From 23d833635309819d444f214e931d4a7c987ea9d8 Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Thu, 1 Feb 2024 23:45:10 +0100 Subject: [PATCH 01/14] add notebooks for 2/2 --- .gitignore | 1 + _doc/articles/2024/2024-03-01-route2024.rst | 20 + _doc/notebook_gallery.rst | 2 + _doc/practice/index_ml.rst | 2 + _doc/practice/ml/gradient_boosting.ipynb | 6114 +++++++++++++++++++ _doc/practice/ml/lasso_rf.pickle | Bin 0 -> 2979576 bytes _doc/practice/ml/optimzed_rf.pickle | Bin 0 -> 1354238 bytes _doc/practice/ml/ridge_lasso.ipynb | 1979 ++++++ 8 files changed, 8118 insertions(+) create mode 100644 _doc/practice/ml/gradient_boosting.ipynb create mode 100644 _doc/practice/ml/lasso_rf.pickle create mode 100644 _doc/practice/ml/optimzed_rf.pickle create mode 100644 _doc/practice/ml/ridge_lasso.ipynb diff --git a/.gitignore b/.gitignore index 8400738..202baee 100644 --- a/.gitignore +++ b/.gitignore @@ -48,6 +48,7 @@ _doc/practice/algo-compose/paris_54000.* _doc/practice/algo-base/*.csv _doc/practice/algo-base/*.txt _doc/practice/algo-base/*.zip +_doc/practice/ml/catboost_info/* _doc/practice/py-base/*.csv _doc/practice/py-base/*.json _doc/practice/py-base/*.jpg diff --git a/_doc/articles/2024/2024-03-01-route2024.rst b/_doc/articles/2024/2024-03-01-route2024.rst index 2eb5612..c3e9d80 100644 --- a/_doc/articles/2024/2024-03-01-route2024.rst +++ b/_doc/articles/2024/2024-03-01-route2024.rst @@ -22,3 +22,23 @@ Séance 1 (26/1) * `ChatGPT `_, `LLM `_, (Large Language Model), SLLM (Small LLM) + +Séance 2 (2/2) +============== + +* arbre de régression, arbre de classification +* random forest, boosting trees + (:epkg:`xgboost`, :epkg:`lightgtbm`, :epkg:`catboost`), + :ref:`nbl-practice-ml-ml_a_tree_overfitting` +* Gradient Boosting, :ref:`nbl-practice-ml-gradient_boosting` +* Régression Linéaire et contraintes sur les coefficients, + `Ridge `_, + `Lasso `_, + `ElasticNet `_, + :ref:`nbl-practice-ml-ridge_lasso` +* Notion de `Pipeline `_ + ou comment intégrer les prétraitements dans le modèle +* prétraitements : tout convertir en numérique, + données numériques, catégorielles, textuelles +* un jeu de données : + `load_diabetes `_ diff --git a/_doc/notebook_gallery.rst b/_doc/notebook_gallery.rst index a47dbbe..2dac1fd 100644 --- a/_doc/notebook_gallery.rst +++ b/_doc/notebook_gallery.rst @@ -178,3 +178,5 @@ Machine Learning practice/ml/ml_features_model practice/ml/timeseries_ssa practice/ml/ml_a_tree_overfitting + practice/ml/gradient_boosting + practice/ml/ridge_lasso diff --git a/_doc/practice/index_ml.rst b/_doc/practice/index_ml.rst index b06c1b3..4037a9b 100644 --- a/_doc/practice/index_ml.rst +++ b/_doc/practice/index_ml.rst @@ -20,6 +20,8 @@ Machine Learning ml/winesr_knn_cross_val ml/winesr_knn_hyper ml/ml_a_tree_overfitting + ml/gradient_boosting + ml/ridge_lasso .. toctree:: :maxdepth: 1 diff --git a/_doc/practice/ml/gradient_boosting.ipynb b/_doc/practice/ml/gradient_boosting.ipynb new file mode 100644 index 0000000..919005c --- /dev/null +++ b/_doc/practice/ml/gradient_boosting.ipynb @@ -0,0 +1,6114 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient Boosting\n", + "\n", + "Le notebook explore l'algorithme du [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Premier exemple\n", + "\n", + "On considère les paramètres par défaut de la classe [GradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from numpy.random import randn, random\n", + "from pandas import DataFrame\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "rnd = randn(1000)\n", + "X = random(1000) * 8 - 4\n", + "y = X**2 - X + rnd * 2 + 150 # X^2 - X + 150 + epsilon\n", + "X = X.reshape((-1, 1))\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", + "df = DataFrame({\"X\": X_train.ravel(), \"y\": y_train})\n", + "ax = df.plot(x=\"X\", y=\"y\", kind=\"scatter\")\n", + "ax.set_title(\"Nuage de points X^2 - X + 150 + epsilon\");" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GradientBoostingRegressor(max_depth=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GradientBoostingRegressor(max_depth=1)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "model = GradientBoostingRegressor(max_depth=1)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy\n", + "\n", + "ind = numpy.argsort(X_test, axis=0)\n", + "y_ = model.predict(X_test)\n", + "df = DataFrame(\n", + " {\"X\": X_test[ind].ravel(), \"y\": y_test[ind].ravel(), \"y^\": y_[ind].ravel()}\n", + ")\n", + "ax = df.plot(x=\"X\", y=\"y\", kind=\"scatter\")\n", + "df.plot(x=\"X\", y=\"y^\", kind=\"line\", ax=ax, color=\"r\")\n", + "ax.set_title(\"Prédictions avec GradientBoostingRegressor\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rien d'imprévu jusque là. Essayons autre chose. On regarde avec une seule itération." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GradientBoostingRegressor(learning_rate=0.5, max_depth=1, n_estimators=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GradientBoostingRegressor(learning_rate=0.5, max_depth=1, n_estimators=1)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = GradientBoostingRegressor(max_depth=1, n_estimators=1, learning_rate=0.5)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_ = model.predict(X_test)\n", + "df = DataFrame(\n", + " {\"X\": X_test[ind].ravel(), \"y\": y_test[ind].ravel(), \"y^\": y_[ind].ravel()}\n", + ")\n", + "ax = df.plot(x=\"X\", y=\"y\", kind=\"scatter\")\n", + "df.plot(x=\"X\", y=\"y^\", kind=\"line\", ax=ax, color=\"r\")\n", + "ax.set_title(\"Prédictions avec GradientBoostingRegressor\\net une fonction en escalier\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Essayons de montrer l'évolution de la courbe prédite en fonction du nombre de marches et revenons à 100 estimateurs." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GradientBoostingRegressor(max_depth=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GradientBoostingRegressor(max_depth=1)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = GradientBoostingRegressor(max_depth=1)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(0, model.estimators_.shape[0] + 1, 10):\n", + " if i == 0:\n", + " df = DataFrame({\"X\": X_test[ind].ravel(), \"y\": y_test[ind].ravel()})\n", + " ax = df.plot(x=\"X\", y=\"y\", kind=\"scatter\", figsize=(10, 4))\n", + " y_ = model.init_.predict(X_test)\n", + " color = \"b\"\n", + " else:\n", + " y_ = sum(\n", + " [model.init_.predict(X_test)]\n", + " + [\n", + " model.estimators_[k, 0].predict(X_test) * model.learning_rate\n", + " for k in range(0, i)\n", + " ]\n", + " )\n", + " color = \"r\"\n", + " df = DataFrame({\"X\": X_test[ind].ravel(), \"y^\": y_[ind].ravel()})\n", + " df.plot(x=\"X\", y=\"y^\", kind=\"line\", ax=ax, color=color, label=\"i=%d\" % i)\n", + "ax.set_title(\"Prédictions\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## learning rate et itérations\n", + "\n", + "Et si on choisissait un *learning_rate*, plus petit ou plus grand..." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GradientBoostingRegressor(learning_rate=1.99, max_depth=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GradientBoostingRegressor(learning_rate=1.99, max_depth=1)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model01 = GradientBoostingRegressor(max_depth=1, learning_rate=0.01)\n", + "model01.fit(X_train, y_train)\n", + "modela = GradientBoostingRegressor(max_depth=1, learning_rate=1.2)\n", + "modela.fit(X_train, y_train)\n", + "modelb = GradientBoostingRegressor(max_depth=1, learning_rate=1.99)\n", + "modelb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n", + "ind = numpy.argsort(X_test, axis=0)\n", + "\n", + "for i, mod in enumerate([model01, modela, modelb]):\n", + " df = DataFrame(\n", + " {\n", + " \"X\": X_test[ind].ravel(),\n", + " \"y\": y_test[ind].ravel(),\n", + " \"y^\": mod.predict(X_test)[ind].ravel(),\n", + " }\n", + " )\n", + " df.plot(x=\"X\", y=\"y\", kind=\"scatter\", ax=ax[i])\n", + " df.plot(x=\"X\", y=\"y^\", kind=\"line\", ax=ax[i], color=\"r\")\n", + " ax[i].set_title(\"learning_rate=%f\" % mod.learning_rate);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une trop faible valeur de *learning_rate* semble retenir le modèle de converger, une grande valeur produit des effets imprévisibles. Pour comprendre pourquoi, il faut détailler l'algorithme..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## L'algorithme" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspiration\n", + "\n", + "\n", + "L'algorithme est inspiré de l'algorithme de la descente de gradient. On considère une fonction réelle $f(x)$ et on calcule le gradient $\\frac{\\partial f}{\\partial x}(x)$ pour construire une suite :\n", + "\n", + "$$x_{t+1} = x_t - \\epsilon_t \\frac{\\partial f}{\\partial x}(x_t)$$\n", + "\n", + "La suite $(x_t)$ converge vers le minimum de la fonction $f$. On applique cela à une fonction d'erreur issue d'un problème de régression.\n", + "\n", + "$$f(x) = \\sum_{n=1}^N l(F(X_i), y_i)$$\n", + "\n", + "Le plus souvent, on applique cette méthode à une fonction $F$ qui dépend d'un paramètre $\\theta$\n", + "\n", + "$$f(\\theta, x) = \\sum_{n=1}^N l(F(\\theta, X_i), y_i)$$\n", + "\n", + "Et c'est la suite $\\theta_{t+1} = \\theta_t - \\epsilon_t \\frac{\\partial f}{\\partial \\theta}(\\theta_t)$ qui converge vers le minimum de la fonction $f$ de sorte que la fonction $f(\\theta, x)$ approxime au mieux les points $(X_i, y_i)$. Mais, on pourrait tout-à-fait résoudre ce problème dans un espace de fonctions et non un espace de paramètres :\n", + "\n", + "$$G_{t+1} = G_t - \\epsilon_t \\frac{\\partial f}{\\partial G}(G_t)$$\n", + "\n", + "Le gradient $\\frac{\\partial f}{\\partial G}$ est facile à calculer puisqu'il ne dépend pas de $G$. On pourrait donc construire la fonction de régression $G$ comme une suite additive de fonctions $F_k \\sim - \\epsilon_t \\frac{\\partial f}{\\partial G}(G_t)$. \n", + "\n", + "$$G_t = \\sum_{k=1}^t F_k$$\n", + "\n", + "Et nous pourrions construire la fonction $F_k$ comme solution d'un problème de régression défini par les couples $(X_i, z_i)$ avec :\n", + "\n", + "$$\\begin{array}{rcl} z_i &=& - \\epsilon_t \\frac{\\partial f}{\\partial G}(G_t(X_i), y_i) \\\\ f(X_i, y_i) &=& l(G_t(X_i), y_i)\\end{array}$$\n", + "\n", + "Voilà l'idée." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Algorithme\n", + "\n", + "Je reprends ici la page wikipedia. On cherche à construire un modèle qui minimise l'erreur $L(y,F(x)) = \\sum_{i=1}^n l(y_i, F(X_i))$. On note $r$ le learning rate.\n", + "\n", + "**Etape 1 :** on cale un premier modèle de régression, ici, simplement une constante, en optimisant $F_0(x) = \\arg \\min_\\gamma \\sum_{i=1}^n L(y_i, \\gamma)$. $F_0(x)$ est une constante.\n", + "\n", + "On note ensuite $F_m(x) = \\gamma_0 \\sum_{k=1}^m r \\gamma_k h_k(x)$ où $\\gamma_0$ est la fonction constante construire lors de la première étape.\n", + "\n", + "**Etape 2 :** on calcule ensuite les erreurs $e_{im} = l(y_i, F_m(x_i))$ et l'opposé du gradient $r_{im} = - \\left[ \\frac{\\partial l(y_i, F_m(x_i)) }{\\partial F_m(x_i)} \\right]$\n", + "\n", + "**Etape 3 :** on choisit la fonction $h_{m+1}(x)$ de telle sorte qu'elle approxime au mieux les résidus $r_{im}$.\n", + "\n", + "**Etape 4 :** on choisit le coefficient $\\gamma_{m+1}$ de telle sorte qu'il minimise l'expression $\\min_\\gamma \\sum_{i=1}^n l\\left(y_i, \\gamma_0 + \\sum_{k=1}^m r \\gamma_k h_k(x_i) + \\gamma h_{m+1}(x_i)\\right)$.\n", + "\n", + "On retourne l'étape 2 autant de fois qu'il y a d'itérations. Lorsque l'erreur est une erreur quadratique $l(y, F(x)) = (y-F(x))^2$, les résidus deviennent $r_{im} = -2 (y_i - F_m(x_i))$. Par conséquent, la fonction $h$ approxime au mieux ce qu'il manque pour atteindre l'objectif. Un learning rate égal à 1 fait que la somme des prédictions de chaque fonction $h_m$ oscille autour de la vraie valeur, une faible valeur donne l'impression d'une fonction qui converge à petits pas, une grande valeur accroît l'amplitude des oscillations au point d'empêcher l'algorithme de converger.\n", + "\n", + "On voit aussi que l'algorithme s'intéresse d'abord aux points où le gradient est le plus fort, donc en principe aux erreurs les plus grandes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Régression quantile\n", + "\n", + "Dans ce cas, l'erreur quadratique est remplacée par une erreur en valeur absolue. Les résidus dans ce cas sont égaux à -1 ou 1." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GradientBoostingRegressor(alpha=0.5, learning_rate=2.01, loss='quantile',\n",
+       "                          max_depth=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GradientBoostingRegressor(alpha=0.5, learning_rate=2.01, loss='quantile',\n", + " max_depth=1)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alpha = 0.5\n", + "model = GradientBoostingRegressor(\n", + " alpha=alpha, loss=\"quantile\", max_depth=1, learning_rate=0.1\n", + ")\n", + "model.fit(X_train, y_train)\n", + "model01 = GradientBoostingRegressor(\n", + " alpha=alpha, loss=\"quantile\", max_depth=1, learning_rate=0.01\n", + ")\n", + "model01.fit(X_train, y_train)\n", + "modela = GradientBoostingRegressor(\n", + " alpha=alpha, loss=\"quantile\", max_depth=1, learning_rate=1.2\n", + ")\n", + "modela.fit(X_train, y_train)\n", + "modelb = GradientBoostingRegressor(\n", + " alpha=alpha, loss=\"quantile\", max_depth=1, learning_rate=1.99\n", + ")\n", + "modelb.fit(X_train, y_train)\n", + "modelc = GradientBoostingRegressor(\n", + " alpha=alpha, loss=\"quantile\", max_depth=1, learning_rate=2.01\n", + ")\n", + "modelc.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 5, figsize=(12, 4))\n", + "ind = numpy.argsort(X_test, axis=0)\n", + "\n", + "for i, mod in enumerate([model, model01, modela, modelb, modelc]):\n", + " df = DataFrame(\n", + " {\n", + " \"X\": X_test[ind].ravel(),\n", + " \"y\": y_test[ind].ravel(),\n", + " \"y^\": mod.predict(X_test)[ind].ravel(),\n", + " }\n", + " )\n", + " df.plot(x=\"X\", y=\"y\", kind=\"scatter\", ax=ax[i])\n", + " df.plot(x=\"X\", y=\"y^\", kind=\"line\", ax=ax[i], color=\"r\")\n", + " ax[i].set_title(\"learning_rate=%1.2f\" % mod.learning_rate);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Concrètement, le paramètre *max_depth=1* correspond à une simple fonction $f(x) = \\mathbb{1}_{x > s}$ et le modèle final est une somme pondérée de fonctions indicatrices." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## learning_rate et sur-apprentissage" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:01<00:00, 15.47it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.6061998151266149, 0.5241640887093781],\n", + " [0.6865162453017377, 0.5868617679417452],\n", + " [0.6643588529036899, 0.5882688129311655]]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "from tqdm import tqdm\n", + "\n", + "\n", + "def experiment(models, tries=25):\n", + " scores = []\n", + " for _ in tqdm(range(tries)):\n", + " rnd = randn(1000)\n", + " X = random(1000) * 8 - 4\n", + " y = X**2 - X + rnd * 2 + 150 # X^2 - X + 150 + epsilon\n", + " X = X.reshape((-1, 1))\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y)\n", + " scs = []\n", + " for model in models:\n", + " model.fit(X_train, y_train)\n", + " sc = model.score(X_test, y_test)\n", + " scs.append(sc)\n", + " scores.append(scs)\n", + " return scores\n", + "\n", + "\n", + "scores = experiment(\n", + " [\n", + " GradientBoostingRegressor(max_depth=1, n_estimators=20),\n", + " RandomForestRegressor(max_depth=1, n_estimators=20),\n", + " ]\n", + ")\n", + "scores[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"GradientBoostingRegressor\")\n", + "ax.plot([_[1] for _ in scores], label=\"RandomForestRegressor\")\n", + "ax.set_title(\"Comparaison pour une somme pondérée de fonctions en escalier\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ce résultat est attendu car la forêt aléatoire est une moyenne de modèle de régression tous appris dans les mêmes conditions alors que le gradient boosting s'intéresse à l'erreur après la somme des premiers régresseurs. Voyons avec des arbres de décision et non plus des fonctions en escaliers." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:02<00:00, 11.19it/s]\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "scores = experiment(\n", + " [\n", + " GradientBoostingRegressor(max_depth=5, n_estimators=20),\n", + " RandomForestRegressor(max_depth=5, n_estimators=20),\n", + " DecisionTreeRegressor(max_depth=5),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"GradientBoostingRegressor\")\n", + "ax.plot([_[1] for _ in scores], label=\"RandomForestRegressor\")\n", + "ax.plot([_[2] for _ in scores], label=\"DecisionTreeRegressor\")\n", + "ax.set_title(\"Comparaison pour une somme pondérée d'arbres de décisions\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le modèle *GradientBoostingRegressor* est clairement moins bon quand le modèle sous-jacent - l'arbre de décision - est performant. On voit que la forêt aléatoire est meilleure qu'un arbre de décision seul. Cela signifie qu'elle généralise mieux et que l'arbre de décision fait du sur apprentissage. De même, le *GradientBoostingRegressor* est plus exposé au sur-apprentissage." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:03<00:00, 6.40it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.8505309865561821,\n", + " 0.7409584480906592,\n", + " 0.8372610096704007,\n", + " 0.8433616064058848],\n", + " [0.8610474948662841,\n", + " 0.7463887159097458,\n", + " 0.8469931884879516,\n", + " 0.8553184433763386]]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = experiment(\n", + " [\n", + " RandomForestRegressor(max_depth=5, n_estimators=20),\n", + " GradientBoostingRegressor(max_depth=5, n_estimators=20, learning_rate=0.05),\n", + " GradientBoostingRegressor(max_depth=5, n_estimators=20, learning_rate=0.1),\n", + " GradientBoostingRegressor(max_depth=5, n_estimators=20, learning_rate=0.2),\n", + " ]\n", + ")\n", + "scores[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"RandomForestRegressor\")\n", + "ax.plot([_[1] for _ in scores], label=\"GBR(5, lr=0.05)\")\n", + "ax.plot([_[2] for _ in scores], label=\"GBR(5, lr=0.1)\")\n", + "ax.plot([_[3] for _ in scores], label=\"GBR(5, lr=0.2)\")\n", + "ax.set_title(\"Comparaison pour différents learning_rate\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Diminuer *learning_rate* est clairement une façon d'éviter le sur-apprentissage mais les graphes précédents ont montré qu'il fallait plus d'itérations lorsque le learning rate est petit." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:02<00:00, 8.43it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.8595510594071162,\n", + " 0.47103349192372335,\n", + " 0.6460977028277105,\n", + " 0.7953273631823825],\n", + " [0.8418307915852581,\n", + " 0.4894523688410177,\n", + " 0.6494948729040753,\n", + " 0.7754015641398699]]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = experiment(\n", + " [\n", + " RandomForestRegressor(max_depth=5, n_estimators=20),\n", + " GradientBoostingRegressor(max_depth=1, n_estimators=20, learning_rate=0.05),\n", + " GradientBoostingRegressor(max_depth=1, n_estimators=20, learning_rate=0.1),\n", + " GradientBoostingRegressor(max_depth=1, n_estimators=20, learning_rate=0.2),\n", + " ]\n", + ")\n", + "scores[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"RandomForestRegressor\")\n", + "ax.plot([_[1] for _ in scores], label=\"GBR(1, lr=0.05)\")\n", + "ax.plot([_[2] for _ in scores], label=\"GBR(1, lr=0.1)\")\n", + "ax.plot([_[3] for _ in scores], label=\"GBR(1, lr=0.2)\")\n", + "ax.set_title(\"Comparaison pour différents learning_rate et des fonctions en escalier\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plus le modèle sous-jacent est simple, plus le *learning_rate* peut être élevé car les modèles simples ne font pas de sur-apprentissage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gradient Boosting avec d'autres librairies\n", + "\n", + "Une somme pondérée de régression linéaire reste une regréssion linéaire. Il est impossible de tester ce scénario avec *scikit-learn* puisque seuls les arbres de décisions sont implémentés. Mais il existe d'autres librairies qui implémente le gradient boosting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### XGBoost" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "from xgboost import XGBRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:02<00:00, 9.09it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.8362392926836479,\n", + " 0.5284643193472292,\n", + " 0.6696397339277518,\n", + " 0.7786586369744302],\n", + " [0.8866993680319293,\n", + " 0.5135892626305283,\n", + " 0.6803157400434685,\n", + " 0.8171169617436257]]" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = experiment(\n", + " [\n", + " RandomForestRegressor(max_depth=5, n_estimators=20),\n", + " XGBRegressor(\n", + " max_depth=1,\n", + " n_estimators=20,\n", + " learning_rate=0.05,\n", + " objective=\"reg:squarederror\",\n", + " ),\n", + " XGBRegressor(\n", + " max_depth=1,\n", + " n_estimators=20,\n", + " learning_rate=0.1,\n", + " objective=\"reg:squarederror\",\n", + " ),\n", + " XGBRegressor(\n", + " max_depth=1,\n", + " n_estimators=20,\n", + " learning_rate=0.2,\n", + " objective=\"reg:squarederror\",\n", + " ),\n", + " ]\n", + ")\n", + "scores[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"RandomForestRegressor(5)\")\n", + "ax.plot([_[1] for _ in scores], label=\"XGB(1, lr=0.05)\")\n", + "ax.plot([_[2] for _ in scores], label=\"XGB(1, lr=0.1)\")\n", + "ax.plot([_[3] for _ in scores], label=\"XGB(1, lr=0.2)\")\n", + "ax.set_title(\n", + " \"Comparaison pour différents learning_rate\\net des fonctions en escalier \"\n", + " \"avec XGBoost\"\n", + ")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les résultats sont sensiblement les mêmes." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/25 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"RandomForestRegressor(5)\")\n", + "ax.plot([_[1] for _ in scores], label=\"XGB(5, lr=0.05)\")\n", + "ax.plot([_[2] for _ in scores], label=\"XGB(5, lr=0.1)\")\n", + "ax.plot([_[3] for _ in scores], label=\"XGB(5, lr=0.2)\")\n", + "ax.set_title(\n", + " \"Comparaison pour différents learning_rate\\net des arbres de décisions \"\n", + " \"avec XGBoost\"\n", + ")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LightGbm" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from lightgbm import LGBMRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 8%|▊ | 2/25 [00:00<00:02, 10.71it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000073 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.276912\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000094 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.276912\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000050 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.276912\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.344856\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.344856\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.344856\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.231196\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000032 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.231196\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000049 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.231196\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 24%|██▍ | 6/25 [00:00<00:01, 12.90it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.434819\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.434819\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000044 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.434819\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.363485\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.363485\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000038 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.363485\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.646337\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.646337\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.646337\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 32%|███▏ | 8/25 [00:00<00:01, 13.40it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000051 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.458236\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000181 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.458236\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.458236\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000036 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.527945\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000031 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.527945\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000034 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.527945\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.110538\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.110538\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000041 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.110538\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 48%|████▊ | 12/25 [00:00<00:01, 12.90it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.536138\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000080 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.536138\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000038 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.536138\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000044 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.140867\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.140867\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000049 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.140867\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.416133\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000053 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.416133\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.416133\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 56%|█████▌ | 14/25 [00:01<00:00, 11.40it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000055 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.396002\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.396002\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.396002\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.328942\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000050 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.328942\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.328942\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000041 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.560493\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.560493\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000034 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.560493\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 72%|███████▏ | 18/25 [00:01<00:00, 12.66it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.999049\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000036 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.999049\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.999049\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.935543\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000037 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.935543\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.935543\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000136 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.395022\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.395022\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000031 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.395022\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000034 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.189666\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 80%|████████ | 20/25 [00:01<00:00, 12.98it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.189666\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000033 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.189666\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000040 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.055646\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.055646\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000048 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.055646\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000041 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.380804\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.380804\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.380804\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.240350\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 96%|█████████▌| 24/25 [00:01<00:00, 13.63it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.240350\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000577 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.240350\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.005265\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000034 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.005265\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000049 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.005265\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.258938\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000037 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.258938\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.258938\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:01<00:00, 12.91it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.386138\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000049 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.386138\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.386138\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.8773032128441518,\n", + " 0.48214820701023675,\n", + " 0.6539572474605444,\n", + " 0.7984627882777451],\n", + " [0.8555575315832163,\n", + " 0.4977691173485087,\n", + " 0.6367319006906368,\n", + " 0.7683900978469463]]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = experiment(\n", + " [\n", + " RandomForestRegressor(max_depth=5, n_estimators=20),\n", + " LGBMRegressor(max_depth=1, n_estimators=20, learning_rate=0.05),\n", + " LGBMRegressor(max_depth=1, n_estimators=20, learning_rate=0.1),\n", + " LGBMRegressor(max_depth=1, n_estimators=20, learning_rate=0.2),\n", + " ]\n", + ")\n", + "scores[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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TkpOTsWjRIuzevZtLkFHg1fNRnLKce0JI1aOgh5B3jLGxMerUqYP79++Xab3SToBYUoamksrZKwkKSuOff/7B+++/jw4dOuCXX36Bra0thEIhtm7dil27dhVZvvAd3PJuo7AOHTogLCwMR44cwcmTJ7Fp0yb88MMPWL9+fYlpk9+kIs/P2yjpfS4YkP8qsVhc7dJ5q9VqAMCcOXNKbEV69YL51c9IwTYCAwOLDTz09LT/fb5tZrKPP/4Yffv2xeHDh3HixAl89dVXWLp0Kc6ePYumTZuWejtqtRo8Hg/Hjh0rtk6GhoYANO9nt27dkJycjM8++wz16tWDgYEBoqOjERAQwB1/gde9z2/z2dXV5764vwlDhw7FpUuXMHfuXDRp0gSGhoZQq9Xo0aNHkfNRnNKee0KIblDQQ8g7qE+fPti4cSMuX75c7B3UwpycnKBWq/HkyROuVQAA4uLikJqaWmQiyLelVqvx9OlTrqUCAB4/fgwA3IDuAwcOQCKR4MSJE1qDmbdu3Vrq/bztNszMzDB27FiMHTsWmZmZ6NChAxYuXIgJEyZw5yQkJKTIeo8ePYKFhUWFpsh98eJFkZajV8+Zk5MT7t69C7VarXXx+ujRI+514GUL06sTL5a3JaiApaUlpFJpsV0CiztPb6OgpVAoFKJr167l2kZBdy0rK6tyb+NVb7px4OrqitmzZ2P27Nl48uQJmjRpgpUrVyIoKKjU+3B1dQVjDHXr1tX6Dr3q3r17ePz4MbZv344xY8Zw5a9mIKwOnJycEBoaWqS8uLLySklJwZkzZ7Bo0SJ8/fXXXHlxn9eS3sfSnntCiG5Ur9tzhJAq8emnn8LAwAATJkxAXFxckdfDwsLw448/AgB69eoFAFi9erXWMqtWrQKA12Y5K6+1a9dyjxljWLt2LYRCId577z0AmrvDPB5Pq/UhIiIChw8fLvU+3mYbhVPYApo7uG5ublwXOVtbWzRp0gTbt2/XCh7u37+PkydPcue0oiiVSq3Uz3K5HBs2bIClpSV8fHwAaN7H2NhY7NmzR2u9NWvWwNDQkEv97OTkBIFAgL///ltrH7/88stb1VEgEMDPzw+HDx/Gs2fPuPKHDx/ixIkTb7XtV1lZWaFTp07YsGEDYmJiirxeMOfP6/j5+cHY2BjfffcdFApFubbxqoKMZ68GlNnZ2cjNzdUqc3V1hZGRUZG08G8ycOBACAQCLFq0qEhrCWOM++wWtEQUXoYxxn3vqxM/Pz9cvnwZt2/f5sqSk5Oxc+fOCttHcecDKPp3DwB3c+HV97G0554QohvU0kPIO8jV1RW7du3CsGHDUL9+fYwZMwYNGzaEXC7HpUuXuFTGAODt7Q1/f39s3LgRqamp6NixI65du4bt27ejf//+6Ny5c4XWTSKR4Pjx4/D394evry+OHTuGo0ePYv78+dyYid69e2PVqlXo0aMHPvjgA8THx+Pnn3+Gm5sb7t69W6r9vM02vLy80KlTJ/j4+MDMzAzXr1/H/v37tRIwfP/99+jZsydat26N8ePHcymrTUxMuLlZKkqdOnWwbNkyREREwMPDA3v27MHt27exceNGbhzBhx9+iA0bNiAgIAA3btyAs7Mz9u/fj3///RerV6/mEluYmJhgyJAhWLNmDXg8HlxdXfHnn38WGeNQHosWLcLx48fRvn17TJ06lQu6GjRoUOr3rbR+/vlntGvXDo0aNcLEiRPh4uKCuLg4XL58Gc+fP8edO3deu76xsTHWrVuH0aNHo1mzZhg+fDgsLS3x7NkzHD16FG3bttUKzktDKpXCy8sLe/bsgYeHB8zMzNCwYUMolUq89957GDp0KLy8vKCnp4dDhw4hLi4Ow4cPL9M+XF1dsXjxYsybNw8RERHo378/jIyMEB4ejkOHDuHDDz/EnDlzUK9ePbi6umLOnDmIjo6GsbExDhw48NaJFCrDp59+iqCgIHTr1g0zZszgUlY7OjoiOTm51F1vX8fY2BgdOnTA8uXLoVAoYGdnh5MnTyI8PLzIsgU3Er744gsMHz4cQqEQffv2LfW5J4ToSFWniyOEVB+PHz9mEydOZM7OzkwkEjEjIyPWtm1btmbNGq1UvwqFgi1atIjVrVuXCYVC5uDgwObNm6e1DGOaFLTFpXIGwKZNm6ZVVlwKYn9/f2ZgYMDCwsJY9+7dmb6+PrO2tmYLFixgKpVKa/3Nmzczd3d3JhaLWb169djWrVuLpO8tad9l3car6ZMXL17MWrZsyWQyGZNKpaxevXpsyZIlXHroAqdPn2Zt27ZlUqmUGRsbs759+7IHDx5oLVOwv8LpuBkrPsVvcTp27MgaNGjArl+/zlq3bs0kEglzcnJia9euLbJsXFwcGzt2LLOwsGAikYg1atRIKwV1gYSEBDZo0CCmr6/PTE1N2aRJk9j9+/eLTVltYGDw2vq96sKFC8zHx4eJRCLm4uLC1q9fX6pzXtaU1YwxFhYWxsaMGcNsbGyYUChkdnZ2rE+fPmz//v3cMgXn+b///iu2vufOnWN+fn7MxMSESSQS5urqygICAtj169ffeB6KO65Lly5xx4/89NWJiYls2rRprF69eszAwICZmJgwX19ftnfv3pJP5Gv2wRhjBw4cYO3atWMGBgbMwMCA1atXj02bNo2FhIRwyzx48IB17dqVGRoaMgsLCzZx4kQu3XRp3+eSvl+vvn8lpawu7u9Fx44dWceOHbXKbt26xdq3b8/EYjGzt7dnS5cuZT/99BMDwGJjY0s4O0WV9H1jjLHnz5+zAQMGMJlMxkxMTNiQIUPYixcvik0z/u233zI7OzvG5/OLHFdpzj0hpOrxGKviUbKEEFKCgIAA7N+/H5mZmbquSo3RqVMnJCYmljkxBSE13ccff4wNGzYgMzPzrRNJEEJqPxrTQwghhJBqLScnR+t5UlISAgMD0a5dOwp4CCGlQmN6CCGEEFKttW7dGp06dUL9+vURFxeHzZs3Iz09nZuHKTMz840txJaWlhQgEfIOo6CHEEIIIdVar169sH//fmzcuBE8Hg/NmjXD5s2b0aFDBwDAihUrsGjRotduIzw8nEvhTgh599CYHkIIIYTUaE+fPsXTp09fu0y7du0gkUiqqEaEkOqGgh5CCCGEEEJIrUaJDAghhBBCCCG1GgU9hJBKsXDhwgqZNLCsAgMDUa9ePQiFQshksirff2no6tyQ0uHxeFoTyG7btg08Hg8RERE6q1NN9zaf+YJ1ExMTK7hWhJB3CQU9hBAtL168wMKFC3H79m1dV6XMHj16hICAALi6uuLXX3/Fxo0bdVaX7OxsLFy4EOfPn9dZHQipCp06dULDhg11XQ0AwHfffYfDhw+X+Hp4eDimT58ODw8P6OvrQ19fH15eXpg2bRru3r2rtWxBsFXww+fzYWtriz59+uDKlStay0ZERHDLLV68uNh9jxw5EjweD4aGhm99nISQsqOghxCi5cWLF1i0aFGNDHrOnz8PtVqNH3/8EQEBARg6dKjO6pKdnY1FixYVG/R8+eWXReYdIdXX6NGjkZOTAycnJ11Xpcaqqs/864KeP//8Ew0bNkRgYCC6du2KH374AT/++CN69uyJv/76C02aNEFkZGSR9datW4fAwEBs27YN06dPx/3799GhQ4di/0ZKJBL89ttvRcqzsrJw5MgRSqRAiA5RympCSK0RHx8PANW2W1sBPT096OnRn9+aQiAQVOj8LllZWTAwMKiw7dUEuv7Mh4WFYfjw4XBycsKZM2dga2ur9fqyZcvwyy+/gM8vei948ODBsLCw4J73798fDRs2xL59+9CkSROtZXv16oWDBw/izp078Pb25sqPHDkCuVyOHj164OzZsxV7cISQUqGWHkLeIdHR0Rg3bhysra0hFovRoEEDbNmyhXv9/PnzaNGiBQBg7NixXHeNbdu2vXa7Fy9eRIsWLSCRSODq6ooNGzaUuGxQUBB8fHwglUphZmaG4cOHIyoqSmuZJ0+eYNCgQbCxsYFEIoG9vT2GDx+OtLS0Erfr7OyMBQsWANBMQvjquIxffvkFDRo0gFgsRp06dTBt2jSkpqZqbaOgm86DBw/QuXNn6Ovrw87ODsuXLy+yv9zcXCxcuBAeHh6QSCSwtbXFwIEDERYWhoiICFhaWgIAFi1axJ3HgvoUN75BqVTi22+/haurK8RiMZydnTF//nzk5eUVOc4+ffrg4sWLaNmyJSQSCVxcXLBjxw6t5RQKBRYtWgR3d3dIJBKYm5ujXbt2OHXqVInnsEBqaio+/vhjODg4QCwWw83NDcuWLYNareaWKejOs2LFCmzcuJGrd4sWLfDff/+9cR+l3Q8A7N69Gz4+PjAyMoKxsTEaNWqEH3/8sci2PvnkEzg7O0MsFsPe3h5jxozhxoHI5XJ8/fXX8PHxgYmJCQwMDNC+fXucO3fujfUsaUzPsWPH0L59exgYGMDIyAi9e/dGcHCw1jIBAQEwNDREWFgYevXqBSMjI4wcObLEfUVGRmLq1Knw9PSEVCqFubk5hgwZorXv69evg8fjYfv27UXWP3HiBHg8Hv7880+u7E3f+wKv+0y/reI+8zk5Ofjoo49gYWEBIyMjvP/++4iOji7y3S2QmpqKgIAAyGQymJiYYOzYscjOzuZe5/F4yMrKwvbt27nvXEBAAABg+fLlyMrKwtatW4sEPIAmKPvoo4/g4ODwxmOxsbHh1nlV69atUbduXezatUurfOfOnejRowfMzMzeuH1CSOWgW42EvCPi4uLQqlUr8Hg8TJ8+HZaWljh27BjGjx+P9PR0fPzxx6hfvz6++eYbfP311/jwww/Rvn17AECbNm1K3O69e/fQvXt3WFpaYuHChVAqlViwYAGsra2LLLtkyRJ89dVXGDp0KCZMmICEhASsWbMGHTp0wK1btyCTySCXy+Hn54e8vDzMmDEDNjY2iI6Oxp9//onU1FSYmJgUW4/Vq1djx44dOHToENatWwdDQ0M0btwYgOaCa9GiRejatSumTJmCkJAQrFu3Dv/99x/+/fdfCIVCbjspKSno0aMHBg4ciKFDh2L//v347LPP0KhRI/Ts2RMAoFKp0KdPH5w5cwbDhw/HzJkzkZGRgVOnTuH+/fvo2rUr1q1bhylTpmDAgAEYOHAgAHD1Kc6ECROwfft2DB48GLNnz8bVq1exdOlSPHz4EIcOHdJaNjQ0FIMHD8b48ePh7++PLVu2ICAgAD4+PmjQoAF3zEuXLsWECRPQsmVLpKen4/r167h58ya6detWYj2ys7PRsWNHREdHY9KkSXB0dMSlS5cwb948xMTEYPXq1VrL79q1CxkZGZg0aRJ4PB6WL1+OgQMH4unTp1rntbz7OXXqFEaMGIH33nsPy5YtAwA8fPgQ//77L2bOnAkAyMzMRPv27fHw4UOMGzcOzZo1Q2JiIn7//Xc8f/4cFhYWSE9Px6ZNmzBixAhMnDgRGRkZ2Lx5M/z8/HDt2rUid+zfJDAwEP7+/vDz88OyZcuQnZ2NdevWoV27drh165bWJJhKpRJ+fn5o164dVqxYAX19/RK3+99//+HSpUsYPnw47O3tERERgXXr1qFTp0548OAB9PX10bx5c7i4uGDv3r3w9/fXWn/Pnj0wNTWFn58fgNJ974E3f6ZdXV3LdH5KIyAgAHv37sXo0aPRqlUrXLhwAb179y5x+aFDh6Ju3bpYunQpbt68iU2bNsHKyor7XAQGBnKf9w8//BAAuHr/+eefcHNzg6+vb5nrmZycDABQq9WIjo7Gt99+C4lEUmL32REjRiAoKAj/+9//uAQMJ0+eRGBgII4fP17m/RNCKggjhLwTxo8fz2xtbVliYqJW+fDhw5mJiQnLzs5mjDH233//MQBs69atpdpu//79mUQiYZGRkVzZgwcPmEAgYIX/xERERDCBQMCWLFmitf69e/eYnp4eV37r1i0GgO3bt6/Mx7hgwQIGgCUkJHBl8fHxTCQSse7duzOVSsWVr127lgFgW7Zs4co6duzIALAdO3ZwZXl5eczGxoYNGjSIK9uyZQsDwFatWlWkDmq1mjHGWEJCAgPAFixYUGI9C9y+fZsBYBMmTNBabs6cOQwAO3v2LFfm5OTEALC///5b6xjFYjGbPXs2V+bt7c169+5d/Il6jW+//ZYZGBiwx48fa5V//vnnTCAQsGfPnjHGGAsPD2cAmLm5OUtOTuaWO3LkCAPA/vjjjwrZz8yZM5mxsTFTKpUlbuvrr79mANjBgweLvFbwfiiVSpaXl6f1WkpKCrO2tmbjxo3TKn/1fdu6dSsDwMLDwxljjGVkZDCZTMYmTpyotV5sbCwzMTHRKvf392cA2Oeff15i/Qsr+B4Wdvny5SKfy3nz5jGhUKh17vPy8phMJtM6ntJ+70vzmS5Jx44dWYMGDV67zKuf+Rs3bjAA7OOPP9ZaLiAgoMj5L1j31fdpwIABzNzcXKvMwMCA+fv7a5WlpaUxAKx///5F6pWSksISEhK4n8Lnv2C/r/7IZDJ2/Phxre0UfB++//57dv/+fQaA/fPPP4wxxn7++WdmaGjIsrKymL+/PzMwMHjtuSKEVA7q3kbIO4AxhgMHDqBv375gjCExMZH78fPzQ1paGm7evFnm7apUKpw4cQL9+/eHo6MjV16/fn3uTnOBgwcPQq1WY+jQoVr7t7Gxgbu7O9fNqKAl58SJE1pdV8rr9OnTkMvl+Pjjj7X660+cOBHGxsY4evSo1vKGhoYYNWoU91wkEqFly5Zas70fOHAAFhYWmDFjRpH9lSct719//QUAmDVrllb57NmzAaBIHb28vLhWOEDTnc/T01OrjjKZDMHBwXjy5EmZ6rJv3z60b98epqamWu9T165doVKp8Pfff2stP2zYMJiamnLPC+pVuC5vsx+ZTIasrKzXdss7cOAAvL29MWDAgCKvFbwfAoEAIpEIgOaOfXJyMpRKJZo3b17mz/6pU6eQmpqKESNGaNVdIBDA19e32C5zU6ZMKdW2pVIp91ihUCApKQlubm6QyWRa9Rw2bBgUCgUOHjzIlZ08eRKpqakYNmwYgLJ97yv6M/0mBS0eU6dO1Sovbv8FJk+erPW8ffv2SEpKQnp6+mv3VfB6cVnTOnXqBEtLS+7n559/LrLMgQMHcOrUKZw8eRJbt26Fh4cHBg0ahEuXLhW7vwYNGqBx48ZcQoNdu3ahX79+r23hI4RUPureRsg7ICEhAampqdi4cWOJaZwLkgCUdbs5OTlwd3cv8pqnpyd3MQ9oxukwxopdFgDXFapu3bqYNWsWVq1ahZ07d6J9+/Z4//33MWrUqBK7tr1OQTYmT09PrXKRSAQXF5ci2Zrs7e2LXOSZmppqpbMNCwuDp6dnhQ3MjoyMBJ/Ph5ubm1a5jY0NZDJZkToWDjAL1zElJYV7/s0336Bfv37w8PBAw4YN0aNHD4wePfq1XewAzft09+5dbkzSq179nLxal4IAqHBd3mY/U6dOxd69e9GzZ0/Y2dmhe/fuGDp0KHr06MEtGxYWhkGDBr12fwCwfft2rFy5Eo8ePYJCoeDK69at+8Z1X607AHTp0qXY142NjbWe6+npwd7evlTbzsnJwdKlS7F161ZER0eDMca9VnhMm7e3N+rVq4c9e/Zg/PjxADRd2ywsLLh6leV7X9Gf6Tcp+My/eu5f/Q4U9rrP2qvnvDAjIyMAmm6Qr9qwYQMyMjIQFxendbOjsA4dOmglMhg8eDDc3d0xY8YM3Lhxo9h1PvjgA6xcuRKffPIJLl26hPnz55dYP0JI1aCgh5B3QMHA8FGjRhUZA1DgTRfDFVEHHo+HY8eOFZsJq/Bd2JUrVyIgIABHjhzByZMn8dFHH2Hp0qW4cuVKqS8ey6ukLF2FLz4rS2nvqJemjh06dEBYWBh3Djdt2oQffvgB69evx4QJE0rctlqtRrdu3fDpp58W+7qHh0eZ6/I2+7GyssLt27dx4sQJHDt2DMeOHcPWrVsxZsyYYgfylyQoKAgBAQHo378/5s6dCysrKwgEAixdurTMA/ULvk+BgYHcoPbCXg0cxGJxsVnBijNjxgxs3boVH3/8MVq3bg0TExPweDwMHz68SIKHYcOGYcmSJUhMTISRkRF+//13jBgxgtt/dfjeV6TyftZMTExga2uL+/fvF3mtYIxPWSaeNTQ0hK+vL44cOVJiJr4RI0Zg3rx5mDhxIszNzdG9e/dSb58QUjko6CHkHWBpaQkjIyOoVCp07dr1tcuWpSuLpaUlpFJpsV2oQkJCtJ67urqCMYa6desWuXAuTqNGjdCoUSN8+eWXuHTpEtq2bYv169eXOPFfSQrmVgkJCYGLiwtXLpfLER4e/sbzURxXV1dcvXoVCoWixMH6ZTmPTk5OUKvVePLkCerXr8+Vx8XFITU1tdzzw5iZmWHs2LEYO3YsMjMz0aFDByxcuPC1QY+rqysyMzPLdV7Koiz7EYlE6Nu3L/r27Qu1Wo2pU6diw4YN+Oqrr+Dm5gZXV9diL2gL279/P1xcXHDw4EGt96Yg419Z6w5oArKKPk/79++Hv78/Vq5cyZXl5uYWyTQIaIKeRYsW4cCBA7C2tkZ6ejqGDx/OvV6W731pPtMVqeAzHx4ertX6Gxoa+lbbLel717t3b2zatAnXrl1Dy5Yt32ofgCY5BaBpPSou6HF0dETbtm1x/vx5TJkyhVLUE1IN0JgeQt4BAoEAgwYNwoEDB4q9OExISOAeF/wDL+4iq7jt+vn54fDhw3j27BlX/vDhQ5w4cUJr2YEDB0IgEGDRokVF7swyxpCUlARA0/++4IKiQKNGjcDn84ukby6Nrl27QiQS4aefftLa7+bNm5GWlvbabFElGTRoEBITE7F27doirxXso6D/fmnOY69evQCgSGa0VatWAUC56lhwPgsYGhrCzc3tjedw6NChuHz5cpH3D9Acy6vvTXmVdj+vHgefz+daJwqOZdCgQbhz506RLHfAy/ejoJWg8Gfg6tWruHz5cpnr7ufnB2NjY3z33Xda3eQKFP4+lZVAICjy/VizZg1UKlWRZevXr49GjRphz5492LNnD2xtbdGhQwetbZX2e1+az3RFKhjz98svv2iVr1mz5q22a2BgUOx37tNPP4W+vj7GjRuHuLi4Iq+X5RiTk5Nx6dIl2NjYwMrKqsTlFi9ejAULFrx2nBIhpOrQrQdC3hH/+9//cO7cOfj6+mLixInw8vJCcnIybt68idOnT3NpWV1dXSGTybB+/XoYGRnBwMAAvr6+JY57WLRoEY4fP4727dtj6tSpUCqVWLNmDRo0aKA1DsbV1RWLFy/GvHnzEBERgf79+8PIyAjh4eE4dOgQPvzwQ8yZMwdnz57F9OnTMWTIEHh4eECpVCIwMJC7gCsrS0tLzJs3D4sWLUKPHj3w/vvvIyQkBL/88gtatGhRYj/+1xkzZgx27NiBWbNm4dq1a2jfvj2ysrJw+vRpTJ06Ff369YNUKoWXlxf27NkDDw8PmJmZoWHDhmjYsGGR7Xl7e8Pf3x8bN25EamoqOnbsiGvXrmH79u3o378/OnfuXOY6enl5oVOnTvDx8YGZmRmuX7+O/fv3Y/r06a9db+7cufj999/Rp08fLg12VlYW7t27h/379yMiIkJrfEN5lXY/EyZMQHJyMrp06QJ7e3tERkZizZo1aNKkCdcqNnfuXOzfvx9DhgzBuHHj4OPjg+TkZPz+++9Yv349vL290adPHxw8eBADBgxA7969ER4ejvXr18PLy6vYsR6vY2xsjHXr1mH06NFo1qwZhg8fDktLSzx79gxHjx5F27Ztiw0eSqNPnz4IDAyEiYkJvLy8cPnyZZw+fRrm5ubFLj9s2DB8/fXXkEgkGD9+fJFudKX93pfmM/06CQkJxbbC1q1bt9h5iXx8fDBo0CCsXr0aSUlJXMrqx48fAyh/8gQfHx+cPn0aq1atQp06dVC3bl34+vrC3d0du3btwogRI+Dp6YmRI0fC29sbjDGEh4dj165d4PP5xXaf3b9/PwwNDcEYw4sXL7B582akpKRg/fr1r61nx44d0bFjx3IdByGkElRlqjhCiG7FxcWxadOmMQcHByYUCpmNjQ1777332MaNG7WWO3LkCPPy8mJ6enqlSl994cIF5uPjw0QiEXNxcWHr168vkqK2wIEDB1i7du2YgYEBMzAwYPXq1WPTpk1jISEhjDHGnj59ysaNG8dcXV2ZRCJhZmZmrHPnzuz06dNvPL7iUlYXWLt2LatXrx4TCoXM2tqaTZkyhaWkpGgtU1LqXX9/f+bk5KRVlp2dzb744gtWt25d7lwOHjyYhYWFcctcunSJOy8olIa3uHOjUCjYokWLuO05ODiwefPmsdzcXK3lnJycik1F3bFjR9axY0fu+eLFi1nLli2ZTCZjUqmU1atXjy1ZsoTJ5fLiTp2WjIwMNm/ePObm5sZEIhGzsLBgbdq0YStWrODWL5yi91VA8am6y7Of/fv3s+7duzMrKysmEomYo6MjmzRpEouJidHaVlJSEps+fTqzs7NjIpGI2dvbM39/fy5Vs1qtZt999x1zcnJiYrGYNW3alP3555/Fvrev1v/VlNUFzp07x/z8/JiJiQmTSCTM1dWVBQQEsOvXr3PLlDVFcUpKChs7diyzsLBghoaGzM/Pjz169Ig5OTkVScXMGGNPnjzhUilfvHix2G2W9ntfms90cQpSvRf389577zHGiv/MZ2VlsWnTpjEzMzNmaGjI+vfvz0JCQhgA9r///Y9brqTvdXHvy6NHj1iHDh2YVCplAIqcs9DQUDZlyhTm5ubGJBIJ992YPHkyu337ttayxaWsNjAwYK1bt2Z79+7VWvZ134fCKGU1IbrDY6wKRucSQgghhLzB7du30bRpUwQFBRXbQkQIIeVFY3oIIYQQUuVycnKKlK1evRp8Pl9rbBIhhFQEGtNDCCGEkCq3fPly3LhxA507d4aenh6XkvzDDz+Eg4ODrqtHCKllqHsbIYQQQqrcqVOnsGjRIjx48ACZmZlwdHTE6NGj8cUXX1CKZ0JIhaOghxBCCCGEEFKr0ZgeQgghhBBCSK1WI9qP1Wo1Xrx4ASMjo3Ln7ieEEEIIIYTUfIwxZGRkoE6dOkXmJytJjQh6Xrx4QYMaCSGEEEIIIZyoqKhiJxUuTo0IeoyMjABoDszY2FjHtSGEEEIIIYToSnp6OhwcHLgYoTTKFfT8/PPP+P777xEbGwtvb2+sWbMGLVu2LHZZhUKBpUuXYvv27YiOjoanpyeWLVuGHj16lHp/BV3ajI2NKeghhBBCCCGElGnYS5kTGezZswezZs3CggULcPPmTXh7e8PPzw/x8fHFLv/ll19iw4YNWLNmDR48eIDJkydjwIABuHXrVll3TQghhBBCCCFlVuaU1b6+vmjRogXWrl0LQJNkwMHBATNmzMDnn39eZPk6dergiy++wLRp07iyQYMGQSqVIigoqFT7TE9Ph4mJCdLS0qilhxBCCCGEkHdYeWKDMrX0yOVy3LhxA127dn25AT4fXbt2xeXLl4tdJy8vDxKJRKtMKpXi4sWLJe4nLy8P6enpWj+EEEIIIYQQUh5lCnoSExOhUqlgbW2tVW5tbY3Y2Nhi1/Hz88OqVavw5MkTqNVqnDp1CgcPHkRMTEyJ+1m6dClMTEy4H8rcRgghhBBCCCmvSp+c9Mcff4S7uzvq1asHkUiE6dOnY+zYsa/NqT1v3jykpaVxP1FRUZVdTUIIIYQQQkgtVaagx8LCAgKBAHFxcVrlcXFxsLGxKXYdS0tLHD58GFlZWYiMjMSjR49gaGgIFxeXEvcjFou5TG2UsY0QQgghhBDyNsoU9IhEIvj4+ODMmTNcmVqtxpkzZ9C6devXriuRSGBnZwelUokDBw6gX79+5asxIYQQQgghhJRBmefpmTVrFvz9/dG8eXO0bNkSq1evRlZWFsaOHQsAGDNmDOzs7LB06VIAwNWrVxEdHY0mTZogOjoaCxcuhFqtxqefflqxR0IIIYQQQgghxShz0DNs2DAkJCTg66+/RmxsLJo0aYLjx49zyQ2ePXumNV4nNzcXX375JZ4+fQpDQ0P06tULgYGBkMlkFXYQhBBCCCGEEFKSMs/Tows0Tw8hhBBCCCEEKF9sUOaWHkIIIYQQQl6Vp1Thn8eJOBEcC32RANO6uMHKSPLmFQmpAhT0EEIIIYSQcikIdI7ei8HpB3HIyFNyr/1+5wUW92+E3o1tdVhDQjQo6CGEEEIIIaWWq1DhnyeJ+KuYQMfaWIyeDW1xNTwZD2PSMW3XTRwProNv+zWATF+kw1qTdx0FPYQQQggh5LUKBzqnHsQh85VAp1cjW/RuZItmjqbg83mQK9VYc/YJfjkfhj/uvMCVp0lYNqgRutSz1uFRkHcZJTIghBBCCCFFlDXQKc6dqFTM2nsbYQlZAIChze3xVR8vGEmEVXIMpHYqT2xAQU8ZKVVq6AnKNKcrIYQQQkiNUBDoHL37AqcfxmsFOjbGEvRsZPPGQKe4ba48GYJNF8PBGGAnk2L54MZo62ZRWYdBajkKeiqZWs3Qd+1FNKhjjMkdXeFiaaizuhBCCCGEVIRchQp/P07QjNEpIdDp09gWTR1KH+gU51p4Mubsu4NnydkAAP/WTvisZz3oi2i0BSkbCnoq2ZWnSRi+8QoAgMcDejW0xZROrmhoZ6KzOhFCCCGElNWbAp1ejWzRu7HNWwc6r8rKU2LpsYcIuvIMAOBsro+VQ73h42RWYfsgtR8FPVXgRmQK1p0PxemH8VxZRw9LTO3kipZ1zcDjVdwfBkJI+UUlZyMtRwGZvhAyfREMRAL6fpJyycxTQsDjQSLk02eI1GgFgc7RezE4U4WBTnH+eZKAT/ffRUxaLng84MMOLvikqwckQkGl7pfUDhT0VKFHselYl5+RRJ1/Bn2cTDGtsys6e1rRP0ZCdESuVOP7E4/w6z/hWuVCAQ8mUpEmCJIKIdMXaj83EHHlsvxyE30hjMR69H1+RzDGEJ2ag/vR6XjwIg3BL9IR/CIdsem5ADQt/FKhAPoiAaQiAfSFeprfooIyPegLBUXLCh4LBdAXvbpOfplQAEElX2SSd9PrAh1bEwl6Nqy6QOdVaTkKfPPHAxy4+RwA4GFtiJVDmqCRPfWgIa9HQY8ORCZlYcPfT7H/+nPIVWoAQD0bI0zp5IrejWxrZNKDtBwFTj2Iw8UnCejf1A6dPK10XSVCSiU8MQsf/XYL96LTAACWRmKk5SggV6rLvU0BnweZVBMAaYIiEffcVD8/OCpUXkcmhaWRuKIOiVQSlZohPDETwS/ScT/6ZYCTlqPQWZ3Eevz8YEgTGDW2N8GMLu6oa2GgszqR6k+lZohLz8XzlBxEp2YjOiUn/3H+75Qc7voEKBzo2KKpg6zKA53inAyOxfxD95CYKYcen4dpnd0wvYsbhDXwGopUDQp6dCg+PRebL4Yj6EoksuQqAICTuT4mdXDFIB87iPWqd3Nteq4Cpx/E4ejdGPz9JAEKleZjIRTwsHFMc3SmwIdUY4wx7L/xHAt+D0a2XAVTfSGWD/ZGNy/NfBC5ChVSsuVIzVYgNVuBtBzN45RsBVJz5EjLL0/JliMtR/M4NUeOXEX5giV3K0O0dbNAOzcL+LqYUWpWHctTqvA4NhPB+a0391+k4VFMBnIUqiLL6vF58LA2QoM6xpofOxN42hhBwOMhW65CjlyFbIXy5WO5CtlyJfc4R6F5rv26CjlF1lEhR65EtkKF1/0XFvB5GNrcATPfc4eNiaQSzxKprhQqNWJSc/G8SECTjejUHMSk5kKpfv2lXHUMdF6VnCXHV4fv4+i9GABAQztjrBzSBJ42RjquGamOKOipBtKyFdh+OQJb/w1HSrbmjqG1sRgT2rngA19HGIirT4aSjFwFTj/MD3QeJ2rdCfK0NoJMX4ir4ckQ6/GxbWxLtHY112FtCSleeq4CXx66j9/vvAAAtHYxxw/DmlTIBWKuQsUFQIUDppRXgqeCgCk1W4G4jFyti1gBn4cmDjIuCGriIINIj+5eVpbMPCUexmi33jyJyyj2olAqFKC+rREa2pnkBzkmcLc2rNKbVIwx5CnVRYKnlGw5dlyOxNlHmvGjYj0+/Ns4Y0pHV5ga0Kz2tUmuQoUXqdotMwUBzfOUHMSl5+INMQ30+DzUkUlhJ5PC3lQKO1Mp7E31Xz6XSatloFOcP+68wFdH7iM1WwGRgI9Z3T0wsb0Ldf8kWijoqUay5Ur8di0Km/55ipg0TX9wE6kQ/m2cMbaNs87+aWXmKXHmYRz+vBuDC48TtLr9uFkZok9jzURj7tZGUKjUmBJ0A6cfxkNfJEDgeF/4OJnqpN6EFOfmsxTM3H0LUck5EPB5mNXNA5M7uur0n2NqthyXw5JwMTQR/4YmIiIpW+t1fZEAvnXNNEGQuwU8rY1ozFA5JWXmcYHN/RdpePAiHRFJWcW2nMj0hVxgU/C7roVBtb+Q+i8iGcuPP8J/ESkAACOxHj7s4IJx7epWq5to5M1C4zNw+WmyJqAp1GKTkJH3xnVFenzYywqCmYLgRp97bmUkqfaf5bKIT8/FvIP3cCY/6G/mKMPKoU2oqyfhUNBTDcmVahy+FY31F8LwNFEzG7G+SIARLR0xsb1LlXRXyMpT4syjeBy9+wLnQrQDHRdLA/RpXAd9GtvCw7poE3KuQoWJO67jnyeJMJLo4beJrWpdiu5/QxOx+78oSPT4MDMQwdRABDN90cvH+T/GEhrQXl2o1AzrL4Rh1anHUKkZHMyk+HF4UzRzrH5BeVRyNi6FJeJiaBIuhSYiKUuu9bqFoQhtXDWtQG3dLWAnk+qoppWDMQaFikGhUkOpYpCr1FCq1VAoGRRqNRSqQo+VaijV+cvkr6P5KVhfjYRMOR68SMP96JcJBl5layJBgzrG8MoPcBramaCOiaTGfn8ZYzgfkoDlJ0LwMCYdgOZzM62zGz7wdaz23affVYwx3H2ehhPBsTgeHIunCVklLqsvEmi10tjJ9Au12EhhYSCuMS01FYUxhn03nuObPx4gM08JiZCPeT3rY3Qrp3fuXJCiKOipxlRqhuP3Y/HL+VAEv9D80xIKeBjUzB6TOrpW+N2LbLkSZx/F4+jdGJx9FI+8woGOhQF6N9b07S3NXeZsuRL+W67hv4gUmBmIsOfDVnAvJkCqifbfeI7PDtyF6k19B6DpPiDTF8HMQMgFQqb6IpgXCo5M9V8GSWYGIkq9WQli03LxyZ7buPw0CQDwvncdLB7QEMY1YNyMWs3wKDYD/4Ym4mJoIq6FJxcZV1LXwgBt3czRzs0CrV0sYKKv2+PKyFXgRWou1/0mJi0HL1JzEZ2ag6w8pVZQohXYFAQqpfhuvY26FgavtOAYw9ywdiaSUKsZ/rwXg1UnQ7gWRDuZFB93dcfAZva16k5/TaVUqfFfRApOBMfiZHAsXqS9DMxFAj58XczgamlYpLXGVF9YY4PyyhadmoO5++7gUpjmb34bV3MsH9wY9qb6Oq5ZxWGMIVehRmqOHClZ2l2qXz7WdK1Oyy/LU6ohzc8WWZBVUiJ8mSlSmp8VUl8kgEQk4DJLSgs9frmOZtmalJafgp4agDGGv58k4udzobgWngwA4POAno1sMbWTKxrUKX8rSo5chXMhmkDnzKM4rUHYzub6mkCnUR3Uty17d5qMXAVGbrqKu8/TYGUkxt5JreFcw5uZN1wIw9JjjwAAvRvZwquOMVKy5EjOkiM5W655nC1HcqacS05RVvoiARcImRrkB0j5gZOpgQgGIj2I9fgQ6fEh1hNALORDJOBDLMx/zr2meS4U8GrMH6TKcOpBHObuv4PUbAX0RQJ8068hBjWzq7HnJE+pwq1nqVwQdCcqVavvPp8HNLIz4cYDNXMyrdBAWqFSIy49FzFpL4OaF6k5WkFORq7yzRsqI6GAB6GADz0+DyI9vuZxfpmQz4dQjwc9vua7wJUL+BAKeDCS6KG+rab1pr6tMQzfwS5eCpUa+64/x49nHiMuXdM1ys3KEHO6e8CvgU2N/T7UVHlKFf4NTcTx+7E4/TAeyYVac/VFAnT2tIJfQxt09rSkpCblpFYzBF2NxNK/HiFHoYKhWA9f9/HCkOb21e7zXngsaEqW9jjQ1Bw5UvODmsIBTEr222UZrUhFAiguiNI87+ZljQFN7XVdTQp6apobkcn45VwY12cVADp5WmJqJze0rFu6mYlzFSqcD4nHn3c1+fcL3zV2NCsIdGzRoI7xW/9hSM2WY/jGK3gUmwE7mRR7JrWqkXda1GqGpccecvO4fNjBBZ/3qPfa5vKCP2LJrwZFJTxPyZZzGfAqmjg/CBLlB0UFAdLL4OhlkKQVNAk1zx1MNZ+LmjQeIFehwnd/PcSOy5EANFl9fhreFC6WhjquWcVKz1XgSlgS/g1NxL9hSQiNz9R6XazHR8uC8UBuFvCyNS7xc8sYQ3qO8mUgk1YQ1OTmBzalGyANaMbD1DGR5g+UlsBWJoWtiQQmUiFEAj6EepoApnBwUvC4IGgpCGD0+O924F6RchUqbL8UgXUXwpCanzjH294En/aoh7ZuFjquXe2WmafE+ZB4HL8fi/MhCVpz38j0heha3xo9GtignbsFtfhXoPDELMzZdwc3IjVj3Dp7WuJ/gxrD2rhyhwrkyFWIS89FfEYe4tJzEZeeiwTucd7L7KBvkfUTeNmjRFZ4igR9IUzzJ9k2kb6cKkGsx0eOQpMRsuB3drGPlflZJVXIVWgvU1BeloBrckdXfN6zXrmPsaJQ0FNDPYzRTHT6592XE522cDbF1E5u6ORpWeQCIVehwoXHCTh6NwanH8Yhu1ArhIOZFL0b1UHvRrZoaPf2gc6rEjLyMGzjZTxNyIKzuT72TmoNq0r+Y1ORFCo1Pt1/F4duRQMA5veqhw87uFb4fhhjyMxTIiVLgaQszR/E5CwFUrLkSMp62YqUq1AhT6FGnkqNPIXmD08e96NCnlJdKXd/jCR6GOLjgDGtnap9i93juAzM2HULIXEZAICJ7etirl+9dyIDWmxariYAym8Jin9lwLOpvhBtXC3g42SKrDxlfmDzMqjJLkULpVDAg62JFHVkEi77Ux1ZoQDHRFqjAuR3UXquAr/+/RSbL4Zz73lbN3PM9auHJg4y3VauFknOkuP0wzicuB+Lf0ITtf422xhL0L2BJtBpWdesRs7RV1Oo1AybLz7FihOPIVepYSIV4pt+DfC+d50yX/PkyFWIz9AELtzvV4Kb+Iy8Mrd4F57fzVRfpBXAFJ4Mu/Bcb5reHwKd3BRSqdnLQKhQ6v1Xg6NchQoN6hjDx6l0N+YrEwU9NVxEomai0wM3Xk50Wt/WGFM7ueK9+la4+CQRR+/F4PSDOK3uVnYyqSbrWmNbNLIzqfQvTExaDoZuuIyo5By4Wxliz6TWMKsBKVSz5UpM3XkT50MSIODzsHxQYwzy0X0T7ZswphkjkadUI0+h1jxWvAyIuACp4LXCjxWa5wXL5ShU+OdJIsLzk2rweEAnD0v4t3FGB3fLajU4lDGGnVef4ds/HyBPqYaFoRgrh3qjo4elrqumE4wxhMZnclnhrjxN1rq7XBJzA1F+EFM0qKkjk7yTA6Rrq4SMPPx8LhQ7r0ZyLc1+Dawxp7tnrRmHWdVi0nJwMjgOx+/H4mp4klbLqLO5Pno0tIVfA2t421fPuW9qs8dxGZi99w43GXWvRjb4tl9DmBuKuWAmvlBrTHxGLuLT87RabMoSzEiFAlgbi2FlJIGVsRjWxhJYGYlhZSyGuYE4v0VGBBN9IYzElPioslHQU0vEpedi0z9PsfPqM+6uHZ8HrT+2dUwk+ckI6sDbvvIDnVdFJWdjyPrLiE3PRYM6xtg1sRVMpNW3r3JKlhxjt/2H21GpkAj5WDfSB53rvZsTrqrVDH8/ScD2SxE4F5LAlbtYGGBMaycM8rHXeb/z1Gw5Pt1/FycfxAEAOnpYYsUQb1ga1c4B6uWhUKlx93kqLj5Jwr3oNJjqC18JajRBDnWtefdEJWdj9eknOHTrOdRM8/9jQFN7fNzVHQ5mNa9LclV7mpCJ48GxOBEchztRqVqvedkao0dDG/g1sIGHtSFd2OqYQqXGL+fCsObsEyjVDIZiPfB5QHoZghmJkA9rYwms84MZKyMJrLWCGk05BTLVCwU9tUxqthzbL0Vi66VwpGYrYGsiQa9GmhadJtXgrlJYQiaGbbiMxEw5mjnKEDjet1p2g4lOzcGYzVcRlpAFE6kQWwJa0HxD+cITs7DjcgT2X3+OjPxWAwORAIN97DGmjTNcdTBm5srTJHyy5zZi0nIhFPDwWY96GNe2rs4/74TUNE/iMrDiZAhOBGtuHggFPIz0dcK0zm50A6EQxhiCX6TjRHAsTgTH4nHcy7F0PB7g42jKBToUNFZP96PTMGvvba33rnAwY2kshnV+MGOV/9jKWBPQUDBTM1HQU0tly5WISctFXXODanfh9zAmHcM3XkFajgKtXcyxdWyLanVn+UlcBsZsuYaYtFzYmkiwY1xL6uZRjMw8JQ7dfI5tlyIQVmguifbuFgho44zOnlaV/tlTqtT46cwTrDkXCsY0LU8/jWha6+aFIqSq3Y5KxfcnHuHfUE3KX32RAOPb1cXEDi41ItV7ZVCpGW4+S8Hx+5pA53lKDveaHp+HNm4W8GtgjW5e1rAyqjnjVt9lcqUaD2LSYSgWUDDzDqCgh+jEnahUjNx0FZl5SnTytMTG0c2rxSDzG5EpGLftP6TlKOBmZYgd41qiTi2b+LGiMcZwMTQR2y9F4MyjeG5meydzfYxu5YQhzR0qpRtjVHI2Pt5zm8vIM8THHgvfb1AtWw4JqakuPknE9yce4c5zzRgImb4QUzq6wr+Nc7W6WVVZchUqXA5LwskHsTj1IB6JmS8Tg0iEfHTysIJfQ2t0qWddrbtrE0Io6CE6dC08GWO2XEWuQo2eDW2wZkRTnWavOfsoDlN33kSuQo2mjjJs8W8B0xqQbKE6eZaUjcArEdjzXxTXP1pfJMCApnYIaONcYS1mf9x5gfmH7iEjVwkjsR6WDGyE973rVMi2CSHaGGM4ERyLFScfcynRbYwl+Og9dwxpbg9hLcs6lpajwPmQeJwMjsP5kHitJEDGEj10rW8Nv4Y26OBuCamo9gd+hNQWFPQQnfrnSQLGb7sOuUqNAU3tsHKIt0664x248RyfHrgLlZqhk6clfhnZDPoiajEor2y5EodvvcC2S+Fa/aXbuJojoI0z3qtvXa6Z4LPlSiz8PRh7rz8HADR1lOGn4U2pzzwhVUCpUuPgrWj8ePoJolM1XbuczfXx0Xvu6OhhCXPDmjvmJzYtF6cexuFkcCwuhyVBWSgLUEFq6e5eNvB1Mat1QR4h7woKeojOnXoQhylBN6BUM4xo6YjvBjSs0j61G/8Ow3d/PQIADGxqh2WDG9M/tQrCGMPlp0nYfikCpx7EcdkE7U2lGN3KCcNaOECmX7rWtPvRafjot1t4mpgFHg+Y1skNM7u603tFSBXLU6qw88oz/HwuFElZcq7c2VwfTR1N0cxRhqaOpqhnY1St554Jjc/AieA4nHxQNOOau5UhF+g01kG2U0JIxaOgh1QLf9x5gZm7b0HNgHFt6+KrPvUr/Z+MWs3wv+OPsPHvpwA0E1jO61m/2iV+qC2ep2Qj6Moz7P7vGTcTvETIR/8mdvBv44z6tsV/T9Vqhi3/hmP58RDIVWrYGEuwapg32rjS7PGE6FJmnhJbL4bj8O1orWQmBaRCARrbm2gFQrrMAKdWM9x+noqTwXE4+SAWTwvVmccDmjrI4NfABt28rOGigyyUhJDKRUEPqTb2XY/C3P13AQAzurhhdnfPStuXQqXGZwfu4uDNaADAvJ71MKmja6Xtj7yUq1Dh99svsPVSBB7GpHPlvnXNENDGGd28rLm7w4mZeZiz7w7O588N1M3LGssHNaaxVoRUM2nZCtyKSsGtZ6m4+SwFt6NSi53E0cFMimaOpmjqIEMzJ1PUtzWu1NZauVKNS2GJOPkgDqcexCEh42UiApGAjzZu5ujuZYOu9a1gZUwZ1wipzSjoIdVK4OUIfHUkGADwaQ9PTO3kVuH7yJYrMW3nTZwLSYCAz8OyQY0x2Me+wvdDXo8xhv8iUrD9UgSOB8dCld/3rY6JBCNbOcHV0gBfHg5GYmYexHp8fNm7Pka1cqJuJoTUAGo1Q1hCJm4+exkIPYnPxKtXD2I9Phrbm2gCIUcZmjmavnXwkZGrwPmQBJx8EIfzj+K5+cQAwFCsh871rNDdyxqdPC11PqkyIaTqUNBDqp0NF8Kw9JhmjM2Cvl4Y27ZuhW07JUuOcdv/w61nqZAI+fhlZDN0qWddYdsn5ROTloOdV57ht2vPtMYIAICHtSHWjGgGTxuaK4mQmiw9V4E7Uam4GZnKtQql5SiKLGcnk6Jpfne4Zo4yNKhj8sYpDeIzcnH6QTxOBMfiUlgiFKqXlymWRmJ087KGXwMbtHIxg1iPMq4R8i6ioIdUS6tOPcZPZ54AAJYNaoRhLRzfepsvUnMwZss1hMZnwkQqxJaA5vBxMnvr7ZKKk6tQ4c+7Mdh+KQL3otMwqpUjvuzt9U7MB0LIu0atZniamIVbz1Jw81kqbj1LQUhcRpHWIJEeHw3rGOe3BpmimZMMtiZSPE3IxMkHmoxrt6JStdZzsTBA9wY26N7AGk3sZTRWkxBCQQ+pnhhj+O6vh/j1n3DweMDqYU3Qr4ldubcXGp+B0ZuvISYtFzbGEuwY3xIeFTRnDKl4jDFky1U00Sgh75jMPCXuRKVqBUIp2UVbg0ykwiKtRN4OMnT3soZfA2u4WdHfd0KItvLEBnQVQiodj8fD/F71kaNQIejKM8zaewcSoQB+DWzKvK2bz1Iwbtt/SM1WwNXSADvG+8JOJq2EWpOKwuPxKOAh5B1kKNZDWzcLtHXTZGdkjCEiKRs3I1O48UGPYtORlqOAHp+H1q7m6O5ljW5eNrAxoUQEhJCKRS09pMqo1Qxz99/FgZvPIRLw8at/c3T0sCz1+udC4jEl6AZyFWo0cZBha0ALyvxFCCE1WFaeEqHxmXC2MICJlBIREEJKpzyxQfWdaYzUOnw+D8sGNULvRraQq9T4cMd1XHmaVKp1D958jonbryNXoUYnT0vsmuhLAQ8hhNRwBmI9eDvIKOAhhFQ6CnpIldIT8PHDsCboUs8KeUo1xm/7Dzefpbx2nV//fopZe+9AqWYY0NQOv45pDn0RdZcihBBCCCGlQ0EPqXIiPU166bZu5siSqxCw5RqCX6QVWY4xhqV/PcSSvx4CACa0q4uVQ7wrdfI7QgghhBBS+9DVI9EJiVCAX8c0R3MnU6TnKjF68zWExmdwrytUaszZdxcb/n4KAJjXsx6+7ONFqUoJIYQQQkiZUdBDdEZfpIctY1ugkZ0JkrPk+ODXq4hMykKOXIVJgTdw4OZzCPg8fD+4MSZ1dNV1dQkhhBBCSA1F2duIzqVkyTF84xWExGXATiaFlbEYt56lQiLk4+cPmuG9+ta6riIhhBBCCKkmKHsbqZFMDUQInNASLhYGiE7Nwa1nqTCRCrFzgi8FPIQQQggh5K1R0EOqBSsjCYIm+MLNyhCOZvrYN7k1fJzMdF0tQgghhBBSC1DeX1Jt1JFJceLjDuABlLCAEEIIIYRUGAp6SLUioGCHEEIIqbFUmZlI3roNTC6H5Yzp4IloInFSPVDQQwghhBBC3gpTq5H+xx+IW7ECqoREAIA8MhJ2q1aCp0eXm0T3aEwPIYQQQggpt5zgYER+MBIvPvscqoRECO3twRMKkXHyJF589jmYSqXrKhJCLT2EEFJeTKWCIioKuY8fI+/xE+Q9fgwI+JANGACDdu3A49N9JUJI7aVMSUHCD6uRum8fwBh4+vqwmDwZZgH+yLr4L55/9BHSjx4FTyiE7XdL6G8i0Smap4cQQt6AMQZVYuLL4OaJJsDJCw0Fy80tdh2RkxNMR34AkwEDIDAyquIaE0JI5WFKJVL27EHCT2ugTksDABj37g2rT+dCaP1yqon0kycR/cksQKWCbMhg2CxaRIEPqRDliQ0o6CGEkELUWVnICw3Var3Je/wYqpSUYpfnicUQu7lB7O4OsYcHlHFxSD14EOqMDM3r+vqQ9e8H0w8+gNjNrSoPhRBCKlz2f/8hdvES5IWEAADE9erB5ssvoN+8ebHLpx09ihdzPwXUaph+MALWX30FHo+SFpG3Q0EPIYSUElMqIY+IQN7jx1oBjuL58+JX4PEgcnSE2MOj0I87RI6O4AkEWouqs7KQ9scfSNm5E3lPQrly/datYDZqFAw7dSqyDiGEVGeK2FjEL/8e6X/9BQDgm5jAcuZHMB069I2JClIPH0bMvPkAYzDz94fV559R4EPeCgU9hJDXkkdGQp2bB4mnh66rUmUYY1DGxXEtNgUBjjwsDEyhKHYdgaUFJO4e2gGOqwv4UmmZ95199RpSdgYh48xZQK0GAAjt7GD6wQjIBg2CQCZ720MktYQ6L08TiIeGQv40HHwDA+i3bAlJ/XoUJBOdUcvlSN66DYkbNoBlZwM8HmRDh8Ly45nQMzUt9XZS9+9HzJdfAQDMJ06A5axZFPiQcqOghxBSLGVKChJ++gmpe/YCjKHOiu9h0ru3rqtVadR5eUhctw7Z/11H3pMnUKenF7scX1+f65bG/fb0KNM/8tJSREcjZfdupO7dB1V+H3ieWAzjvn1gNmoUJPXqVfg+SfWkzs5G3tNwyMNCkRcahrynTyEPDYU8KooLjAvjGxtDv3lzGPi2hL6vL8QeHjQuglSJjPPnEbd0KRSRzwAA0mbNYPPlF5B4eZVreym//YbYRd8AACymToXlRzMqrK7k3VJlQc/PP/+M77//HrGxsfD29saaNWvQsmXLEpdfvXo11q1bh2fPnsHCwgKDBw/G0qVLIZFISrU/CnoIKR+mVCLlt91IWLuWG2wKABAIYP/TjzB67z3dVa6SqOVyPJ82HVn//POyUCCAqK4zJB7arTfCOnWq/OJRnZuL9KNHkRy0E3kPH3Ll0uY+MBs5EkZdu4InFFZpnUjlUGVkQB4WhrywsPzgJgzy0DAooqNLXIdvbAyxqytEri5QJSYh+/p1qDMztZYRyGTQb9EC+r6+MPBtCZGbG90xJxVKHhGBuKX/Q+aFCwAAPUtLWM2dA+O+fd/6s5a8YwfivlsKALD8eCYsJk9+6/qSd0+VBD179uzBmDFjsH79evj6+mL16tXYt28fQkJCYGVlVWT5Xbt2Ydy4cdiyZQvatGmDx48fIyAgAMOHD8eqVasq7cAIeddlXbqEuKVLuTElYk9PWM/7HGmHDiHtyO/gCYWw/+UXGLZvp+OaVhwml+P5RzORef48eBIJrOfNg7SJN0R164JfzWYFZ4wh59YtpAQFIf3kKUCpBADoWVvDdPgwyIYOhZ65uY5rSUpDmZKiCW5CNQFOQaCjjIsrcR2BmZkmuHFzhdjVDWI3V4hdXSGwsNC6qGRKJXIfPkT21avIunoN2TduaLoYFd6WuTn0W7aAga8v9Fv6QlTXmYIgUi7qrCwkrt+A5G3bNN1/hUKYjRkNiylTITA0qLD9JG3ahPgVKwEAVnPnwnz8uArbNnk3VEnQ4+vrixYtWmDt2rUAALVaDQcHB8yYMQOff/55keWnT5+Ohw8f4syZM1zZ7NmzcfXqVVy8eLFU+6Sgh1Q0xhiU8Qn54zxCoEpNg3GvnpDUr6/rqr01eVQU4pYtQ+ZpzXdOIJPB8uOZkA0ZAp5AAKZUInr2HGScOAGeRALHXzdCv0ULHdf67TGFAtGzZiHj1GnwxGI4bFgPg1atdF2tUlHExSF1zx6k7NkLVVISAIAnFMK4V0+YjhoFaaNGOq4hKUhbzrXahIVCHvYUeWFh3HtWHD1ra4hdXSBydYPY1RViN1eIXF3L3YWSKRTIuX8f2VevIfvaVWTfvFUkbbqelRX0W7aEvm9LGPj6QujgQEFQFVOlp0Px/Dnkz59D8Txa8zj6OdTpGZDUrwdJ48aQentD5Fw9AlTGGNKP/oX477/ngnWD9u1hPW8exC51K2WfievWIeHHnwAA1vPnw2zM6ErZD6mdKj3okcvl0NfXx/79+9G/f3+u3N/fH6mpqThy5EiRdXbt2oWpU6fi5MmTaNmyJZ4+fYrevXtj9OjRmD9/frH7ycvLQ15entaBOTg4VIugJ3X/fkgaNKgVF8fvCnVWFvKePHmZoSskRJOCuHB3r3wGbVrDbOw4GLRrWy3+EZWFOisLiRs2InnrVs0dOoEAph98AMvp0yAwMdFalsnleD7jI2ReuAC+vj4ct26B1NtbRzV/e0ypRPScucg4fhw8kUjTgtWura6rVWZquRwZx48jeedO5N65y5VLGjeG2aiRMOrRo9q1WNVmypQUpP/xJzJOnkTukyfaXURfIaxTp0irjcjVtdLnaFLL5ci9exdZV68i++o15Ny+DSaXay2jZ2sLg5Ytue5wQju7Sq3Tu0CdmwtFdHSxgY3ieXSJ4whfJTAxgcS7MaSNvSH19oa0caMif68rW+6jR4hdvBg5128AAIQODrCe9zkMO3eu9P+D8T/+iKR16wEANgsXwHT48ErdH6k9Kj3oefHiBezs7HDp0iW0bt2aK//0009x4cIFXL16tdj1fvrpJ8yZM0dzd12pxOTJk7Fu3boS97Nw4UIsWrSoSLmug57ckBCEDxoMQJN5xGLKFLoAqUaYUgn5s2eaDF0hIS9TEEdFFb8Cnw+RszPEnh6AmiHj9GlApQIAiD08YDZ2LEx69wKvmr/HTK1G+h9/IH7FSigTEgBogjfrefMgdncvcT11Xh6iJk1G9pUr4Bsbw2n7thoZzDOVCi8+/Yyb9dt+7RoYduyo62q9tZx79zRd3/46xmWZE5ibw3TYUMiGDYfQumh3YvL2mEqFrH//ReqBg8g4exYonOGPz4fQwV4T2HCtNm4Q13UG36Diuv68DXVuLnJu30H2NU13uJy7d7WPAYDQ3p5rBdL39dWaTPJtMMagzsqGOjMD6owMqDIzoc7M1DzOyIQ6M78sI/Pl6xkZUGdlgSeVQGBoBL6x0cvfRsbgGxlCYGwMvqERBMZGL38bG1fq/1+mVEIRGwvF8+fFBjaqhMQ3bkNgZgahvT1E9nYQ2tlDaG8Pvr4+coODkXPnDnKDg4sEqAAgcnGBtHFjSJtoAiGxu/sbU0KXhyo1FQk//YSU3XsAtRo8iQQWkyfBbOxY8MXiCt9fcRhjSFi5EkmbNgMAbJcshmzQoCrZN6nZqmXQc/78eQwfPhyLFy+Gr68vQkNDMXPmTEycOBFfffVVsfupri09ysRExH67GBknTgAARG6uqLNkSY2+Q14TFXQzyQ15XCgNcQjkoWHF/gMB8lMQe3hyA9glnh4Qubpq/WFXREcjeUcgUvftgzq/z7yelRVMR4+C6bBhEFTDrpU59+4hbvES5Ny5AyD/Dt3nn8GwS5dS3aFTZ2Xh2YSJyLl1CwJTUzgFBULs6lrZ1a4wTKVCzPz5SDvyO6CnB/uffoJRl866rlaFUiYlIXXvXqT8thvK+HhNoZ4ejLp1hdmoUZA2a1bjWiWro7zwcKQdOoy0w4dfnmcAYq/6kA0YCP2WLSBydq6yi8GKos7ORvatW5rucFevIuf+fe7mTgGRkxP0fX2h79sSEi8vMLk8P1DJgDozU/M7QxPAqDIzigYtmZlcgFNc9rnKwhOJwDc2hsDQUPPbyAh8I6OXvwsHSVy5MQRGmuXV2dmaQCa6aGCjiI0tcp5exTcwgNDevkhgI7S3g8jO7o2BMJPLkRvyGDl37mh+7t7hsqRpHadUCmmDBpA28YbE2xvSxt5vddODqVRI3bcfCatXQ5WaCgAw6tkD1p9+CqGtbbm3W+76MIa4pUuRsiMQ4PFQZ9n/YPL++1VeD1KzVMvube3bt0erVq3w/fffc2VBQUH48MMPkZmZCX4pMidVtzE96cdPIPbbbzX9uPl8mAUEwHLG9DLP4UHeTJ2djbzQ0PzWm5dBjiolpdjleVJpfuph95dBThlTEKvS05G6dy+SdwRyFz98fX2YDB4EszH+ENnrvmuIMiEB8at+QNqhQwAAnr4+LCZPhlmAf5nvfqoyMvAsYCxyg4OhZ2kJp51BEDk6Vka1KxRTqxHz1VdIO3AQEAhg98MqGHfvrutqVRqmUCDjzBkkBwVx3VAAQFy/PsxGjYRx797glzIjJtFQZWYh48RxpB48hJwbL8+pQCaDcd++kA0cUCNbP19HlZmFnJs3uO5wuQ8eVHygoqenCUSMjDQtNQaG+UGHIfiG+WVGRuAbGIJvZAi+gQFYbh5UGelQZ2Ryv9UZ6VClZ7wMwAoFYqiC2TZ4QiGEdepoAhkHe4jyAxxNcGMHgUxW4TcclMnJyLl7V9MSdOcOcu7eK5K9D9B0WZR653eJ824MiZdXqb7/2TdvInbxYuQ90GSOFLu7w/qLL2DQyrdCj6OsGGOI/eYbpP62G+DzYbfiexj36qXTOpHqrcoSGbRs2RJr1qwBoElk4OjoiOnTpxebyMDHxwddu3bFsmXLuLLffvsN48ePR0ZGBgSlmHCtugU9gKavd9zSpUj//Q8AgNDJEXUWL64VA8IrC2MMTKEAy86GOjsb6pwcqLNz8h9ng+U/V0RHI++JJshRREUV/8+Nz4fI0ZELasQeHpB4eGgG7FZQCmImlyPtr7+QvGUr8h4/5vZr3MMPZmPHQdqoYYXspyzUcjlSAgOR+Ms6qLOyAAAm/d6H5azZb3XnT5mSgmdj/JH35AmEderAKSgQwjp1KqraFY4xhtiFi5C6Z4/mH+TKFTDu2VPX1aoyuQ8fInnnTqT/8SdYfqu4QCaDbMgQmH4wQid3a2sKxhhybtxA6oGDSD9x4mUmND4fBu3bQTZgIAy7dH5nui6r0tORff2GJjvctWuQR0SAb2DABS0CI8P84OSVoMXwlQDGsOB1Q/AkkkptfWRqNdRZWVCnp2sHQxkZUKVnvDF4UmVkaLr88XjQs7GByM6Oa7ER2ttxwY2elZXO50NiajXkT59qWoJu30HO3bvIe/KkaKCqpwdJvXpcECT19obQ0ZF7HxTx8YhfsYK7ZuEbGcFyxgyYfjCiUrrOlQdTqxHz9ddI239AcyNr9Q8w7tZN19Ui1VSVpaz29/fHhg0b0LJlS6xevRp79+7Fo0ePYG1tjTFjxsDOzg5Ll2pysC9cuBCrVq3Cxo0bue5tU6ZMgY+PD/bs2VNpB1ZVMs6fR+yChVy2E9MPPoDlrFkVmtpRV1RpaVAmJ2uCkZz84CQ7p1CAUvA8B+rsLC5o4ZbNydEEOIWev6m7QHEE5ubaLTceHhC7uVZZyxpjDFn/XkLyli3IunSJK9dv0QJm48bCsGPHSv/HyBhD5vnziPvf/7juD5JGjWDzxXxImzSpkH0oExIQOXoM5BEREDk5wSkoEHqWlhWy7YrEGEPct4uRsmsXwOejzrJlMOnbR9fV0gllSgrSDh5Eys5dULx4oSkUCGDUtSvMRo+C1MeHur7lU8TGIu3wEaQeOqjVhUjk7AyTgQNh0u/9ChvbQqo3xhhYXh54fH61H7NZHFVmFnLv38/vEncXObdvF5tBUCCTQeLdGCJ7B6QdOqTpts3jQTZ4ECw//rhapsRnajVi5s3TdFkWCjXzyXWuXV2WScWosslJ165dy01O2qRJE/z000/w9dU0jXbq1AnOzs7Ytm0bAECpVGLJkiUIDAxEdHQ0LC0t0bdvXyxZsgQymazSDqwqqTIyEL/8e6Tu2wdAk8XH5ttvYNi25mWPAoCc4GAkbfwVGSdPVloXAp5QCJ6+Pvj6+uBLpZoffX3w9KXQM7fQBDmemiCnOv1hzn30CMlbtyLt6F/cvCoiFxeYjQ2AyfvvV0p//7ynTxH33VJk5ad4F1hYwGrWLJj071fhwZYiJgaRI0dB8eIFxO5ucNyxo9ypdSsDYwzx//sfkrfvAHg82H73HWQD+uu6WjrHVCpknjuH5MAgZBcaW/mud31Ty+XIPHMGqQcPIevff7m743x9fRj16gnZwIGQNm1KgSGp0RhjUES/QM6d28i9exc5t+8g98EDLgFKAYl3Y9h8+WW1T4HPVCq8mPsp0v/6q1bOJ0cqRpUFPVWtugc9BbIuX0bMl19xs22bDBoI688+q5YD4IuTfeMGEtdv0JrJnm9oCL5UCp6+FHx9g0IBSn6QIpWCL80PXvSlRZ5zwUz+74L1a/qM84rYWCQHBiJ1z16uv7XA3Bxmo0ZCNnx4hQQKqvR0JP78C5J37tQEWEIhzP3HwHzyZAgMDd96+yWRR0UhcuQoKOPjIfHyguO2rdXiM8wYQ/yKFUjevAUAYLv4W8gGD9Zxraqf3JDHSAkKQtoff3DztwhkMsiGDoXpiOHvRNe33AcPNN3X/vxTKzW9fvPmMBk0CMbdu1WbbGuEVAa1XI68hw+Rc0fTHU6/uQ+M+/bVeXe90tLMuzYbGadO1bh510jVoKCnGlBnZSF+9Y9ICQoCGIOepSVsFi2EUZcuuq5asRhjyLp4EYkbNrwcHM3nw7hXL5hPnAiJp4duK1jNqTIzkbpvP5J37IAyJgYAwJNIIBs4AGb+/hA5OZV5m0ylQuqBA0hY/SNUyckAAMPOnWH92acQOTtXZPVLlPf0KSJHjYYqORnSJk3guHmTTi8SGWNIWP0jkjZsAEDzOZSGKjUVqQcOIHnnTihfaD6bEAhg1K0bzEaNrHVd3wrm1Ek9eBB5jx5x5Xo2NjDp3w+yAQPK9X0khOgGk8vxfObHyDx3DjypVDORdvPmuq4WqSYo6KlGsm/cQMwXX0IeEQEAMO7dG9ZfflFtugoxtRoZp04jacMGTfYeaLqcmQwYAPMJ42tE9q7qhCkUSD9xEklbNnNZccDjacZWjBsL/aZNS7Wd7Bs3ELtkCbcNkYsLrOfN00nTfu6jR4j0D4A6LQ36vr5w2LBeZ12kEtasReLPPwMArL/8EmajRuqkHjURUyqRce4cUgKDkH3tGleu6fo2CsZ9ete4NMwFmFKpmVPn4CGtOXV4QiEMu74H2cBBMGjTGrxSJMwhhFQ/arkcz6dNR9Y//4Cvrw+HzZtK/f+UlE/BtCBcCvfo51BER3PPzUaNhJm/v66rSUFPdaPOzUXi2rVI2rIVUKshMDODzVdfwqhHD53dYWUKBdL+PIqkX3+F/OlTAJo0z6bDhsFsbAAN5H1LjDFkX72GpK1bkHXhb65c2rQpzMaNhVGXLsVegCliYhD//Qqk//UXgPzMOtOnwfSDD3TaFTDn7l08GzsO6qwsGHTsAIc1a6p84G/i+vVIWP0jAMDq889gHhBQpfuvTXJDQpAStBNpv//+Muubqakm61sN6vqWFx6OtIOHkHbkiNacOhIvL01Sgj69ISjlmFFCSPWmzs1F1JQpyL58BXxDQzhu3VLtxyVVZ4wxqFJT8wMaTVAjf64JbArKWKG5Ml9l+sEI2Hz9dRXWuHgU9FRTOffuIWb+F5o0kwAMu74Hm6+/htCq6mZUV+fmIvXgQSRv2sxleeIbG8Ns1EiYjh5dbVqgapO8J0+QtG0b0n//gxtQKnRyhHlAAEz69wdfKoU6NxdJmzcj6ddNmvEXPB5kQ4bA8uOZ0DMz0/ERaGRfv45nEyaC5ebCqHt32K1aWWUpTpM2bUL8ipUAAKs5s2E+YUKV7Le2U6akIO3AASTv2lW069vo6jXhKVOpoIiK0szXFRqKzH8uFp1T5/2+kA0cCEm9ejqsKSGksqizsxH14SRkX78OvrExnLZvq3VzaFUkVWYWN+lu4VYaTWDznJvyokR8PvRsrCGys4eQS+mumXRXVLcu9CwsquZAXoOCnmqMyeVI3LARiRs2AEol+MbGsP78c5gM6F+pFxeqzCyk7v4NSdu2Q5WYCCB/wH2AP0xHjKjUAfFEQxEfj5Sdu5CyezfU+YOqCy7UMk+f4YJQaXMf2MyfD4mXly6rW6zMf//F88lTwBQKGL/fF3X+979KHxCbtG0b4v+nmd/L8uOZsJg8uVL39y5iSiUyzp5FStBO7a5vXvVhNmo0jHv3qrKub0ylgvzZM8jDwjQBzpNQ5IWFQf70KZhcrr1wwZw6AwfBsHOnd2ZOHULeZarMLERNmICc27chkMnguGM7JB7v3rhjxhjUWVlQxsdD8fyVVpr8x6rU1DduR2BpoQlq7AsCm5dzVAmtrat9OncKemqA3JAQxMz/ArnBwQAAg3btYPvNogqfCFKZkoKUwCAkBwVBnZ4OANCrYwvz8eMhGzTonUxfq2vqrCykHjyE5O3boXj+nCvXs7WF9dw5MOrZs9rcXS9OxtmzeP7RTECphGzoUNgsWlhp9U0ODELckiUAAItp02A5Y3ql7Ie8pOn6FoS03//Q7vpWkPXNxqZC9lMQ3OSFhmoCnCeaFhx5eHjR4CYfTyyGyNUFYjc3SLy8YNyz11tNxksIqZlUGRl4Nm48cu/dg8DcHE6BOyB2cdF1td4KUyigSk2FMjkFqpQUqFKSoUxJgerV5ympUCUnQ5WSUiQdeXEEMlnRgKag1aZOnRp/HUhBTw3BlEokbd2KxDVrweRy8PX1YfXpXMiGDn3ru+eKuHgkb92KlL17uZnGRXXrwnziRJj07VPjU0XXBkypRMbp00g9eBDSxt4wHz+uyiZafVvpf/2F6DlzAbUaZv5jYPX55xUe+KTs3o3YhYsAAOaTJsHy45nVOhisbUrs+ta9G8xGjy71vDZMqYQ8v1uaPDQUeaFhbw5uJBKIXVwgcnOF2M0dYjc3iN1cIbSzo2QEhBAAmonTIwPGIu/hQ+hZWsIpcEeVZTZ9k4JWGFVKClTJJQQv+c+VKclQpaRyN6bLim9gkN/tzB4ie7uXAU1+lzSBYe1Oy09BTw2T9/QpYr74Ejm3bgEA9Fu0gO3ib8uVVlUeFYWkTZuRdvAgdwdA7FUfFh9OglG3rnTBQCpM6sFDiJk/HwBgPnkSrD7+uMK2nbJvH2K/0gyQNBs/DlZz5lDAoyNc17fAIGT/9x9X/mrXN6ZUQv4sCnlhZQ9uxO5uELm6aYIbdzcI69Shv1WEkDdSpqTgmX8A8h4/hp6NDZyCAiGyt6+0/RUM/lfGxkIRGwtlXDwUcbFQxsZBGRcHZXJymVphiuDxIJDJIDAzg8BUBj1TMwhMTSEwM4WeqammXFboualpjblZWlko6KmBmEqFlJ27EP/DD2A5OeBJJLD8eCbMRo8u1T//vCdPkPjrr0g/+hegUgEApM2awWLyJBi0b08XjKRSJO/ahbhvvgUAWH7yCSwmffjW20w9dFgTTDEG0zGjYT1vHn1+q4mSur7pWVlpxtyU8E9eK7hxc4PYNT+4sbOrMZMkEkKqJ2VSEiLH+EMeFgZhnTpwCgos11ABplJBmZioCWji4qCMjdMENHHxL8vi4kq8iVMcnlRafPBSXDBjagqBsTHd8CkjCnpqMHlUFGK+/ArZV68CAKTe3rD9bgnErq7FLp9z7x4SN2xA5ukzXJlB+/awmPQhTd5FqkTS5s2I/34FAMB6/jyYjRlT7m2l/fEHXnz6mSbg+eADWH/1JQU81ZAyJQWp+/cjZddv3GS8gOYfvNjFBWI3V01wk/9DwQ0hpDIp4uPxbPQYyCMjIXRw0AQ+habeUMvlUMbnBy+xcVDGFRPYJCRwN43fRGBuDqG1NfRsbKBnbQWhtQ30rK2hZ2GhuRFkRq0wVYWCnhqOMYbUvfsQv3w51FlZ4AmFsJg2Debjx4EnFGrmgLn2H5I2bEDWpUualXg8GHXrBvNJH0LaoIFuD4C8cwpPGmrz7TcwHTKkzNtIP3YM0bPnAGq1JkHCwgV0oVzNMaUSWZevgCkU1HJDCNEpRWwsIkeNhuL5cwjt7SF2d+e6nqmSk0u3EYEAelZWmoDG2hpCG2voWdvk/9Y81rOypEyR1QgFPbWEIiYGMQsWIOvvfwDk96EfOQqp+/Yh5/ZtzUICAUz69oX5xAkltgYRUtkYY4j/fgWSt2wBeDzUWb4MJn37lnr99JMnEf3JLEClgsmggbD99lu6eCaEEFIm8ufRiBwz+mXylUJ4YjH0bKwhtNK00AhtrKFnZa0ps8lvqTE3p+5lNQwFPbUIYwzpv/+O2O+WcnO7AABPJIJs8CCYjRsPkb2dDmtIiAZjDHHffouUXb8BAgHsVv8A427d3rhe4RTYJv3eh+1339E/HUIIIeWiiItH+rG/wJfqF2qhsYZAJqPu0rUQBT21kDIhAbFLvkP2lSuaYMffH3qWlrquFiFamFqNmC++RNqhQ4BQCIef18KwQ4cSl8+8cAFR02cACgWMe/dGneXLKOAhhBBCSKlQ0EMI0RmmUiF6zhxkHDsOnlgMh40bYeDbsshymRf/xfOpU8Hkchj16AG7Fd+Dp6engxoTQgghpCYqT2xAnecJIRWCJxDAbvlyGHbuDJaXh6gpU5CdPwdVgazLl/F82jRNwNOtK+y+X04BDyGEEEIqHQU9hJAKwxMKYbf6Bxi0aQOWnY2oDychJzgYAJB17RqipkwFy8uDYadOsFu5EjyhUMc1JoQQQsi7gIIeQkiF4ovFsF+7BlIfH6gzMhA1fgJSDxxA1OQpYLm5MGjfHnY//Qgepf4khBBCSBWhoIcQUuH4+vpw2LAekkaNoEpNRcwXX4JlZ8OgTWvYr/mJ5joghBBCSJWioIcQUikEhoZw/HUjxJ6eAAB9X1/Y//wz+BKJjmtGCCGEkHcNjSAmhFQagUwGp51ByL5yBQbt2lHAQwghhBCdoKCHEFKpBIaGMOraVdfVIIQQQsg7jLq3EUIIIYQQQmo1CnoIIYQQQgghtRoFPYQQQgghhJBajYIeQgghhBBCSK1GQQ8hhBBCCCGkVqOghxBCCCGEEFKrUdBDCCGEEEIIqdUo6CGEEEIIIYTUahT0EEIIIYQQQmo1CnoIIYQQQgghtRoFPYQQQgghhJBajYIeQgghhBBCSK1GQQ8hhBBCCCGkVqOghxBCCCGEEFKrUdBDCCGEEEIIqdUo6CGEEEIIIYTUahT0EEIIIYQQQmo1CnoIIYQQQgghtRoFPYQQQgghhJBajYIeQgghhBBCSK1GQQ8hhBBCCCGkVqOghxBCCCGEEFKrUdBDCCGEEEIIqdUo6CGEEEIIIYTUahT0EEIIIYQQQmo1CnoIIYQQQgghtRoFPYQQQgghhJBajYIeQgghhBBCSK1GQQ8hhBBCCCGkVqOghxBCCCGEEFKrUdBDCCGEEEIIqdXKFfT8/PPPcHZ2hkQiga+vL65du1bisp06dQKPxyvy07t373JXmhBCCCGEEEJKq8xBz549ezBr1iwsWLAAN2/ehLe3N/z8/BAfH1/s8gcPHkRMTAz3c//+fQgEAgwZMuStK08IIYQQQgghb1LmoGfVqlWYOHEixo4dCy8vL6xfvx76+vrYsmVLscubmZnBxsaG+zl16hT09fUp6CGEEEIIIYRUiTIFPXK5HDdu3EDXrl1fboDPR9euXXH58uVSbWPz5s0YPnw4DAwMSlwmLy8P6enpWj+EEEIIIYQQUh5lCnoSExOhUqlgbW2tVW5tbY3Y2Ng3rn/t2jXcv38fEyZMeO1yS5cuhYmJCffj4OBQlmoSQgghhBBCCKdKs7dt3rwZjRo1QsuWLV+73Lx585CWlsb9REVFVVENCSGEEEIIIbWNXlkWtrCwgEAgQFxcnFZ5XFwcbGxsXrtuVlYWdu/ejW+++eaN+xGLxRCLxWWpGiGEEEIIIYQUq0wtPSKRCD4+Pjhz5gxXplarcebMGbRu3fq16+7btw95eXkYNWpU+WpKCCGEEEIIIeVQppYeAJg1axb8/f3RvHlztGzZEqtXr0ZWVhbGjh0LABgzZgzs7OywdOlSrfU2b96M/v37w9zcvGJqTgghhBBCCCGlUOagZ9iwYUhISMDXX3+N2NhYNGnSBMePH+eSGzx79gx8vnYDUkhICC5evIiTJ09WTK0JIYQQQgghpJR4jDGm60q8SXp6OkxMTJCWlgZjY2NdV4cQQgghhBCiI+WJDao0exshhBBCCCGEVDUKegghhJBqJkuRBaVaqetqEEJIrVHmMT2EEEK0qZkaF6MvYufDnUjLS8Mwz2Ho49oHQr5Q11Uj1ZxKrUJURhRCUkIQkhyCJylPEJISgpisGJhJzDDFewoGeQyizxIhhLwlGtNDCCHllKfKwx9hf2DHgx0ITwvXes3ByAEfNv4QfVz6QI9P95cIkC5P1wQ1ySF4nPIYj1MeIzQ1FDnKnNeu52jkiI+afYTuTt3B4/GqqLaEEFJ9lSc2oKCHEELKKDk3GXse7cHukN1Izk0GABgKDTHYYzDMJGbYFryNK3c0csQk70noVbcXBT/viILWm8cpjxGSEoLHyZoA50XWi2KXlwgkcDd1h4epB/fjInPBqYhTWHdnHZJykwAADc0b4hOfT9DStmVVHg4hhFQ7FPQQQkqUnJuMby9/iwxFBlZ3Wg1DkaGuq1TjhKeFY8eDHfgj7A/kqfIAALYGthhVfxQGug/kzmm2Iht7QvZg6/2tSMlLAQA4Gzvjw8YfolfdXhDwBTo7BlKxMuQZXKtNQQvO61pvbA1s4WnqCXdTd3iaecLD1AOORo4lfiayFdnY/mA7tt3fhmxlNgCgnV07fNzsY3iaeVbacVWV5xnPceH5BTgaOaKVbSsIBbWzG59cJcflF5cRkR6BAe4DYCyqvdcyjDEcjziOLEUWBrkPotZJUiko6CGEFOtR8iPMPDuTu9M83HM4vmj1hY5rVTMwxnA97jq2B2/HhecXuPKG5g3h38AfXZ26ltiCk63Ixm+PfsO24G1IzUsFoAl+JntPRg/nHhT81CBqptaMvckPbApacF7XeuMmc4OnWX6AY+oJDzOPcl/sJuUkYcPdDdgXsg9KpgQPPPRx6YPpTaejjmGdtzm0Kqdmavwb/S92h+zGP8//AYPmMsRYZIwujl3Q3al7rQiAFCoFLsdcxomIEzj37BwyFBkAAHtDe6zstBJe5l46rmHFy1JkYeGlhTgecRwAMNl7MqY1mabjWpHaiIIeQkgRxyOO4+t/v0aOMgfW+taIy44DDzzs6LkDTaya6Lp61ZZCrcDJiJPYHrwdD5MfAgB44KGTQyeM8RoDH2ufUt/BzFJkccFPWl4aAMDFxAWTvSeju1N3Cn6qKTVT43j4cewO2Y1HyY9KbL2xMbDRBDWmHvAw84CnqedrW2/exrP0Z1hzaw13USnkC/FBvQ8wodEEyCSyCt9fRUrLS8Ph0MPYE7IHURlRXHkzq2aITI/kuvEBNTcAKinQAQBLqSX4PD7isuMg4ovwWcvPMMRjSK1pCXmc8hizz89GRHoEBDwBVEwFAFjQegEGewzWce1IbUNBDyGEo2ZqrL21Fr/e+xUA0LZOWyzrsAzf//c9joQdgYuJC/b13QeRQKTjmlYvGfIMHHh8ADsf7URsViwAQCwQo59rP4z2Gg1nE+dybztLkYVdD3dhW/A2pMvTAQCuJq6Y3EQT/PB5NItAdcAYw/mo81hzew2epDzhysUCMdd6U3j8jYnYpMrreD/xPn648QOuxV4DABgJjTC+0XiMrD8SEj1JldfndYKTgrH70W4cCz/GdQs1Ehqhn1s/DPMcBmcTZ6jUKtyMv4kTESdwOvJ0sQGQn7MffG19q10mO61AJ+ocMuTagU5Xp67wc/ZDU6umyJBn4MuLX+L88/MAgF51e2FB6wXQF+rrqPYV43DoYSy5sgS5qlxY61tjRccV+Cf6H2y8uxECngA/dfkJHew76LqapBahoIcQAkBz4T7vn3lcd6yxDcZiZrOZEPAFSM1NRb8j/ZCcm4yp3lMxpckUHde2eniR+QJBD4Nw8MlBZCmyAABmEjOMqDcCwzyHwVRiWmH7ypRnYufDndj+YDt3geQmc8Nk78no5tSNgh8duhJzBT/d/An3Eu8B0Fyc+zfwRzfnbnAycqpWrXKMMVx6cQk/3PgBISkhAAArfStMazIN77u+r9PEGXmqPJyMOIndj3bjbuJdrtzT1BPD6w1Hr7q9SrzQLxwAnYo8xSUFATQB0HuO76G7c3edBkAFgc7JiJM4G3VWK9CxkFqgm1M3dHfqjqZWTYt8Zhhj2B68HatvroaKqVDXpC5WdlwJd1P3qj6Mt5ajzMF3V7/D4dDDAIC2dm2xtN1SmEpMwRjDV/9+hSNhRyDVk2KL3xY0tGio2wqTWoOCHkIIItIi8NG5jxCeFg6xQIyFbRaij0sfrWWOhx/H3L/nQo+vh/1998NV5qqj2upecGIwtgdvx8nIk1x3DFcTV4xpMAa9XXpDLBBX2r4z5BkIehiEwOBArhuMu6k7pnhPwXuO71HwU4Vux9/GmltruJYTqZ4UI+uPRECDAJ205JSFmqlx9OlRrL21lhtj5GriipnNZqKTQ6cq7T71IvMF9obsxcEnB7kkHnp8PXR36o4R9UbA29K7TPWpTgGQQqXAlZgrOBl5EmeenSkS6HR1fNmiU5rg+Fb8Lcy5MAfx2fGQCCT4stWX6OfWrzIPoUJFpEVg1oVZeJLyBHweH9OaTMOERhO0/m4p1ArMODMD/774F2YSMwT2DISjsaMOa01qCwp6CHnHXYy+iE8vfIoMRQas9a3xY+cf0cCiQZHlGGOYfnY6/n7+N5pYNsH2ntvfqQtsNVPjQtQFbH+wHTfibnDlvra+8PfyR1u7tlV6PtLl6Qh6EITAB4HIVGQC0NwRn+I9BV0cu9SaPv/VUUhyCNbcWsO1igr5Qgz1HIoJjSbAQmqh49qVTZ4qD3se7cHGexu5sWPNrJrhE59PKnX8npqpceXFFfwW8hv+fv431EwNALDWt8ZQz6EY6D6wQs7l6wIgE7EJujh0qfAAqHCgc/bZWa5bKvAy0Onu3B3NrJqVqxUwOTcZ8/6Zh0svLgEABrgNwDzfeZDqSSuk/pXleMRxLPh3AbKV2TCXmGN5h+UlplLPUmRh7PGxeJj8EI5GjgjsFQgziVkV15jUNhT0EPKOYoxhW/A2rL65GmqmRhPLJvih8w+vvdCIyYxB/yP9ka3Mxhe+X2B4veFVWGPdyFHm4I+wPxD4IBAR6REAAD2eHnrW7YkxDcagnlk9ndYvLS8NgQ8CEfQwiOtiV8+sHqZ4T0Fnh84U/FSg8LRw/HL7Fy4hgIAnQD+3fpjceDJsDW11XLu3ky5Px5Z7WxD0MIgbQ9PFoQtm+syEi4lLhe7nSOgR7AnZg8j0SK7c19YXIzxHoKNDx0rrYqdSq3Aj7gZORp4sMQDyc/ZDS9uWZQ6AFGoFrsZcxckITYtO4UDHXGKu6br2FoFOccfy671fse7OOqiZGu6m7ljVcdVbjR+sLHKVHCuur8Bvj34DADS3bo7lHZbDUt/ytesl5iRi1F+jEJ0ZjUYWjbCp+6YaP46J6BYFPYS8g3KVuVhwaQH+Cv8LADDIfRDm+84vVYKCXQ93Yem1pTAQGuBwv8OwMbCp7OrqRGJOInY/2o09IXu41NFGQiMM9hyMD+p9UO2OOy0vDduDt2Pnw53c3Cz1zepjapOp6GjfkYKft/Ai8wXW3VmH38N+51okejr3xNQmU6vlRebbiMuKw7o763Ao9BDUTA0+j48BbgMwtclUWOlblXu7Ickh+O3Rb/gr/C8uo52h0BDvu76PYfWGVWhgVRoVEQAVDnTORp3lWsoATaBTkIygogKd4lyNuYpP//4UybnJ0NfTx6I2i9Cjbo9K2Vd5RGdGY875ObifdB8AMKHRBExrMq3UgW14WjhGHxuNtLw0dLTviNWdV9OEzaTcKOgh5B0TmxWLmedm4kHSA+jx9PBZy88wzHNYqS+KVWoV/I/7407CHXSy74SfuvxUqy6ow1LDsOPBDvwZ9ifkajkAwM7QDqO9RmOA24Bqf6cxNTcV2x9sx66Hu7jgp4F5A0xtMhXt7drXqveqsiXmJGLj3Y3Y93gflGolAKCTQydMbzK9Vkzy+TphqWH48eaPOBd1DoBmDqHRXqMxtuFYGImMSrUNhUqBU5GnsDtkN27F3+LK3WRuGFFvBPq49KkW36c3BUDvOb6H7k7dua5Y12KucWN0Cgc6ZhIzdHPqVumBzqsSshMw9++5XLfb4Z7DMbfFXJ1n2bwQdQHzL85HujwdxiJjLG2/tFzZ2G7H38aEkxOQp8rDYI/B+LrV1/R3jJQLBT2EvENuxt3EJ+c/QXJuMkzFpljZaSVa2LQo83ZCU0Ix5M8hUKqVWNFxBfyc/SqhtlUrQ56BLy5+wV3kAUBji8bwb+CPLo5datzdxZTcFGwL3obfHv3G3VlvaN4QU5pMoeDnDdLy0rDl/hbsergLuapcAJruVzOazoC3pbeOa1e1bsbdxA83fsDthNsAAJlYhg8bf4hhnsNKvKiOzYrFvsf7cODxAS6NtB5PD+85vYfhnsPLNF9VVXtTAASgWgQ6r1Kqlfj59s/YdG8TAM2NjhUdV8DeyF4ndVlzaw223N8CAGhk0QgrOq54qwlxz0SewSfnPwEDw/Qm0zHJe1JFVZe8QyjoIeQdsf/xfiy5ugRKtRKepp74qctPb/VP6OfbP2P9nfUwk5jh9/6/V/tsVa+jUqsw/ex0XIy+CB546OLYBf4N/NHEskm1vTgrreTcZGy7vw27Q3ZzwU9ji8aY0mQK2tZpW+OPryJlKbIQ+CAQ24O3c8khGls2xkdNP4Kvra+Oa6c7jDGcjTqLH2/+iPC0cACa1s/pTaejV91e4PP4YIzhauxV7Hm0B+eiznFZDa2kVhjsORiD3Qe/cQxHdVMQAJ2IOIHTz05zAVBBoNPdqTt8rH2qVUryv5//jfkX5yMtLw1GIiMsabsEnR07V9n+47PjMffCXNyMvwkAGFl/JGb7zK6QiWJ/e/Qbvrv6HQDg27bfor9b/7feJnm3UNBDSC2nUCuw7Noy7AnZAwDwc/bDN22+eetuJXKVHIP/GIzwtHAMcBuAb9p+UxHV1YlV11dha/BWiAVibPbbXCvv5iflJGHr/a3YE7KHa73o6dwTC9ssrBZdjHQpV5mLPSF7sPneZi5lsoepBz5q+hE62HegwDCfUq3EkdAj+OX2L4jPiQegSZrRzakbjj49iqdpT7llm1s3x4h6I9DZsXO1mxi0PFRqFdfa1cSySbUKdF4VkxmDOX/Pwd0EzVxHAQ0C8FGzjyr9fbgScwWf/f0ZknOTYSA0wDdtvkF35+4Vuo8fbvyALfe3QI+nh7XvrUVbu7YVun1S8W7G3URjy8bVorcEBT2E1GLJucmYdX4WbsTdAA88fNTsI4xvOL7CLuJuxt2E/3F/AMCm7ptq5N3w38N+xxcXvwAALO+wHD3r9tRxjSpXYk4itt7fil0Pd0HJlHCTueGHTj/UugH5paFQK3DoySFsuLOBu4h3MnbC9CbT0d25+zuVkr0scpQ5CHoQhC33t3AtYgCgr6ePvq59McxzWI2cNLM2UagUWHVjFYIeBgHQBGrfd/y+UhKwqJkaG+9uxC+3fwEDg4epB1Z1WgUnY6dK2df8i/Nx9OlRSPWk2NZjG7zMvSp8P+TtxWbFYsX1FTgRcQKft/wcI+uP1HWVKOgh5HVUahVeZL1ARFoEItMjEZEegYh0zeP0vHS0rtMafs5+6GjfsdrdLX+U/Agfnf0IMVkxMBAaYFn7Zejo0LHC97P4ymLsCdkDByMHHHz/ICR6kgrfR2W5k3AHY4+PhUKtwMRGE/FRs490XaUqcyv+Fmafn42EnAQYCA2wpN0SvOf4nq6rVSVUahX+Cv8Lv9z+Bc8znwMAbAxsMNV7Kvq69q0WdyRrgpTcFGy+txmPUx6js2Nn9HXpC0ORoa6rRQo5HXkaX/37FTIVmTAVm2Jp+6UV2jqSnJuM+f/Mx78v/gWgyQT6ecvPK/X/gEKlwJQzU3A15irMJeYI6hWkk7FLpHhylRw7HuzAxrsbkaPMAZ/Hx/iG46vF/1cKeqpAliILBkIDndaBlIwxhuTcZO2gJk3zOCojCgq14o3bkAgkaG/fHj2ce6C9fXudTxJ3PPw4vvr3K+SqcuFk7ISfOv8EF1nlpITNkGeg/5H+iM+Ox7iG4/CJzyeVsp+KFpsVixFHRyAxJxGdHTpjdefV79yd/cScRMw+P5vrfz++4XjMaDqjWnfdeRuMMZx5dgZrb61FWFoYAM34jA8bf4ghHkN0nu2KkMoQlR6F2Rdm42HyQ/DAw8TGEzHVe+pbf89vx9/G7AuzEZ8dD4lAgi9bfYl+bv0qqNavlynPRMDxAISkhMDZ2BmBPQMhk8iqZN+kZP88/wf/u/Y/PMt4BkAz0fF83/nVJtslBT2VTKlWovv+7nA2cUY/137o5tSt2rUIvCuyFdl4lvFME9jkt9xEpkciIi0CGYqMEtcT8UVwNHZEXZO6cDJ2gpOxE5yNnSHkC3Hm2RkcjziOqIwobnmpnhQd7Tuih3MPtLVrW6UtHyq1Cmtvr+Uy+LS1a4vlHZbDWFS534Gzz85i5rmZEPAE2N1nt84n7HyTXGUu/I/740HSA7jJ3BDUK+idvTGhUCvww40fEPggEADQyrYVlnVYVqtmP2eM4dKLS1hzaw2Ck4IBAEYiI4xrOA4f1PuA/iaTWi9PlYfl15Zj7+O9AICWNi2xrMOy105GXRLGGHY82IHVN1ZDyZRwNnbGqk6rqrxLY3x2PEb9NQoxWTHwtvTGpu6balRPg9okKiMKy/9bjvNR5wEAFlILzG4+G73r9q5WYyIp6Klkt+NvY8yxMWDQnDKpnhTdnbqjv1t/NLNuVqvuLMtVctxNuAu5Wg4hX6j5EQhfPi6hrCLvKivVSrzIfMF1QSvcLS0uO67E9XjgoY5hHS6gKfjtbOIMGwOb175PjDE8TH6I4xHHcTLiJKIzo7nX9PX00cmhExcAVead5Ax5Bj7/53P8/fxvAMDYhmMxs+nMKrtrP+v8LJyKPAUvcy/s7LWz2nYRYozhs38+w7HwY5CJZfit92/UNQLAsfBjWHBpAXKUObAxsMGqjqvQyLKRrqv11u4k3MEPN37g5jCR6kkx2ms0/Bv4V/rNAEKqm6NPj2LR5UXIUebAQmqB5R2Wl2nagnR5Or66+BXORp0FoEmGsqDNAp3dNApLDcPoY6ORIc9AF4cuWNVpVa1tqa6OcpQ52HJ/C7bc2wK5Wg49nh5GeY3CpMaTqmVXVwp6qkBsViz+CPsDR8KOIDI9kiu3N7TH+27v433X92FnaKfDGpZftiIb/774F6cjT+Pv539rDWotLT6PzwVAeny9EoMjPb5e0SAq/3lqXioi0yMRlRHFTSJYHJlY9jKoMXHmHjsYOVTIHSLGGIKTgnE8/DhORJ5AbFYs95qh0BBdHDWzfLe2bV0hKTwLRKRFYMbZGYhIj4BYIMaiNovQ26V3hW2/NBJzEvH+4feRIc/AnOZz4N/Av0r3X1qb7m3Cjzd/hB5PDxu7byzXPEX/b+/O42O63geOfyb7HhKykUQIat/FVqViKVq0VVqK/pTytRSllqqt1VqqVClatbQUXWzVUktRte/7GkssiS2SyDrJzP39cWRIY0kiyUzieb9e85K5c++dM2Pmzn3uOed5Cqpzd84xYMsALsVewtbKluEhw3m99OsWdaUus24m3GTagWmsDlsNqB7bDs91oHvF7ng6epq5dUKYz/mY83yw5QPORZ/DSmdF36p96V6p+xMvwp64fYIPtnzAlbgr2FrZMrTWUN4o+4bZjw/7r++n5/qe6I16OpbtyIiQEWZvU0GnaRp/h//NpL2TuBZ/DVC1zEbUHpFrQ+lzggQ9eUjTNA7fPMzKcytZd3Ed8Snxpsdq+9SmTXAbQgNCLX6oRaw+lq2Xt7Lx0ka2X9tOsiHZ9Jingycejh6kGFJINaaSYkwx3VKNqWq59uigJCfYW9sT4BagemoeCHACXQPzdMyvUTNy9NZR1l1Yx/pL67mRcMP0mKudK00CmtC8RHNCfEOeKpXotivbGPrPUO6m3MXbyZuvXvyKCp4VcuIlZNlvZ35jzM4xOFg7sLzNcvxd/c3SjkfZHL6Z9ze/j4bGx3U+5o2yb5i7SRbnrv4uI/8dabqS2y64HSNCRuSbYSMphhQWn1zM7COzTcfYtsFt6VO1T65krhIiP0pISWD87vGmiwINijXg8wafP/Q3UtM0fjnzCxP3TERv1FPMpRhTXphChSLm+Z15mPUX1zN462A0NAZUH0D3St3N3aQC60LMBSbsmcCOazsAlQTmw1ofEhoQavHBpgQ9ZpKYmsim8E2sOreK3RG7TcPfnGycaFaiGW1KtbGoqtW3Em+x+fJmNl3axO6I3ekCl2IuxWga2JQmAU2oXLTyE68WGTXj/YDIkJIuMEoLih62PN39/yxztnUm0C2QILcgvJ29LW7YoFEzcvjmYVMAdCvxlukxd3t3QgNCaVaiGbV9amd6WJimacw/Pp9p+6ehoVHNqxpfNvoyW2O0c4qmaXRf3529kXup61uXOU3nWMxn+Oyds3T+szMJqQl0KNuBkXVGmrtJFkvTNOYdm8f0g9MxakbKeZRjauOpFt8jvePqDj7f8zkXYy8CqhL88NrDC8QwPSFymqZprDy3kvG7x5NsSMbH2YfJDSdT1auqaZ2ElATG7hzLnxf+BKCRfyM+rf+pRRajXnRiERP3TgTg8+c/p3XJ1mZuUcGSkJLA7COz+fHEj6QaU7G1suWdiu/QvWJ3i79Yn0aCHgsQERfB7+d/Z9W5VaaMF3B/+FubUm3wc/EzS7s2hm9k46WNHLxx0BSYAQQXCqZJQBNCA0MpW7isxZzY5gcGo4GDNw6y7uI6NlzaYKryDVDYvjChgaG0KNHisZW+k1KTGL1jtOmH6LXSr/FRyEc5OmQuuy7FXuLVVa+iN+oZ32A8r5R6xdxNIjopmo5/dORq3FVq+dRiTtM5BaJgYm7beW0nQ/8Zyp3kO7jbuzPx+YkWWQzwyt0rTNo7ic2XNwMqI9uA6gNoE9zG4i6ACGFpTked5oOtH3Ap9hI2OhsG1hjI2+Xf5nzMeQZtGcT5mPNY66wZUH0AXSt0tejf+y/2fsHCEwuxsbJhVugs6vjWMXeT8j1N01h3cR1f7P3CVM+sYfGGDK01lAC3ADO3Lmsk6LEgmqZx8MZBVoWt4q+Lf6Ub/hbiE0Kb4DY0CWiSqxH1hZgLbArfxMZLG01ZjtJU8KxAaGAoTQKaEOQelGtteJYYjAb2Xd/HXxf/YuOljaZq8KCGCqYFQNW8qpkCoIi4CN7f/D4no05io7NhWO1hFjGu+kFp82bc7d1Z1WaVWedQpBhTeG/De+yN3Esxl2IsabWEwg6Fzdae/CYiLoJBWwZx7PYxdOjoU7UPPSr3sIhgIjE1ke+Pfs/8Y/PRG/VY66x587k3+V/V/+Fq52ru5gmRb8Tp4xi9YzTrL60H1JD7o7eOkpiaiJejF5NfmEx17+pmbuWTGTUjw/4ZxtqLa3G2dWZhi4UWky45Pzp75yyf7f6Mfdf3Aepi/LDaw3Kl5l9ekKDHQiWkJKjhb2Gr2BOxJ93wt+YlmtMmuA3Vvao/9YmupmmcvnOajZdUj05a7QpQGc2qe1cnNEAFOr4uvk/1XOLxUo2p7Incw/qL69kYvpGY5BjTY0Udi9I0sCkVi1Tki31fEJUURWH7wnzZ6Etq+tQ0Y6sfLsWYwptr3uT0ndO0DGrJxIYTzdaWtOKpTjZOLGq5SCrFZ0OyIZkJeybw65lfAXih+At89vxnZst+pmkaGy5t4It9XxARHwGoSbTDaw+nVKFSZmmTEPmdpmksPb2USXsnmRIC1fGtw4TnJ+Sr5B96g55eG3uxN3IvXo5eLGq5SM5fsihWH8usQ7NYcmoJBs2Ag7UD71Z6l24Vu2FvbW/u5mWbBD35wLW4a6bsbw/Wg/F39eeVUir7W1aGvxk1I0duHlGBTvjGdCmWbaxsCPENITQglEb+jcw6P+RZlmJMYXfEbv66+BebwjdxV5++jtBzHs/xVeOvzDLsMbOO3TpGpz87YdSMzGwyk4bFG+Z5G34+/TOf7PoEHTq+avwVjQMa53kbCpIVZ1fw6a5P0Rv1+Lv6M7XR1Dy/inruzjkm7JnA7sjdAPg6+zKk1pB8MYlWiPzg2K1jTDswjTq+dXinwjv5MgV0rD6Wrmu7ci76HCXdS/LDSz9Y5DwkS2PUjKwOW83U/VNNQ++bBjZlcM3BFn2+kVkS9OQjDw5/W3dhHQmpCabHnjT8LcWYwv7r+9l4aSN/h//NzcSbpsccrB2oX6w+TQKa8IL/C1K7wsKkGFLYGbGTvy7+xbYr26hfrD4f1/k4X0wcnLx3Mj+c+AFfZ19WtlmZp23eG7mXnut7kqql8n7193m30rt59twF2YnbJxi4eSDX4q/hYO3AqLqjeLnUy7n+vP+98mhvbc//Vfw/3qn4Do42jrn+/EKI/CUyPpJOf3biRsINqntV59tm3+brXorcduL2CT7b/RmHbx4GoIRbCYaHDKeeXz0ztyznSNCTT5mGv51bZbriCeBs66yGv5VqQ3nP8uyK2MXGSxvZcmVLuuFSLrYuvOD/AqEBodTzq5cvTqBF/pOQksCrq1/latxVOpfrzNDaQ/PkeS/fvcxbf7xFdHI0LwW9xMTnJ0ovQA6KTopm2LZhbL+2HYA3n3uTITWH5EoiDaNmZOW5lXx14CvTlccmAU0YUmuIxWeTE0KY15k7Z+i6titxKXE0C2zG5BcmW8R8REsSnRTN1we/5pczv6Ch4WTjRK8qvehcrrNFJEfKSRL0FADX4q6xOmw1q86t4krcFdNya501Bs1gul/YvjAvBrxIk4AmhPiGYGdtZ47mimfM9qvb6bWxFzp0LGq5iMpFK+fq88WnxNP5z86ciz5Hec/yLGyxMN/UmMlPDEYDsw7PYs6ROQBULVqVKY2m4OXklWPPcfTmUT7b/RnHbh8DIMg9iGG1hxWoK49CiNy1J2IPvTb2IsWYkqcX3yydwWjgt7O/Mf3gdNNF8ZZBLRlUYxDezt5mbl3ukKCnANE0jQM3DrDqnMr+lpCagLeTtynjWjWvapmuASNEThqxbQS/n/+d4ELB/Nz651y7emTUjAzYPIDNlzdTxLEIS1stLbAHb0ux5fIWRmwbwd2Uu3g6eDL5hcnU8qn1VPu8lXiLrw58xcpzKwHVg927Sm/eKveWpBoXQmTZ2gtr+fCfDwEYXHMwXSt0NXOLzOvQjUN8tvszTkadBKB04dKMqD3CIhMj5SQJegqohJQEbibexN/VX7pyhdndSbpDm5VtuJN8h37V+tGzcs9ceZ7pB6bz3dHvsLOyY36L+bneqySU8NhwBm4ZyJk7Z7DWWTOwxkC6lO+S5SGFKcYUlpxcwqzDs4hLiQPglVKvMLDGQEmqIoR4KguOLWDK/ikATG44mRZBLczcorx3K/EW0/ZPY1XYKgBcbV3pU60PHcp2eCYuikvQI4TIE2vOr2H4tuHYWtny6yu/UtK9ZI7u/8EreZZSFPVZkpiayNidY/nj/B8ANC/RnHH1xmV6vuCuiF1M2D3BlDa/vGd5htcenq46vBBCZJemaUzcO5HFJxdja2XLnKZznrpXOifalGpMJcWYcv9mSHno/cyu96htkw3JbLm8xXRBqV1wO96v/n6+Skf+tCToEULkCU3T6L2pN9uvbqe6V3Xmt5ifY72Qx28fp+variQbkulWoRsf1PwgR/YrskbTNJacWsLkvZNJ1VIp6V6SaY2nPbaY8bW4a3yx7ws2XNoAqLmH71d/n3al20kvtRAiRxmMBob8M4QNlzbgauvKwpcW5ljtthRjCtFJ0UQlRZlud5LupLuftuxO0h0SDYmmekh5qbxneT4K+eiZHAkhQY8QIs9cjbtKu1XtSExNZFTdUbQv0/6p93kz4SYd/+jIjYQbNCjWgBkvzsiXdSUKkkM3DjFoyyBuJt7E2daZT+t/SmhgaLp1klKTmH9sPt8f+55kQzLWOms6PteR3lV6Sz0NIUSuSTYk03N9Tw7cOIC3kzeLWi7Cx9knw3oGo4Ho5OjHBi8P3o/Vx+ZI++ys7LC1tsXW6oHbA/dtrGzu/21tk369/6z73/vFXIrRJKDJM/sbKUGPECJP/XjiRybtnYSLrQur2q56qmxfyYZk/m/d/3Hk1hGC3INY3HIxrnauOdhakV23Em8xeOtg9l/fD8D/Vfw/+lXrh7XOmr/D/2byvsmmwsi1fGoxrPYwyhQuY84mCyGeETHJMXRZ24XzMecp6V6SWj61TEFM2r/RydFoZO1010pnRSH7Qng4eJhuhR0KZ7hf2KEwTjZOGYISa521lFfIRRL0CCHylMFooPOfnTl2+xhNApowrfG0bO1H0zRGbh/J6rDVuNq5sqTVEgLdAnO2seKppBhTmLZ/Gj+c+AGA2j61sdZZszNiJwDeTt4MrjWY5oHN5YdeCJGnIuIi6PRnp3TF2v9Lhw53e/eHBi8PW+Zm5/bM9qLkBxL0CCHy3Omo03Rc05FULZWpjaZmGPqUGQuPL+SLfV9grbPmm9BvpHaLBVt3cR2jto8iMTURUMM3ulXsRveK3aUwshDCbMJjw/nt7G/YWNng4eCBp4OnKZAp7FCYQvaFnomsZs8KCXqEEGaRll66qGNRVrZdiZtd5r+n265so+/ffTFqRobVHkancp1ysaUiJ4RFhzF251iKOhZlQI0B+Lv6m7tJQgghniES9AghzCLZkMzrq1/nYuxFXi/zOqPrjs7UdudjztPpj07EpcTxaulXGVN3jAyNEkIIIcRjZSc2kByiQoinZm9tz6i6owD49cyv7I3c+8RtYpJj6P93f+JS4qjuVZ2RISMl4BFCCCFErpCgRwiRI2r51OK10q8BMG7nOJINyY9cN9WYypCtQ7gUewlfZ1++bPQltta2edVUIYQQQjxjJOgRQuSYQTUHUcSxCBdjLzLn8JxHrjdl3xR2RuzE0caR6S9Of6aqSAshhBAi70nQI4TIMW52bnwU8hEA84/N53TU6QzrrDi7gkUnFwEwvsF4nvN4Lk/bKIQQQohnjwQ9QogcFRoYyov+L5KqpTJ251gMRoPpsYM3DjJu1zgA/lflfzQNbGquZgohhBDiGSJBjxAix40IGYGLrQtHbx1lyaklgCoeN2DzAFKNqTQNbMp7Vd4zcyuFEEII8ayQoEcIkeO8nb0ZWGMgANMPTicsOoz+m/sTlRRF2cJl+bT+p1jp5PAjhBBCiLwhZx1CiFzxepnXqe5VncTURDqu6cipqFN4OHgw/cXpONk6mbt5QgghhHiGSNAjhMgVVjorRtcbja2VLUmGJGysbJjaaCp+Ln7mbpoQQgghnjES9Aghck1J95IMqjEIOys7xtQdQ3Xv6uZukhBCCCGeQdkKembOnEmJEiVwcHAgJCSEPXv2PHb96Oho+vTpg6+vL/b29pQpU4Y///wzWw0WQuQvnct3Zl/nfbQJbmPupgghhBDiGWWT1Q2WLVvGoEGDmD17NiEhIUybNo3mzZtz+vRpvLy8Mqyv1+tp2rQpXl5e/PrrrxQrVoxLly5RqFChnGi/ECIf0Ol05m6CEEIIIZ5hOk3TtKxsEBISQq1atZgxYwYARqMRf39/+vXrx7BhwzKsP3v2bCZPnsypU6ewtbXNViNjY2Nxd3cnJiYGNze3bO1DCCGEEEIIkf9lJzbI0vA2vV7P/v37CQ0Nvb8DKytCQ0PZuXPnQ7dZvXo1devWpU+fPnh7e1OxYkU+++wzDAbDQ9cHSE5OJjY2Nt1NCCGEEEIIIbIjS0HPrVu3MBgMeHt7p1vu7e1NZGTkQ7c5f/48v/76KwaDgT///JOPP/6YKVOm8Omnnz7yeT7//HPc3d1NN39//6w0UwghhBBCCCFMcj17m9FoxMvLi2+//ZYaNWrQoUMHPvroI2bPnv3IbYYPH05MTIzpdvny5dxuphBCCCGEEKKAylIigyJFimBtbc3169fTLb9+/To+Pj4P3cbX1xdbW1usra1Ny8qVK0dkZCR6vR47O7sM29jb22Nvb5+VpgkhhBBCCCHEQ2Wpp8fOzo4aNWqwadMm0zKj0cimTZuoW7fuQ7epX78+586dw2g0mpadOXMGX1/fhwY8QgghhBBCCJGTsjy8bdCgQXz33XcsXLiQkydP0rt3b+Lj43nnnXcA6NKlC8OHDzet37t3b6Kionj//fc5c+YMf/zxB5999hl9+vTJuVchhBBCCCGEEI+Q5To9HTp04ObNm4waNYrIyEiqVq3KunXrTMkNwsPDsbK6H0v5+/vz119/MXDgQCpXrkyxYsV4//33GTp0aM69CiGEEEIIIYR4hCzX6TEHqdMjhBBCCCGEgDyo0yOEEEIIIYQQ+Y0EPUIIIYQQQogCTYIeIYQQQgghRIEmQY8QQgghhBCiQJOgRwghhBBCCFGgSdAjhBBCCCGEKNAk6BFCCCGEEEIUaFkuTmqpjEYjer3e3M0QQmSCnZ1duiLGQgghhBC5qUAEPXq9ngsXLmA0Gs3dFCFEJlhZWREUFISdnZ25myKEEEKIZ0C+D3o0TSMiIgJra2v8/f3l6rEQFs5oNHLt2jUiIiIICAhAp9OZu0lCCCGEKODyfdCTmppKQkICfn5+ODk5mbs5QohMKFq0KNeuXSM1NRVbW1tzN0cIIYQQBVy+7xYxGAwAMkxGiHwk7fua9v0VQgghhMhN+T7oSSNDZITIP+T7KoQQQoi8VGCCHiGEEEIIIYR4GAl6hBBCCCGEEAWaBD3PIJ1Ox8qVK83dDGHh9Ho9wcHB7NixI9PbrFu3jqpVq0r6eCGEEEJYFAl6zKRbt27odDp0Oh22trYEBQXx4YcfkpSUZO6m5Zi01/fgrUGDBmZv038DvgULFpjaZ2Vlha+vLx06dCA8PNw8jbQQs2fPJigoiHr16pmWlShRIsP/6YQJE0yPt2jRAltbWxYvXmyOJgshhBBCPJQEPWbUokULIiIiOH/+PFOnTmXOnDmMHj3a3M3KUfPnzyciIsJ0W716dbb3lZKSkoMtS8/NzY2IiAiuXr3Kb7/9xunTp2nfvn2uPV+a3HxN2WEwGDAajWiaxowZM+jevXuGdcaNG5fu/7Rfv37pHu/WrRvTp0/PqyYLIYQQQjxRgQt6NE0jQZ9qlpumaVlqq729PT4+Pvj7+9O2bVtCQ0PZsGEDALdv3+bNN9+kWLFiODk5UalSJZYsWZJu+0aNGtG/f38+/PBDPDw88PHxYcyYMenWOXv2LA0bNsTBwYHy5cub9v+go0eP8uKLL+Lo6Iinpyc9e/YkLi7O9Hi3bt1o27Ytn332Gd7e3hQqVIhx48aRmprKkCFD8PDwoHjx4syfPz/DvgsVKoSPj4/p5uHhAagClePGjaN48eLY29tTtWpV1q1bZ9ru4sWL6HQ6li1bxgsvvICDg4Op92Du3LmUK1cOBwcHnnvuOb755hvTdnq9nr59++Lr64uDgwOBgYF8/vnngOqlAGjXrh06nc50H1QPkI+PD76+vtSrV4/u3buzZ88eYmNjTeusWrWK6tWr4+DgQMmSJRk7diypqammx0+dOkWDBg1M7/XGjRvT9SzlxmvSNI0xY8YQEBCAvb09fn5+9O/f37TtnTt36NKlC4ULF8bJyYmXXnqJs2fPmh5fsGABhQoVYvXq1ZQvXx57e3vCw8PZv38/YWFhtGrVKsP/qaura7r/U2dn53SPv/zyy+zbt4+wsLAM2wohhBBCmEO+L076X4kpBsqP+sssz31iXHOc7LL3lh47dowdO3YQGBgIQFJSEjVq1GDo0KG4ubnxxx9/8Pbbb1OqVClq165t2m7hwoUMGjSI3bt3s3PnTrp160b9+vVp2rQpRqORV199FW9vb3bv3k1MTAwDBgxI97zx8fE0b96cunXrsnfvXm7cuMG7775L3759WbBggWm9v//+m+LFi/PPP/+wfft2unfvzo4dO2jYsCG7d+9m2bJlvPfeezRt2pTixYs/8fV+9dVXTJkyhTlz5lCtWjXmzZvHK6+8wvHjxyldurRpvWHDhjFlyhSqVatmChJGjRrFjBkzqFatGgcPHqRHjx44OzvTtWtXpk+fzurVq/n5558JCAjg8uXLXL58GYC9e/fi5eXF/PnzadGiBdbW1g9t240bN1ixYgXW1tamdbZt20aXLl2YPn06zz//PGFhYfTs2ROA0aNHYzAYaNu2LQEBAezevZu7d+/ywQcfPHT/OfmafvvtN6ZOncrSpUupUKECkZGRHD582PRc3bp14+zZs6xevRo3NzeGDh1Ky5YtOXHihKkoaEJCAhMnTmTu3Ll4enri5eXFihUrKFOmDK6urhnaP2HCBD755BMCAgJ46623GDhwIDY29z/3AQEBeHt7s23bNkqVKvXEz4IQQgghRG4rcEFPfrJmzRpcXFxITU0lOTkZKysrZsyYAUCxYsUYPHiwad1+/frx119/8fPPP6cLeipXrmwaEle6dGlmzJjBpk2baNq0KRs3buTUqVP89ddf+Pn5AfDZZ5/x0ksvmbb/6aefSEpK4ocffjBdsZ8xYwYvv/wyEydOxNvbGwAPDw+mT5+OlZUVZcuWZdKkSSQkJDBixAgAhg8fzoQJE/j333/p2LGjaf9vvvlmuuBi0aJFtG3bli+++IKhQ4ea1p04cSKbN29m2rRpzJw507T+gAEDePXVV033R48ezZQpU0zLgoKCOHHiBHPmzKFr166Eh4dTunRpGjRogE6nMwWRAEWLFgXu9z49KCYmBhcXF9VTmJAAQP/+/U3vydixYxk2bBhdu3YFoGTJknzyySd8+OGHjB49mg0bNhAWFsaWLVtM+x4/fjxNmzbN8P+ek68pPDwcHx8fQkNDsbW1JSAgwPT5SAt2tm/fbpqXs3jxYvz9/Vm5cqVp+F5KSgrffPMNVapUMe330qVLps/Mg/r370/16tXx8PBgx44dDB8+nIiICL788st06/n5+XHp0qUM2wshhBBCmEOBC3ocba05Ma652Z47Kxo3bsysWbOIj49n6tSp2NjY8NprrwFqbsVnn33Gzz//zNWrV9Hr9SQnJ+Pk5JRuH5UrV05339fXlxs3bgBw8uRJ/P3905281q1bN936J0+epEqVKumGKNWvXx+j0cjp06dNQU+FChWwsro/GtLb25uKFSua7ltbW+Pp6Wl67jRTp04lNDQ0XftiY2O5du0a9evXT7du/fr10/VSANSsWdP0d3x8PGFhYXTv3p0ePXqYlqempuLu7g6ono2mTZtStmxZWrRoQevWrWnWrBlP4urqyoEDB0hJSWHt2rUsXryY8ePHmx4/fPgw27dvT7fMYDCQlJREQkICp0+fxt/fP10w9WBwmluvqX379kybNo2SJUvSokULWrZsycsvv4yNjQ0nT57ExsaGkJAQ0349PT0pW7YsJ0+eNC2zs7PL8DlKTEzEwcEhQ9sHDRpk+rty5crY2dnx3nvv8fnnn2Nvb296zNHR0RQ8CiGEEEKYW4ELenQ6XbaHmOU1Z2dngoODAZg3bx5VqlTh+++/p3v37kyePJmvvvqKadOmUalSJZydnRkwYAB6vT7dPtKGKKXR6XS5ki74Yc+Tmef28fExvcY0D86TeZIHg7G0eUbfffdduhN5wNSbVL16dS5cuMDatWvZuHEjb7zxBqGhofz666+PfR4rKytTO8uVK0dYWBi9e/fmxx9/ND332LFj0/XQpHlYcJBXr8nf35/Tp0+zceNGNmzYwP/+9z8mT57M1q1bM90eR0dHdDpdumVFihTh6NGjT9w2JCSE1NRULl68SNmyZU3Lo6KiTD1rQgghhBDmVuASGeRXVlZWjBgxgpEjR5KYmMj27dtp06YNnTt3pkqVKpQsWZIzZ85kaZ/lypXj8uXLREREmJbt2rUrwzqHDx8mPj7etGz79u2mYWy5wc3NDT8/P7Zv355u+fbt2ylfvvwjt/P29sbPz4/z588THByc7hYUFJRu/x06dOC7775j2bJl/Pbbb0RFRQEqeDMYDE9s47Bhw1i2bBkHDhwAVOBx+vTpDM8bHBxseq8uX77M9evXTfvYu3fvE58nJ16To6MjL7/8MtOnT2fLli3s3LmTo0ePUq5cOVJTU9m9e7dpP7dv3+b06dOPfZ8BqlWrxqlTp56YnOPQoUNYWVnh5eVlWpaUlERYWBjVqlV74usXQgghhMgL+aNL5BnRvn17hgwZwsyZMyldujS//vorO3bsoHDhwnz55Zdcv379iSerDwoNDaVMmTJ07dqVyZMnExsby0cffZRunU6dOjF69Gi6du3KmDFjuHnzJv369ePtt982DW3LDUOGDGH06NGUKlWKqlWrMn/+fA4dOvTE+i5jx46lf//+uLu706JFC5KTk9m3bx937txh0KBBfPnll/j6+lKtWjWsrKz45Zdf8PHxoVChQoDK4LZp0ybq16+Pvb09hQsXfujz+Pv7065dO0aNGsWaNWsYNWoUrVu3JiAggNdffx0rKysOHz7MsWPH+PTTT2natCmlSpWia9euTJo0ibt37zJy5EiADL0oOfmaFixYgMFgICQkBCcnJxYtWoSjoyOBgYF4enrSpk0bevTowZw5c3B1dWXYsGEUK1aMNm3aPLZNjRs3Ji4ujuPHj5uGMe7cuZPdu3fTuHFjXF1d2blzJwMHDqRz587p3sddu3Zhb2+fYSilEEIIIYS5SE+PBbGxsaFv375MmjSJDz74gOrVq9O8eXMaNWqEj48Pbdu2zdL+rKysWLFiBYmJidSuXZt333033ZwUACcnJ/766y+ioqKoVasWr7/+Ok2aNDElVMgt/fv3Z9CgQXzwwQdUqlSJdevWsXr16nSZ2x7m3XffZe7cucyfP59KlSrxwgsvsGDBAlOviKurK5MmTaJmzZrUqlWLixcv8ueff5rmI02ZMoUNGzbg7+//xJ6IgQMH8scff7Bnzx6aN2/OmjVrWL9+PbVq1aJOnTpMnTrVlFTA2tqalStXEhcXR61atXj33XdNAeaThr89zWsqVKgQ3333HfXr16dy5cps3LiR33//HU9PT0DVSapRowatW7embt26aJrGn3/+mWFo4n95enrSrl27dEGovb09S5cu5YUXXqBChQqMHz+egQMH8u2336bbdsmSJXTq1CnD/DMhhBBCCHPRaVktLmMGsbGxuLu7ExMTg5ubW7rHkpKSuHDhAkFBQVmeWyFEbtq+fTsNGjTg3Llz+TJ185EjR2jatClhYWG4uLhkaptbt25RtmxZ9u3bl2543n/J91YIIYQQ2fW42OBRZHibEDlkxYoVuLi4ULp0ac6dO8f7779P/fr182XAAyo728SJE7lw4QKVKlXK1DYXL17km2++eWzAI4QQQgiR1yToESKH3L17l6FDhxIeHk6RIkUIDQ1lypQp5m7WU+nWrVuW1q9Zs2a6lNxCCCGEEJZAgh4hckiXLl3o0qWLuZshhMjPNA2uH4dTa6BQIFR909wtEkKIAkGCHiGEyClGI2gGsH58ogghMrhxCo4vh2PL4fbZ+8vvXIRGw+AJWSCFEEI8ngQ9QgiRE5LvwqLX1FX6RsMgpJcEP+Lxbp1Tgc7xFXDjxP3l1vZQvCZc2g5bJ0BqEoSOkcBHCCGeggQ9QgjxtFL18HMXuHyvEOz6kXBoCbSeCgEh5m2bsCxRF+4HOpFH7y+3soXgJlDhVSj7Eji4wa5ZsG4YbJ+mAp8WEyTwEUKIbJKgRwghnobRCKv7QtjfYOsEDQbCrm/gxnGY1wyqd1VX6Z08zN1SYS7R4SrIOb4Crh28v9zKBko2UoHOc63AsVD67er0Bms7+GMQ7J4NqcnQ6kuwkhJ7QgiRVRL0CCHE09g0Bo4sUyewb/wIpUOhZnfYOAoOLoIDC9Wk9GafQpU35Ur9syLmKpxYpXp1ruy9v1xnBUENVaBT7uUnB8O1uoONgwqs989XgU+bGWBlnbvtF0KIAkaCHiGeJUYDaEaZa5JTds2G7V+pv1/5WgU8AM6e0GYmVO0MawbCzZOwsjccXAytpoDXc+Zrs8g9d6/fD3TCdz7wgA5KNIAKbaFcG3ApmrX9VusENvawvCcc/gkMydBujnyP8xN9AmybouZptZ4KXuXM3SIhnjnSR/4M0ev1BAcHs2PHjhzd75gxY6hatWqO7tOchg0bRr9+/czdjJynj1eT7G+cUPMDxNM5tlzNtwBoMgqqvpVxncC60GsbhI5VQ98u/Quz68PGseokSOR/8bdg7/ewoDVMKQtrh9wPePzrwEuT4INT0G0N1Ho36wFPmkqvQ/sFau7Psd/gl26q10dYvnObYFZd2PaF+mz89q6aByiEyFMS9JhJt27daNu27WPXOXjwIB06dMDX1xd7e3sCAwNp3bo1v//+O5qmAXDx4kV0Op3pZmdnR3BwMJ9++qlpnTSzZ88mKCiIevXqmZaNHz+eevXq4eTkRKFChXL6ZT6V8PBwWrVqhZOTE15eXgwZMoTU1NTHbhMVFUWnTp1wc3OjUKFCdO/enbi4ONPj/32/0m67du0yrTN48GAWLlzI+fPnc+215bmkWLh9TqVT1owQe83cLcrfLmyDFe8BGtTqAQ0GPXpda1toMAD67IYyL4ExFf79Er4JgTN/5VWLRU5KiIL9C+GHNvBFGTXn5uI2QINiNaH5ZzDwBHT/C0LeA1efnHne8q9Ax8Uqu9upNbCsM6TIBQyLFXdTBTiLXlWpx92KgaMHXD8G/0wyd+uEeOZI0GOhVq1aRZ06dYiLi2PhwoWcPHmSdevW0a5dO0aOHElMTEy69Tdu3EhERARnz55l7NixjB8/nnnz5pke1zSNGTNm0L1793Tb6fV62rdvT+/evXPttej1Wb+iZTAYaNWqFXq9nh07drBw4UIWLFjAqFGjHrtdp06dOH78OBs2bGDNmjX8888/9OzZM8N6ae9X2q1GjRqmx4oUKULz5s2ZNWtWltttkRKiIOq8CnZsndWypBhIjnv8duLhIo/B0rfAoIdyr8BLEzM3T6dQALy1FDr+BG7F1eT2n95QJ64xV3O/3eLpJEar4YmLXoMvSsPv/eH8FnUhwbeq6s17/wj02AR1+4B7sdxpR5nm6nNk4whn18OSDqoXV1gOTYMDP8CMmnD0FzWPK6S3uvDReqpaZ9uXcHW/edspxDNGp/23O8ACxcbG4u7uTkxMDG5ubukeS0pK4sKFCwQFBeHg4KAONilmGjZi65TpScrdunUjOjqalStXZngsPj6ewMBAGjZsyPLlyx+6vaZp6HQ6Ll68SFBQEAcPHkw3xCw0NJSyZcsyc+ZMAPbt20dISAjR0dG4urpm2N+CBQsYMGAA0dHRmWr/g8aMGcPKlSs5dOhQutdWq1YtZs6cib29PRcuXMjSPteuXUvr1q25du0a3t7egOqpGjp0KDdv3sTOzi7DNidPnqR8+fLs3buXmjVrArBu3TpatmzJlStX8PPze+T79V8//PADH330EZcvX85Suy1O3E2IvaL+digMhQMg5gok3Faf1yJlzDKxPsP3Nr+IvgzfN4W7ERBYHzovB9tstD85DrZOhJ0z1UmznQs0Gn6vto9MtbQYmqYCmz3fwrmNKtBN411JzdGp0A48S+V92y7+Cz91AH2c+iy+tQzsMx7bRR67eQbWDFBzdwB8KsHL06FY9fvr/Pp/aohikbLw3j/ZO4YI8Yx7XGzwKAXv1zUlAT7zM89zj7gGds5PvZv169dz+/ZtPvzww0euo3vMieq+ffvYv38/Xbp0MS3btm0bZcqUeWjAkxs2bdqEm5sbGzZsMC3r1asXixYteux2aUPRdu7cSaVKlUwBD0Dz5s3p3bs3x48fp1q1ahm23blzJ4UKFTIFPKCCPysrK3bv3k27du1My1955RWSkpIoU6YMH374Ia+88kq6fdWuXZsrV65w8eJFSpQokaXXbhE0De5GQlykuu9cRPUu6HTg6guJd9R3JfGOpFLOrIQodZX/bgQULaeGGWX3ZMXeBZp9AlU6wppBcHkXrP8IDi+F1l+Cf+2cbbvIGqMBTv4O/06FiEP3lxd9TmVdq9AOipYxW/MAlRjh7RXqM3lpO/zYDjr9mjHttcgbKUnq8/Lvlyo4tnWCxiNUD89/L2S0/EINkb11GjaPV8cCIUSuK3hBTwFw5swZAMqWLWtatnfvXho3bmy6v3TpUlq3bm26X69ePaysrNDr9aSkpNCzZ890Qc+lS5fw88u7YNDZ2Zm5c+em65EZN24cgwcPztT2kZGR6QIewHQ/MjLykdt4eXmlW2ZjY4OHh4dpGxcXF6ZMmUL9+vWxsrLit99+o23btqxcuTJd4JP2Xl26dCn/BT2adq8355a67+oDLj73e3SsbcHFW528x14Dh0JS9+NJUhJhSUd1kuJWDDr/Bo6Fn36/3hXgnbVwaBFsGAXXj6qepBrdoMloCUjzWmqySj++/Ss1Bw7UMLLqb0ONd8C7vHnb91/+taHLKhXwXNkLP7wCb6/MP58bQ6pKw33oJyhSGmr3hOI1n7ydpbmwTfXupH1mSjdTgU3hwIev7+QBL38FS9+EHV/Dc62liLEQeaDgBT22TqrHxVzPnUsqV65sGj5WunTpDBP6ly1bRrly5UhJSeHYsWP069ePwoULM2HCBAASExPzdBhRpUqVMgxB8/LyyhCU5LUiRYowaND9See1atXi2rVrTJ48OV3Q4+joCEBCQj7LsKUZ4c4lSIpW992Lg/NDskU5e6msU8YUiL+RcxOtCyJDKvzaHS7vBgd3FfDk5HwNKyuo3gXKtlKBz6FFsH8BnEyr7dNRavvktuQ49Z7vnAl37/1+OLirk/CQXqqn1FIVqw7d/lBJFSIOqyxyXVZlP0tcXjm3Ef76CG6eUvevHVABp191lfyhQjuVptuSJUTB+o/VdxbUxaQWE1Tbn/Sdfa6lqtt1eIlKZ9/rX7DLvXMIIURBTGSg06khZua45dCJSenSpQE4ffq0aZm9vT3BwcEEBwc/dBt/f3+Cg4MpV64c7du3Z8CAAUyZMoWkJJXZp0iRIty5cydH2pcZzs4Zh/n16tULFxeXx97S+Pj4cP369XTbp9338Xn4CbqPjw83btxItyw1NZWoqKhHbgMQEhLCuXPn0i2LiooCoGhRCz9xeJDRALfP3wt4dFC4xMMDHlAn2m73ev7iroMhJY8amc9oGvw5GE7/oTJmvbk09+prOHtC25nQ7U81jCrhFqzspU5ib55+8vYi6+Jvw+bPYGoFNbzw7jU1/LPZpzDwOLw40rIDnjQ+FeGdP1WP7o3jsKAlxEaYu1UPd/MMLG6vhuXdPKWymYWOhcodwdpOBT8r3lP/J39/apmZJjUNDi9TiQrSAp4a70CfPVDx1cyfC7SYAK5+EBUGm8blXnuFEEBBDHoKgGbNmuHh4cHEiROzvQ9ra2tSU1NNmdOqVavGqVOnMqSxzkvjxo3j0KFDj72lqVu3LkePHk0XxGzYsAE3NzfKl3/4EJO6desSHR3N/v33M+L8/fffGI1GQkIePXTg0KFD+Pr6plt27NgxbG1tqVChQjZfbR4zpKihFfq7KlOQR8knD79yLKx6JzWjGuomMvpnshp+gw5emwuB9Z64yVMrUR/e2wahY9TQqkv/wqz66qRIavvkjOjLsHaoOrHeOlFdKPAopSacv38Y6vXLf0kBipZVgY9bcbh1Bua/pF6npUiIgj8/hG/qqKxzVjZQpw/0P6BSur86R6X5fnGkCgTib6rv37RKqibRpR0q2DC3qPNqOOGKniohTNFy8H/r4eVpWZ9P5VhIFTUG2D1LJacQQuSagje8LR+JiYlJd6IP4Onpib+/P3PnzqVDhw60atWK/v37U7p0aeLi4li3bh2ggpoH3b59m8jISFJTUzl69ChfffUVjRs3NmW0aNy4MXFxcRw/fpyKFSuatgsPDycqKorw8HAMBoOpPcHBwel6XnJCVoa3NWvWjPLly/P2228zadIkIiMjGTlyJH369MHeXg152LNnD126dGHTpk0UK1aMcuXK0aJFC3r06MHs2bNJSUmhb9++dOzY0TRHZ+HChdjZ2ZkSISxfvpx58+Yxd+7cdM+/bds2nn/+edMwN4uWmgy3w1SVdisbFfBkJqGGTqfmp9w+q368nYuCbT54vXnlwA9qkjFAy8mqRkpesbGDBgPVpPm1H8KZdaqa+9Ff1VyBMs3yri0Fyc3T8O80OPqzqpcE4FtFvdflXgEr68dubvE8S6nAZ+HLcOcCzG8JXVeDR5D52mRIUcVbt3x+f9ht2ZbQ9BMo8p+RCy5FoeEQqD9A1SHa851K0nB8hbp5V4KQnlCpfd4fq1L1sPNr2DpJFXe2tocXPoR6/dX3NbtKh6rhrQd+gJX/g947VKITIUTO0/KBmJgYDdBiYmIyPJaYmKidOHFCS0xMNEPLsq9r164akOHWvXt30zp79+7VXn/9dc3Ly0uzsbHRPD09tebNm2tLly7VjEajpmmaduHChXTbW1tba8WLF9d69Oih3bhxI91zvvHGG9qwYcMy1Y7Nmzeb1gkMDNRGjx79yNcyevRorUqVKun22aZNm2y/N2kuXryovfTSS5qjo6NWpEgR7YMPPtBSUlJMj2/evFkDtAsXLpiW3b59W3vzzTc1FxcXzc3NTXvnnXe0u3fvmh5fsGCBVq5cOc3JyUlzc3PTateurf3yyy8Znrts2bLakiVLnvo15Dp9gqZFHNW0qwfUv/psfA9uh6ntb53L+fY9gsV/b0+t1bQxhTVttJumbRxr3rYYjZp24ndNm1JetWe0m6Yt7axp0VfM26785PJeTVvy1v33b7Sbpi1orWnnNqn3t6CJvqJpX1VTr/OL5zTt5pm8b4PRqGmn12na9Br33/OZdTUtbHPW9hNxRNNW9dW0T7zv72dCoKat/1jToi7mRsszurRL02bWeeCz83LOHi8TYzTtywpq378PzLn9mtPxlZq2d17B/H4Ji/C42OBRCl6dHvFIR44coWnTpoSFhWW6FychIQFPT0/Wrl1Lo0aNcreBFmLt2rV88MEHHDlyBBsbC+4M1cerHh7NADYOanhOdq44pibBjVOApvbhkLl890/Dor+3l/eqK+WpiVC1E7SZaRmJBJLjYOsE2PnN/do+jUdA7fekts/DaBqE/a3SCF/cdn/5c61Vz05+zBKWFXcjVXKDm6dU4pKuq3NvPtp/XT8Bf42A85vVfaciatha9S7Z701LiIKDP8LeuaqwL6ihvGVeUr0/QS/k/Pc0MRo2jYV98wENnDyh+WdQuUPOP9f5rSr7HqgMfKUaP3Z1i7ZvHqwZqP5u9qkaLiryv5gr8Pv7ahhwbhVfzoLs1OmRoOcZs2DBAmrUqEGlSpUytf4ff/zBN998wx9//JHLLbMcv/76K/7+/o+dB2R2SbFq+IpmVPNyPEo93YlvzBU1ht7GQU2iz+WTfIv93t46C983g8QoCG4Kby5RKb4tSeQxdUJxZY+6711JVXn3r2XedlkKowFOrr5XY+ewWmZlo05U67+v5r48K+JvwQ9tVSp0J091Mu1bOXefb/Nnah6cZlSJCer0huc/UNnwcoLRAGf+gj1zVOHYNEWfg9o9VEKEpx0epmlwYqWa9xV3L6FO1c6qnk5upgP/YzDs/U7Ny/rfjpx7z/LSiVXwc1fUoBFUYNp5ef4O4oS6kLHoNZXsJThUZTE1Mwl6LOnkSYjckhB170qnpiZbFw56+rkIhlS4cUL1ILj753rGKov83t6NVDVyosPBrxp0XWO5Y+uNRnXVe8Oo+9n6mn0K9fqau2Xmk5qsirtu/0plwwJ1QaBGN6jbR6VvfxalFdW9duBeyvUVULxGzj5HajLsngP/fAHJMWpZuVeg6bjcnU908zTs+RYOLYGUeLXM3k310NbuoeY4ZVV0uAo+zv6l7nsGQ+tpEPR8jjX7kZLjYHZ9uHMRqr0NbWbk/nPmpAv/qM+aQa++d6l6OPyTytDXc8uj6xYJy3Zxu6oplRQDRcrC28st4ngqQY+lnDwJkVvibkLsFfW3Q2EoHKCupOXIvm9A7FV1VdyrfK5O6ra4721SrErzG3lUJYL4v/WWX+cE1JX19SNVrQ9QJ2c13zFrk/Jc8l01/GjnTIi7V7jYoZCqr1O7p0oF/qxLilFpoi/vBjtX6PwrBNR5+v1qGpz6Q30G71xQy3wqQ4vPoUSDp99/ZiXFqMBnz7f3A15QvbUh70GpJk8uwGxIhd2zVfKSlASwsoXnB0GDQWCbh8eoSztUAgo0eOuX/JO0JOIwzG+lMoiWexnaL1TBz7wWEHFIfS66r5dkOfnNidXw27sqUZJ/HTX6wUKKH0vQYwknT0LkBk1TPRFpJ3XORVXmtZwchqYZ4cZJ9UPl4gNuvk/eJpss6nubqofFr8OFrep97b5eBT75ycYxajhXWmrtSq+bu0W5L/6WOknd86066QWV6rheX6je1XJ76cwlOQ6WdFTzm2yd4a2lENQw+/uLOKLm7aTNl3LxhiajVMFNc2XBMxrVPK49c+DsBkxDrDxKqZ6fqm89fMjY1QNqrkLkEXU/oJ5KQW2uoZDrRsCumapm1P92Prn8gLlFnYfvm6tC1yWeh06/3g8Uoy/Dty+oDKGVO0K72ZYxR1I82Z7v4M8hgKaKZ7/+vUUFrRL0mPvkSYjcoGkQc1n9aID6IXTxzp0fjsToe1dsrdSk56dJxfoYFvO9NRpheQ849qtKDNBtjRralt+kFVHdOxd01tBhkar4XhBFh8OOGSrFb2qiWuYZrNIcV+6Qa5/ZAkGfAMs6qcDAxgE6Llbj87Pi7nXY/Ckc+BHQVOrmev1UrR1Lqm10O0ylyj646P6QO1tnqNJR9QB6Pad6Cf8er4IkzagCoqafqKFlT+oZyk0piTC7gaq9VrkDvPqt+dryJHevw7xmakiedyV454+MgeWFf9TcMs0AL01SvW/CcmmaKgy87Qt1v8Y7qlSChSXMyU7Qk61v9cyZMylRogQODg6EhISwZ8+eR667YMECdDpdupsEJ0JkkmZUPyZpAY97cXD1yb0rZQ7u92r8PCMFSzd8rAIeKxt444f8GfCA+jy8NFldSdUMqpjjg5O8CwKjUc1hml5NnaSmJqr/rzd+hD57oPrbEvA8iZ0TdFyiMp6lJsGSN+HUn5nbNiUJtn0JX1dXASeaqiPVbx80+diyAh5Q83lafAaDTkCrL1UR0ZR42Pc9fBOiMjTODFFFQTWjqv3Tdx/U6GregAfU1fS2s9XQ5SPL4OQa87bnUZJiYPFr6jeqcAk1uf1hPWlBDVUSCIB1w9UcEWGZDCmwuu/9gKfxRypRjoUFPNmV5W/2smXLGDRoEKNHj+bAgQNUqVKF5s2bc+PGjUdu4+bmRkREhOl26dKlp2q0EM8Eo0FdrUybqF64hBp+lZvSCpaCymCmT8jd5zOnnTNh572Jwm1mQnAT87bnaVlZqdfxXGs1/nrJWyr9dkGQqocVPVWSAmMqlGwEXVZBj82qaGx+Lyqal2wdVIBf7hU1lPXnt1Xhz0fRNPX4zFoqfbM+DorVUPPe2s+HQgF51/bssHeBWt3VMLGuv6vvh85K9T7EXoVCgepk/bW54JK54tl5wr+WKnwKsGYAxN82a3MySEmCpZ3UPEjnovD2CnD1fvT6df6nAkvNAL90hZireddWkTn6eFj6luod1Vmp1NQvfFighiNmOej58ssv6dGjB++88w7ly5dn9uzZODk5MW/evEduo9Pp8PHxMd28vR/zxRBCqKstt8+qEwydlZpjklfjuu2cVZIEUCcFlj8CNuuO/qrmIwCEjlFDXgoCaxt4fZ4KClLi1VXYyKPmbtXT0cerzEFHf1E9cq/OVQFPyUYF6sc4T9nYwevzodIbKoj89f/g8LKM6107CPNfUj2H0eFqzlS7b6H7Rgiw4JT+D6PTqR6Hjovh/cPQcAg0Hgn/25X1IX55pfEI1UMVfxP+/MDcrbnPaIDl76r5XHauKmh80jxInU6dRHtXUq/n57dV4CQsQ/xt1ft5dj3YOELHn1SvZwGTpaBHr9ezf/9+QkPvHyCsrKwIDQ1l586dj9wuLi6OwMBA/P39adOmDcePH3/s8yQnJxMbG5vuJp6eXq8nODiYHTt25Oh+x4wZQ9WqVXN0n7nlxIkTFC9enPj4eHM35dFSk1W9mJREdZLnGZwnBUPTcfMFdCroSipg37/zW2FFL/V3SC81F6QgsbFXP1j+IWr4yY/t4NY5c7cqexKi1FyAcxtV+uk3l0Hl9uZuVcFgbaMmlVfrrIZ3rXjv3rA1IDYCVvSGbxtB+E51EtRouBrKVqWD+Yd/Pa1CAapY6gtD1JA/S2VjD+1mqXl6x1fAseXmbpG6CPbHIDj5u6rD9OZP4Fslc9vaOUHHReoC3tX9ah5iQbyolt/cuajmZV3dr/5vuq6Gsi+Zu1W5IktHrlu3bmEwGDL01Hh7exMZGfnQbcqWLcu8efNYtWoVixYtwmg0Uq9ePa5cufLI5/n8889xd3c33fz9/bPSzHyhW7dutG3b9rHrHNy3hw6vtcXX2wt7ezsCi/vRuukL/P7jN2g3TsGts1w8uDXdfCk7OzuCSwbx6ahhaHE31UlDUgwk32X2jK8IKhFIvdo1VE+C0cj4Tz+lXr16ODk5UahQoTx57ZkVHh5Oq1atcHJywsvLiyFDhpCamvrYbcaPH//Y11O+fHnq1KnDl19+mUutfkopiSrgMSSrHxTP0vfm2OQxG/v7Qz1ir6qTooIg4ogakmFMgfJtofnnBbO3wM4Z3voZfO5dVf2hjcqilJ/EXlOpe6/sUSmou6yC0hZ6RT6/srKGl7+Gmt0BDVb3U+lpv66u6quAmifWbz80GmaeY9Gzzq+aKu4K8McHqrSAOW3+DPYvwJQpMqsZAAuXgNe+VyMYDv6oCtkK84k4rApy3z6navT933rwr23uVuWaXL9cU7duXbp06ULVqlV54YUXWL58OUWLFmXOnDmP3Gb48OHExMSYbpcv57Mf66dhSIX4W6z6cRZ16jcgLvo2C6eN4eSW31i3+GvatWjEyM+nEXP7hroKr48DYOPSWUQcXM/Zf1cwdtC7jJ88jXlzZkD0JYg6j3brLDNmzKD7a01VEcrrxyDyMPqocNo3r0fvLu3vpSw+BTfPqCvDURdy5Cq/Xq/P+ttgMNCqVSv0ej07duxg4cKFLFiwgFGjRj3xudq3b0/v3r0fuc4777zDrFmznhhA5Tl9vAp4jCkqs5Jn6bytD/FfLt6qp8mQbHnjybPjziWVmlp/FwIbQLs5+f+K9eM4FlKFKD1Lq9pOP7Qx/wlTZt06p1Lg3jypshX+37oC/UNsVlZW0GoK1Omj7h/9RdWpKV4b3v0bXp0D7sXM28ZnXcMh6gJGYhSsGWi+3pHd38I/k9Tfrb+E8m2yt5/gJiq9OcCfH8LlRyfDErno/BZVWynuOnhVgO4boGgZc7cqV2UpHUORIkWwtrbm+vXr6ZZfv34dHx+fTO3D1taWatWqce7co4db2NvbY29vn5Wm5W/GVNUbkxgNyXeJT0ig+/sjaNWkAcsXzlInL/ZqeFO5ui3o3ncwmtGggpRodfDzLB6MT8nyoBkJLFOR+b/8yYETYXS3cwHNyP6DRwi7dIVWoS8AOtLqF4wdrIKDBctWq2VpKWDTJEWr6N+5SKZfTrdu3YiOjqZWrVrMnDkTe3t7Lly4kKW3ZP369Zw4cYKNGzfi7e1N1apV+eSTTxg6dChjxozBzu7hWZrGjh2rXs+CBY/cd9OmTYmKimLr1q00adJEBRuJd9QQAmtb1cOS9m9eTZBOilWpojWjGsbjUcr82VKsrFWmuJgrKpObU2EVBOVHaVXp0w7uHRebN6DMKy5FVQ/JvBaqaOMPbVVabgspLvdQ1w7Cotch4Zb6Hry9Qiq55zadDpqPV4VcT/0BdfuozGwFsRc0P7KxU9ncvm0Ep9bAkZ/VMMO8dGw5rP1Q/d1oBNT8v6fbX/0B6rt+YhUsexve26p+b0TeOPqrGuZtTFG1lTosUueaBVyWzmDs7OyoUaMGmzZtMg3NMhqNbNq0ib59+2ZqHwaDgaNHj9KyZe7UkNA0jcT/nrjnEUcbR3SZ/ZHQNJU55/Z5SI7FVEQNWP/vfm7fiebDEWMeWRzN9CxO93pinIuAmx8A+/btY/+RE3Tp/h4UKQ3AtuN/UqZMGVxL173//Jrx3s0AbnvUSb9HqfvLk2NVMBBzWbXV1TfTP4KbNm3Czc2NDRs2mJb16tWLRYsWPXa7uDjVc7Vz504qVaqUbihl8+bN6d27N8ePH6dateynFrazs6Nq1aps27aNJnWrqQm6POLKmSkQeiAYssrhwCgh6n4b7F2hcJDlZKNyKqKKQKYmqXoM+fGKrz4BfnpDJYZwK66q0T8DB3cT92LQZaWakH7jOCxur+5bWophUBm1lryleuN8q0Cn31TgJnKfTqeGUT1vQRPmxX0+FaHRUFU/Ze0QNawsFwtIpxO2GZb3BDSo1UNl9HpaOh20+UaNLLl5En7uAl3XSNr5vLBz5v1EPuXbqjpQNs9GR0OWL9sOGjSIrl27UrNmTWrXrs20adOIj4/nnXfeAaBLly4UK1aMzz//HIBx48ZRp04dgoODiY6OZvLkyVy6dIl33303Z1/JPYmpiYT8ZJ6sMrvf2o2T7WMmRRoN9wKJaBVMpCTeL5pm46BOxBwKc+b6egDKVqhk2nTv3r00btzYdH/p0qW0bt3adL9evXpYWVmh1+tJSUmhZ8+edOnSxfT4pUuX8PPzu98WnU6d0GMN2KoCc5B+wrxjYbU8LlJdITekQCF/NRb3CZydnZk7d266Hplx48YxePDgJ24LEBkZ+dC5Y2mPPS0/Pz8unTuphv+BOgG0tlOv0aBX/2oGdUs1qJP+R3mawCjuhpozA+r9LhSQqfc3z+h0KpiOOq/mhjgXyV8HR8O9zFRX9qp5IZ1/M10ceKZ4loK3V8KClnB1n6rP8mDVdEtw8nf1f2XQqyuPHX/K+wQeQliy+gNVT9y1g/B7fzVvL7d7464egGWd78+DfGlizj2nvYvqdf+2MVzeDX8NV0MtRe4wGmHjKNjxtbpf+z1oMaFgD/P+jywHPR06dODmzZuMGjWKyMhIqlatyrp160wnpOHh4Vg98AbeuXOHHj16EBkZSeHChalRowY7duygfPnyOfcqLJlmVEOXEqNVgPPghHCdlZo34VhYFSN7jMqVK3Po0CEASpcunWE+yrJlyyhXrhwpKSkcO3aMfv36UbhwYSZMmABAYmJi1ovC6nTqSpK1rertSYxSwYDHk3siKlWqlGEImpeXF15eFlAHwWjE0dpAQqzqVcLF++G9WEbD/SDImJI+IMqJwMiQqobwgKpz4FbMMoeT2LuptKT6u2pyuUeQuVuUOZoGfwyEM2vVRYW3lqkq7M8q7/Iq6Fv4iko1+0s36PCj+iya2/6FqhaJZoRyL6u01JYUkAlhCaxt1DC3OQ1VauGDi1RR3txyO0z1DOvjIOgF1SOQ06MQPEvBa9/BTx1g71zwrZq7r+lZlaqHVX3g6M/qfugYNcTQEs85clG2Buj37dv3kcPZtmzZku7+1KlTmTp1anaeJlscbRzZ/dbuPHu+/z43cG9oWJzqzUmKUSfFaazt7vXouIHe6qFXnUuXVkPSTp8+TZ06dQA1zyk4OPiRz+3v7296vFy5coSFhfHxxx8zZswYHBwcKFKkCEePZrNeh3MR1e47F9SJ762z4FlSLXvUJs4Zs/xkZXibj48Pe/akn9yYNpcss/PHHsqQAlHnibp9i1KB/qpnxcnz4etaWavb406+ciIwcvVVgZelHnx0OnD3g5un1RwvfXz+yOK0ZYJKwauzUtmCAuqYu0XmV6yGCv4WvaaCwRW9cudEJrM0Df6dqopeAlTvAq2nWc7wTiEsjddz8OJHsGEUrBuu6lUVyoUMt7ER8GNbdWHOt4rqkcmtXv4yzVVNos3jVTps7/LqWCVyRvJdNW/q/GY1L/eVGVD1TXO3yizy6azkR9PpdI8fYpZbNE19sJKiVa/Og4GOla0KdBwLq0nqOt1jJ4Q3a9YMDw8PJk6cyIoVj6mU/RjW1takpqai1+txcHCgWrVqzJo1C03TMj/v6EEObioLVFSYSnZw84y6QvOEHqoHZWV4W926dRk/fjw3btww9Q5t2LABNze37PcSpiSqYVoGPcdOhfH6G289OuDJrGwFRveCIWOqGnJlyZPK09g6qXYmRKnEBkXKWG6QZjTAxtH3u/BbfgHlWj9+m2dJiQbwxo+q4OexX1UA+/JXef//aTTCho9h5wx1v8EgldHJUj9XQliKun3h5BqVzn11XzV0NSe/N4nR6sJIdLgqOtrpt9yfA/j8YDVs7/Sf6gS951aZz5cT4m6orKURh8HWGd744ZlO/V/ggp48pWmq2zcxWgU7xgeGnFnZqBNax8LqpOIhB6SYmBjTkLU0np6e+Pv7M3fuXDp06ECrVq3o378/pUuXJi4ujnXr1gEqqHnQ7du3iYyMJDU1laNHj/LVV1/RuHFj3NzUmPjGjRsTFxfH8ePHqVixomm78PBwoqKiCA8Px2AwmNoTHByMi4tL+gbbOamT3dthKo3xrbNZGuqUleFtzZo1o3z58rz99ttMmjSJyMhIRo4cSZ8+fUyZ/fbs2UOXLl3YtGkTxYoVe/zrKV4Ul5RboBm5ePUWVyNvEPpSHp0IZyYwyg9cfdVnPSVBfd4dC5u7RRkl31V1Rs6o7wkvfgy1upu3TZaoTDN49Tv4rTscWKhOaJp9mncBhyFF1YQ5vETdbzYe6mUuGY4Qzzwra2g7C2Y3UGmH983LueNcSiIs6aiSnrh4q+yJeRF8WFmpYrnfNVFJZ37pphKuWMLw2/zqdhgselUVH3UqAp1+fuZ70J6d2Us5RdPU8J6YK3D9uCrolHBLBTw6a9Vz4BkM3hVVl7O9yyNPJLZs2UK1atXS3dJSLrdr144dO3bg5OREly5dKFu2LC+++CJ///13hiQGAKGhofj6+lKiRAl69uxJy5YtWbZsmelxT09P2rVrx+LFi9NtN2rUKKpVq8bo0aOJi4sztWPfvn2mdUqUKMGYMWPUHRt7FfjYOqverNth6iCZw6ytrVmzZg3W1tbUrVuXzp0706VLF8aNG2daJyEhgdOnT5OSkvLk17PlTzXs0M6FJev30KxZMwIDJQ1ulljbPVCw9Jq6Um9J7lxSRdbOrFMJOF77HhpmrmfxmVTxVXh5uvp75wz4Z3LePG9KopoYfXiJOma2nS0BjxBZVSQYQkerv9d/rOrqPa20xC/hO8HeHTovV8VE84qDu0pgYucKl/5VQ/hE9lzdr34P71xU/4fd1z/zAQ+ATtPMVeUq82JjY3F3dycmJsbUc5EmKSmJCxcuEBQUlPWJ+lmVkqAOLIYHim3qrNUX1bHwvQDHcuPII0eO0LRpU8LCwjL24jxCQkICnp6erF27lkaNGt1/wGiE6ItqzhKouUnOXpY1NEXTVHCalizAyRO9oxely5Tlp59+on79+uZtX35kNMCNk2qYnpufuhKYDTn+vQ3fBUs7qf9rF2/1w1m85tPv91mw8xuVNQlUJp86jy7s+9QSo9VV5PCdKrlE+wVQ9qXcez4hCjKjERa+rAKEwAbQ9ffsZ+LSNDVU7uAiddHo7RVQwky/kSfXwLJO6u923+Z9TaLMSkmC3bNgxwx1UTCgzv2bd0XzzU08uxF+fluds/pWUZk6XSwgiVQOe1xs8CgyvC0rrO3VlRCd1QOBjqtFBzoPqly5MhMnTuTChQtUqlTpyRsAmzdv5sUXX0wf8IA6sBYOUumW42+qK/+GFMvJQGZMVVc4ku+q+/eCsvCwMEaMGCEBT3ZZWauMftHhqm6Po6f5i6geXqqGShn0qmr5m0vBvbh525Sf1P2fSqW/5XNYN0wd06p1zvnnuRup5glcP6auIr+1DALr5vzzCPGssLKCNjNgVn0V+Oz5Fur0yt6+No1TAY/OCtrPN1/AA2oOZsMhqvf59/4qeYNvFfO157+MRjj2m0rAEnP5/vLjy9UNwM4FiteCgLoQEALFaqoL47nt0BIVvBpToWRjlaHTEmuymYn09GSVPh5sHJ+pvOZP9GCtGQd3KFTCvO9ParJKWJCapA7ghQKfrWKUuU3TVCa31ESV2c8965mDcuR7azTC35/Av1+q+8+1VpnI8kNmOUujabB+pBrmprOC1+dBhXY5t/+o8/BjO3UhwsVbDZvxqfjEzYQQmbB3LvzxgTo36fWvGvqWFbtmqQseoIa81uia823MKqNBpbE+twHcA6DnFnB+ysRDOeHidnWsvHZA3XcrpuaOuhdXIw4u74LLe+4VnX+AzlpdlAuoe783yPUpMtH+138zYVZ6A9rMLNDFXrPT0yNBj8gZiXfUnAo0Nd/Ho6R5egCS41RqbWOqyprnUVIlYBA5K/mums+GDoo+l+UkDU/9vU2OgxXvwak16v7zH0DjkXIx4mlomrqqeuAH9d15cwmUbvr0+404onp44m+oseVvr8w/tZ6EyA80TaWXPr8FiteG/1uX+aFVR36B5feKxb/4sWXNg0y8owqX3rmgUnN3+s18IwtunVNZQdN+c+xcoMFAqNsnYxZbowFunFBBUPguVXj1wR6hNIVLgH/akLi6ar50dn7DjAaVvnzPHHW/Xn8IHVvgfw8l6JGgx7yS49QVXc2ghgJ6lsq9vP4PkxClhl2hqYOQx+NrCYmndDtMXc2yd1d1m7Lgqb630ZdhyZtw/aj6/31lhuWO+c5vjAaV/e74cjXnpvPypxvmcnG7msOTHAvelVRxVNfszQMTQjxG9GWYVU9915p+AvX7P3mbsxthSQd1kTCkl5rTZwnD0x90/QTMDYWUeKj/PjQd9+RtclL8bdg6EfZ9fz9hVY2u0Gh41ubJRF9WwU9aIHT9GPCf02+HQioA8g9RQZBftSdfUExJUhcAT6xU95t/pgKxZ4AEPRL0mF9KkqrlY9CrtN0eJXN/uJGmqfkCcZHqvoO7GtImBQ5zV0oS3Dyp/vYMztK44Wx/by/vhaVvqV4D56IqYYF/7Sw2XDyWIUUlhTj7l8qi1HU1FKue9f2cXqvSzqYmQUA91XMkw0yFyD0HflTzOazt4b1/1FyYR7myTyVBSEmAiq+rFPaW2jNwbDn8+o76+/X5KvNkbktJUj0n/0yB5HsJm8q0UD0oj3tfMyspBq7svR8EXdmnhow/yNpOBT4BdVSPkH9I+iF+idHqWH3pX9U73242VHr96duWT0jQI0GPZTCkqF6A1EQ1P6BwCRWI5AajEaIvqboxoK68uPpZ3tWqgir6ssqYZuMIRctm+n3P1vf26K+w8n+qRpRXBXhrKRQKeIrGi0dKSYTF7eHiNpWw5Z214FUu89sfXKySS2gGKNtSzRHKQiFjIUQ2aBr89AacXQ9+1aH7hocPB7t5BuY1h8QoKPUivLnM8ud+bBgF279ShbLf3QjeFXLneTTtfpKC6HC1zKeSqmNWslHuPCeo86bII/eCoJ0Qvltd3PuvImVUEFS8Fuyareop2blCx8VQ8oXca58FkqBHgh7LYTSocbhp2dPc/dWk95xkSFHD6VISAN2957CAiY7PEkOKSmGtGVQA4pS59z9L31ujUWUW+2eSul+2pUpYIBlpclfyXfihjar34OID/7dW9dw+yfbpsOFj9XeVt+CVr82f4U+IZ0VsBHwTonoSXhypsqA9KOaqqt8Se0XVbemyOm+yij0tQyosfk3NWyocBD0353yB7Es7Yf1H6pgH6gJqk4+hcse87wXTNHV+c3n3vSBoF9w6k3E9F2+Vktq3ct62zwJI0CNBj2XRjKonIDFK3XfxBlffnOmFSUlUBwSDXo2x9QiSk2BzuXsd7l5T3ete5TI1rDDT31t9PKzoBSdXq/v134cmo2XoYl5JiIIFrdSk3EIB8M46cC/28HU1TU303f6Vul+3r5pbYKlDZoQoqA4vgxU91TG555b7mRITomD+S3DzFHiWhv/7K39dKEyIgm9fUD0wwU1V2vuc+C24HaaOXSd/V/dtne8nKbCkREjxt1UQdPnekDidNbSblbcFZC2IBD0S9Fie/863cfSAQv5PV9soKUalvtWMauyyR8ksZw8TOchoVHN7DHoV1GYiDWemvrex19Qk+IjD6sf75a+gWqccbrx4orvXYX4LdZGhSBk11O2/vbaGVFgzAA7+qO6HjoUGA/K6pUIIUL+7yzqrTGM+leDdv9Uk/B/bqpNmVz/o/lf+HB4ccUT1VKUmql6sF0dmf18JUbB1Euz97l6SAiuo3gUajZCEK/lAdoIeuQT3DNHr9QQHB7Njx44c3e+YMWOoWrXqwx/U6VQxy7RaLolRcPu8Gv6WVZqmagJFnVcBj52LOgnLQsCzbt06qlatitFozPrzi4ezslLBDkDcdTXk7WldPaBSlUYcVkPmuv4uAY+5uHpDl1WqHsWtM6reTlLM/cdTkuCXrirg0VmpbHoS8AhhPjodtJ6qLjJGHoWtE1RSkcu71fzazr/lz4AH1DCuV6arv/+ZDCfXZH0fqcmw42uYXhV2z1IBT3BT6L1DXVyTgKfAkqDHTLp160bbtm0fu87Bgwfp0KEDvr6+2NvbExgYSOvWrfn9999J66C7ePEiOp3OdLOzsyM4OJhPP/2U/3bizZ49m6CgIOrVq2daNn78eOrVq4eTkxOFChXK6Zd5n3MR8CilTor0d+HWWdUz8Bjh4eG0atUKJycnvLy8GNK/N6lRl9SDTh4qJfYDcwUuXrxI9+7dCQoKwtHRkVKlSjF69Gj0+vvP06JFC2xtbVm8eHGuvMxnlmNhNcFUM8LdiKfb17HlaghGXCQULQc9/obAujnTTpE9hQJU4ONURE22XfyGGnqYFAuLX1dXlK3t4Y0fofrb5m6tEMLFC1pNUX9vm6KyMdo4wFs/g3d587btaVV+A+r8T/29opcqlp0ZmqZ+X2bUUgVGk2LAuyK8vQI6/5q1ZC0iX5Kgx0KtWrWKOnXqEBcXx8KFCzl58iTr1q2jXbt2jBw5kpiYmHTrb9y4kYiICM6ePcvYsWMZP3488+bNMz2uaRozZsyge/fu6bbT6/W0b9+e3r1759prMQUdDm5qHLGVjeqavnlGzc15CIPBQKtWrdDr9ez49x8Wfv05C35axqjJs8DNT1Vo/s8QuVOnTmE0GpkzZw7Hjx9n6tSpzJ49mxEjRqRbr1u3bkyfPj1XXuszS6dTPQEACbcf+f/6WJoGWyaq1KSpSVC6GXRf/8yOV7Y4RUpDl5XqSvHlXSp1+IJWKsObnau6elyutblbKYRIU/FVqNBO/a2zhvYLVeavgqDpOAhsoC6iLu2kLsA8Tvhu+L6p+n2JvqSSs7SZqVJ7l3oxb9oszE7m9JhJt27diI6OZuXKlRkei4+PJzAwkIYNG7J8+fKHbq9pGjqdjosXLxIUFMTBgwfTDTELDQ2lbNmyzJw5E4B9+/YREhJCdHQ0rq4ZJ/wvWLCAAQMGEB0dneXXMmbMGFauXMmhQ4fSvbZatWoxc+ZM7O3tuXDhwv0NUpPVxEFD8iOTEKxdu5bWrVtzLfwC3rbxkJrE7B9/Y+hn07l58xZ2dplLrzl58mRmzZrF+fPnTcvCw8MJDAzk3LlzlCpVKsuvVzxG1AWVPtzOVfXEPSJpRYbvbUoirOqjUoXCvUnw4yRhgSW6vAd+aKuKBYLq/en8G/hVNWerhBAPk3gHtkxQJ/Zlmpu7NTkr7qZKbBB7Fcq2gg6LMiZOiToPG8fAiVXqvq2zSohTr2/u1xAUuSo7c3oKXB5RTdPQErNxlTkH6Bwd0eVAZrL169dz+/ZtPvzww0c/12OeZ9++fezfv58uXbqYlm3bto0yZco8NODJDZs2bcLNzY0NGzaYlvXq1YtFixbdX0nTMFUkvtdrExcXB8DOnTupVLEC3tYxkJoKVrY0b/sWvYeN5/jx41SrVi1T7YiJicHDwyPdsoCAALy9vdm2bZsEPTnNzU8NGdDfVSmPHTJxILobCUvehGsHVC9gqy9VxWthmfxrw5s/qf8z56JqaIinfI+EsEiOheGlieZuRe5wKQodfoR5L8HpP2DbF/DCvfOmhCj45wvY8y0YU9Q5RrXO0PijTCXbEQVTwQt6EhM5Xb2GWZ677IH96JyePr3hmTMqF3vZsmVNy/bu3Uvjxo1N95cuXUrr1veHktSrVw8rKyv0ej0pKSn07NkzXdBz6dIl/Pz8nrptmeXs7MzcuXPT9ciMGzeOwYMH31/JaFS1AtJq+bh4q0BIpyPyyiW8C7uoCYY2juBZEm+3VAAiIyMz1YZz587x9ddf88UXX2R4zM/Pj0uXLmX/BYqHs7FXJ8LxN9TVN3vXx6cov3EKfnlTpbx2LKzmhAQ9n3ftFdlTshEMOqGSiVjbmrs1QohnVbEa0PpLNVJg82dqXk50uMrKlla0vFQTaPZJ7hU0FflGgQt6CqrKlSubho+VLl2a1NTUdI8vW7aMcuXKkZKSwrFjx+jXrx+FCxdmwoQJACQmJubp8L9KlSplGILm5eWFl5dX+hW10urkOP6muh97BXQ2qqcATc0fKBR4b5hT+tf8OFevXqVFixa0b9+eHj16ZHjc0dGRhISELL4qkSmu3mpeT2oSJNxSQdDDpCTA8l4q4ClSBt5cKj0G+UlOFwYUQojsqNYZrh2EvXNVqu40XhWg2TgIDjVf24RFKXBBj87RkbIH9pvtuXNC6dKlATh9+jR16qhJh/b29gQHBz9yG39/f9Pj5cqVIywsjI8//pgxY8bg4OBAkSJFOHr0aI60LzOcnTOOlc0wvC0dDTSNuLPbAfApWoQ9R06rysv3egquX7+uHvN5fNf0tWvXaNy4MfXq1ePbb7996DpRUVEULfqIk3HxdKxs1PCB2Ktq6JqjR/q5OZqmgtz4WyqhRakm8Po8cCxktiYLIYTIx5p/DpHHVJIVF29Vv6dqJ5kXKtIpeEGPTpcjQ8zMqVmzZnh4eDBx4kRWrFiRrX1YW1uTmpqKXq/HwcGBatWqMWvWLFMCBHPIMLztv5JiANW2ug1fZPz077lx86apd2jDhg24ublRvvyj021evXqVxo0bU6NGDebPn4/VQ6rBJyUlERYWlul5QSIbnIuooMaQrGr3uN0bWmk0Qkw4xEep+5U7QpNh6VKPCyGEEFliY6cSqlzYCkEvgL2LuVskLJCcaZhRTEyMachaGk9PT/z9/Zk7dy4dOnSgVatW9O/fn9KlSxMXF8e6desAFdQ86Pbt20RGRpKamsrRo0f56quvaNy4sSmjRePGjYmLi+P48eNUrFjRtF14eDhRUVGEh4djMBhM7QkODsbFJWcPGg8d3vZfqcmARrNXKlG+/HjefvttJk2aRGRkJCNHjqRPnz7Y29sDsGfPHrp06cKmTZsoVqwYV69epVGjRgQGBvLFF19w8+ZN024f7B3atWsX9vb21K0rtV9yjc5KBTp3LqiCsk5FVI9d1Hk1rA1UD1C1wRLwCCGEeHr2LvBcK3O3QlgwOdswoy1btmTobejevTtz586lXbt27Nixg4kTJ9KlSxeioqJwd3enZs2aGZIYgEpRDSoY8vX1pWXLlowfP970uKenJ+3atWPx4sV8/vnnpuWjRo1i4cKFpvtp7dm8eTONGjUCoESJEnTr1o0xY8bk5Mt/OBsV0FgDa9asoXfv3tStWxdnZ2e6du3KuHHjTKsmJCRw+vRpUlJSANUTdO7cOc6dO0fx4sXT7fbBzOxLliyhU6dOOOXzHkGL5+CuJrrr49TE0tSke1l0rKFQMZXsQAghhBAiD0idnmfIkSNHaNq0KWFhYZnuxUlISMDT05O1a9eagqD87NatW5QtW5Z9+/YRFBRk7uYUfPoEuPVAtWxre/AoSZIB+d4KIYQQIluyU6cn44QHUWBVrlyZiRMnpi8U+gSbN2/mxRdfLBABD8DFixf55ptvJODJK3ZO4HSvTpKdCxQtA7YS5AghhBAib0lPjxAid2mamsdj62TKxCffWyGEEEJkV3Z6emROjxAid+l0YJcxhbkQQgghRF6R4W1CCCGEEEKIAk2CHiGEEEIIIUSBVmCCnnwwNUkIcY98X4UQQgiRl/L9nB5bW1t0Oh03b96kaNGi6O5NlBZCWCZN07h58yY6nQ5bW1tzN0cIIYQQz4B8H/RYW1tTvHhxrly5wsWLF83dHCFEJuh0OooXL461tbW5myKEEEKIZ0C+D3oAXFxcKF26NCkpKeZuihAiE2xtbSXgEUIIIUSeKRBBD6geHzmJEkIIIYQQQvxXgUlkIIQQQgghhBAPI0GPEEIIIYQQokCToEcIIYQQQghRoOWLOT1pNT1iY2PN3BIhhBBCCCGEOaXFBFmp+5cvgp67d+8C4O/vb+aWCCGEEEIIISzB3bt3cXd3z9S6Oi0flEY3Go1cu3YNV1dXsxcfjY2Nxd/fn8uXL+Pm5mbWtoj8TT5LIqfIZ0nkBPkciZwinyWRUx71WdI0jbt37+Ln54eVVeZm6+SLnh4rKyuKFy9u7mak4+bmJl9kkSPksyRyinyWRE6Qz5HIKfJZEjnlYZ+lzPbwpJFEBkIIIYQQQogCTYIeIYQQQgghRIEmQU8W2dvbM3r0aOzt7c3dFJHPyWdJ5BT5LImcIJ8jkVPksyRySk5+lvJFIgMhhBBCCCGEyC7p6RFCCCGEEEIUaBL0CCGEEEIIIQo0CXqEEEIIIYQQBZoEPUIIIYQQQogCTYIeIYQQQgghRIEmQU8WzJw5kxIlSuDg4EBISAh79uwxd5NEPjNmzBh0Ol2623PPPWfuZol84J9//uHll1/Gz88PnU7HypUr0z2uaRqjRo3C19cXR0dHQkNDOXv2rHkaKyzakz5L3bp1y3CcatGihXkaKyzW559/Tq1atXB1dcXLy4u2bdty+vTpdOskJSXRp08fPD09cXFx4bXXXuP69etmarGwVJn5LDVq1CjDcalXr15Zeh4JejJp2bJlDBo0iNGjR3PgwAGqVKlC8+bNuXHjhrmbJvKZChUqEBERYbr9+++/5m6SyAfi4+OpUqUKM2fOfOjjkyZNYvr06cyePZvdu3fj7OxM8+bNSUpKyuOWCkv3pM8SQIsWLdIdp5YsWZKHLRT5wdatW+nTpw+7du1iw4YNpKSk0KxZM+Lj403rDBw4kN9//51ffvmFrVu3cu3aNV599VUztlpYosx8lgB69OiR7rg0adKkLD2P1OnJpJCQEGrVqsWMGTMAMBqN+Pv7069fP4YNG2bm1on8YsyYMaxcuZJDhw6ZuykiH9PpdKxYsYK2bdsCqpfHz8+PDz74gMGDBwMQExODt7c3CxYsoGPHjmZsrbBk//0sgerpiY6OztADJMTj3Lx5Ey8vL7Zu3UrDhg2JiYmhaNGi/PTTT7z++usAnDp1inLlyrFz507q1Klj5hYLS/XfzxKonp6qVasybdq0bO9XenoyQa/Xs3//fkJDQ03LrKysCA0NZefOnWZsmciPzp49i5+fHyVLlqRTp06Eh4ebu0kin7tw4QKRkZHpjlHu7u6EhITIMUpky5YtW/Dy8qJs2bL07t2b27dvm7tJwsLFxMQA4OHhAcD+/ftJSUlJd1x67rnnCAgIkOOSeKz/fpbSLF68mCJFilCxYkWGDx9OQkJClvZrk2MtLMBu3bqFwWDA29s73XJvb29OnTplplaJ/CgkJIQFCxZQtmxZIiIiGDt2LM8//zzHjh3D1dXV3M0T+VRkZCTAQ49RaY8JkVktWrTg1VdfJSgoiLCwMEaMGMFLL73Ezp07sba2NnfzhAUyGo0MGDCA+vXrU7FiRUAdl+zs7ChUqFC6deW4JB7nYZ8lgLfeeovAwED8/Pw4cuQIQ4cO5fTp0yxfvjzT+5agR4g89NJLL5n+rly5MiEhIQQGBvLzzz/TvXt3M7ZMCCGUB4dDVqpUicqVK1OqVCm2bNlCkyZNzNgyYan69OnDsWPHZI6qeGqP+iz17NnT9HelSpXw9fWlSZMmhIWFUapUqUztW4a3ZUKRIkWwtrbOkHHk+vXr+Pj4mKlVoiAoVKgQZcqU4dy5c+ZuisjH0o5DcowSuaFkyZIUKVJEjlPiofr27cuaNWvYvHkzxYsXNy338fFBr9cTHR2dbn05LolHedRn6WFCQkIAsnRckqAnE+zs7KhRowabNm0yLTMajWzatIm6deuasWUiv4uLiyMsLAxfX19zN0XkY0FBQfj4+KQ7RsXGxrJ79245RomnduXKFW7fvi3HKZGOpmn07duXFStW8PfffxMUFJTu8Ro1amBra5vuuHT69GnCw8PluCTSedJn6WHSEkJl5bgkw9syadCgQXTt2pWaNWtSu3Ztpk2bRnx8PO+88465mybykcGDB/Pyyy8TGBjItWvXGD16NNbW1rz55pvmbpqwcHFxcemuaF24cIFDhw7h4eFBQEAAAwYM4NNPP6V06dIEBQXx8ccf4+fnly4rlxDw+M+Sh4cHY8eO5bXXXsPHx4ewsDA+/PBDgoODad68uRlbLSxNnz59+Omnn1i1ahWurq6meTru7u44Ojri7u5O9+7dGTRoEB4eHri5udGvXz/q1q0rmdtEOk/6LIWFhfHTTz/RsmVLPD09OXLkCAMHDqRhw4ZUrlw580+kiUz7+uuvtYCAAM3Ozk6rXbu2tmvXLnM3SeQzHTp00Hx9fTU7OzutWLFiWocOHbRz586Zu1kiH9i8ebMGZLh17dpV0zRNMxqN2scff6x5e3tr9vb2WpMmTbTTp0+bt9HCIj3us5SQkKA1a9ZMK1q0qGZra6sFBgZqPXr00CIjI83dbGFhHvYZArT58+eb1klMTNT+97//aYULF9acnJy0du3aaREREeZrtLBIT/oshYeHaw0bNtQ8PDw0e3t7LTg4WBsyZIgWExOTpeeROj1CCCGEEEKIAk3m9AghhBBCCCEKNAl6hBBCCCGEEAWaBD1CCCGEEEKIAk2CHiGEEEIIIUSBJkGPEEIIIYQQokCToEcIIYQQQghRoEnQI4QQQgghhCjQJOgRQgghhBBCFGgS9AghhBBCCCEKNAl6hBBCCCGEEAWaBD1CCCGEEEKIAu3/AVuPTBSfrW9lAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"RandomForestRegressor(5)\")\n", + "ax.plot([_[1] for _ in scores], label=\"LGB(1, lr=0.05)\")\n", + "ax.plot([_[2] for _ in scores], label=\"LGB(1, lr=0.1)\")\n", + "ax.plot([_[3] for _ in scores], label=\"LGB(1, lr=0.2)\")\n", + "ax.set_title(\n", + " \"Comparaison pour différents learning_rate\\net des fonctions en escalier \"\n", + " \"avec LightGBM\"\n", + ")\n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 8%|▊ | 2/25 [00:00<00:02, 8.95it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.156120\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.156120\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.156120\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000049 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.182360\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000048 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.182360\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.182360\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000036 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.345161\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 16%|█▌ | 4/25 [00:00<00:02, 10.20it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000038 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.345161\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000037 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.345161\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.125674\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.125674\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000041 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.125674\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000038 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.173663\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.173663\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000040 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.173663\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 24%|██▍ | 6/25 [00:00<00:01, 10.88it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000057 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.600185\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000048 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.600185\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.600185\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.499798\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000052 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.499798\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.499798\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.414976\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 32%|███▏ | 8/25 [00:00<00:01, 10.56it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.414976\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.414976\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.525282\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.525282\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000072 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.525282\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 40%|████ | 10/25 [00:01<00:01, 9.77it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.312694\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.312694\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.312694\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.210617\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.210617\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.210617\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000044 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.757290\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.757290\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000051 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 154.757290\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 48%|████▊ | 12/25 [00:01<00:01, 10.15it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.371614\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.371614\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000059 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.371614\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.379656\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 56%|█████▌ | 14/25 [00:01<00:01, 9.10it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000856 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.379656\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.379656\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.051009\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.051009\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000033 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.051009\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 60%|██████ | 15/25 [00:01<00:01, 8.81it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.140242\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.140242\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000077 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.140242\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.432455\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.432455\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 72%|███████▏ | 18/25 [00:01<00:00, 8.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.432455\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000085 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.319151\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000059 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.319151\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000041 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.319151\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 80%|████████ | 20/25 [00:02<00:00, 9.29it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.163655\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.163655\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.163655\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000036 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.329555\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000044 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.329555\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000041 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.329555\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 88%|████████▊ | 22/25 [00:02<00:00, 7.86it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.444763\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000044 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.444763\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000077 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.444763\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.058841\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.058841\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 251\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.058841\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:02<00:00, 9.30it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.162133\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000041 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.162133\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000043 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.162133\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.433042\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000032 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.433042\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.433042\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.500633\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.500633\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000053 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 250\n", + "[LightGBM] [Info] Number of data points in the train set: 750, number of used features: 1\n", + "[LightGBM] [Info] Start training from score 155.500633\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.8384828369380417,\n", + " 0.7470535641154521,\n", + " 0.8395674441554806,\n", + " 0.8460697933405346],\n", + " [0.8966181473711606,\n", + " 0.7767690018820292,\n", + " 0.8808463799894176,\n", + " 0.8937462469421166]]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = experiment(\n", + " [\n", + " RandomForestRegressor(max_depth=5, n_estimators=20),\n", + " LGBMRegressor(max_depth=5, n_estimators=20, learning_rate=0.05),\n", + " LGBMRegressor(max_depth=5, n_estimators=20, learning_rate=0.1),\n", + " LGBMRegressor(max_depth=5, n_estimators=20, learning_rate=0.2),\n", + " ]\n", + ")\n", + "scores[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QGRnJ1KlT2bBhgyapR4YXr0d23uTaC4Lw/omGkSAIWZibm+Ps7MytW7feaLvXnUQyp8xTOS2XX0iq8DpOnjzJF198Qd26dfntt99wcnJCT0+P1atXs379+izlM78Jftt9ZFa3bl0ePnzIjh07OHDgACtXrmTBggUsW7Ysx5TRr5Kb1+dd5HSfM5IIvMjAwOCDS2WuUqkAGD16dI69US8+VL/4GcnYx9q1a7NtnOjqav+JfdeMa8OHD6d169Zs376d/fv3M3HiRGbNmsWRI0eoUKHCa+9HpVIhSRJ79+7Ntk6mpqaA+n42adKEyMhIvv/+e4oXL46JiQmBgYH06tVLc/4ZXnaf3+Wzm1+f++y+Ezp37syZM2cYM2YM5cuXx9TUFJVKRbNmzbJcj+y87rUXBCF/iIaRIAjZatWqFStWrODs2bPZvonNzNXVFZVKhbe3t6Z3ASAkJITo6Ogsk2m+K5VKha+vr6bHA+DBgwcAmkHoW7duxdDQkP3792sNwF69evVrH+dd92FtbU3v3r3p3bs38fHx1K1blylTpvD1119rrsn9+/ezbHfv3j1sbW1zNT1wUFBQlh6oF6+Zq6srN27cQKVSaT3g3rt3T7MenvdUvTh55dv2KGWws7PDyMgo2/DD7K7Tu8jocdTT06Nx48ZvtY+M0DB7e/u33seLXvVywcPDg1GjRjFq1Ci8vb0pX7488+bNY926da99DA8PD2RZxs3NTet36EU3b97kwYMHrFmzhh49emiWv5hZ8UPg6uqKj49PluXZLXtbUVFRHD58mKlTpzJp0iTN8uw+rzndx9e99oIg5I8P6xWeIAgfjO+++w4TExO+/vprQkJCsqx/+PAhixYtAqBFixYALFy4UKvM/PnzAV6ave1tLVmyRPN/WZZZsmQJenp6NGrUCFC/ZZYkSasXw8/Pj+3bt7/2Md5lH5nT94L6TXCRIkU04XhOTk6UL1+eNWvWaDUwbt26xYEDBzTXNLekp6drpb1OTU1l+fLl2NnZUalSJUB9H4ODg9m4caPWdosXL8bU1FST9trV1RUdHR1OnDihdYzffvvtneqoo6ODl5cX27dvJyAgQLP87t277N+//532/SJ7e3vq16/P8uXLefr0aZb1GXMivYyXlxfm5ubMnDmTtLS0t9rHizIyub3Y6ExMTCQ5OVlrmYeHB2ZmZllS4r9K+/bt0dHRYerUqVl6XWRZ1nx2M3o0MpeRZVnze/8h8fLy4uzZs1y7dk2zLDIykr///jvXjpHd9YCs33uA5gXEi/fxda+9IAj5Q/QYCYKQLQ8PD9avX0+XLl0oUaIEPXr0oHTp0qSmpnLmzBlNGmeAcuXK0bNnT1asWEF0dDT16tXjwoULrFmzhrZt29KgQYNcrZuhoSH79u2jZ8+eVKtWjb1797J7925++OEHzRiOli1bMn/+fJo1a0b37t0JDQ3l119/pUiRIty4ceO1jvMu+yhZsiT169enUqVKWFtbc+nSJbZs2aKVNGLOnDk0b96cGjVq0LdvX026bgsLC83cNbnF2dmZ2bNn4+fnR9GiRdm4cSPXrl1jxYoVmnEN/fv3Z/ny5fTq1YvLly9TuHBhtmzZwunTp1m4cKEmGYeFhQWdOnVi8eLFSJKEh4cHu3btyjLm4m1MnTqVffv2UadOHQYPHqxpmJUqVeq179vr+vXXX6lduzZlypShX79+uLu7ExISwtmzZ3ny5AnXr19/6fbm5uYsXbqUr776iooVK9K1a1fs7OwICAhg9+7d1KpVS6sB/zqMjIwoWbIkGzdupGjRolhbW1O6dGnS09Np1KgRnTt3pmTJkujq6vLvv/8SEhJC165d3+gYHh4ezJgxg3HjxuHn50fbtm0xMzPj0aNH/Pvvv/Tv35/Ro0dTvHhxPDw8GD16NIGBgZibm7N169Z3Tv6QF7777jvWrVtHkyZNGDZsmCZdd6FChYiMjHztMN+XMTc3p27duvz888+kpaVRoEABDhw4wKNHj7KUzXjZMH78eLp27Yqenh6tW7d+7WsvCEI+ed9p8ARB+Lg8ePBA7tevn1y4cGFZX19fNjMzk2vVqiUvXrxYK81xWlqaPHXqVNnNzU3W09OTCxYsKI8bN06rjCyr0+9ml8YakIcMGaK1LLv0yz179pRNTEzkhw8fyk2bNpWNjY1lBwcHefLkybJSqdTa/o8//pA9PT1lAwMDuXjx4vLq1auzpC7O6dhvuo8XU0fPmDFDrlq1qmxpaSkbGRnJxYsXl3/88UdNauwMhw4dkmvVqiUbGRnJ5ubmcuvWreU7d+5olck4XuZU5LKcfXrj7NSrV08uVaqUfOnSJblGjRqyoaGh7OrqKi9ZsiRL2ZCQELl3796yra2trK+vL5cpU0Yr/XaGsLAwuUOHDrKxsbFsZWUlDxgwQL5161a26bpNTExeWr8XHT9+XK5UqZKsr68vu7u7y8uWLXuta/6m6bplWZYfPnwo9+jRQ3Z0dJT19PTkAgUKyK1atZK3bNmiKZNxnS9evJhtfY8ePSp7eXnJFhYWsqGhoezh4SH36tVLvnTp0iuvQ3bndebMGc358yx1d3h4uDxkyBC5ePHisomJiWxhYSFXq1ZN3rRpU84X8iXHkGVZ3rp1q1y7dm3ZxMRENjExkYsXLy4PGTJEvn//vqbMnTt35MaNG8umpqayra2t3K9fP02q7de9zzn9fr14/3JK153d90W9evXkevXqaS27evWqXKdOHdnAwEB2cXGRZ82aJf/yyy8yIAcHB+dwdbLK6fdNlmX5yZMncrt27WRLS0vZwsJC7tSpkxwUFJRtivXp06fLBQoUkBUKRZbzep1rLwjC+yfJ8nsetSsIgvAOevXqxZYtW4iPj8/vqnw06tevT3h4+Bsn0xCEj93w4cNZvnw58fHx75z8QhCET58YYyQIgiAIwkcvKSlJ6+eIiAjWrl1L7dq1RaNIEITXIsYYCYIgCILw0atRowb169enRIkShISE8McffxAbG6uZpyo+Pv6VPc12dnaiESUInzHRMBIEQRAE4aPXokULtmzZwooVK5AkiYoVK/LHH39Qt25dAObOncvUqVNfuo9Hjx5p0tcLgvD5EWOMBEEQBEH45Pn6+uLr6/vSMrVr18bQ0PA91UgQhA+NaBgJgiAIgiAIgvDZE8kXBEEQhA/GjRs3mDJlCo8fP87vqgiCIAifGdEwEgThgzJlypRcmYzxfatfvz6lS5fO72q8lfd1zV91nJiYGNq1a0dUVBQFCxbM9f1nx8/PD0mS+PPPP9/4eMLbkyTprScxliRJa6JkQRCE3CIaRoIg5IqgoCCmTJnCtWvX8rsqwkeqd+/eVKhQgQULFuR3VYS3dOzYMSRJYsuWLfldFc6cOcOUKVOIjo7Odr1KpeKvv/6iSZMm2Nraoqenh729PU2bNmXFihWkpKRolZckSeufiYkJJUuWZMaMGSQmJmqV7dWrF5IkYW5uniWNOIC3t7dmP3Pnzs21cxYE4d2IrHSCIOSKoKAgpk6dSuHChSlfvnx+V0f4yPj5+VG5cmVGjhyJQvF27+wmTJjA2LFj32gbV1dXkpKS0NPTe6tjCm8nKSkJXd28fQQ5c+YMU6dOpVevXlhaWmY5frt27di/fz81a9Zk9OjRODg4EBkZyfHjxxk8eDDnz5/njz/+0NquSZMm9OjRA1Cn/z558iQTJ07k+vXrbN68Wausrq4uiYmJ7Ny5k86dO2ut+/vvvzE0NCQ5OTn3T1wQhLcmGkaCIAjvICEhARMTk3faR3JyMvr6+m/dIPgUFC5cmB9++OGd9qGrq/vGD9uSJIksZPkgv6/5iBEj2L9/PwsXLuTbb7/VWjdq1Ci8vb05ePBglu2KFi3K//73P83PAwcOJDU1lW3btpGcnKx1XgYGBtSqVYt//vknS8No/fr1tGzZkq1bt+bymQmC8C4+37/CgiC8tsDAQPr06YODgwMGBgaUKlWKVatWadYfO3aMKlWqAOpwqIwQkVeN2zh16hRVqlTB0NAQDw8Pli9fnmPZdevWUalSJYyMjLC2tqZr165ZBuh7e3vToUMHHB0dMTQ0xMXFha5duxITE/PSepw8eZJOnTpRqFAhDAwMKFiwICNGjMgSAtOrVy9MTU15+PAhLVq0wMzMjC+//FKrzOXLl6lZsyZGRka4ubmxbNkyrfUZoUYbNmxgwoQJFChQAGNjY2JjYwE4f/48zZo1w8LCAmNjY+rVq8fp06e19hEXF8fw4cMpXLgwBgYG2Nvb06RJE65cufLS84Tcv+bv8zjnz5+nRYsWWFlZYWJiQtmyZVm0aJFmfXZjjA4ePEjt2rWxtLTE1NSUYsWKaTXAchpjdOTIEerUqYOJiQmWlpa0adOGu3fvapXJOJ6Pj4+mV8LCwoLevXtnCa16VT1ysnr1aho2bIi9vT0GBgaULFmSpUuXapVp1aoV7u7u2W5fo0YNKleurLUst673u8hujNGxY8eoXLmy1mfmZePGtm/fTunSpTXfSfv27dOsmzJlCmPGjAHAzc1N853k5+fH48ePWblyJc2aNcvSKMrg6enJ4MGDX+tcHB0dkSQp20Z59+7d2bt3r1Y438WLF/H29qZ79+6vtX9BEN4f0WMkCMJLhYSEUL16dc2AZzs7O/bu3Uvfvn2JjY1l+PDhlChRgmnTpjFp0iT69+9PnTp1AKhZs2aO+7158yZNmzbFzs6OKVOmkJ6ezuTJk3FwcMhS9scff2TixIl07tyZr7/+mrCwMBYvXkzdunW5evUqlpaWpKam4uXlRUpKCsOGDcPR0ZHAwEB27dpFdHQ0FhYWOdZl8+bNJCYmMmjQIGxsbLhw4QKLFy/myZMnWcJj0tPT8fLyonbt2sydOxdjY2PNuqioKFq0aEHnzp3p1q0bmzZtYtCgQejr69OnTx+t/UyfPh19fX1Gjx5NSkoK+vr6HDlyhObNm1OpUiUmT56MQqHQPBifPHmSqlWrAuq31Fu2bGHo0KGULFmSiIgITp06xd27d6lYseJ7u+bv8zgHDx6kVatWODk58e233+Lo6Mjdu3fZtWtXjg+3t2/fplWrVpQtW5Zp06ZhYGCAj49Plobmiw4dOkTz5s1xd3dnypQpJCUlsXjxYmrVqsWVK1eyTADauXNn3NzcmDVrFleuXGHlypXY29sze/bsd6oHwNKlSylVqhRffPEFurq67Ny5k8GDB6NSqRgyZAgAXbp0oUePHly8eFHzggLA39+fc+fOMWfOnPdyvd/F1atXadasGU5OTkydOhWlUsm0adOws7PLtvypU6fYtm0bgwcPxszMjF9++YUOHToQEBCAjY0N7du358GDB/zzzz8sWLAAW1tbAOzs7Pj7779RKpVaPT+vKzk5mfDwcEDdW3z69GnWrFlD9+7ds20YtW/fnoEDB7Jt2zbNd8D69espXrz4S39XBUHIJ7IgCMJL9O3bV3ZycpLDw8O1lnft2lW2sLCQExMTZVmW5YsXL8qAvHr16tfab9u2bWVDQ0PZ399fs+zOnTuyjo6OnPmryc/PT9bR0ZF//PFHre1v3rwp6+rqapZfvXpVBuTNmze/8TlmnENms2bNkiVJ0qpfz549ZUAeO3ZslvL16tWTAXnevHmaZSkpKXL58uVle3t7OTU1VZZlWT569KgMyO7u7lrHValUsqenp+zl5SWrVCqturm5uclNmjTRLLOwsJCHDBnyxueZ29f8fR0nPT1ddnNzk11dXeWoqCitspmv1eTJk7X2v2DBAhmQw8LCcqzro0ePsnxuM+5ZRESEZtn169dlhUIh9+jRI8vx+vTpo7XPdu3ayTY2Nm9Uj5xk99n08vKS3d3dNT/HxMTIBgYG8qhRo7TK/fzzz1qf4dy+3tnJ+Hy/6vcQkCdPnqz5uXXr1rKxsbEcGBioWebt7S3r6urKLz6qALK+vr7s4+OjWXb9+nUZkBcvXqxZNmfOHBmQHz16pLX9iBEjZEC+du2a1vKUlBQ5LCxM8+/F7zwg239t27aVk5OTtcr27NlTNjExkWVZljt27Cg3atRIlmVZViqVsqOjozx16lTNZ2/OnDkvvVaCILw/IpROEIQcybLM1q1bad26NbIsEx4ervnn5eVFTEzMa4VvvUipVLJ//37atm1LoUKFNMtLlCiBl5eXVtlt27ahUqno3Lmz1vEdHR3x9PTk6NGjAJoeof3792cJY3oVIyMjzf8TEhIIDw+nZs2ayLLM1atXs5QfNGhQtvvR1dVlwIABmp/19fUZMGAAoaGhXL58Watsz549tY577do1TXhNRESE5jwTEhJo1KgRJ06cQKVSAWBpacn58+cJCgp67XPMi2v+vo5z9epVHj16xPDhw7P0VL0sPXdG2R07dmiu3as8ffqUa9eu0atXL6ytrTXLy5YtS5MmTdizZ0+WbQYOHKj1c506dYiIiNCER75NPTJk/ozExMQQHh5OvXr18PX11YSImpub07x5czZt2oScac72jRs3Ur16dc19yOvr/baUSiWHDh2ibdu2ODs7a5YXKVKE5s2bZ7tN48aN8fDw0PxctmxZzM3N8fX1feXxMu6Lqamp1vI9e/ZgZ2en+efq6ppl2zZt2nDw4EEOHjzIjh07GDduHPv27aN79+5a1z6z7t27c+zYMYKDgzly5AjBwcEijE4QPlCiYSQIQo7CwsKIjo5mxYoVWg8MdnZ29O7dG4DQ0NC32m9SUhKenp5Z1hUrVkzrZ29vb2RZxtPTM0sd7t69qzm+m5sbI0eOZOXKldja2uLl5cWvv/76yvFFAAEBAZoHYVNTU+zs7KhXrx5Alu11dXVxcXHJdj/Ozs5ZEjEULVoUUI9lyczNzS3LeYK6wfTiea5cuZKUlBRNXX7++Wdu3bpFwYIFqVq1KlOmTHnlA2FeXPP3dZyHDx8CvPE8UV26dKFWrVp8/fXXODg40LVrVzZt2vTSxom/v3+2dQV14y6jsZpZ5gYggJWVFaAOrXzbemQ4ffo0jRs31ox1srOz04xNyvzZ7NKlC48fP+bs2bOA+ppdvnyZLl26aMrk9fV+W6GhoSQlJVGkSJEs67JbBlmvOaive8Y1fxkzMzNAnVUus1q1amkaPU2bNs12WxcXFxo3bkzjxo354osvmDlzJjNmzGDbtm3s2rUr220yxiNu3LiRv//+mypVquR4XoIg5C8xxkgQhBxlPLj973//o2fPntmWKVu2bJ7XQZIk9u7di46OTpb1md/6zps3j169erFjxw4OHDjAN998w6xZszh37lyOjRmlUkmTJk2IjIzk+++/p3jx4piYmBAYGEivXr2yPLwaGBjkSva4zD0BGecJMGfOnBzTnWeca+fOnalTpw7//vsvBw4cYM6cOcyePZtt27bl+Ib9TbzJNf+Qj2NkZMSJEyc4evQou3fvZt++fWzcuJGGDRty4MCBbI/5NnLaT0YPwtvW4+HDhzRq1IjixYszf/58ChYsiL6+Pnv27GHBggVan83WrVtjbGzMpk2bqFmzJps2bUKhUNCpUydNmfd1X9+HV13zlylevDgAt27doly5cprldnZ2NG7cGFAnqHhdjRo1AuDEiRO0bt06y3oDAwPat2/PmjVr8PX1feuJbQVByHuiYSQIQo7s7OwwMzNDqVRqHhhy8iYhNnZ2dhgZGWl6STK7f/++1s8eHh7Isoybm5um9+VlypQpQ5kyZZgwYQJnzpyhVq1aLFu2jBkzZmRb/ubNmzx48IA1a9Zo5icBsk3V+ypBQUFZ0nc/ePAAIMuA/RdlhAWZm5u/8loDODk5MXjwYAYPHkxoaCgVK1bkxx9/zLFhlJfXPK+Pk3Ftbt269VrXJjOFQkGjRo1o1KgR8+fPZ+bMmYwfP56jR49mu6+M8KkX6wpw7949bG1t3yo9+5vWA2Dnzp2kpKTw33//afWQZBfKaGJiQqtWrdi8eTPz589n48aN1KlTRys07X1c77dhb2+PoaEhPj4+WdZlt+x15fSd1Lx5c3R0dPj777+zZJV8G+np6UDWHqjMunfvzqpVq1AoFHTt2vWdjykIQt4QoXSCIORIR0eHDh06sHXrVm7dupVlfVhYmOb/GQ+LOc0y/+J+vby82L59OwEBAZrld+/eZf/+/Vpl27dvj46ODlOnTs3yNliWZSIiIgD1uIGMB5QMZcqUQaFQZJnB/sW6ZOwr837fJi1xenq6Vlrq1NRUli9fjp2dHZUqVXrptpUqVcLDw4O5c+dm+4CVca2VSmWW8D57e3ucnZ1feZ65fc3f13EqVqyIm5sbCxcuzPL5elkPQWRkZJZlGb1xOV0rJycnypcvz5o1a7SOdevWLQ4cOECLFi1yPF5u1gOy/2zGxMSwevXqbMt36dKFoKAgVq5cyfXr17XC6CDvr/fb0tHRoXHjxmzfvl1r3JyPjw979+596/3m9J1UqFAh+vTpw969e1myZEm2277Jee7cuRNAq/fpRQ0aNGD69OksWbIER0fH1963IAjvl+gxEgThpX766SeOHj1KtWrV6NevHyVLliQyMpIrV65w6NAhzUOfh4cHlpaWLFu2DDMzM0xMTKhWrVqWsTQZpk6dyr59+6hTpw6DBw8mPT2dxYsXU6pUKW7cuKEp5+HhwYwZMxg3bhx+fn60bdsWMzMzHj16xL///kv//v0ZPXo0R44cYejQoXTq1ImiRYuSnp7O2rVrNY27nBQvXhwPDw9Gjx5NYGAg5ubmbN269bXGKrzI2dmZ2bNn4+fnR9GiRdm4cSPXrl1jxYoV6OnpvXRbhULBypUrad68OaVKlaJ3794UKFCAwMBAjh49irm5OTt37iQuLg4XFxc6duxIuXLlMDU15dChQ1y8eJF58+a99Bi5fc3f13EUCgVLly6ldevWlC9fnt69e+Pk5MS9e/e4fft2lgZXhmnTpnHixAlatmyJq6sroaGh/Pbbb7i4uFC7du0c6z9nzhyaN29OjRo16Nu3ryZdt4WFxVuFQb1tPZo2bYq+vj6tW7dmwIABxMfH8/vvv2Nvb8/Tp0+zlM8YyzJ69OhsP/d5fb0z27p1K/fu3cuyvGfPnhQsWDDL8ilTpnDgwAFq1arFoEGDUCqVLFmyhNKlS3Pt2rVXHi87GS8jxo8fT9euXdHT06N169aYmJiwcOFCHj16xLBhw9iwYQOtW7fG3t6e8PBwTp8+zc6dO7MdZ/bgwQNNmF1iYiLnzp1jzZo1FClShK+++irHuigUCiZMmPBW5yEIwnv0XnLfCYLwUQsJCZGHDBkiFyxYUNbT05MdHR3lRo0ayStWrNAqt2PHDrlkyZKaFLuvSt19/PhxuVKlSrK+vr7s7u4uL1u2LEvK5Qxbt26Va9euLZuYmMgmJiZy8eLF5SFDhsj379+XZVmWfX195T59+sgeHh6yoaGhbG1tLTdo0EA+dOjQK8/vzp07cuPGjWVTU1PZ1tZW7tevnyb9b+ZzyJyC90X16tWTS5UqJV+6dEmuUaOGbGhoKLu6uspLlizRKveqdMZXr16V27dvL9vY2MgGBgayq6ur3LlzZ/nw4cOyLKtTCo8ZM0YuV66cbGZmJpuYmMjlypWTf/vtt1eepyzn7jV/38c5deqU3KRJE815ly1bVis984v7P3z4sNymTRvZ2dlZ1tfXl52dneVu3brJDx480JTJLl23LMvyoUOH5Fq1aslGRkayubm53Lp1a/nOnTtaZTKO92Ia7tWrV2uliX6deuTkv//+k8uWLSsbGhrKhQsXlmfPni2vWrUq2zTUsizLX375pQzIjRs3znGfuXW9s5Px+c7p38mTJ2VZzpquO+M6VahQQdbX15c9PDzklStXyqNGjZINDQ21ygHZpqt3dXWVe/bsqbVs+vTpcoECBWSFQpHlmqWnp8urV6+WGzZsKFtbW8u6urqyra2t3KhRI3nZsmVyUlJSluNm/qejoyO7uLjI/fv3l0NCQrTKvuy7IoNI1y0IHx5JlvOgX1wQBEEQBOEdtW3bltu3b2c7Zk0QBCG3iTFGgiAIgiDku6SkJK2fvb292bNnD/Xr18+fCgmC8NkRPUaCIAiCIOQ7JycnevXqhbu7O/7+/ixdupSUlBSuXr2a7bxYgiAIuU0kXxAEQRAEId81a9aMf/75h+DgYAwMDKhRowYzZ84UjSJBEN4b0WMkCIIgCIIgCMJnT4wxEgRBEARBEAThsycaRoIgCIIgCIIgfPY+mTFGKpWKoKAgzMzMkCQpv6sjCIIgCIIgCEI+kWWZuLg4nJ2dUShery/ok2kYBQUFZTubtiAIgiAIgiAIn6fHjx/j4uLyWmU/mYaRmZkZoD55c3PzfK6NIAiCIAiCIAj5JTY2loIFC2raCK/jk2kYZYTPmZubi4aRIAiCIAiCIAhvNMRGJF8QBEEQBEEQBOGzJxpGgiAIgiAIgiB89kTDSBAEQRAEQRCEz55oGAmCIAiCIAiC8NkTDSNBEARBEARBED57omEkCIIgCIIgCMJnTzSMBEEQBEEQBEH47ImGkSAIgiAIgiAInz3RMBIEQRAEQRAE4bP3Vg2jX3/9lcKFC2NoaEi1atW4cOFCjmXT0tKYNm0aHh4eGBoaUq5cOfbt2/dO+xQEQRAEQRAEQchNb9ww2rhxIyNHjmTy5MlcuXKFcuXK4eXlRWhoaLblJ0yYwPLly1m8eDF37txh4MCBtGvXjqtXr771PgVBED5Wiw97M33XHdKUqvyuiiAIgiAImUiyLMtvskG1atWoUqUKS5YsAUClUlGwYEGGDRvG2LFjs5R3dnZm/PjxDBkyRLOsQ4cOGBkZsW7durfaZ3ZiY2OxsLAgJiYGc3PzNzklQRCE9+JpTBI1Zh0BoFvVgsxsVwZJkvK5VoIgCILw6XmbtsEb9RilpqZy+fJlGjdu/HwHCgWNGzfm7Nmz2W6TkpKCoaGh1jIjIyNOnTr11vvM2G9sbKzWP0EQhA/ZKe9wzf//ufCY3449zMfaCIIgCIKQ2Rs1jMLDw1EqlTg4OGgtd3BwIDg4ONttvLy8mD9/Pt7e3qhUKg4ePMi2bdt4+vTpW+8TYNasWVhYWGj+FSxY8E1ORRAE4b077aNuGBVzMANgzv777LgWmJ9VEgRBEAThmTzPSrdo0SI8PT0pXrw4+vr6DB06lN69e6NQvNuhx40bR0xMjObf48ePc6nGgiAIuU+WZU55h/PN1c3MufgH4xziARiz+QbnfSPyuXaCIAiCILxR68TW1hYdHR1CQkK0loeEhODo6JjtNnZ2dmzfvp2EhAT8/f25d+8epqamuLu7v/U+AQwMDDA3N9f6JwiC8KF6EBKP9RMfmvufR+/KBeoun8KKa6twD3tEv78u4RMal99VFARBEITP2hs1jPT19alUqRKHDx/WLFOpVBw+fJgaNWq8dFtDQ0MKFChAeno6W7dupU2bNu+8T0EQhI/FKZ9wGgVcBkDPxQX09Cjod4cFJxYz4tgKxs/bTlhcSj7XUhAEQRA+X28czzZy5Eh+//131qxZw927dxk0aBAJCQn07t0bgB49ejBu3DhN+fPnz7Nt2zZ8fX05efIkzZo1Q6VS8d133732PgVBED52Z+8HUy9QPU2B4+RJFNm3F4sO7UGhoHrwHabsmMmh7v2IeeCTzzUVBEEQhM+T7ptu0KVLF8LCwpg0aRLBwcGUL1+effv2aZInBAQEaI0fSk5OZsKECfj6+mJqakqLFi1Yu3YtlpaWr71P4dUm77jFWd8INvSvgbWJfn5XRxCETFLTVaSeOY1FaiKylTUmNWog6eri/OOP2PTti9/chaiOHKS890WetGlDfPt22A8ZjJ6zc35XXRAEQRA+G288j9GH6nOexygmMY2KMw6iVMlMbl2S3rXc8rtKgiBkcuFRJNf6DqJO0A2sevbEcVzW+dmuHDnHrWlzqBJ8BwBJTw/Lrl2xHdAfXVvb911lQRAEQfio5fk8RsKH6YR3GEqVun275+bTfK6NIAgvOnfDj+rBtwGwbNsm2zIVG1bH5pcljKwzlOu2HshpaUStXYtPk6aEzl+AMibmfVZZEARBED47omH0CThyL1Tz/0v+UYTEJudjbQRBeFHC/v3oqZQkuRTGoHjxHMu1LOtE+6+aM7bWQH6o1Z8Uj2LISUlErFiBT+MmhC9bhioh4T3WXBAEQRA+H6Jh9JFTqmSO3Vc3jCyM9JBl2Ct6jQThgxGXnIbn9ZMAWLZpgyRJLy3/dR03etQszFW7onQp35+UyT9h4OmJKi6OsIWL8GnSlMi//kKVIjLYCYIgCEJuEg2jj9y1x9FEJaYx8crf/LVrMrZJ0ey5GZzf1RIE4ZlLZ25RKuIRKiQKdm73yvKSJDG5dSkal7AnRSnTx8cYaeU6nOfMQc+1EMrISEJmzuKhVzOiNm9GTkt7D2chCIIgCJ8+0TD6yB25F4JDQgQ1A66iHxdNM7/zXPSPJFSE0wnCByHs3x0AhBQpjd5rZtrUUUj80q0CZV0siExIpfdfl1E2bIrHrl04TpuKrqMj6cHBBE+cxMNWrYjZtRtZpcrL0xAEQRA+IrH79hOze3d+V+OjIxpGH7kj98Jo8OSq5ueWgZeRVCr23Ra9RoKQ32RZxuH8EQD0mrd8o22N9XVZ2bMyBSyN8ItIpN9fl0hBgVXnznjs34fDuLHoWFuT5h9A0OjRPGrbjrgjR/hEEo0KgiAIbynh3DkChw8naNRo4o4eze/qfFREw+gjFhSdxN2gGBo8vqJZZhkfSflQb3bfEOOMBCG/BZ6+gH1sGMk6epTu0vaNt7c3M2RNnyqYG+py2T+KUZuuo1LJKAwMsO7ZkyIHD2A3/FsUZmakPHjAk8FD8OvSlYSzZ3P/ZARBEIQPnjI+gcAfxmt+DpowkfSoqHys0cdFNIw+Ykfvh1IkJpBC8aFIBgZYtG0LgJf/eS74RRIaJ8LpBCE/BWzYCsCdIpWwsrV4q30UsTdjRY/K6OlI7L75lJ/23dOsU5iYYDtwIEUOHcSmf38kIyOSb9wgoHcf/Hv2IvHq1ZfsWRAEQfjUhM6bizIoiGBjK/zNHFBFRBA8eYqIJnhNomH0ETt6L5SGjy8DYNaoIdY9ewBQM/g2ZskJ7L8lwukEIb+oUlMxOq0Oo0tp6PVO+6rubsOcjuUAWHHCl7/O+mmt17GwwH7kCIocPIDVV18h6emReP48/t2683jgIJLv3ctmr4IgCJB8/wF+//sfsQcP5ndVhHeUcO4c0f9sAGBhhc7MqdSNdElB3IEDxO7alc+1+ziIhtFHKjlNyZkHIdR7cg0A89atMSxRAoOSJdBVKWnw5Aq7RdpuQcg38ceOY5iUQLihOcWbNXjn/bWtUIDRTYsCMOW/2xy6E5KljK6tLY7jf8Bj/z4sOnYAHR3ijx3jUdt2BI4chTI+/p3rIQjCp0OWZZ5OmkjSpcsEjRpN0s1b+V0l4S2pEhJ4On4CALvcamBbpxayZzH+Lt4UgOBp00l7Kp4LX0U0jD5SZ30jKB70AOuUOHQsLTGtXRsAyw4dAPDyv8AF3wjC4sRcJ4KQH4I2bwPgZKFKVHS3zZV9DmlQhK5VCqKSYdg/V7nxJDrbcnrOzjjPmIH7rp2Yt2gBQOyePYQt+iVX6iEIwqchdtcukq/fAEBOTeXJsGGkh4fnc62EtxEydy5pgYEEG1uxrtwXTPmiFN828mSTZwO8rQuhiosj6IcfRAbTVxANo49U5jA68xYtkPT0ALBo1QpJXx+32KcUiXosstMJQj5Ij4pCeeYUAOE1G2Kop5Mr+5UkieltS1O3qB1JaUr6/HmJx5GJOZY3cHOjwPx5uPz2KwDRmzaRFhqaK3URBOHjpkpMJHTuPABsvu6Lvpsb6cHBPPl2OHJqaj7XTngTL4bQ9W5ckoLWxrQq64y7owWzK3RFqadP4tlzRK3/J59r+2ETDaOPkCzLnLoRQK2nNwGw+KK1Zp2OhQVmTdXdpk39L7JHZKcTPgLJd+8S9ttvqFI+jR7OuH37UCjTeWjuTNEaFXJ133o6Cn7tXoHijmaEx6fQ+8+LxCS+fJJX0wYNMCpfHjklhchVq3O1PoIgfJwiVq0mPSQEPWdnbIcOxeXXX1GYmpJ0+TLBs2bld/WE1/RiCF1ksbIMrOcBqOfE+7aRJ4Fm9vxZRv2sGDp3Lim+j/Ktvh860TD6CHmHxuN67zKGyjR0CxbEsFw5rfWWHdXhdPWfXOWa91PC4z+Nh03h06SMT+DxwEGE/7KYiJUr87s6uSJ6x38AHC5UidpFcieMLjMzQz1W966Co7khPqHx9F97iZR0ZY7lJUnCdshgAKI2bCA9IiLX6/QhkGWZJ8NH4F2vPk+GfUPEn3+SdOMGctrLG46C8LlJCw7WfN/ajxmNwtAQA3c3nOfOAUki+p8NRG3alM+1FF5H6Lx5pAUGEmJsxaqSLZncqpRWlELLMk4UdTBla8FqhBcvj5ycTNDYscjp6flY6w+XaBh9hA7fDaXBszA6yy++QJIkrfXGVaui5+KCSXoyNQJvsk9kpxM+YOGLF5Meok4kELV2HarEnEPDPgapfn4kX7uGEolrnlUo6WSeJ8dxsjBide8qmBrocv5RJN9vufHSdKwmtWtjWLYscnIyEatW5Umd8lv8kSPE7dtHekgIcQcPEvrTbPw6d+F+1Wr49+xF6KJFxJ88JZJQCJ+90HnzkZOTMapUCbNmzTTLzerXx+7bbwAInj6DxCsi5f+HLOHc89C4BRU6U6NMIRqXdNAqo1BIfNuoKLKkYIJnWyRTM5Jv3CB8xYr8qPIHTzSMPkIXLntTMcwbAIvWrbKslxQKLNq3A9RzGu0R2emED1Ty3btErlsHgMLCAmV0NNGbN+dzrd5NzH87AbhqX5SSZTxQKKRXbPH2SjiZ89uXFdFRSGy/FsS8Aw9yLCtJEraDBwEQtf4f0iMj86xe+UFWKglbuAgAiw7tsRs1EtMGDdCxsEBOSiLx/Hkili7jcb9+PKhaDd927QmeNp2Y3btJCxYvj4TPR9K1a8Tu3AmShMO4cVlertoMGKAOyU9L48m335AWkjUDppD/MofQ7S5cg7tOxZjcumS2ZZuXdqS4oxn+OqZcbfc1AOG/LSXp1u33Vt+PhWgYfWSiE1OxOH8cHVmFolRp9AsXzracZbt2IEmUjfDF/8Z9EU4nfHBklYrgKVNBqcSseTPsR44EIGL1nx/twF9Zlon571kYXcG8CaN7Ud2idsxqVwaAJUd92HAhIMeypvXqYViyJHJSEpF/rsnzur1Psbt3k+LtjcLcHIfvvsO2Xz8KLv0Nz7NncN+1E8epU7Fo0wa9ggVBpSLl7l2i1q8naNRofOo3wLthQwJHjyHqn39Ivn8fWZlzaKIgfKxkWdaMH7Jo2xaj0qV4FJ7AgLWXOPNQnY1OkiScZ83EwNMTZVg4T4Z988mM//yUZITQhZtY80eplgys646rjUm2ZRUKieGNPQH4MaUQBo2aQHo6Qd9/jyo5+X1W+4MnGkYfmeMPwqgfoA6js2vXNsdyek5OmDxL4d3Y/yL7RXY64QMTvXkLSdevozAxwWHsOCzatUXXzo704GBidn6cE9ElXblC2pMnJOoacNap9HtpGAF0rlKQYQ2LADB++y2OPwjLtpzWWKN161BGR7+X+uU1OTWVsMVLALD5+mt0LCw06ySFAoMiRbDq0hnn2T9R5OABipw4ToGFC7Hq8RWGpUqBjg7pQU+J3bWL4KnTeNSmLQ+q1yCgf3/Cly0j4cIFVElJ+XV6gpBrYnftJvn6DSRjY+yGDyddqWL4hqvsvx3CwLWXNVkuFSYmuPy6BIWFBck3bhA8ddpLQ3WF9yvh3HlNCN3c8p2wtrNiUP0iL92maUlHSjiZE5+q5L8G/0PH1pbUhw81Pe2CmmgYfWSunLpGsejHqBQKzJs3e2nZjDmNGgdcZN/1wPdRPUF4LekREYTOnw+A3bffoOdgj0JfH+tevQCIWLnyo3xjH/Ms6cIp5zI42FtS0Nr4vR17ZJOitKtQAKVKZvC6y9wJis22nGnDhhgUL44qMZGINZ9Gr1H01q2kPX6Mjq0t1v/78pXl9eztMW/mheMPP+C2dQvFLpyn0OpV2A4biknNmiiMjVHFxZFw4iRhCxcR0KMn96tW41GXLoTM/pm4Q4c+uVBE4dOnSkoidJ46Pbdt/37oOdiz8tQjrj+JASA2OZ3Bf18hOU393atfqBAF5s8DhYKYbduI+nt9vtVdeE4dQjcegL1uNblu58mk1iUx0n/5tBCZe41+vxGJ2YTJAESuWUPC+Qt5W+mPiGgYfUSUKhm9YwcBUFWqhq6NzUvLmzZsABYW2CbHknr2DJEJH2d4kvDpCf15DqqYGAxKlMCqe3fNcssuXVCYm5P66BFxhw7nYw3fnColhdh9+wA4UrAStd5Tb1EGSZKY3aEsNdxtSEhV0ufPizyNydrLoTXWaO06lDEx77WeuU2VlET4b0sBsB04EIXxmzdGFSYmmNSogd2QIRRa9QdFL5yn8NYtOIwfj1nzZuja20NaGsnXbxC5ejVPhg7Du2YtHjZrTtD48URv3UpqQM4hjILwIYj4YxXpwcHoOTtj3asXD8PimX9QPS5xZJOiWBnrcTMwhhm772i2Ma1VC/vRowEImTVLPEB/ADJC6GLMbfm9ZAvqFbWj6QsJF3LStKQDpZzNSUhVskZ2wbJTJ5BlgsaNFUlpnhENo4/IVf9IavpeBMClU7tXllfo62Pdtg0ATfzOi3A64YOQcOECMTt2gCThNGUykq4usiyTnKZEx9QEqy/VDaWIFSs+qtCN+KPHUMXGEmVixQ1bj/cWRpeZvq6CZV9VwtPelODYZHqvvkhsctZU1WaNG2Pg6YkqPp7Iteveez1zU9T69aSHhaFXoABWnTvlyj4lXV2MSpXC+qv/4bJgAUWOH8Pj0CGcf56NZZcuGHiq37qm+vkRs3UbT8dP4GFTL0Lnzv2oPrPC5+PF9NyyvgHfbblBarqKukXtGNawCAu6lEeSYN25AHZcex5lYt27F+atW4NSSeDw4aQFigiU/JI5hO6nMh1QGhoz5YtSWRJo5ESSJEY0LgrAX2f90Bk6HD0XF9KDnhIyU8xdBaJh9FG5uu8kTomRpOobYtGk8WttY/EsnK5a8B2On7uXl9UThFeSU1MJnjoNAMsunTF6NgfXpB23KTNlP9ceR2P91VdIhoYk375Nwpkz+VndN5KRdOGAcwVQKKjhkbVHVyWrGHN8DO12tGPa2Wns9t1NcELuvrCwMFLPcWRnZsC94DiG/H2FNKVKq4ykUGh6jSL/+gtlXFyu1uF9UcbFEbHidwBshw5F0tfPk+NIkoS+SwEsvvgCp6lTcN/5H0XPncVl2VJs+vfHqFIlACJW/sHTiRM/yjBQ4dMWOv9Zeu6KFTFr1oy/zvpx2T8KE30dZrUvgyRJ1C9mz9AG6nEq47bdxCdU/b0gSRJO06dhWLIkyqgoHg8bJsbc5YPMIXTHitXmmp0nX9dxw802+4QLOWlUwp6yLhYkpipZeTkE559mgSQRs20bcYc/rkiNvCAaRh+Tg+ownaRqdVAYGb3WJoZFiyKVKIWurML05CERTifkq4jVf5L68CE6NjbYjxgBwLXH0aw950+aUmbNGT90ra3V3ftAxO8fx4Sv6ZGRxJ84AcCRghUp5WyOtUnWh/STT06yz28fPtE+bH6wmbEnx9JkSxOab23OhFMT2O6zncdxj9+518HFyphVPatgpKfDSe9wfth2M8s+zby80C/ioe7lWvdx9hpFrl6NMiYGfQ8PLL5o/V6PrWNpiVn9+tiPHEHhv9fh9OOP6rEYW7YSOGIkqo80s6Lw6Um6fp3YZ9MIOIwbR0BkIj/vuw/AuBYlKGD5/HlieOOi1PSwITFVyaB1V0hMVU8CqjA0xGXJYnSsrUm5c5enEyaK3tH3LHTefNICA0m0tuOXIs1wtjBkaMOXJ1zIzou9RgnFymDTtw8ATydO+mQnAH9db9Uw+vXXXylcuDCGhoZUq1aNCxdeHnO6cOFCihUrhpGREQULFmTEiBEkZ0oPqFQqmThxIm5ubhgZGeHh4cH06dPFL10mT8JiKeutDqMr3LXDG23r0LUzoA6nO3BLzGkk5I/UJ08IX6oeC+Lw/Xfq+WVkmR8zxbPvuxVMfEo6Nr17ga4uiefOkXTjRj7V+PXF7tkL6emEO7sRYO6Y4/iiVbfUE6t6FfaiR8kelLQpiUJS8CT+CTse7mDi6Ym02NaCxlsa8/2J79l0fxO+0b5v9V1YxsWCJd0roJBg8+UnLD7io7VeUiiwHajuNYr4cw3K+IQ3PkZ+So+IIOJZynG7b79B0nn5wOO8ZtmhPQUWLUTS0yPuwAGeDByIKuHjuqbCp0eWZU2IlEW7dhiUKsXYrTdJSlNS3d2a7lULaZXXUUgs6loBezMDvEPjmfDvLc33j56zMy6LFoKuLrG7dxO5avX7Pp3PljqETp38YlapDiTpGTKhVUmM9XXfan/1i9lRvqAlyWkqlh9/iO0336jTs0dG8nTy5M/6+fuNG0YbN25k5MiRTJ48mStXrlCuXDm8vLwIDQ3Ntvz69esZO3YskydP5u7du/zxxx9s3LiRH374QVNm9uzZLF26lCVLlnD37l1mz57Nzz//zOLFi9/+zD4xV7fswTwtkThjCxzq136jbc1bNEepp0+huFCu7T+VRzUUhJzJskzI9BnIyckYV6umjldH3RC66BeFoZ6CApZGJKUp2XdLPTjY4lmZj2F2bk0YXYGKANmOL7oWeo0roVfQU+jxXZXvGFNlDBtbbeR019MsbbyUvqX7Ut6uPLoKXUITQ9nzaA/Tz02nzY421N9Un5HHRvL33b+5H3kflazKsv/sNCrhwNQ2pQGYf/ABR+9pf0+bN2+GfuHCqGJiNH90PxYRK1YgJyZiWLo0Zk2a5Hd1ADBv0oSCK5ajMDYm4cxZ/Hv3IT0qKr+rJXzGYnftJun6dU167n8uBnDWNwIjPR1mdyib7QTUdmYGLO5WAR2FxLargWy4+FizzrhKFRzGjQXUSQDiT4pnirymSkjg6QT1RK7Xyjfgkk0RahexpXlpx7fepyQ9z1C39pw/4ckqnH+eDXp6xB86TMz2HblS94/RGzeM5s+fT79+/ejduzclS5Zk2bJlGBsbs2rVqmzLnzlzhlq1atG9e3cKFy5M06ZN6datm1Yv05kzZ2jTpg0tW7akcOHCdOzYkaZNm76yJ+pzkr5/LwBRNRpk+2Z0zsU5dN3VlcjkrClkdczM0GukfnBwPH2AKBFOJ7xncYcOEX/8OOjp4Th5EpIkkZKu5Kd96nFv/eu4061qQQC2XXkCgM3XfUGSiD90mBQfnxz3nd9SfB+RfOMG6Ojwn21p9HUVVClsnaVcRm9Ra4/W2Bvba5ab6ptSu0BthlcaztoWaznT7Qwrm65kULlBVHGsgoGOAZHJkRz0P8hPF36i486O1N5Qm2GHh/HnrT+5GXaTdFV6jvX7qrorPWu4AjBn/32tN4GSjg62gwYC6rC0j6WHIy0oSDMA2W7EcK2Bx/MP3KffX5c4ei80X956mtSoQaE1f6JjaUnyjRv4f/UVaSEh770egvBieu5QAzNm7VF/547xKpbjZKAA1dxtGN20GACT/7vNrcDn2SutunfHokN7UKkIHDWKVH//PDwLIXTefNKePCHd1oFpBRqhpyO9UcKFnNQrakeFQpakpKtYevwhhiVKYDd0KAAhP/742SbZeKOGUWpqKpcvX6Zx4+cD/xUKBY0bN+bs2bPZblOzZk0uX76saeT4+vqyZ88eWrRooVXm8OHDPHigTht5/fp1Tp06RfPmzd/4hD5FiZHRuD24AkChLu2zrH8c95i1d9ZyO+I2/9z7J9t9FPyyCwB1Aq9z6MqjvKusILxAlZBAyI8zAbDp2wcDd3cA1p71xz8iETszAwbU86BdRRcAzvpGEBidhIGHB2aNGwEf9lijmP/Ub9aiS1cixsCMKoWtMNTTfnnhG+PL0cdHkZDoWarnS/dnpGtENadqDC4/mFVeqzjT7Qx/Nf+Lbyp8Qy3nWhjrGhOXGsexJ8eYd3ke3fd0p9Y/tRhwcAC/3/idKyFXSFVqv/wY3rgopga63Hkay4E72g/p5i1boudaCGVUFFEbNubCFcl7Yb/9hpyWhnHVqpjUrKlZftk/iiWnTnPE/yS9/zyP18ITbL70mNT01+thyy1GZcrg+vc6dB0cSPV5iH+37qT6+b3XOghCxKrn6bmtevZk3LabxKekU8nVip41C79y+wF13WlU3J7UdBVD1l/RZLiUJAnHyZMxKlcOVWwsT4YO/WheqnxsEs5f0PTmL67UmSQ9Q/rUdqOIvelLt0tTpjHg4AB67+tNQlr290aSJEY2UY81+vt8ACGxydj07YNR+fKo4uMJ+mE8sur9fnd+CN6oYRQeHo5SqcTBQTtfuoODA8HB2WdW6t69O9OmTaN27dro6enh4eFB/fr1tULpxo4dS9euXSlevDh6enpUqFCB4cOH8+WXOU/Ul5KSQmxsrNa/T9WNf7ZjoEon0MKRYrUrZVm/+cFmZNRvRjfe20hyenKWMkaVK5No74xxegp+23bmeZ0FIUPYr7+p/zi7uGA7UN07EZWQyi+HvQEY07QYJga6FLA0ooa7DbIM26+q31TZ9O8PQMzu3R/k2ytZpdIMaj7tVgUg2/FFf976E4AGBRvgbuH+RsfQ19Gngn0F+pXtx7Imyzjd7TT/tPyH0ZVHU9+lPmb6ZiSmJ3Im6Ay/XP2Fnvt6UvOfmvTZ34dfr/3KuafnMDRQ0uvZg9CiQ97avUa6utgOUN+XiFWrPvhsUym+j4j5dzuQtbdo9sELGBf+DeNCqzB1W8rD2JuM2XKDOj8fYdnxh9mmLs8rBh4eFF7/N/qurqQFBeH35f9Ivnv3vR1f+Lyp03P/AajTc2+7Hc6JB2Ho6yqY3aEsOtmE0L1IoZCY17kcBSyN8I9IZMzm65rvDoW+PgV++QVdOztSvH0IGjvus3yIzkuZs9A9ruXFASNXHM0N+aah5yu3XX17NWeCznAp5BLjT43PMfy6dhFbKrtakZquYumxh0i6ujjP/gnJyIjE8+eJWrs2V8/pY5DnWemOHTvGzJkz+e2337hy5Qrbtm1j9+7dTJ8+XVNm06ZN/P3336xfv54rV66wZs0a5s6dy5qXzMo+a9YsLCwsNP8KFiyY16eSb5L27AYgtHoDFArtW5aiTOFf738B0FfoE5USxW7f3Vn2IUkSZu3Ucx+5XzhMdKIIpxPyXvL9+0Q++z12nDgBhaEhAIsOexObnE5xRzM6VHLRlG9fsQAAW688QZZljMqUwbhGdUhPJ+IDHOibeOkSaUFBKExNWa+jDld7cXxRaGIoO33VjafepXu/8zF1FbqUti1Nz1I9WdxoMae6nmJL6y2MrTqWJq5NsDa0JkWZwsXgiyy7vox+B/pRc31NbipnY2oSm22vkUXrVui5uKCMiCBq44fdaxS+ZDEolZg2aIBxhQqa5ed8I7iesAFJJwUAyfAxxoWXY1l4PaFJgfy09x41Zx3hx913sp34Ni/oFSiA6/q/MShRAmVEBP5f9SDx0qX3cmzh8xY6fz5yUhJGFSuSWLM+03epk9yMaFz0lb0NmVka6/PrlxXR05HYfzuEP049jzjRc7DHZfEv6oQjBw8SsXx5rp/H5ywjhE5ycOQ723oAjG9ZAhODlydcCIgNYPl19b2QkDgccJiVN7OPupAkiRHPeo3Wnw/gaUwS+q6uOHz/naYOH3Ioe154o4aRra0tOjo6hLwQLx0SEoKjY/aDwCZOnMhXX33F119/TZkyZWjXrh0zZ85k1qxZqJ69XRgzZoym16hMmTJ89dVXjBgxglmzcp5saty4ccTExGj+PX78OMeyH7PUp0+xf3gbAOf2bbKs3++3n+iUaJxMnBhaQR0buvbO2mxj6926d0YlSZSM8OP4wYt5W3HhsyerVARPmQpKJWZNm2JaT/3F7hsWz7pz6pj0CS1Lar25bF7GCSM9HXzDErj2OBoA22e9RtFbtnxwaUQzki6k1m5ApFKBhZEepZwttMqsu7OOdFU6Fe0rUt6+fK7XQSEpKGZdjC9LfMn8+vM51vkYO9ruYFKNSbRwa4G9sT3pcjpXwi7iXGQXIGftNdLTw2aA+jpH/PEHquSsvc4fguQ7d9QZAAG74d9qlsuyzI+HDqJncRmARQ0W0aloJxSSAqXRDcw9F+Lotp/4tFh+P/mIOrOPMnLjNe4+zftIA10bG1z/WoNx5cqo4uMJ6Ps1cceO5flxhc9X5vTc9uPGMmH7bWKT0ynrYkG/Om5Zyt+PvE+vfb349dqvxKVmndOsfEFLJrQsCcBPe+9x2f/5WGaj8uVxmDQRgLBFvxB35GhenNJnJ3MI3aYGPYmW9KnpYUOrsk4v3U6WZaadm0aqKpUaTjWYXGMyAEuuLuHEkxPZblPTw4aqbtakKlX8dvQhAJZdumBSuzZyaipB349FTnt/ve357Y0aRvr6+lSqVInDmSaAUqlUHD58mBo1amS7TWJiYpZeDp1nyQMy/jDnVEb1km5ZAwMDzM3Ntf59ih5u3IYCmdu27lSrUTrL+o331G93OxXtRMeiHTHRM+FhzEPOBGWdGFPPwZ6IUpUBiNq6LW8rLnz2YrZtI+nqVRTGxjj8ME6zfNbee6SrZBoWt6e2p3bviqmBLs2eZdrZdkUdOmdcvTqGpUsjp6QQ+QF166uSk4nbtx+Aa8XV3381PWy0GnpxqXFserAJgD6l+7yXekmShLuFO52KdmJ23dkc6niITa02oa/Q52nqDUyt72bba2TZpg16zs4ow8KJ3rzlvdT1TYUuWgSox0UZFiumWX7KOxwf5TokSaahSzMaFmrIpBqT2NJ6C7UK1EIlp5NgeBSHkgsoUuQK6ao0tl0NpPmik/RYdYHTPuF5mqhBx8yMgit/x7R+feSUFJ4MHUbMThHSLOS+F9NzH1Rac+huCHo6Ej93LIuuTtaok+9OfMflkMssu76M5tua8+etP7OE5Peo4UrLsk6kq2SGrr+qNSeiVadOWHXvBkDQmDGk+Prm8Vl+2jKH0CU2bc2qZDt0FRJTXyPhwi7fXZx/eh4DHQMmVp9Ih6Id6Fy0MzIyY0+MxT82a6KMzPMabbz4mMDoJPWkvj/OQGFhQfLt24Qv+3x6A984lG7kyJH8/vvvrFmzhrt37zJo0CASEhLo3VsdItKjRw/GjXv+ENS6dWuWLl3Khg0bePToEQcPHmTixIm0bt1a00Bq3bo1P/74I7t378bPz49///2X+fPn0+5Z6NfnLH7nLgCeVKqLkb72gO7bEbe5EX4DXYUu7TzbYaZvRrsi6mv2152/st2fUzf1nEZFr58kOiYxD2sufM7So6IInTMXANthw9B71qN85mE4B++EoKOQ+KFF8Wy3zQin23kjiJR0JZIkYdO/HwBRf69HGR//Hs7g1eKPHEEVH4+usxO7UWeZe3F80ab7m0hIS6CIZRHquNTJj2oiSRIlbErQp4y6YWbqtAek1Ky9Rvr6mjFdEb//jiolJV/qm5PEy5dJOH4CdHSw+2aYZrksy0w9uh5dY390MGBc9VGadZ5WnixrvIzljZdTxLIIico4QvQ2UaTiMqqVCkQhyZx4EMaXK8/TeskpdlwLJF2ZN+MkFIaGuCz+BfMvWkN6OkFjviNy3d95cizh8xW7e48mPbdO/0FM+U8dcTK0gSfFHbO+QF52fRm+Mb5YG1rjbuFOTEoM8y7Po+W2lmy6v4k01fOEC7M7lMXd1oSnMckM33gNler594fDuHHqXtGEBJ4MHoIyLmvPk/B6QucvIO3JE3SdnBjvUB+A3rUK4+lg9tLtopOjmXNxDgADyw2koLl6iMnYqmMpb1eeuLQ4vj3ybbbJGGp42FDdXd1r9OtRdeicnoMDjs96A8OXLSPp5s3cOsUP2hs3jLp06cLcuXOZNGkS5cuX59q1a+zbt0+TkCEgIICnT59PIjphwgRGjRrFhAkTKFmyJH379sXLy4vlmWJRFy9eTMeOHRk8eDAlSpRg9OjRDBgwQGsc0uco+f59TAP9SFPoYP9FyyzrN91Xv4lu4toEWyP1A9mXJb5EISk4E3QG7yjvLNsU+cKLWCNzrFLiOLfh43xjKaenIyuV+V0N4SVC58xFGRODQbFiWH/1PwBUKpkfd6sHn3evWogi9tl/ydf0sMXR3JDoxDSO3gsDwKxxY/Td3VHFxRG9YcP7OYlXiNmhDqMzbtmKy4/VqWwzjy9KVaay7u46AHqV6oVCyvMhnS/Vp3QfnE2cSVCFY+pwPPuxRu3boevoSHpoKNFbt+ZTTbOSZZnQ+QsAsOzQAX1XV826A3ce81RnMwBfleiFo0nWsO6aBWqypfUWJteYjI2hDSFJT7ijWkzVmhtpXSUdQz0FtwJj+XbDNerNOcaqU49ISMk5BfrbkvT0cP7pJ6z+p/6dCJkxg7Bff/2sJ1MUco8qKYnQuc9eSPXvx7SzYUQlplHCyZzBDTyylL8dcZvVt9RjNydVn8TWL7YyvdZ0nEycCE0KZfq56bTd3pY9vntQySpMDXT57X8VMdRTcOJBGEuOPh97IunpUWDRQnSdnEj18yNo9Bjxd/otJJy/QNTf6hcmFzoN4l6cjL2ZAd8+69F5mXmX5xGVEkURyyJa2U/1dPSYX38+dkZ2PIx5yMTTE7P9zsnoNdp86TFPotQvzi1atsS8RQtQKgn67vsPPjlPbnirv9RDhw7F39+flJQUzp8/T7Vq1TTrjh07xp9//qn5WVdXl8mTJ+Pj40NSUhIBAQH8+uuvWFpaasqYmZmxcOFC/P39SUpK4uHDh8yYMQN9ff23PrFPQei27QBccChBvUraX2oxKTHs8d0DQNdiXTXLXcxcaFRIneI446EsM0lPj8ha6vVpO7fnQa3zVlpwMD4NGvKgeg2efDuc6C1bxBwhH5jEy5eJ2aYO1XScMhlJVz1QdNvVQG4HxWJmoKuZWC47OgqJthWeJ2EAkBQKbL7+GoCINWvyvTcjPTyc+FPqiQ19y9clTSlTwNIIVxtjTZmdD3cSnhSOg7EDLdxa5LSr98ZI14jvqqgH1OpYHUfSC8/Sa6TQ18em37PrvOJ3VKkfRpKWhFOnSLp8GUlfH9shgzXLZVlmxqmlKPRiMFbYMqTi1znuQ0ehQ8eiHdndfjf9y/bHUMeQ25HXOBY/gRYNj9GvvjU2JvoERicxbdcdav50hLn77xMWl7ufNUmhwGH8D9gOU48JDV+8hJCZs0RGL+GdZU7PfblKM3bfeIqOQmJOx7LovRBCl6ZMY+LpiShlJV6FvWjk2ghdhS5ti7RlV7tdjK06FmtDawLiAvj+5Pd02tmJ44+PU8zBjOnPJo1ecOgBp33CNfvUtbHBZfFiJAMD4o8fJ2zx4vd6/h87VWKiJoROr017pj9VJ8kY37IEpq9IuHAx+CLbfbYDMLnGZPQUelrr7YztmF9/ProKXQ76H+SPW39k2Uc1dxtqFbEhTSlreo0AHCdNRNfOjtRHjzQvqD5l+fsKU8iRrFIR/SyM7n7pWhSwNNJa/9/D/0hWJuNp5UkF+wpa63qU7AHAroe7iEjKOli9SE91LLDbwxtEBgTlRfXzhCzLBE+bTnpYGKq4OOL27+fphIn41KuP7xdtCJ07l4TzF5A/kIe5z5GclkbwlCkAWHbqpMkalpiazpz96okFhzYsgo2pwUv3kxFOd/ReqCaW3aJVS3SdnFCGhRPz7795dAavJ3bPHlAqMSxThhPJ6sZQ7SK2mvhvlaziz9t/AvBVya/Q09HLaVfvVcNCDanpXBMV6Zg47c5+rFHHjuja25MeHKxJi52fZJWK0AXqP8ZWX36JXqbpIjZfu0WUvnqc1+jKIzHUNXzl/kz0TBhWYRg72+2ktXtrAA4G7OHf8GF82fw2E79wp7CNMTFJaSw56kOt2UcYt+0GD8NyL4RTkiTshgzB4dls9lFr1xI09vMa4CzkrszpuY2HDWf8XvWD7cB67pQuYJGl/O83f8c7yhsrAyt+qPaD1jp9HX2+LPEle9vvZViFYZjpmfEg6gFDjwyl576euLmE0LmyC7IM3264Skjs8/FIRqVL4TR9GgARy5YT+2wcpvBqGVnodJ2d+MWzGclpKqq6WfNFOeeXbpeqTGXaWfU171y0c45Jfsrbl2d8NXXD65crv3DyycksZZ73Gj3hcaS610jH0hKnmT8C6u+qhBzmLf1UiIbRByrxwkX0IsOJ0zPCvmlDrXUqWcXG++qkC12Ldc0yGK+cXTnK2pYlVZWqCbfLzLNKGXwd3NGRVdz4Y33enUQui9t/gPgjR0BPjwILF2I7dCiG5cqCJJHy4AERK/8goGdPHtSoyeOhQ4nauIm0oI+n4fcpiFyzhhRvH3SsrLAfNVKz/PcTjwiJTcHFyui1JhYs6mBGmQIWpKtkdl5X30NJXx+bZ2MZI/5YhZye+6FOrysjjM6iTRtO+ahfPtTKlEjiaMBR/GL9MNM3o2PRjvlSx+xIksTYqmPRVegimdxFx/Ru1l4jA4PnvXPLl+f7i4a4AwdIuXMXhYmJZqwZqEMz515cgKRIw16vOB2Lt3qj/TqaODKzzkw2tNpAZYfKpChTWH37D/56PJBBrcP59ctyVChkSWq6in8uPKbx/OP0++sSl/wiX73z12T9vy9xnvMz6OgQ+99Onnzz7QebEVCWZRIuXCBw9BgeDxj4wWWI/NyFLVigSc89N8WF8PgUitibMiybOW/uR97n9xu/A/BDtR+wNrTOdp/Gesb0L9ufvR320qd0Hwx1DLkaepXe+3sTZf4b7gWiCI9PZdj6q1pj8yy++ALrXr0ACBo3juT793P/hD8xmUPowgeM5j+fWHQUEtPavDrhwh83/8Av1g9bI1u+rfTtS8t2LNqRjkU7IiPz/cnvCYgN0FpfubA1dTxtSVfJLD7yfDiGaZ06WHbtAkDQuB9QfsJzh4qG0Qcq+lka4FPOZalfxkVr3bmn5/CP9cdEz4RW7lkfBiRJ4quSXwGw4f4GUpRZQ0ESG6vHLOkd2PVRxLcrY2II/nEGALb9vsa8mRd2Q4fgtnEjnmdO4zx3LhZtvkDHxgZVQgLxhw4TPHkyPg0b8bBVK0J+mk3CmTMfTGjQpygtMJCwX38DwP6779B5Fi4bEpvMsuPqFKBjmxfHUE8np11oyTynUQbLjh3QsbQk7fHjfHsTmeLjQ/Lt26CrS3r9RpqUz7U8bAD1A+SqW6sA9YsLEz2TfKlnTtws3DS9ykaOu7gTHJG116hzJ3RsbUkLCtKkJM8Pcno6YYt+AcC6d290raw065adP0KSwUWQJWbWm/DKh4eclLIpxSqvVSxssBBXc1cikyOZcX4aK32HMfILFZsH1qBxCQdkGQ7eCaHjsrO0/+00+24Fo1S9+3enRevWuPy6RB1+dPQoj7/u90ENXFdGRxO5Zg2+LVsR0KMnsbt2EX/8OAEfWD0/Z0nXr2te1jzu3p9t14JQSDCnY9ks37dpKnUIXbqcTqNCjfAq7PXK/VsYWDCi0gh2t99Nl2Jd0JV0Off0DGHmszEtuJ6LQfeZe+CB1jb2o0dhUrMGclIST4YMRRkdnWvn+6nJHEJn1qkT4x+roxB61HDNNmFGZr4xvvx+U93I/b7q95jrvzpD87iq4yhnV4641Di+PfotiWnaibiGP+s12nolEP+I54kaHL77Dr1ChUgPDibkxx9f/wQ/MqJh9AFSpaQQ8+yh73yRqlQoaKm1PiNF9xceX2CsZ/zi5gA0dm2Mk4kTkcmRmrFImZX7XwcSdQ2wjgoh7Mz53D2BPBA6dx7KsHD03dywGTBAa52ulRUWrVriPHs2nidPUHjLFuy+/QajihVBoSDV5yGRf/5JQJ++PKhWnccDBxG5fj2pT57kcDThbQTPnIWclIRx5cpYtH0+59a8A/dJSlNSsZAlLcu8fA6GzL4o54yuQuLGkxi8Q9QPYApjY6x6qBv9Eb//ni+N+owHENO6dTkXoX5LWtLJXBMeeDnkMjfCb6Cv0Kd7ie7vvX6vY0DZAdgb2SPpRaBvcyJrr5GhITZ9+wIQvmx5voV4xezYQeqjR+hYWmLd6/lg4jSlkt/vqMPrPE0aUK1AuXc6jiRJNCrUiH+/+JexVcdiYWCBT7QPgw8PZqX3OL77wpxDI+vRtUpB9HUUXAmIZuC6yzSef5y/z/uTnPZug8zN6ten0B8rUZiaknjpEv49e+Zrj4wsyyRdu0bQ2HF416tPyKyfSPX1RTI2xrJTR3RsbUm5e5fHAwd9FoOxP2SZ03Mbtf6C0TfUv6t9a7tRoZBVlvKrb63mbuRdzPXNmVD9zV4o2BvbM6H6BP5r9x+t3FshISGZ3sDEfT6rH8xmy7UbmrKSri7O8+ah5+JC2pMnBI4cma+9/B+yzCF0u2t2xDc8AVtTA83EqzmRZZnpZ6eTpkqjdoHaeLm+upEL6lDJ+fXnY2tki0+0DxNOT9D6/q/kakW9onYoVTKLjzwfa6QwNsb5p59AoSBmx3/E7j/wdif8gRMNow9Q/NFjSIkJhBhZ4lirmta8A8EJwRx7cgyALsW65LgPXYUu3YurH8r+uvNXlgfIom4OXPdQz2n08M8PO5wu4cIFojers045TZ+GwiDn8SmSQoFR6VLYDhpE4fV/U/TsGQosmI9F+/bo2tkhJyURf+wYIdOm87BxEx42a07wzJnEnzz1wYawfAzijhwh/vBh0NXFcfIkzR/b20ExbL6sboBOaFXyjf4I25gaUL+YOg32tquBmuXWX36JwtiYlPv3iT9+PBfP4tVklUoz/4zFF19wyludNS/zfEyrb6uzPLUp0kaTLfJDY6xnzOgqowHQtznK3XC/LL1GVl06o2NtTdqTJ8Ts2v3e66hKTSVsya8A2PTvj46pqWbdjOPrSNf1B5UB8xqPzbVj6uno8WWJL9ndbjc9SvZAV6HLmaAzdNrZiXU+cxndwplTYxswpIEH5oa6PApPYPy/t6j10xEWHHzAvlvBXPaPJCAikcTUN3sINK5cGde/1qBjY0PKnbv4f/k/0gIDX71hLlLGJxC1YQOP2rXHr2s3YrZvR05JwaB4cRynTMbzxHGcpk+n0MrfUZiZkXT5Mk++/Tbfwy0/Z5nTc68p2ZyQ2BQK2xgzskmxLGV9onxYdn0ZoE7h/LbfTwXNCjKrziy2fLGFBgUbIEky+paXmHqtBxNP/KgZ26xrZYXLr78iGRmRcOYsofPmv/2JfqISLjwPodP/fiILz6hDx8c1L4654cvHpm732c6lkEsY6hi+VSN3Qf0FmmQMGVEOGTIaZduuPOFR+PNeI+OKFTSh1sGTJ5MeFvbax/xYiIbRByjjwetowYo0LKmdenbzg82oZBVVHKvgYZk1/WZm7Yu2x1jXGJ9oH84GZR0sJ7VQDzw2OXf8g5kb5kWqlBSCJ6lnbrbs3BnjypXfaHsdCwvMmzfHeeaPFDlxHLd/t2E3cqR6Pzo6pPr5EfXXWh7368eD6jUI6N+fyL/Wkurnlwdn82lSJSYSPEMd5mjTuzcGnuqYdllWp+eWZWhdzpmK2by9fJUOz8Lptl8N1IQt6VhYYNlVnYkxYsXvuXEKry3xwgXSg4NRmJlhUr8ep7zVGZky5i/yjvLmxJMTSEj0KtXrvdbtTTUr3IwqjlWQFOkY2O/O2mtkbIxNX/XcRxHLlr33t73RGzaQ/vQpug4OmskjAWKT49nutwKAatZdcLN6/V7I12VhYMGYKmP4r81/NHFtgkpWsdV7Ky23tWT7o78Y2siVs+MaMalVSQpYGhGRkMqiw94MXHeZDkvPUnfOUUpO2k/JSfuo+/NROiw9w4C1lxj/700WHHzAunP+mkaUf0SCJjW4YcmSFP57HXrOzqT6+eH35f9Iefgw18/vRcl37vB00mS869YleMpUUu7dQzIwwKJdOwpv+Ae3f7dh1bWrpnFqWLw4BZcvQzI0JOHESXXiCJGa+b1TJSUROm8eAPHtu7P6vvoBdnaHslnmPUxXpTPpzCTSVGnUdambbRj+mypqVZRfGv7Cn15rMUwvBpKS7Y820GJbC5ZcXUJcahyGxYriPEvdoxW5erWY2DgTdQidOgGLZadO/BxuQVKaksquVppQ8pxEJkcy77L63g8uP5gCpi8vn53y9uUZV1U97+iiK4s4HXj6+bqCljQsbo9KhsWHtad+sRs6BIPixVFGR/N00uSPYjjGm3h5/j/hvUuPitK8BT9WsCIjitpp1qUp09j6QD23yMt6izKY65vT3rM96+6u46+7f1GzQE2t9TXbNMDvd3sKxYcSun0nTv/rlsOe8k/4smWk+vmha2eH/ehRr97gJSRJwrBECQxLlMC2vzo+PuHsWRJOniT+xEnSQ0JIOHGShBMnCZkJegULklKlJL4lLDnrEEu8bjqTakz6YHsB8kv40qWkBz1Fz9kZ28GDNMuP3AvlzMMI9HUVfOeV9e3l62hYwh4LIz2exiRz9mGEpmfGumdPotauJenKFRIvXXrjBvPbygijM2/enIB4JUExyejrKKhSWN3oy8hE19i1MYXMC72XOr0tSZIYV3UcnXZ2AvPb3A+4xIE7nniVev4yxqprVyJ+X0mqvz+xe/Zg8cUX76VuqoQEzUzrtoMHozB8nm3u+8MLUenEQpoNPzcZkqf1KGhekPn153Ml5ApzL83lZvhNFl9dzKb7m/i24rf0qtWSr2q4sufmU/bcfEpoXAphcSmEx6eQnKYiMVVJQGQiAZGvnkzbWF8HW1MDbE31Kdz+ezptmoNl8BPud+lO+ISfsKhQ7tl6A0xekbr3daiSkojds5eojRtJvvE8BErf3R2rrl2waNMGHYus2cw09a1YEZfFi3k8eDCxe/aiMDXDceqUtx7rJby5iFWrSH/6FB0nJ0ZTClDSs4Yr1dxtspRde2ctN8NvYqZnxqTqk3L1PlVyLM+/7f+i5e9/kGa+h0SjJyy/sZwN9zfQt3RfujXuhs2AAUQsX87TCRPRd3PHqHSpXDv+xyp0