vaee2113wybaUulM8ShDHGH5gNXA90AMYbYzp4GHWRiEQ7H28729YHngC6AV2BJ4wxxa8iukxdbuW+L4Ua/t688cYb/PTTT7z44otlGr+8Jac1QajqzJd7EF2BfSJyQERygYVAQhnbDgG+EZHTInIG+AYY6qM4q7VLvdy3p3kALF68mK5du3Lddde5xrfZbMyYMcO1fG+88YbHdZKfn8/EiROJjIxkzJgxZGZmAvD000/TpUsXwsPDmTJliqscgadl8lY22V18fDznzp2jW7duLFq0iEOHDjFw4EAiIyMZOHCgq0x30ZLW5Skt/cgjj7B//36io6N9tpeiVEX5slhfU+CwW/cRHHsERY02xvQF9gAPiMhhL22bFm1ojJkCTAFo0aJFJYV93h//+EevBcgqKjo6mpdfLlsRQJvNxn/+8x9uv/12wFHJce/evfz444+ICPHx8axZs4aUlBSuueYali1bBjjqBpVFz549iY+PZ/jw4YwZMwZwlGI4ePAgwcHBHg9tFezN/PLLL+zatYvBgwezZ88eEhMTGT58eLH15Wke4PiQ//HHH1m+fDlPPfUUK1eu5J133iE0NJSNGzeSk5NDr169GDx4cLHqr7t37+add96hV69eTJ48mddee42HHnqIqVOnuko/3HrrrXzxxReMGDHC4zI9++yzDBgwgHfffRer1UrXrl35/e9/X6iwWmJiIrVr13Yt04gRI/jDH/7AxIkTeffdd5k2bRqffvop4KgltHLlSvz9/Xn00Uc9TnvOnDlMnz6dCRMmkJubi81m47nnnmPbtm2Vvp0pVRl8uQfhqbRg0boenwNhIhIJrAQKKrmVpS0i8qaIxIpIbKNGjS4o2OqkoNx3gwYNOH36tMdy3zExMezatYu9e/cSERHBypUrefjhh1m7dq2rzHNFFNSQmT9/fqEqrgXWrVvHrbfeChQu0Vxe7iWrk5OTXcv373//m+joaLp160ZqaqrHG7g0b97cVcPmlltuYd26dQCsWrWKbt26ERERwbfffsv27du9LtOKFSt47rnniI6Opn///q6yySXZsGEDN998M+BIQAXzhcIlrb1Nu0ePHvztb3/j+eef59ChQ8UqjipV3fhyD+II4F6AvhlQ6N6JIpLq1vkW8Lxb2/5F2q6u9AhLUdZv+pXtciv37YmnktUiwquvvsqQIUNKbFu0rLExhuzsbO69916SkpJo3rw5Tz75pGuZPS1TSWWTy8o9Dvc9j/KUlm7dunWF56+Ur/lyD2Ij0MYY08oYEwTcBCS6j2CMcS8nGQ/sdL7+GhhsjKnnPDk92NnvinK5lPsua8nqIUOG8Prrr7tKJe/Zs4dz584VG+/XX39lw4YNACxYsIDevXu7kkHDhg3JyMhwlaT2tkzeyiaXpGfPnixcuBBwVC8teiMg9+Uoa2npyi7nrVRl8lmCEJF8YCqOD/adwEcist0Y87QxJt452jRjzHZjzBZgGjDJ2fY08AyOJLMReNrZ74pzOZT7Lq1Ec4E77riDDh06EBMTQ3h4OHfddZfHXwW1b9+e9957j8jISE6fPs0999yDxWLhzjvvJCIigpEjR7ruzGWz2Twuk7eyySV55ZVXmDt3LpGRkbz//vv885//9DheeUpLN2jQgF69ehEeHq4nqVW1o+W+tdy3UuoKoeW+lVJKVQpNEEoppTzSBOHB5XLYTSmlClTkc00TRBEhISGkpqZqklBKXTZEhNTUVEJCQsrVzpfXQVySmjVrxpEjR0hJSanqUJRSqtKEhITQrFmzcrXRBFFEYGBgsdIOSil1JdJDTEoppTzSBKGUUsojTRBKKaU80gShlFLKI00QSimlPNIEoZRSyiNNEEoppTzSBKGUUsojTRBKKaU80gShlFLKI00QSimlPNIEoZRSyiNNEEoppTzyaYIwxgw1xuw2xuwzxjxSwnhjjDFijIl1docZY7KMMZudjzm+jFMppVRxPiv3bYzxB2YDg4AjwEZjTKKI7CgyXh1gGvDfIpPYLyLRvopPKaVUyXy5B9EV2CciB0QkF1gIJHgY7xngBSDbh7EopZQqpxIThDHmFrfXvYoMm1rKtJsCh926jzj7uU+jE9BcRL7w0L6VMeZnY8x3xpg+XuKbYoxJMsYk6R3glFKqcpW2B/Gg2+tXiwybXEpb46Gf60bPxhg/4CXgTx7GOw60EJFOzhg+NMbULTYxkTdFJFZEYhs1alRKOEoppcqjtARhvLz21F3UEaC5W3cz4Jhbdx0gHFhtjEkGugOJxphYEckRkVQAEdkE7AeuK2V+SimlKlFpCUK8vPbUXdRGoI0xppUxJgi4CUh0NRZJE5GGIhImImHAD0C8iCQZYxo5T3JjjGkNtAEOlL44SimlKktpv2JqZ4zZimNv4Vrna5zdrUtqKCL5zvMUXwP+wLsist0Y8zSQJCKJJTTvCzxtjMkHbMDdInK6DMujlFKqkpSWINpfyMRFZDmwvEi/x72M29/t9VJg6YXMWyml1IUpMUGIyCH3bmNMAxzf7n91nhtQSil1mSrtZ65fGGPCna+bANtw/HrpfWPMHy9CfEoppapIaSepW4nINufr24BvRGQE0I3Sf+aqlFLqElZagshzez0Q5/kEETkL2H0VlFJKqapX2knqw8aY+3Fc0xADfAVgjKkBBPo4NqWUUlWotD2I24GOwCRgnIhYnf27A3N9GJdSSqkqVtqvmE4Cd3vovwpY5auglFJKVb0SE4QxpqSL2RCR+MoNRymlVHVR2jmIHjgqsi7Acb+G0uovKaWUukyUliCuxnHDn/HAzcAyYIGIbPd1YEoppapWiSepRcQmIl+JyEQcJ6b34ai+ev9FiU4ppVSVKfWWo8aYYGAYjr2IMOAV4GPfhqWUUqqqlXaS+j0c92z4EnjK7apqpZRSl7nS9iBuBc7huFnPNGNc56gNICJS7C5vSimlLg+lXQdR2oV0SimlLlOaAJRSSnmkCUIppZRHV3yC8HOugTxbqT/oUkqpK8oVnyDqhdoA2JnatIojUUqp6uWKTxCmdk0A9pxpQmpqFQejlFLViE8ThDFmqDFmtzFmnzHmkRLGG2OMEWNMrFu/mc52u40xQ3wWZEgIACJ+fPaJ3gNJKaUK+CxBGGP8gdnA9UAHYLwxpoOH8eoA03AUAyzo1wG4Cce9KIYCrzmn5zP1OcXSuWm+nIVSSl1SfLkH0RXYJyIHRCQXWAgkeBjvGeAFINutXwKwUERyROQgjhpQXX0YK9FmC9/8UIc0zRFKKQX4NkE0xVEqvMARZz8XY0wnoLmIfFHets72U4wxScaYpJSUlIpFGVIfgE5X7SbPHsDniVKx6Sil1GXGlwnC070jXJ++xhg/4CXgT+Vt6+oh8qaIxIpIbKNGjSoYpWMVtGi2k6YcYek7Zyo2HaWUusz4MkEcAZq7dTcDjrl118FRCHC1MSYZRznxROeJ6tLaVpp6NephCa7DT1cLo83HfPV9HTIyfDEnpZS6tPgyQWwE2hhjWhljgnCcdHbdwlRE0kSkoYiEiUgY8AMQLyJJzvFuMsYEG2NaAW2AH30RZIBfADdcN4Iv/PwZee1XZOcHsnyZHmZSSimfJQgRyQemAl8DO4GPRGS7MeZpY0yJ97J23rHuI2AH8BVwn4jYfBVrQtsEUu02TM+vuIoTLHlbDzMppZQRuTy+LcfGxkpSUlKF2qbnpNPwhYZMt+eR8dTrvB9wGylpwdSoUclBKqVUNWOM2SQisZ6GXfFXUgPUDa5LXKs4PgsMYVTLLziXF8zXX10eiVMppSpKE4RTQtsE9uZlc3XPr2jAKZa8pYeZlFJXNk0QTiOuGwHA8rZ2Ekwin39bk5ycKg5KKaWqkCYIp+ahzYlpEkNiDQujmyWSnhPCym/0MJNS6sqlCcJN/HXxbMi0Et59OaFYWaq/ZlJKXcE0QbhJaJeAIKyMgBF8zqdfh5CXV9VRKaVU1dAE4SaqcRQtQlvwWd2rGHPNJ5zJrsnqVXqYSSl1ZdIE4cYYQ/x18XxzNoXeXZdTiwyWvHW6qsNSSqkqoQmiiPi28WTZcvk+NoDhfMGnXwZj89k13EopVX1pgiiiX1g/6gbXJbF+c0Zf/Qknz9Vm3bqqjkoppS4+TRBFBPkHcf3vrufz9BMM7vIFIWSx5E29WbVS6sqjCcKDhLYJnMw+w46eNbmeL/n480DsertqpdQVRhOEB9e3uZ4AvwA+a3wtoxst5djZuvzwQ1VHpZRSF5cmCA8sIRb6texH4tkUhnf9giByWKqHmZRSVxhNEF7Et41n55kDnIxrwCC+Yeln/lwmldGVUqpMNEF4Ed/WcU+jxKbtGN1wKYesFjZtquKglFLqItIE4UWYJYzIxpF8lnGKhC6fEUAeS/WiOaXUFUQTRAkS2ibw/bFN2Ie2II5VLFmKHmZSSl0xNEGUIKFtAnaxsywskjH1l7AvtT6//FLVUSml1MXh0wRhjBlqjNltjNlnjHnEw/C7jTG/GGM2G2PWGWM6OPuHGWOynP03G2Pm+DJOb2KaxNC0TlM+y0hlZJfP8MPGkjf1MJNS6srgswRhjPEHZgPXAx2A8QUJwM2HIhIhItHAC8A/3IbtF5Fo5+NuX8VZEmMM8W3j+Tp5NXVHtqMva1i6WAszKaWuDL7cg+gK7BORAyKSCywEEtxHEJF0t85aQLU7wh/fNp7MvEz+06YLo+stZcfJRuzcWdVRKaWU7/kyQTQFDrt1H3H2K8QYc58xZj+OPYhpboNaGWN+NsZ8Z4zp42kGxpgpxpgkY0xSSkpKZcbuEhcWR+2g2iRmnOZ/unwBoL9mUkpdEXyZIIyHfsX2EERktohcCzwM/MXZ+zjQQkQ6AQ8CHxpj6npo+6aIxIpIbKNGjSox9POCA4IZ+ruhJO77kiY3RtCT71myUG8zp5S6/PkyQRwBmrt1NwOOlTD+QmAkgIjkiEiq8/UmYD9wnY/iLFVC2wR+y/iNpPA4RluWsuV4Y/bvr6polFLq4vBlgtgItDHGtDLGBAE3AYnuIxhj2rh1DgP2Ovs3cp7kxhjTGmgDHPBhrCW6oc0N+Bt/Pjt7ktFdlgF6mEkpdfnzWYIQkXxgKvA1sBP4SES2G2OeNsbEO0ebaozZbozZjONQ0kRn/77AVmPMFmAJcLeIVNkncv0a9enTsg+Je5fR8tZYYtnIkg+yqyocpZS6KIxcJpcGx8bGSlJSks+m/9KGl3hwxYPsH7uQj3r8zMz050hOhpYtfTZLpZTyOWPMJhGJ9TRMr6QuI1fxvrRjjO62AoCP3zlTlSEppZRPaYIoo2vrX0vHRh35bE8ibSZ1IZItLP13RlWHpZRSPqMJohwS2iaw9tBaTve5hTF1lvD9oeYcK+l3WUopdQnTBFEO8W3jsYmN5aeTGd19NQCfvKuHmZRSlydNEOXQpWkXrq59NYl7PqfDnV1px06WzLVWdVhKKeUTmiDKwc/4MeK6EXy570tyBtzJmNpLWHOgBT6q8qGUUlVKE0Q5JbRNICM3g9VnDjG6+/fY8efTuXqYSSl1+dEEUU4DWg2gZmBNPtv9GVH3OA4z/e054YzmCKXUZUYTRDnVCKzBkGuHkLg7EYZMZe5Vt3HkTB1uuylTb0eqlLqsaIKogIS2CRw9e5Sf0g7T/W8tecH/z3y2oiYvz8qv6tCUUqrSaIKogGHXDcPP+Dn2Iia+xx/v/ZyRfMKfH4Yffqjq6JRSqnJogqiAhjUb0qt5Lz7b/RkEhGD+uop3+91Hc/mVG4edJTW1qiNUSqkLpwmiguLbxrPlxBYOWQ9B3ebUm/s+HzW9kROng5g4Mg27vaojVEqpC6MJooIS2jpur52423mLi1YDiZ07kr+H/Ill60KZ9UxmFUanlFIXThNEBbVp0IZ2DduRuMftHki//1/u++sBxpqPePSpINat0d0IpdSlSxPEBUhom8Dq5NVYs53lNozBTFvKW+OeIkySuWl4ul5lrZS6ZGmCuADxbePJt+fz5d4vz/cMrEHonGUs7jCBU2dDuHXICT0foZS6JGmCuADdmnbj6tpX8+zaZzl57uT5AaFhdPr4Sf5Zdzpf/9yY//uz3r9aKXXp0QRxAfz9/Jn/P/M5cOYAce/F8VvGb+cHtr2eKfMaMN7vQx7/eyirV+RWXaBKKVUBPk0Qxpihxpjdxph9xphHPAy/2xjzizFmszFmnTGmg9uwmc52u40xQ3wZ54UY2HogyycsJ9maTP95/Tl29vwdhMzIZ3ljxvv8jn2MTzjLiRNVGKhSSpWTzxKEMcYfmA1cD3QAxrsnAKcPRSRCRKKBF4B/ONt2AG4COgJDgdec06uW+of156sJX3H07FH6z+vPkfQjjgHGUOeZpSzpdxfW7JpM6HsQm61qY1VKqbLy5R5EV2CfiBwQkVxgIZDgPoKIpLt11gIKyt0lAAtFJEdEDgL7nNOrtvq07MPXt3zNbxm/0W9eP8cFdACBNYn4+B1mXz2N/+xpxTNTkqs0TqWUKitfJoimwGG37iPOfoUYY+4zxuzHsQcxrZxtpxhjkowxSSnV4PekPZv3ZOUfVpKamUq/ef04eOagY0D9a7lt2VD+EPQeT7/bgpVL9C50Sqnqz5cJwnjoV6wgtojMFpFrgYeBv5Sz7ZsiEisisY0aNbqgYCtL16Zd+Xbit6TnpNNvXj/2nd4HgIkZzWv/2k57djJhfD7Hj+ixJqVU9ebLBHEEaO7W3Qw45mVccByCGlnBttVKTJMYVk1cRWZeJv3m9WNP6h4Aat3xPItvfpqM/BqM77abfK0OrpSqxnyZIDYCbYwxrYwxQThOOie6j2CMaePWOQzY63ydCNxkjAk2xrQC2gA/+jDWShd1dRSrJq4iz5ZHv3n92JmyE4yhw9x3mNP2T3x3rANPjv2pqsNUSimvfJYgRCQfmAp8DewEPhKR7caYp40x8c7RphpjthtjNgMPAhOdbbcDHwE7gK+A+0TkkjsmE9E4gtWTViMi9H+vP9tOboOg2ty6ahq313qHZz+NYf7zv7J3L6SkQF5eVUeslFLnGblM7pMZGxsrSUlJVR2GR7tP7WbAvweQa8tl5a0ribo6iqz/fEi3QeH8IpGFxq3pl4klMANL0DksNbKw1MrDUsdGaChY6gdgaRSMpXENLI2DsVxTA0uTmlgaBmCxQGgoBAdX0UIqpS5JxphNIhLrcZgmiItj3+l9xL0XR2ZeJt/c+g0xTWJIf+//+G72LtLOhmDNrIU1qzbWnLpYcy1Y8y1YbRasYsHK+YeNgBLnE2KysAScdSSY4EwsNXOw1M7DEmrHUs8PS4NAQhvXxHJ1LSxNamC5pqbjuZ7BYoGQkIu0QpRS1YImiGqioCRHek46K25ZQZemXYqPZM+HnHTITYOcNDh3GlKPw6nfkNQUMk+kYf0tF2uKHWsqWK2BWNMCsZ6riTWzFmnZtbHm1sWa50wy9sIJJo+gEmMMNtlY/NMJDTyHJfgclpAsLLVysdTJx2IBSwN/LA2DsDSpTeg1dbFcUwtLs1pYGgVhsUCNGmA8/QZNKVUtaYKoRg5ZDxH3XhypWal8NeErejTvUfkzETvknnUkmhwrpJ2EU8eRlN/IOnEG6/Fs0k44k8wZ40gwZ0OwnquBNauWcy+mLmn5oYX2Ys5Qj1xKPoYVSC4W/zTnXkwGoSFZWGpkY6mTh6WuHUt9g6VBIJbGIVia1MHSzEJos7pYmtbCUt+PWrU0wSh1MWmCqGYOpx1mwL8H8FvGb7wT/w6/q/876gbXdT1CAqrBcR4RyM907MVkOvdiUo+TfTwF69FzWI9nYz2ZT9ppwXrGD2t6ENaMEKyZNbFm18aaUwdrfijWfAtp9lDXHkwWNUucrT/5WPzSsPinYwk669iLqZGFpVYOoXVsWELFsRfTKBBL45pYmtbF0rIhlpb1sDQOpnYdowlGqXLQBFENHU0/yoB/D3BdI+EuyD+oUMIIDQ4tuTvE8/DggGpwxjo/25Fk0lMg9Rg5x06QdjQN69EMrCdysabYsJ42pKX7YU0PxnouxLEXk10ba15dR5Jx24vJpFaJs/PDRqhJO78XE5yBJSQTS81sLLVzsdS1EVrPYGnoj+WqYCxNamNpXg9Ly0ZYwhpQp34gflrjWF1BNEFUUxm5GSQdSyI9J530nHTSstNcr9Nz0knL8dydlp1Gnr3038S6J5qKJplqkWhseY4kc/o4eceOkXY4FevRs1iPZWI9mYc11e7ciwnAmhFMWmYNrNm1nHsxziTjPBeTQZ0SZ2WwE2rSCPVLxxKQhiWw8F6MpW4eFovjUFlow0AsV9fE0rQOlhb1sLRqTN3mV+EfqBlGXTo0QVyGcvJziieQMiYY9/HLmmi8JpSg0hNMwfAg/5JPkPuU3QaZZ8j/7ShpySdJO3zGuReT5dyLEaxp/ljTA517MTWx5tQmLa+2cy8mFKtYSCe01FnVJQ2LnxWLfxqhgRlYgs5SMyCHYL9cgvzzCPbLI8gvj2B/53NAHkF++Y5n/3y31zZXv+AAG0EBzu4AG8EB+QQF2AkOdHsOFAID7Bh/P/ArePiff+3vD/7u/YoMK+jn71/kuWBct34BAUX6+Z0f5hdwfrh/wfAA52vns/GHgMDz/fwCnPMJOD+Of4BzHOe0jN/5B36Fu42fnryqoJISRMm/mVTVVnBAMFcFXMVVta6q8DREhBxbToUSzK9pv7rGT8tJI99eet2QYP/gMu2xeOp2b1OhROPnD7UbEvC7hjT4HTSowPrCbsdmPUn6weNYD53Cejgd62/nSDuRgzXVhvUMWNMCsGYEYc0McRwmy63Nr+euJstegxwJIpcgciTY9Vzar8oqIogcgsnBMbdc1+uKP2cX6r6QaflzAfffNR4efqX1M+e7/cz5fu7dhZ4Lxi/y2jWshNfG73w/V3+3fq7hHp4LDXN/eOrn9ij4MnBNS/jTmxVft95Wue5BqAtVkGhKTTAFw3O9J6TyJJryJpmi41fpHo2TiOMK+pwcyM0tx3O2kJsj5GTZyc2xO7qz7Y5+2XZyc+X8ODk4unOMo22ucxq5htxc43jOM+Tk+Tmec/3IzfcjN88Pm71yD5f5+9kce0b+bntL/vmOPSt/555UwZ5VwZ6WX67j2eSe7zZ5BPvlOJ6N4zmIXOfrXMe45nyidPWX7CJJz/ksOQSSgxEBux3s4nhz7FL4tQjY7IW77eIoJeqpjau/882246ENRYbjoTRpKX5XG/aerdB7onsQyqeMMYQEhBBSO4TGtRtXeDoiQnZ+dukJxsPwQ9ZDhc7R2MpQmSUkIKRSfgwQ6B9Y4WU2BoKCHI9ytuT812XfsdnKmbiKPBfv509Ojj+5uUElts3KBWup06r85S14L4KCHFUJCj3X9NK/lOeKtAkOEoICheBAu/MQoh1/Y3cmL7vjjbG7dfv75n5qmiBUtWGMoUZgDWoE1qiURFORczQHrQcLjV/eROMtydQMrOlIokUewQHBnvv7F+4f4BeAqYJj7P7+josfa9S46LMuVcHeV+UkrvI9p6WVPq0Lu3tk8S8Afn7ek0qnTvDBBxe4Qj3QBAH88Y9/ZPPmzVUdhqpi/vhTz/lXwC528u352Ow28u355Mv51za7rVB3mj2N03K60PgF45T7kEFRBvyMn+OB3/nXbg9jjMf+lfEwxmA83qblyuPn5yhJU1pZGik44mQ//+z+uqz9vA3Pz3cko/R02L07Gni50pdVE4RSJfAzfo5zFRe4By8i2MVe+oMyjOP2cJ+uzW4rcbwLVZCAKpyIvCS20hKTe/elxLidp/a16GjfTFcTBPDyy5WfeZWqTuxiJyc/h+z8bHJsjueij4Lhxfp7G78c08nKz8IuF/ALJidvh+C8HrbzL/1wXknTK9omwO/K+si8spZWqSuUn/Fznd+pKvn2/PInoXKOb822eh2WnZ99wcvgb/zLff6oIonIW5vggOCLuielCUIpdVEE+AUQEBRAraCSy6X4ioiQZ8+7j/vyhQAAC+FJREFU4CRUqI2teP+zOWe9TivXduE/vQryDyqWODpf05kFoxdUwloqTBOEUuqKYIwhyD/IVYKmKrgf6qvInpO3Nq0srXwSryYIpZS6SKrDob7yuLR+FqCUUuqi8WmCMMYMNcbsNsbsM8Y84mH4g8aYHcaYrcaY/xhjWroNsxljNjsfib6MUymlVHE+O8RkjPEHZgODgCPARmNMoojscBvtZyBWRDKNMfcALwDjnMOyRMRHv+5VSilVGl/uQXQF9onIARHJBRYCCe4jiMgqEcl0dv4ANPNhPEoppcrBlwmiKXDYrfuIs583twNfunWHGGOSjDE/GGNG+iJApZRS3vnyV0yeCrd4vN7fGHMLEAv0c+vdQkSOGWNaA98aY34Rkf1F2k0BpgC0aNGicqJWSikF+HYP4gjQ3K27GXCs6EjGmN8D/wvEi0hOQX8ROeZ8PgCsBjoVbSsib4pIrIjENmrUqHKjV0qpK5wvE8RGoI0xppUxJgi4CSj0ayRjTCfgDRzJ4aRb/3rGmGDn64ZAL8D95LZSSikf8+kd5YwxN+CoQesPvCsizxpjngaSRCTRGLMSiACOO5v8KiLxxpieOBKHHUcSe1lE3illXinAoQsItyFw6gLa+4rGVT4aV/loXOVzOcbVUkQ8HoK5bG45eqGMMUnebrtXlTSu8tG4ykfjKp8rLS69kloppZRHmiCUUkp5pAnivDerOgAvNK7y0bjKR+MqnysqLj0HoZRSyiPdg1BKKeWRJgillFIeXVEJogzlx4ONMYucw/9rjAm7CDE1N8asMsbsNMZsN8ZM9zBOf2NMmlv588d9HZfbvJONMb8455vkYbgxxrziXGdbjTExFyGmtm7rYrMxJt0Y88ci41yUdWaMedcYc9IYs82tX31jzDfGmL3O53pe2k50jrPXGDPxIsT1ojFml/N9+sQYY/HStsT33AdxPWmMOer2Xt3gpW2J/78+iGuRW0zJxpjNXtr6cn15/Hy4aNuYiFwRDxwX6+0HWgNBwBagQ5Fx7gXmOF/fBCy6CHE1AWKcr+sAezzE1R/4oorWWzLQsIThN+AosmiA7sB/q+B9/Q3HxT4XfZ0BfYEYYJtbvxeAR5yvHwGe99CuPnDA+VzP+bqej+MaDAQ4Xz/vKa7/b+/cg62q6jj++fISC0QyNahJ5U5O2aRWlClaODAojRUZNJGNKfag1IwJpzHNuTZN4Tg4jphFYoINNkD0kNIAk4fTqFAIV8QUqKapGKwsgR7mjV9//NaRzWbvc8/Fc/a5dn+fmT1nP9Ze67d/e+219lrr7O9q5J63wK5OYHYD97nu89tsu3LH5wLXt8FfheVDVXmsP7UgepQfT9uL0vr3gQmSikQHm4aZ7TKzTWl9L/Ak9VVv+xofAO425xHgaEmjKkx/ArDTzF7KV/SHjZmtB57N7c7mo0VAkRrxecBqM3vWzP4GrAbOb6VdZrbKzLrTZlvk9Uv81QiNPL8tsSuVAR8Gvtes9BqlTvlQSR7rTxVEI/LjL4ZJD9JzwDGVWAekLq23Ao8WHD5T0hZJ90t6c1U24Qq8qyT9Sq6em6e3su7N5iOUP7jt8tnxZrYL/AEHjisI026/zeBgef0sPd3zVnBF6vr6Tkl3STv9dQ6w28y2lxyvxF+58qGSPNafKohG5McblihvNpKGAcuBz5vZntzhTXgXymnAPOBHVdiUGGdmbwMmA5dLenfueDt9NgR4P7Cs4HA7fdYI7fTbtUA3sLgkSE/3vNl8E+gATsd12eYWhGmbv4Dp1G89tNxfPZQPpacV7OuVz/pTBdGI/PiLYSQNAkZweM3hXiFpMH7zF5vZD/LHzWyPme1L6/cBg+Uqty3HDsiuPwP8EG/qZ2lI1r1FTAY2mdnu/IF2+gzYXetmS7/PFIRpi9/SQOUFwEWWOqrzNHDPm4qZ7Taz/5rZfuCOkvTa5a9BwIXAkrIwrfZXSflQSR7rTxVEj/Ljabs20j8VeLDsIWoWqX/zTuBJM7u5JMxramMhkt6J37e/ttKulNYrJQ2vreODnFtzwe4FLpbzLuC5WtO3Akrf7Nrls0Q2H30c+HFBmJXAJLm0/UjctytbaZSk84Ev4vL6/ywJ08g9b7Zd2TGrD5ak18jz2womAr82sz8UHWy1v+qUD9XksVaMvPfVBf/HzdP4vyGuTfu+gj8wAEPx7oodwAZgTAU2nY03+7qAzWl5LzATmJnCXAE8gf9z4xHgrIr8NSaluSWlX/NZ1jYB30g+fRwYW5Ftr8AL/BGZfZX7DK+gdgEv4G9sl+HjVj8HtqffV6WwY4EFmXNnpLy2A7i0Art24H3StXxW+8feaOC+eve8xXZ9N+WdLrzgG5W3K20f8vy20q60f2EtT2XCVumvsvKhkjwWUhtBEARBIf2piykIgiDoBVFBBEEQBIVEBREEQRAUEhVEEARBUEhUEEEQBEEhUUEELUeSSZqb2Z4tqbNJcS+UNLUZcfWQzrSkqLkmt/9ESR9tdfovBUlTJJ3SxPi+dBjnXCLptmbZEFRDVBBBFTwPXFjhl8wNIWlgL4JfBnzWzM7N7T8RKKwg0le4fYEpuALoIRymjb2uIIKXJ1FBBFXQjc+ZOyt/IN8CkLQv/Y6XtE7SUklPS5oj6SJJG5L2fkcmmomSHkrhLkjnD5TPf7AxicB9OhPvGkn34B9n5e2ZnuLfKunGtO96/IOlb0m6KXfKHOAc+VwAs9Kb8jJJK4BV6fyrM3bckEnrY+l6Nkuan2wemHyyNdlR5LNjJS1PcW6UNC7tvzXZiqTzJK2XdBauV3VTSqdD0lpJX5O0DrhK0vvk8588JukBScenOIZJuivZ0SXpQ5LmAEemuBaXXUfaf2m6J+uAccVZI+jTtOLr0lhiyS7APuAoXDd/BDAb6EzHFgJTs2HT73jg77ge/hHAH4Eb0rGrgFsy5/8Mf9l5A/4V7FDgU8B1KcwRwC+Bk1K8/wBOKrBzNPB74FhgEPAgMCUdW0vBV+Lk5p0ALkk21L5snYRXjko2/gSfe+BNwApgcAp3O3Ax8HZcorkW39EFad4DnJ3WX4/LMIB/Xf4EcC7wFNBR4uO1wO2Z7ZEcmJ/+E8DctH5jzc+1cNl7lNbLrmNUxpdDgF8At7U7L8bSu6WvNIGD/3PMbI+ku4HPAf9q8LSNlnSdJO0kvZHjb/7Zrp6l5kJv2yX9BngjXjCfmmmdjMArkP8AG8zstwXpvQNYa2Z/Tmkuxgvz3irBrjazmsjjpLQ8lraHJTtOxSuDjS63w5G44NoKYIykecBPM9ecZSJwig5MVXKUpOFmtlfSJ4H1wCwz21nHxqz43OuAJUkTaQhQ881EXPMIAPM5BfJMKLmOMzjYl0uAk+vYE/RBooIIquQWXIb7rsy+blJXZxImG5I59nxmfX9mez8H5928Xozhb+xXmtlB4mSSxuMtiCKaNTlUNn4BXzez+Tk7rgQWmdk1hxghnYZP9nI5PlHNjFyQAcCZZlZU0b4F16ga3Qsb5wE3m9m9yT+dGdt70uIRBdchaUoD5wZ9nBiDCCojvVUvxQd8a/wOfwMFnyVr8GFEPU3SgDQuMQbvXlkJfEYulYykk+Vqm/V4FHiPpFenfvTpwLoeztmLTwVZxkpghlzPH0mvlXQcLrA2Na3X5hg+IQ3kDzCz5cCX8Wkw86zCxQhJ556efk8AvoBPKjNZ0hkN2jgC78KDAwqhRenUJvJ5oebXsuvAfTle0jEp7LQ66Qd9lKgggqqZC2T/zXQHXihvwLslyt7u6/EUXpDfjytv/htYAGwDNsknop9PDy3m1J11DbAGV+fcZGZFMspZuoBu+cx1hwwom9kqfMzgYUmP41PZDjezbcB1+ExkXfh0kKPwGb/WStqMjx0c0sLAu+nGpoHjbcDM1Pq6E5/b+U94JbxA0lB8es6r0yB0R0F8ncAySQ8Bf8ns/yowMg2Yb+FAt963gS5Ji8uuI/myE3gYeABvOQYvM0LNNQiCICgkWhBBEARBIVFBBEEQBIVEBREEQRAUEhVEEARBUEhUEEEQBEEhUUEEQRAEhUQFEQRBEBTyP1lq8Bh8KoXlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for results in results_global:\n",
    "    x_value = [int(NB_TREES/k) for k in [5, 10, 50, 100, 500, 1000]]\n",
    "    plt.xlabel('Number of trees extracted')\n",
    "    plt.ylabel('MSE')\n",
    "    plt.plot(x_value, [elem[1] for elem in results], color='darkorange',\n",
    "             label='Weighted results of the average trees')\n",
    "    plt.plot(x_value, [elem[2] for elem in results], color='red',\n",
    "            label='Weighted results of the average trees normalized')\n",
    "    plt.plot(x_value, [elem[3] for elem in results], color='blue',\n",
    "             label='Mean results of the average trees')\n",
    "    plt.plot(x_value, [elem[4] for elem in results], color='green',\n",
    "             label='Results of a forest train with number of trees extracted')\n",
    "    plt.plot(x_value, [elem[0] for elem in results], color='black',\n",
    "             label='Results of the base forest')\n",
    "    plt.figure(1, figsize=(15, 10))\n",
    "\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    \n",
    "\n",
    "\n",
    "    fig_acc_rec = plt.gcf()\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weight_density(list_weight):\n",
    "    print(list_weight)\n",
    "    X_plot = [np.exp(elem) for elem in list_weight]\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    for kernel in ['gaussian', 'tophat', 'epanechnikov']:\n",
    "        kde = KernelDensity(kernel=kernel, bandwidth=0.5).fit(X_plot)\n",
    "        log_dens = kde.score_samples(X_plot)\n",
    "        ax.plot(X_plot[:, 0], np.exp(log_dens), '-',\n",
    "                label=\"kernel = '{0}'\".format(kernel))\n",
    "\n",
    "    ax.legend(loc='upper left')\n",
    "    ax.plot(X[:, 0], -0.005 - 0.01 * np.random.random(X.shape[0]), '+k')\n",
    "\n",
    "    ax.set_xlim(-4, 9)\n",
    "    ax.set_ylim(-0.02, 0.4)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 125,
   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1437754c10>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for results in results_global:\n",
    "    ax = pd.Series([[e for e in test[5] if e != 0] for test in results][1]).plot.kde(figsize=(15, 10))\n",
    "    \n",
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    "legends = ['Experience '+ str(i+1) for i in range(10)]\n",
    "ax.legend(legends)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.26899689, 0.26359377, 0.2780403 , 0.25029723, 0.26674508,\n",
       "         0.25602716, 0.28057576, 0.2761758 , 0.25817293, 0.26356801],\n",
       "        [0.26899689, 0.26359377, 0.2780403 , 0.25029723, 0.26674508,\n",
       "         0.25602716, 0.28057576, 0.2761758 , 0.25817293, 0.26356801],\n",
       "        [0.26899689, 0.26359377, 0.2780403 , 0.25029723, 0.26674508,\n",
       "         0.25602716, 0.28057576, 0.2761758 , 0.25817293, 0.26356801],\n",
       "        [0.26899689, 0.26359377, 0.2780403 , 0.25029723, 0.26674508,\n",
       "         0.25602716, 0.28057576, 0.2761758 , 0.25817293, 0.26356801],\n",
       "        [0.26899689, 0.26359377, 0.2780403 , 0.25029723, 0.26674508,\n",
       "         0.25602716, 0.28057576, 0.2761758 , 0.25817293, 0.26356801],\n",
       "        [0.26899689, 0.26359377, 0.2780403 , 0.25029723, 0.26674508,\n",
       "         0.25602716, 0.28057576, 0.2761758 , 0.25817293, 0.26356801]],\n",
       "\n",
       "       [[0.27542295, 0.27749768, 0.28513058, 0.26038702, 0.27043376,\n",
       "         0.2655008 , 0.28448981, 0.28333658, 0.27387447, 0.2769381 ],\n",
       "        [0.27746557, 0.27723817, 0.28723859, 0.26434651, 0.27067318,\n",
       "         0.26330039, 0.28196962, 0.28240111, 0.27509222, 0.28088583],\n",
       "        [0.28995364, 0.29198289, 0.29873153, 0.27618004, 0.2848853 ,\n",
       "         0.27857491, 0.29298835, 0.30077324, 0.2886711 , 0.28905086],\n",
       "        [0.32365526, 0.32322906, 0.32710513, 0.29903915, 0.31318329,\n",
       "         0.30669926, 0.32434317, 0.32110736, 0.31463418, 0.321466  ],\n",
       "        [0.39986111, 0.42484653, 0.42855969, 0.39370378, 0.39935977,\n",
       "         0.38460084, 0.41563938, 0.40814036, 0.39929003, 0.38932494],\n",
       "        [0.58523066, 0.55891364, 0.59428021, 0.60547191, 0.5266932 ,\n",
       "         0.54086835, 0.57100958, 0.54292164, 0.53241884, 0.59593718]],\n",
       "\n",
       "       [[0.27521601, 0.27770826, 0.28523181, 0.26038166, 0.27049563,\n",
       "         0.26550442, 0.28434805, 0.28336574, 0.27317227, 0.27730912],\n",
       "        [0.27744027, 0.27741863, 0.28736133, 0.26435666, 0.2708491 ,\n",
       "         0.26327205, 0.28195336, 0.28236196, 0.2744054 , 0.28125145],\n",
       "        [0.29039242, 0.2925128 , 0.29908397, 0.27622541, 0.28540109,\n",
       "         0.27863392, 0.2930088 , 0.30074197, 0.28805612, 0.28951146],\n",
       "        [0.32484297, 0.32460791, 0.32853218, 0.29964306, 0.31475447,\n",
       "         0.30729241, 0.3249432 , 0.32151156, 0.31435099, 0.32265734],\n",
       "        [0.40624496, 0.4302276 , 0.43530539, 0.39867506, 0.40663919,\n",
       "         0.38977503, 0.42116368, 0.41272401, 0.40457793, 0.39346472],\n",
       "        [0.61110865, 0.57982481, 0.62469263, 0.63199171, 0.54630055,\n",
       "         0.56558963, 0.59272349, 0.56177889, 0.55353829, 0.61516357]],\n",
       "\n",
       "       [[0.27065035, 0.26652983, 0.27690332, 0.25221968, 0.26834736,\n",
       "         0.25713719, 0.27970688, 0.27633639, 0.26457284, 0.26459536],\n",
       "        [0.27745036, 0.27779328, 0.28618091, 0.26025249, 0.27057945,\n",
       "         0.26101474, 0.28177424, 0.27897506, 0.27042052, 0.27522275],\n",
       "        [0.29080491, 0.29277669, 0.29888537, 0.27686572, 0.28509987,\n",
       "         0.2785929 , 0.29306583, 0.29749568, 0.28659403, 0.28920128],\n",
       "        [0.32643403, 0.32525994, 0.32820348, 0.30076919, 0.31430383,\n",
       "         0.30854091, 0.3247721 , 0.31945693, 0.31341696, 0.32260233],\n",
       "        [0.40894501, 0.43023553, 0.43584108, 0.40142063, 0.40520717,\n",
       "         0.39108446, 0.4205632 , 0.41085099, 0.40442852, 0.39712488],\n",
       "        [0.61110865, 0.57982481, 0.62469263, 0.63199171, 0.54630055,\n",
       "         0.56558963, 0.59272349, 0.56177889, 0.55353829, 0.61516357]],\n",
       "\n",
       "       [[0.26184144, 0.25626252, 0.26511056, 0.24293248, 0.25853787,\n",
       "         0.24899595, 0.27433988, 0.27443584, 0.24968665, 0.25521777],\n",
       "        [0.27008137, 0.26487907, 0.27600019, 0.25217323, 0.26622961,\n",
       "         0.25721161, 0.28795328, 0.28488014, 0.25274144, 0.26086461],\n",
       "        [0.28205227, 0.2757543 , 0.29624245, 0.27094063, 0.29016011,\n",
       "         0.27193868, 0.30978997, 0.29614998, 0.26827511, 0.27353369],\n",
       "        [0.30842355, 0.30288144, 0.32913279, 0.30527809, 0.32101279,\n",
       "         0.31426529, 0.3350261 , 0.33831256, 0.30012365, 0.30287159],\n",
       "        [0.39608144, 0.38109562, 0.41662116, 0.40638002, 0.41791456,\n",
       "         0.40641226, 0.44955332, 0.44545138, 0.40050687, 0.39659829],\n",
       "        [0.52593732, 0.54251735, 0.57760869, 0.56082674, 0.56808121,\n",
       "         0.55389761, 0.59152879, 0.62432776, 0.52053009, 0.54411424]]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(\n",
    "    [\n",
    "        [\n",
    "            [results[i][k] for results in results_global]\n",
    "        for i in range(len(results_global[0]))]\n",
    "    for k in range(5)])"
   ]
  },
  {
   "cell_type": "code",
Léo Bouscarrat's avatar
Léo Bouscarrat committed
   "execution_count": 127,
   "metadata": {},
Léo Bouscarrat's avatar
Léo Bouscarrat committed
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0019333893291256,\n",
       "  1.0002339744254798,\n",
       "  0.9965284128922761,\n",
       "  0.9930020768164572,\n",
       "  0.9713515521464255,\n",
       "  0.9355587965148584],\n",
       " [0.9964172479701133,\n",
       "  0.9965133913097529,\n",
       "  0.9921861297905141,\n",
       "  0.9842416540561515,\n",
       "  0.9682575760922218,\n",
       "  0.933691714442025],\n",
       " [0.9983332596273122,\n",
       "  0.9980542966631237,\n",
       "  0.9953384960946179,\n",
       "  0.9863737744133908,\n",
       "  0.9713936104999941,\n",
       "  0.9322186005941735],\n",
       " [1.0038094772994455,\n",
       "  1.0018194366490565,\n",
       "  0.9977426702506628,\n",
       "  0.9901515373986598,\n",
       "  0.9656704215772347,\n",
       "  0.9225711413699638],\n",
       " [0.9990610710125596,\n",
       "  0.997516190665299,\n",
       "  0.994208907140429,\n",
       "  0.9866287246076226,\n",
       "  0.9654844823617819,\n",
       "  0.9349411984209011],\n",
       " [0.9988517681736824,\n",
       "  0.9978719264484801,\n",
       "  0.9951808165785492,\n",
       "  0.9869717461401798,\n",
       "  0.96626061534528,\n",
       "  0.9275270848174226],\n",
       " [1.0036372042892556,\n",
       "  1.0021281690286359,\n",
       "  0.9992184310564347,\n",
       "  0.9936213667248184,\n",
       "  0.9726814533989055,\n",
       "  0.9287908504721321],\n",
       " [1.0072738855021581,\n",
       "  1.0058719271210204,\n",
       "  1.0008091849171328,\n",
       "  0.9924905567327407,\n",
       "  0.9726743101033102,\n",
       "  0.9357752656379753],\n",
       " [1.0124396297482838,\n",
       "  1.0124081517188515,\n",
       "  1.008883039483941,\n",
       "  1.0028553033696677,\n",
       "  0.9743542569764307,\n",
       "  0.942597999044375],\n",
       " [0.9944072082781984,\n",
       "  0.9943119815126942,\n",
       "  0.9936514348889314,\n",
       "  0.9866366329633924,\n",
       "  0.9668343182393178,\n",
       "  0.9188473811851859]]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[sum(elem[5]) for elem in results] for results in results_global] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_global[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entraînement de la forêt aléatoire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = RandomForestRegressor(n_estimators=NB_TREES, random_state = RANDOM_SEED)\n",
    "\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accès à la la liste des arbres\n",
    "\n",
    "tree_list = regressor.estimators_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Création de la matrice des prédictions de chaque arbre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# L'implémentation de scikit-learn est un peu différente que celle vue en réunion, D est de même taille que X \n",
    "# et chaque élément est composé de d signaux, d'où la création suivante de D où on créé une liste pour chaque\n",
    "# élément comprenant les valeurs prédites par chaque arbre\n",
    "\n",
    "D = [[tree.predict([elem])[0] for tree in tree_list] for elem in X_train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "omp = OrthogonalMatchingPursuit(n_nonzero_coefs=NB_TREES_EXTRACTED)\n",
    "omp.fit(D, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Matrice avec poids de chaque arbre\n",
    "\n",
    "omp.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcul des résultats des différentes méthodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Résultat de la forêt de base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_squared_error(regressor.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Résultat de la forêt extraite avec l'OMP, où chaque arbre est multiplié par son poids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = [sum([tree_list[i].predict([elem])[0] * omp.coef_[i] for i in range(NB_TREES)]) for elem in X_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Résultat de la forêt extraite avec l'OMP, où on prends la moyenne des arbres extraits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = [mean([tree_list[i].predict([elem])[0] for i in range(NB_TREES) if omp.coef_[i] != 0])for elem in X_test]\n",
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Résultat d'une forêt avec le même nombre d'arbre que le nombre d'arbre extrait"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor_small = RandomForestRegressor(n_estimators=NB_TREES_EXTRACTED, random_state=RANDOM_SEED)\n",
    "regressor_small.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_squared_error(regressor_small.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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