/gLSHj9G18kJ34592f3PLRQSTGtT+pX3Z+7FucSkxFDMqhj/K/m/t65Dp6KduBNxh63eWxlzYgwbW27UTAw7vLEnR+6Fsv1aIEMaFsHDTv1iRNLXx3n2bPw6diT+6FFitm3DskOHt67Dh0aSP5GmXmxsLBYWFsTExGBu/urBZx+qqA0bCJ4ylYcWzqzpPZ3NA583ZvY+2st3J77DzsiO/R33Z8lTn53HcY9pua0lMjLb22zP0ss0s+d42p3fRlKR4lTclb8pkF+UfP8Bjzp0gPR0CixahLlX0zw7lizLhNy6iN/+f0k7cwGr+0/RUT7/1UjVgTuFJPxruzFixEYMjE1fsrfPR/KDBzxqr75HLr/9hlnDBgCkKVV4LTyBb1gCA+t5MLZ58Zfu589bf7Lj4Q5+rvsznlbaWZQmbL/JunMBtK9QgPldymuWP500mehNmzCpV5dCmSaMziuqpCS8a9VGlZiI69/r2Jpmy8Ttt6jubs2G/jV4Gv+UFttakC6n80/LfyhtWzrP65QbZl+Yzbq761Cm2OGaNJE939TX+qMcvnwFYQsWoO/mhvuunUg6r5c8412EL1tG2MJF6LkWwmPXLiQ99XedX0wArf9tA1I6LezGMrvFl3lelwwqWcW+R/tYeGUhTxPUk5iXtCnJ6MqjqeJYRausLMskpCoJj0shLD6F8GeNpbD4VE3DKeNfWJy6EfUis9QEpp1dSfGoxyTqGjCtWi+u26l/N4z0dLAzUzeiDPV0kCTU4z0kdYNXAhSZ/p95uU14IGWuHqHkjVMYpKgbbEqFDr4lq3KncmOCXYsjKRTa+wQUUsb+wcXKmIH1PNBRSMTu20fgyFGgUmHTr59WNkoh76QFB/OweQvkpCROdx/OjEQXClgacWBE3SwN50cxj+j4X0dSValMqzmNdp7t8qROR++H0nv1BXTNblPI4wRhKeqMZ/ZG9gwo3Y9qi46SePwEuk5OuG3ZjK5N1gbc5yLhwgUCeqjHTTotX0GHS0oehiXQs4YrU9u8/G/Huafn6HegHxISf7f4mzJ2Zd6pLqnKVHrv682N8Bt4Wnmyrvk6zfj1r9dc5NDdUNqWd2ZhV+2pYSL++IPQOXNRGBvj9t8O9F1cstt9vnqbtoHoMfrAxPyn7mY+4lKRBsXttdZtuLcBUKdbfJ1GEahjgRsVasShgEOsvbOWKTWnaK03b9OG9AvbMfK5R4q3tyYMKr/JSiVPJ02E9HRMGzXCrGmTXN1/miqNB5EPuBZ2jeth17kRdoPA+ECwA9qAYYqC0v4yVf31qOgrYR6ZTPlHMuUf+XJva00c2nbCsmMHDEuWzNV6fUzU80pNU9+jxo00jSKAfy4E4BuWgLWJfrYzrmd27PExTUjAuJPj+KflP1rz/rSv6MK6cwHsvRXM9Lbpmj/6Nn37EL1lCwnHT5B87x6GxV/e+HpXcYcOo0pMRM/FBaOKFTm97gqgnr8IYO3dtaTL6VR1rPrRNIpAHYax23cPUYTxMHovB+6U0O41+rI7EatWkfroEbH79mHRsmWe1kcZHU3EH+p4d7th32gaRQBjDs8EKR0p2ZPJjV7da56bFJKCFu4taFioIevurmPlzZXcibhDn/19aFiwISMrj8TV3BVQN0JMDXQxNdClsO3LsxJmbkRlbiyFxadyteoMjP/8mUJ+t5l+9g/mVe/BcfuSJKW9fk8UgJ4ynVpBN2jhd5YyEY80y58a27DHrToHC1UmxsAMooHop6+1zzSliuGNi2LerBnKuDiCJ04i4vff0bEw14xBEPJORnru9JJlmJFQACR1CN2LjSKlSsmk05NIVaVS07kmbYu0zbM6NShmz5AGRfj1qETInTIMbxfHloerCEoIYvrFH/GsV4DJPnYQ+JTAb4dTaPUqrd/vz8WLIXQbceZh2D1sTPQZ2fTl0RXJ6clMPzsdgK7Fu75zowieJ2Poursr3lHeTDoziTl15yBJEsMbF+XQ3VB2XA9iaMMiFLE302xn3asXcUeOknT5MkFjx+K6Zs17eXGW10TD6AOS+vgxSVeuoELimEsFBhR/PpHhg6gHXAm9go6kQwfPN+uy/KrkVxwKOMTOhzv5puI3WnMWNKlZgpOOJan19BahGzdTcMIPL9nT+xO1/h+Sr99AYWqK46SJ79ztH54UzvWw6+p/ode5E3GHZKV2sgWFpKCIZRHK2ZVTzwVlV5bC5oUBSPX15eaGZSTu2IVdbBpR69cTtX49BiVLYNmhAxatWr007ORTFPPvdpIuXUYyMsLxh+efm5ikNBYcVKduHdGk6EsHkD6Oe8wPp55vez/qPstvLGdohaGaZRUKWuJma8Kj8AT23QqmQyX1Wyl9V1fMm3kRu2cvESt+p8D8ebl9iloy0lZbfPEFKhnOPHw+vigmJYYtD7YA0Kd0nzytR24z0zdjVOWRTDg9AX27w8w7XIemJVtpfud0TE2x6dWTsEW/EL50KebNmyMp8m54asQff6CKi8OgWDHMWzTXLD8TeJ57caeRZYmvigzDOBfCyd6Goa4hX5f5mnZF2rH0+lI2P9jMkcdHOPHkBNNrT3/jsRuvakSp2q8naNRo4g4eZOy5P/l58hSSG7fQ9DylpKt7m2QZVLKMLIOMusGlFxyI1ZE9WJ88gG5cjLqcQkF0heqE1W9BdIkKlJYkSj3bPmM79f/lZ/t8/n+A0Nhk1pz155fD3tRwt6Gauw1WnTqhio0ldM5cQufOQ2FmjlWXzu9wlYWXSbpxQxPWO6dIC5AkulUtqJUEJsM/9/7hWtg1jHWNmVIj70MdRzQuymX/KM75RrLlmBObBm5nl982VtxYgXdyIN+1lPlpjQIuXSJ41k84TZqYp/X5EGUOoWPgNyxafgmA75sXx8Lo5Q3F32/+TkBcAPZG9nxT4Ztcq5ODiQPz68+nz74+7PfbT0mbkvQp3YfSBSxoWtKBA3dCWHTYh8XdnvcaSTo6OP80C982bUm6dJnINX9h06d3rtUpv4iG0QckdtcuAK7ZFcHIyZGiDs/DtTImam1YqCEOJg7Zbp+TCvYVKG1TmlsRt9h4fyODyj0fB1LUwZTfytSl1tNbRO/4D5fvRiPp6+fC2by9tKdPCXs20739qJFaM92/1vY59Qa9wFzfnLJ2ZSlnV47y9uUpY1smxzlnDDw8qDx+DitberL830U0ugHVfRSk3LlLyJ0ZhP48B7MmTbDs2AHjqlXz9MHxQ5AeFUXonDmAeiCmnvPzmbl/PepDVGIaRexN6ValYI77SFGmMOrYKOJS4yhnV46uxbsy7uQ4Vt5cSYOCDShlq45BlySJDhULMPfAA7ZeeaJpGAHY9OtH7J69xO7bh92336Dv6pon55sWGkrCafXAVIsvWnMrMIbY5HTMDHUpU8CCVbdXkpSeRDGrYtR0rvmKvX14Wnu0ZsO9zdyKuE4AGzlwp4p2r9H//kfEqtWk+jwk7sABzJs1y5N6pIWGErl2HQB2w7/V/B4pVUomnFTPm6GXUJNv6tbNk+O/CRsjGyZUn0C34t2Yc2kOpwNPM/HURKwNrXP1M6DQ16fAgvk8nTKFmC1biZw8CfuEeFxzeACR09KIO3KU6I0bSDjzPOmOrqMjlp06Ytmx4xt/p74oPkXJ1itPGL7xGnu+qYOViT42ffuijIklYsUKgqdMQcfMFPMWLd7pOEJWmdNz+1WuzwmFHY7mhoxrUSJL2cexj1l0ZREAoyqPwsk09xOVvEhXR8Ev3SrQ8pdT3A+JY9rOB8zt1J12Rdrx992/WX1rNYtax/D9Fohev57IQuaU6vXySUk/JQkXLhC1Tv0d5zR9OuNOPCYxVUmFQpZ0rPjyULSH0Q812ePGVRuHqX7uhvRXsK/A2KpjmXF+BouuLKK4VXFqFqjJ8MZFOXAnhF03ghjWsAhFHZ73GukXLIjD2O8JnjSZsAULMKldC8OiL08z/qH7tJ/ePiKyLGvC6I4WrEiD4naaNzvxqfHsfKhe9zpJF14kSRI9SqkndNxwT3vCV0mSKNy8ERGG5ujGxRB35Oi7nso7kWWZ4KnTUCUmYlSxIpZdXn2+4UnhHA44zPzL8+m5tyc119ek6+6u/HThJ/Y+2ktgfCASEkUsi9DBswPTak5jR9sdnOx6kqWNlzKw3ECqO1V/rYk4+5brh2ODZixoq2DMCCuMRg3BoGhR5JQUYnftIqBXbx429SJ86VLSnr5eSMrHKGz+fJRRURh4emLdo4dmeUBEIn+e9gNgfIsSWqnmXzTr/CzuRt7FysCKufXm0sq9FV6FvVDKSsafGq/1OW1bQZ1x56xvBIHRz+dNMSxRApO6dUCl0oRf5YXY3XtApcKoXDn0CxfmlI+6t6iGuw3pcip/31WnW+1duvdHOfhcISmYVGM8IKFncZ3ZR3drZRrSMTPT3Ofw35Yiq7KOickNEcuWIScnY1S+PKb162uWb7q/lbCUR8hKQwaWG/TakwS/Dx6WHvzW6DeauzUnXU5nxNER3I24m6vHkHR1cZo+HetnWQJDf/6Z0AULte5RWlAQoYsW4d2wIYHffqtuFEkSJvXq4vLbbxQ5dBC7IUPeuVEEMK1NKdxtTXgak8yYLTc09bAbMRzLbl1Blgn87nviT55852MJ2mJ37yHp2jVkQyMm2NYBYGb70ll65lWyiklnJpGsTKaqY1U6Fu343upob2bIL10roJDUE3RvuvQYYz1j+pXtx94OeynX7mu21lO/gE2bu4zo4ID3Vrf89GII3Q2nYuy8HoQkwfQ2pVEocv7boZJVTDs7jXRVOvUL1qdRoUZ5UsfOxTrT3rM9KlnFmBNjeBz3mJLO5jQr5Ygsw6JD3lm2sezUCdN69ZDT0gj6fuxHn75fNIw+EMm3bpP66BEpOnqccSpDo0xhdLt8d5GYnoibhRtVHau+1f4buzbGwdgh2wlfm5V34WAhdVaviM1b3v4kckHcvn3EHzsGeno4TZuabc/Lk7gn/HPvH8aeHEuzrc1osKkBw48OZ/Wt1VwJvUKyMhkzfTNqFajF4PKDWd5kOae7nebfNv8ypeYU2nm2w93CHYX05h9/SZKYXms6nlaeBOhEMc7pNM7bNlJ48yYsu3RBYWpK2pMnhC36BZ9GjQno15/Yffs/+i+KzBKvXCX62efEccpkrRjx2fvukapUUcfTlvrF7HLaBdt9trPVeysSEj/V/QlHE3XvxPhq47E2tOZhzEN+vfarpryLlTHV3a2RZXXq7sxs+/cHIObff0kLCc2188xME0b3bOLa088aRnU8bfnv4X9EJkfibOL8WrPIf6hK2JSgrbs6TDdY7x/23tK+ztY9vkJhakrKgwfEHT6c68dPffyYqE3q+crsRozQNDDjUuNYcOkXAAzjm9Or+oc3fkshKZhRawbVHKuRmJ7I4MODs+2lfheSJOEwZgx2z5IbRCxfTvCUqcQdPcrjgYPwadyEiKXLUIaFo2Nri82AAXgcPEih5csxa9gASTf3AkRMDHT5pVsF9HUUHLobwl9n/TV1dJw4EfOWLSE9nSfDviHx8uVcO+7nLnN67l2lmxBhaEH7CgVoWDxrY3fz/c1cCrmEka4RU2pOeau/d++ihocNo56Nl5m04zZ3gmIBdSr8EZVG0H/eAR4XMMAgHa799uN7rVt+yRxCZz16NJN33Abgy2qFKF3g5aH427y3cSX0Cka6RvxQ9Yc8ewEnSRI/VPuBMrZliE2NZfjR4SSmJTK8iXr8+e6bT7kXHJtlG6cZ09GxtCTl7l3CfvstT+r2voiG0QciZqf6weusYylUxsbU8FBna5FlmY33NwLq3qK3/WXQU6gnLgT1IPHMbxqLO5pxr1w9AJLOnCEtOPitz+NdKGNiCP5xJqB+2DUoUiRLmbsRd2m7oy0zz89kt+/u7HuD2uzgVNdTLGu8jEHlBlHTuSZm+mZZ9vW2jPWM+aXBL1gYWHAr4hbTz03HsHRpnKZOwfPkCZxn/4RxlSqgUpFw8iSBw4drZo9P8c76tuVjIqelETxlCgAWHdpjXKmSZt1l/0h233yKJMEPLUrk+Fm9H3mfGefU8x4NLj9YK+zIytCKyTXU81atub2Ga6HXNOvaPwsz2Hrlidbn17hyZYwqVkROSyNyzZpcOc/Mku8/IOXuXdDTw6xZM5JSlVzyiwKguoeVJkV3j1LqCUE/ZqOrDsdAMkPHMIRZp/7Q7jWysMDqK3Va2PDflub63BXhS5ZAejomtWphUu35C6AlV5eSpIpBmWLHiGq9MND9cHqLMtPX0WdBgwUUtSpKeFI4Aw8OJDo5OtePY9uvH45Tp4IkEb1xI08GDVa/TFKpMK5enQILF+B55DD2I4aj7/Lmc5u8rtIFLBjXQp3w5Mc9dzUPvpJCgfNPszCpVxc5OZnHAweRfDd3e9A+VxGrV5P+9CmJVnasdKqBrakBk1pnTQAUFB/E/MvqyVS/rfgtBc1yDmnOS4PqedCgmB0p6SoG/32Z2OQ0zTp7UwdSu6kTuZjtPP3BzqWYW14MoVt7Ixzv0HisjPUY/YqEC+FJ4Zr7OazCsDwPiTTQMWBB/QXYGNrwIOoBU85MoZiDGS3LqI+bXa+Rrp0djs+eDSJW/E7StWt5Wse8JBpGHwA5PV0dqgMcKViRWh62mlCRSyGX8In2wUjXiC883m0OkQ5FO2Cka4R3lDfnnp7TLJckiSq1y3HDxh1JVhHzb/6k7Q6ZMwdleDj6Hh7YDOifZX2aMo2JpyeSokyhmFUxBpcbzPLG2fQGWb5db9CbcDFzYW69uSgkBf89/I/199YDoDAywqJNG1zX/oXH/n3Y9O+Prp0dyqgoIteswbf1Fzzq3IWojZs+yj8EkWvXkfLgAToWFtiPHq1ZrlLJTNulfvjpUrkgJZyyT4sZlxrHyGMjSVGmULtAbfqXzXqfGxZqSGv31qhkFRNOTyApXR0616KME4Z6CnzDErj+JEZrG5t+6ixY0Rs2oIyJybLPdxHz3w4AzOrXQ9fKiot+kaQqVThbGOKbcI7HcY+xNLCkXZG8SYH7PlkYWDDs2YDeKIOdbL1+T2u9dY8eKIyNSbl7l/ijuRd2m+LtrQklths+XLPcP9afDff+UdctsQOdKxfOtWPmBTN9M35r9BuOJo74xfox7MgwktOTX73hG7Lq0pkCCxYg6eujY2GBde/euO/dg+ufqzFv1uy9jRPtVbMwjUvYk5quYug/V0hMVU9UK+np4bJwIUaVK6GKiyOg79ekPHr0ir0JL5MWEkLE7ysBWOzpRaqOHjPalsbSWPtey7LMlDNTSExPpKJ9RboVz7/5CRUKifmdy1PA0gi/iES+zxR2CVC7+yieWksYJym5u2ZJvtUzr2mH0HUksWwlFj5rXHzXrHiWe/iiny/+TFxqHCWsS7y3++lg4sC8+vPQlXTZ67eXNbfX8G1jTyQJ9t4K1rwIycy8mRfmrVuDSkX8iY83jFY0jD4ACWfPoYyIIMHIlCv2xbTSdGf0FrV0b/nOvR7m+uaah7e1d9ZqrWtRxokDruq3tJFbtubZGIKcJJw7T8wW9eS1TtOmosjmD/vKWyu5H3UfSwNLljVZxqDyg6hZIHd7gzKLSUrj6L1Qft53j07LzlB95mEazj1Gq8Un6bz8LMv3KXCT1GOgZl/4mVH/beW3Yz6sOePH5kuPORSrz91W/yPmzy3IP85Dp24D0NEl+cYNgidPxrtOXYLGjiPx0qWPYubotKdPCVui/uNl/90YdK2sNOt23gji+uNojPV1GNk0+4GXsiwz4dQEAuICcDJxYlbtWTk2YL+v+j32Rvb4x/rzyxV1GJWpgS7NniUE2HbliVZ50/r1MShaFFViIlHr17/zuWrqrFQSu1OdFMX82eSmGWF0NT1sWHVbPa6pW/FumnkfPnb/K9UJG10PJJ0U5lyYr/XZ1LWywupLdc9z+K+/5drnNnTRIpBlzJo2xajM81C5n87/jIp00uOLMbpOG/ReMmbtQ+Fg4sDSRksx0zfjWtg1xp4ci1KlzPXjmDfzwvPkCYqcPIHD999h4OaW68d4FUmS+LljORzMDfANS9CEBoH6JVHBpUsxKFkCZWQkAX37ftLjLvNa2Pz5yElJ+Dp6cMypHC3LOtGstGOWctu8t3H26VkMdAyYVmvaew+he5GViT6/flkRPR2JvbeCWf1sDCqApbE1fi3LAZD89+ZPKuQ8s8g1azQhdPbff8/MPXeJT0mnnIsFXSq/vDfvdOBp9j7ai0JSMLnm5PcalVDJoRLfV/0egAVXFhChvEWrsupESwsPPch2G8cJ43FZthS7b4a9t3rmtg//r8xnICOM7ohTOZQKHU3DKCwxjMP+6lj+t0m6kJ3/lfgfEhInA0/iG+2rWV7CyYzHZWuQoGuIMjCQxAsXc+V4r0OVnMzTyZMAsOzaRSs8K8ODqAesuLECgLFVx2JrlDUt6bsKik5ix7VAJm6/RbOFJyg/7QC9/7zIb8cectEviuDYZHzDE7gVGMuFR5EcvR/GtdtlSYupgIyKfWE/M+fwWSb/d5sxW24w+O8r9Fx1gY6/X6TFRZmm1i3p1mQ8K0u1IsDUHjkpiZjt2/H/31ecqNGAX/pOZMjiAwz5+wpjNl9nyn+3mX/wAX7hCbl+rm8jZOYs5GdJMSzaPe8dSU5T8vO++4A6dMLezDDb7dfcXsORx0fQU+gxv/58LA0tczyWhYEFU2tNBWDd3XVcDFZ/HjPC6f67HkRq+vPGuyRJ2PTrB0DkX2tRJSWRGxLOnSM9NBSFhQWm9dThphmJF5ydArkTcQdDHcN8fSub23QUOvxYR51CN9HgHCsuaPcMWffuhWRkRPLt2yScOPHOx0u6cYP4Q4dBodD6Y3om8Ayngk4gywrsUjvSprzzS/byYSliVYRfGvyCnkKPwwGH+enCT3ny8kPHwiLbl0jvk7WJPgu7VECSYPPlJ+y49nxslY6ZGYV+/x39woVJD3pKQJ++pEdG5mNtX59KJbP+fAAbLwYQk5j26g3yUOb03AuLtcLa1ICpX5TKUi44IZi5l+YC6pCrjHm18lv5gpaMf5Y1b+aeu1wJiNKsK/fVN0SbgElkIqE7tuVXFfOMMj6eiD/VId72o0ZxKTSF7dfUCRemvSLhQlJ6EtPPqecs6l68O6Vsst7zvNalWBfaFmmrScbQtYYxkgQH7oRwKzBrdIaOhQVmmRLnfIxEwyifqRISiDt4CIDDLhUp7mhGAUsjALZ6byVdTqe8XXmKW+fO5JUFzQvSoKB6Is61d5/3GkmS9H/27jqu6usN4PjnJt2NoqAoggkGir3ZNd2c3agLY8bPTZ3t7M3pZm06Y5u5zZpdM0EM7EBFMZDuvtz4/XH1OoZOURBx5/168VLut86XuHzPOc95Hpr7luFw6RoAJP/xR6Fc70XEL1lK7t17yB0dcRw9Ot92tVbNxBMTDdlY2ni8egpYrVbHjZg0fj15lxEbzlF/9iECZh/isw3n+eXkXa5Hp6HTgYe9GR/WLM3cztXYNqQ+mz6qx6r+tVncw4+5nasxpX1lPqn8BbbyckjlmbhU2ECrKrY08XKgjrstlV0tcbczxcHCCDOljGRjC/6o0ISP3h3DqIZD2VumDlkyJY7JMTQ/8TufLBlJzeUzub9jD78cD+e7gzdp+90xdl0q3pHWtMOHSdu/H2QynCdPzpMUY+WJO0QmZ+FiZczAhuWeevyZ6DMsCF0A6Du2L1IAtUGpBoaaXRNPTCQjN4P6nvY4WRqRnJnLoet5Ey1Ytm6Fws0NTVKSITnEq0p9lHTBsk1rpEoliRkqrjwKIbiUvhWAThU6YWNs86xTlEj1y9TE0+QdAH64/DVqjdqwTW5ri00PfUcwbsmrzxrFLVgA6OtDPV5XqNaqmXVqDgC5ifUY3bThv2Y4fBPVcq7FrIazkCBhQ9gGQ5rdt1G98nYMe0e/OPvLLZe5m/BkMEduZ0eZlT8hd3FBdecO9wcOQpOWVlxNfSE6nY7pO68yfsslvvjjErVnHGDwz2fYdSmK7NzCn/17Xlsep+c+WLYWN23cmNKhMvbmRvn2mxY8jfTcdKrZV6OXd6/X2s7n6RvgTtuqLqi1OoauDSUpQz875OdWl+CG+oHOhz8uee3RKkUt6de1aFNSUJYrh2mLFkzerp9V7Vbbjepu1v967LILy4hMj8TZzJlhvsUzAyORSJhQdwJV7KqQkpPCtxe/pG01/Rr4BU9Za/Q2KNkrhd8CaYcO6WcObJwIsynDp49mi9RaNb/d0Gdo6lapW6Fes0/lPhy6f0hf8NV3uOGhrk1VF0aWrUPbiJOk7duHZuIEZJZPXytSWLLDwkhYqX9gcJ40EZlF/rC41VdWczXhKhZKCybVnfRSCShy1BouR6ZwOiKJ03cSOXM3iZSsvKOAMqmEyq6W1CprS213G2q52+JgYfSMM+bVNeMHuu7oSmL2PSzdtrC00ZyntlOr1ZGt1pCRoyEjR02GqheZyamkHDqA0b4dGN+4Sr3oK9SLvkK2pQ2nKvizxcyTob+oCGzsyeetKr32cCJtVhYx0/XJEmz79cXY60moXHx6Dkv+CgdgTEsvTJT5F8bHZcYx5ugYNDoN7cq148OKH77wtcfUHkPww2Ai0yP55sw3TKo3iY6+pfjhyG02hz7IE0oikcuxCxxA9JSpJKxahU23rq+01kKbkUHqo0ELq0dhdI+LupYrlcyZ2JPIJDL6+PR55jlKsm/eHUeHbcHkyu8z/egqpjYdZNhm178/SWvXkX3hIhkngjBvUP+lrpFx8qQ+rbRCgf3QJ0V9N4VtIiL1Nlq1KW7S92hfreTMFv1dS/eWxGXGMef0HBaELsDR1JH25dsXd7OKxPB3PDkZnsCpiESGrz/Hbx8HoJTr36sUrq6UWfkTd3v2IvvqVR588iluK5YjNX767HJx+/bATUPIVzkHM27HZbDvagz7rsZgYSyndRVnOtYohX85O2T/MuJfGFJ36dNzq+RKVlZqTXMfJ9pXy7/4/s/bf3Is8hgKqYJp9achk75ZSUokEgmzP6jK1ahU7sRnMHLTeVb2rY1UKsG5Zx8y/5qP6f040g4fxvKdd4q7uYVCk55B4qpVANh/8jG/nn7A9eg0rE0VjGn574PdN5Ju8POVnwEYX2d8sYZqG8mM+Lbpt3Td0ZWwpDAcXTYhlbzDgWsxXHyQTLXS1sXWtqJQsobg3kKPFxzvc60BEgnvPOoYHbl/hNjMWGyNbWletnmhXtPP0Q8fOx9yNDmGwrEAPi6WqMp7ccfSGV1ODimPCs4WFZ1GQ9SEiaBWY9G8ORbNmuXbJzw5nCXn9akfx9YZi4Pps1NA/11qdi5/hcUyb+91uvwQTLUp+/hgaTCzd1/n4PVYUrJyMVHICChvx2fvVuDXQH8uTG7B9qENmNTeh9ZVXV64UwTgbObM/CbzDQsVV11Z9dT9pFIJpko5DhZGuNubUdnVito+btQd2h/f7X9QbucObAcMQGZnh3FqEo3O7uHbo4v4bdckSs2dwKJPpvHwzIXXOqoWv+wHciMjkbu44PDpp3m2fbv/Buk5aqqWsqJjjfwZsNRaNWOOjiE+Kx5Pa08m1p1YoI6tmcKM6fX1oQS/3fiNoMgg3vfVh9P9FRZLYkbemHSrTp2Q2dujjooiZcfOgt5qHmkHDqDLzERRtgwmNWoAT9YXmdjrF5a2cG9BaYt/L8pXUpWzc8bfugcAWyN+IjHrSfiL3N4em0c1xuIXL36pWSOdTkfso0LONl26GDKoJWcns+icPlW7Kq4Fo9+t8a/hJm+6Xj696OvTF4BJJyYR/DD4OUeUTHKZlAXdamBlouDCgxS+3heWZ7uRhwdlVixHam5O5pkzRH42Al1u8YaoPc2PR/Uz9aCv13RodBN2f9aQjxqXw8XKmLRsNZvOPKDHihACZh9kxs6rXI5MKZJQSW1WFrFf69Nzr6/wDmobO2Z0rJLvPTQuM47Zp2YD+kyf5a3LF3pbCoOFsYIlPf0wkks5HBbHksO3AGhbrQsH/fTj9A+WflecTSxUSWvXoklJQenuTk7Dd5m/T78u538tvLA1e/agnVanZWrwVNQ6Nc3KNKNpmaavq8nP5GzmzDeN9ckYjkXtx7fKJeDtnDUSHaNipI6PJ+PECQD2uNTA2lSBbxn97M36MH0mpvcrvI9SVrgx5BKJxDDKvSFsAyqNyvB6m2qu7CujT8LwOBlCUUlau5bsS5eQWljgNGFCvu0arYZJJyaRq82lYamGtC/37JHW6JRstl94yKRtl2m98BjVp+6j/6rTLP4rnFN3EslRa7EzU9KyshMT2nqzbUh9Lk5pwbpBdRnZvCINKthjbvRqE6g1nWoyts5YABacXcDxyOMFPodR+fI4fT6GCof/otT332HRsiUyKytM1Tn4x1yj1dGNpPTqxtW6ATz4bARJ69eTc/tOkSVvyAkPfzKj9+V4pGZPiuDeiElj/Sl9Yb4Jbb2f+vD63bnvOBtzFjOFGfObzH+pUa86LnUMa3gmBU3CxVZHlVKW5Gp0/HnhYZ59pUZG2PbV/2wnrFjxSh3IxzH9Vh06GB5Ejt+KR6JI5IFK/3Dbv3L/lz5/STC7+WB0OS5opRl8cWhOnm22gQOQGBmRde4cmSdPPuMMz5b+119kX7iIxMQE+48/Mry+5MIS0nJT0WQ742nyLi0r519gXtKMqjWK1u6PCsAeHsn1xOvPP6gEcrU2YW7nagD8ePQ2h8Pyhrsa+/jgtmwpEiMj0o8c4eG48W9U6NS6kHvM3KX/3nzeyos+9dwB8HaxZFxrb0588Q4bBtelex03LI3lxKTmsPzYHdp9f5wW3x5l8V+3uJ+YWWjteZyeO9bUhs2ejZnUvjKOlnln2XQ6HdNPTidNlYaPnQ/9KvcrtOsXBW8XS6Z31IdSz99/g6DweKyMrMh+vxm5MpBcCiMzNLSYW/nqtBl/my369BPm7LtJWo6aKqUs6V6nzL8e+1vYb1yMu4iZwszwTPEmqOVcizG1xwBwS70BhXk4h67Hcv5+cvE2rJCJjlExSt21C7RaEstW4KG5A00qOiCTSridcpuQqBAkSAoUdlQQLdxb4GjqSHxWPLvv7Da83qaqC4fcapIrkZF99WqR1Z/IjYwkdsFCQL8gUeHkmG+fX67+wsX4i5grzJlU70kInVar42ZMGutC7jFy43kazDlE3VkHGb7+HD8H3+VaVCo6HbjbmdK5ZmnmfFCVg6Mbc2ZCM37oXYuBDctR3c26SELSunh14YMKH6BDx+dHP+de6stV9JYoFFg2b07phQuoEByEx+Y/kA35jCtlqpApN0KamkLa3r1ET53G7TZtuNWkKQ+/+ILkzVvIffjw+Rd4ATqdjuip0yA3F/OmTTF/N2+l7Zm7rqHVQcvKTviXs8t3/KF7h1h1Wf+HYVrANDysXj5r1gi/EZSxKENMZgxzT83lg0dJGP6ZnQ7Apnt3pBYWqG7ffulCpLkxMWQ8eth/HEZ3LyGT+4lZGNsdR4uWANcAvO28X/KOSgYHC1Nau3wCwMm4XVyKu2zYpnB0xLpLF0Cfoa4gdBoNcd8uAMC2d2/kDvqZ4FtJt9j4aBY7J6Ydo5o/vcNd0kglUr5q8BV1nOuQkZvBpwc+5WF64fyevmlaVnamTz39ov///XaB2LS86cpNa9Wi9HcLQS4ndccOoqdPfyOycm47H8mXW/Wj4J80Kc+nTfLX0ZNKJdQtZ8es96txekIzfuhdkzZVnVHKpdyMTWfe3jAazv2LzkuD+OXkXcM6mpfx9/TcP/m0pZ63Kx/45Z+V3xOxh7/u/4VcKmd6/eklopZal1pufFizNFodjNx4nuxcDW3r9OJoFf3veswPy4q5ha8ucd06NMnJKMuW5XbVevzx6G/VtPeq/Gv4ZWxmrGE97nDf4TiZ5S/eW5y6V+pOh/Id0KLFwm09EkXiMzPUlVSiY1SMHofRHSrtB2DIRvdbmH5tUePSjXE1L5rYeoVUQY9K+jCZX64+Kfha2dUSG1cHgl302U+S/yj8LDE6nY6oadP0Gc5q1cS6S/7OX0RKBIvO61NDj6k9Bmcz/ajx3YQMms0/QvNvjzJ+yyW2nIvkQVIWUglUKWVJ//ruLOnpx6nx73J4TFO+/rA6XWuXobyDeZFViv67x1WjqztUJ02VxvBDw8nIfbWschKpFGMfHyoO+5g2O9azdtxyRjUcyhrvVtwv6wNKJeqYGFK2bSdq/HhuvfMut1q2JGrSZFJ37UKdkPBS103dvp3MU6eQGBvj9OWXeb5+R2/EcTgsDrlUwtjW+TsH91PvM+G4fhawl3cvWri3eLmbf8RUYcpXDb5CgoRt4duwdwxHLpVw4UEKt2LzLuSWmZtj00P/s53w4/KXeuhK3bEDtFpMatZE6aZPp3r8VjwSWQYKa32GvP5V3u7ZosfGv9MWXZofSHSMPTIVre7JCL/dwEAkCgWZZ86QcerUC58zddcucm7eRGppiV3gAED/vjDvzDy0Og25aT5Usa1JM+/8AyYl1eMCsJ7WnsRlxfHxgY9JySncmltvivFtvKnkbEF8uopRGy+g1eb9HTRv3BjXObP1RWrXbyDu0SBZcdl/NYZRmy6g00HvumX5vOW/F9wEMJLLaFnZmSU9a3JmQjPmdq5GfU87JBI4czeJiVsvU3vGAQJXn2b7hYdkqQqWtCFu/rfosrK4YutOaLmazHy/ar6/YQlZCcwM0RdFH1x1MBVtnl4q4U007b0quFoZE5Oaw/YLD/Fz9CO0WRm0QPaRY2TfKLkP29qMDBJX6gcF7T75mB+O6wdI3/crhV+Zf0/UM+fUHNJz06lqX7XQshEXJolEwqR6k6hsV5lc0jEt/SuHb0Ry9m7S8w8uIUTHqJjk3L5N9uXLIJPxu6U3MqmExhUdyMzNZNstfUHJrpWK9peic8XOmMhNCEsK41S0/qFGIpHQpqoLe8v6A5Dy559oc3IK9bqpu3aRceQoEoUCl2nT8mQ4g0chdEGTyNHkEOAaYKi9FJmcRY/lIdyOz8BYIaVeOTuGv+PJL4F1uDilJTuGNWRy+8q0qeqSL9zgdVLKlHzb5FscTBwITwln/LHxeR4mX4WpUs43PWrRY0Bb/vBpzmDfAXzWYx6SbxZh99FHmFSvDjIZuXfvkbxpE5GjRnOzfgNut+9A9MyZpB06hCY1f2G2f9KkpBAzZy4A9p9+alj/AaDR6pixUz+T2KeeOx72ZnmOzVZnM/LwSNJy0/B19GVUrVGFcu++jr6GEND552ZQ30ufvXFzaGS+fW379EZiZET2pUsFDvPS6XSkbNX/Dj6eLQL9+iKFTTA6SS4+dj74O/u/7K2UKDZmSrqU+xidRsm9jOtsvbnNsE3h5IT1h52BF5810qlUxH33PQB2gYHIrKwAOPrgKEEPg9BpZeTEtGVk84qvZTDjdbJUWrK02VKcTJ24k3KnyArAFjdjhYxFPXwxUcg4fiueZUfD8+1j1bYtzpMnA5Dwww8k/FQ8WftO3IpnyLpQNFod7/uWYmqHygX+ubM0VtCllhtrB9YleOy7fNnGm8qulqi1Og5ej2X4+nPU+mo/ozae5+iNONSaf/97kHXpEinb9L9nP1btwLi23oZstX83M2QmyTnJVLSpyMCqAwvU5uJmopTRN8AdgJXH9cV/mwT04JSX/mtfXD8PhSFpwwY0SUkoypYhuV5T9l6NBuDjxv++9uvog6Psu7sPmUTG5HqT37gEGo8ZyYxY0HQBtsa2SI0fYuyymW8PhD3/wBLipTpGixcvxt3dHWNjY/z9/Tn1nJHCBQsW4OXlhYmJCW5ubowcOZLs7Lx/DCIjI+nVqxd2dnaYmJhQtWpVzpw58zLNKxFS/tTPFiVXqUmKkTk1y9hgbapk953dpOWm4WbhRoBrQJG2wcrIivfKvwfAz1d/NrzetqoL5x0rEGdijTYlhbQDBwrtmuqkJGJm6Ee47D7+CKNy+dM7r7++nnOx5zCVmzKl3hQkEgkxqdn0WH6SyOQsPOzNOPp5U9YPrsuoFl40rODwyuuDCpuDqQMLmi5AIVVw6P4hfrj4Q6GdWyKR0LueO5s+qoeLlTE3knPpdDKXoCYf4r5xAxVDTlJ66RJs+/bFqJI+803OzZsk/fwLDz4dwo269bjzYRdiv5lP+okTT635Ezv/WzSJiSg9y2PXr2+ebZvO3CcsJg0rEwXD380fbjIzZCZhSWHYGtsyr9E8FFJFod37UN+heFh5EJ8Vj9ZmCwBbzkWi+ceItNzODuvOjx7Yf/yxQNfIuX6dnJs39eGMrVoC+s7g8duRKGyCAP1s0dv20P5vhjWuCcn6Wb+5p+eTqnrSubYbOBAUCjJDQsh8gffs5M2byb1/H5mdHba99SmFczW5zDszDwBVYgP8XD1pXPHFEq2UNM5mzixrtgwLhQXnYs8x7ti4IikAW1S0Oi2Zuc9fR+PpaMGUDj4AfLPvRp7aNY/ZdOuKwyj9wEnsvHkk/144afZf1Nm7SQz6+QwqtZaWlZ2Y27naK4duOlsZM6hROXYOb8iBUY0Y2tST0jYmZKg0bD4XSZ+Vp6g76xBT/7zChfvJ+Wa0dTod0Y/Sc+93q4V9LV96PGVNyv67+w0P0dPrT0chK7z32delW50ymCplXI9O48StBDqU78CuAP2a6pQdOwotLPx10mZmGjp19h99zOqQB+h00KiiAxWdnl2MPjM3k69O6rO/9vHpg5ft82cti5OzmTNfN/4aqUSGwuo8IfHbOBNRMmqUPU+BnyY3btzIqFGjWLZsGf7+/ixYsICWLVsSFhaGo2P+sId169YxduxYVq5cSUBAADdu3KBfv35IJBLmz58PQFJSEvXr16dp06bs3r0bBwcHbt68iY3N21Ub5DGdTkfqn/qMb0fL6IuZvuPtiE6nY0PYBkBfVOt1VKzu5dOLjWEbOfrgKHdS7uBh5UFlV0tK2Zmxv0wteoQdIOWPP7Bq27ZQrhc7d57hgdt+0KB82++n3mdhqD6sYnSt0biYuxCfnkPPFSHcTciktI0Jawf6P7OI6JukmkM1JtadyKSgSSw5v4RKNpUKNbuMbxkbdgxrwIiN5zl2M54RG88Tei+JL9t6Y9G0KRZN9ddSJyaSeeoUGSdPknkyBFVEBNmXLpF96RIJy5eDQoFp9eqY1quLWd26oNORvEm/zsNl8uQ8Ka/Tc9R88yizzvB3K2BtmjcxyOabm9lyawtSiZS5jeYWeny0sdyYGfVn0Gt3L84n/YWFrRtRiT6cvJ1Afc+8RX/tBvQnacMGMoNPknXpEiZVq77QNR4nXTB/5x3DbMbVh6lkKk9iLM+ktHlpmpcp3EyRbzobMyV9fHqx+t4pMohl8bnFjPMfB+hTMVu//z7JGzcSv2QpZVb+9MzzaLOzDTNL9h9/jNRUn4xj3fV13E29i05tjirhHUa/9/bNFv2dp40nC99ZyEf7P+LAvQPMPT2XsXXGvtH3rNPpOHjvIF+f+ZrI9Ei8bb2p61qXei718HPyw0iWP4tnl1puHLsZz46LUQxff46dwxtiZZL3Ad5+8CC0qSkkrPiJqEmTkZpbGAYkitLVh6n0X3WKTJWGhhXs+a67L3KZFNWDSDJDTpIRfJLM06fRpKUhkcmQyOUglyGRK/7lc3mebUZyBd1kMrrJ5STlaLibouJucg5ZGlAHydghkXLYzJjyzpZ4lbLBxtIEdVwc2efOkS1TsKF6Oza8Xy3fz0VydrLhIXpAlQH42PkU+derKFiZKPiwZmnWBN/lp+O3WdW/DuUDWnH50Haq3NWQuGYNTuPGFXczCyRpw0Y0iYko3NygeSt+m3cEgIEN/n2N7ZLzS4jKiMLVzJWPq3/8Opr6ymo71+bz2mOYfWo2Rk67mH7Qi22B/Yq7Wa9MoitgAL6/vz+1a9dm0SL9+g+tVoubmxvDhg1j7Nj82TOGDh3KtWvXOPi3RdCjR48mJCSE48f1WbvGjh3LiRMnOHbs2EvfSGpqKlZWVqSkpGBZxLV3XlVm6Dnu9uiBxNSUzi0mkY6cfSMbkSW9Ta9dvTCSGXGg8wGsja1fS3uGHRrG4fuH6VKxCxPr6Svez9p9ja27TrN6/yyQSCi/f3+ecKqXkREczL3+A0AioezatZj6+ebZrtVpCdwbyJmYM9RxrsPyFstJzVLTfXkI16JScbY05reP6+FmW3z5/F/GzJCZrL++HjOFGevarKOc9dOLoL4sjVbHggM3+P6QPvVpDTdrlvT0w/UpoRcAuVFRZISEkHkyhIyTJ1FHRz91P6uOHXGdPSvPa1/vDWPRX7dwtzNl38jGhjolANcSrtFrVy9UWhXDfYczqFr+jm9h+S70O5ZfWo5SYkFi2Gd0qu7F/C418u338IsvSNm2HYvmzSn9/fPTwOrUam42bYomLp7SSxZj8aiexuK/brDk1kCkyiQm+E8o8jDXN1FShoqG3/+AxOVHJEj5rf0mw6im6kEk4a1agVpN2fXrMPX1feo5En76idh5X6NwdaXcnt1IlUoSshJot6Ud6bnpZD38gJq2LdkwuO4b3UkoLHsi9jDmiD7L06iao97YdWu3km4x+/RsQqJCnrrdSGaEn6Mf9VzrUc+1HhVtKhoG9lKzc2n73THuJ2bRtqoLi3r45vve6nQ6oidNJvm330ChwG3JEswbNiiy+wmPS6frD8HEp6toYi9lnmcuuWdOkXkyhNwH+RO6FIc13q3w+t9w+tfP/0A99thYdt7eSXmr8mxqv6nQM9e+ThHxGTT95jA6HRwY1Zhk7XUWLO3Llxu1SExM8Dx0EHkJGSTXZmVxq1lzNAkJuMz4ik0ONZi56zoVnczZO6LRM9/TriVco/vO7mh0Gpa8u4SGpRu+5pa/PJ1Ox8hDYzn4YBdatRnzAlbSxvvN6ai/TN+gQDNGKpWKs2fPMu5vPXipVEqzZs0IDn56bYaAgAB+/fVXTp06RZ06dbh9+za7du2id+/ehn22b99Oy5Yt+fDDDzly5AilSpXi008/ZdBTZhQey8nJIedva19SX2DdxJsi5U/9iHS6f0PSkVPK2oQKjuaMP66fLWrl3uq1dYpAP217+P5htodvZ5jvMKyNrWlb1YUfjthxwbEC1WNvkrJlCw7Dhj73XM+izc4mavIUQJ817J+dItAXdTwTcwYTuQlTAqaQnqOh78pTXItKxd7ciHWD/Etcpwj0ySNuJt3kTMwZhv81nHVt12GpLLzOu0wqYXQLL3zLWDNiw3nO30+m3ffH+a6bLw0q2OfbX+HignXHjlh37IhOpyP37l0yToaQEaKfUdIkJSGztcXx8zF5jnuYnMXyY7cBGNvaO0+nKFWVyqjDo1BpVTQu3ZjAqoGFdn9P83H1jzn84DA3k25i5LyFPZf7Mv29Kpj9I6TSbuBAUrZtJ+3AAXJu335q6ObfZQQHo4mLR2ZtjXmDJw9mu27vQapMwkRmxXue7xXJPb3pbMyU9PNtwYobwSgsLzEzZCarW61GIpGgLF0Kq47vkfL7H/pZo+X5wxc1aWkk/LgcAPuhQ5E+mon8/tz3pOemo80uhTqlJqO7ef0nOkWgf6+PzYhl3pl5zD87H0dTR9qWK5zZ+cKQkpPC0gtL2XB9AxqdBqVUSb8q/ejk2YnzcecJfhjMyYcnic2KJTgqmOCoYDgLtsa21HWpq+8oudTj++5+dF4axM5LUTQ4bZ8vXbFEIsF5ymQ06Wmk7d7Dg+HDKfPTT0/9O/Gq7t+N5pvZ6/gw4ip1km/jnBRF/N93kMkwqVYN07r+mPnXRVHKFZ1aDWo1Oo0GXa4aNGp0ajU6tUa/TfOsbbnw6HWdRpNvmyonl4iYFG7HpBKbmI5Uq0Wm05BsZEHEOx2Z+Shd+N8dvn+Ynbd3IpVImV5/eonuFAG425vRzNuJ/VdjWHniDjM61iS1RjnuHL6FR0wWSevW4TBkSHE384UkbdiIJiEBRenSmLVtx+pv9YP/gQ08nvmeptFqmBo8FY1OQ0v3liWqUwSPCvc2nkqz9ddIkd9h8skxvFPhD4zlb35Uz7MUqGMUHx+PRqPBySlveIyTkxPXrz+9LkOPHj2Ij4+nQYMG6HQ61Go1H3/8MePHjzfsc/v2bZYuXcqoUaMYP348p0+fZvjw4SiVSvr27fvU886aNYupU6cWpPlvBJ1KRdoufXrsII/akAjvejuSlJPE3oi9AHSr1O21tqmWUy28bb25lniN3278xqBqg6hayorSNibsLlOH6rE3Sd6yGftPP0Eie7nFgPGLF5N77x5yJyccRo3Mtz0yPZL5Z/WhlZ/5fYadkQt9fjrFhQcp2JgqWDvQn3IO5q90n8VFIVXwTZNv6LajG3dT7/LF0S9Y9M6iQl9Y+U4lJ3YOb8jHv57lysNUeq8MYXTzinzaxPOZcfMSiQSluztKd3dsunVFp9Wiun0bmZUVclvbPPvO2xtGjlpLHQ9bWlZ+8h6g1Wn58viXPEh/QCnzUsxoMKPIw0CVMiUzG8yk+47uYHmFrLSz7LlclQ9q5i22alShAubvvkv6wYMkLF+B66yZ/3rex2F0lm3bGkIIs1RqIjQ7kMqhU7kuJfoN/1UFNvBgdUgHdObXCY0NZdedXYYHefuPPiJly1Yyjh0j6+JFTKpVy3Ns4qpV+mKH5cph1UFfk+x64nU239RnvsyObk/DCo7U8cj7c/e261O5DzGZMfx89WcmnJiAnYkddV3qFmubNFoNW25t4bvQ70jK0a8NerfMu/yv1v8MBY1LW5SmXbl26HQ6bqfcJvhhMEEPgzgTc4bE7ER23dnFrju7AChnVQ7/Wj6cvGrH1B251CprQ4V/rLeQyGSUmjOH++kZZBw7xv2PP6bsLz9j7PVqay20mZlkng0lM+QkySeCyL12nWH8LVBGIsHIuxJm/nUxq+uPSc1ayMzNnn3CQlYaaADEpeWw4+JDtp5/SEqmipXdauZ7305VpTIteBoAfX36UtXhxcKD33SBDTzYfzWGzaEPGNPCi84VP2Rb3TmM2KYl6de12A0YgNTk6REQbwptVhYJP+nDiO0//og9YQk8TMnGzkzJe08pfv7YhrANXEm4goXCgi9qf/G6mluojOXGfP/uQnrv7k62/B5D941neetvSuwAV5EvYjl8+DAzZ85kyZIlhIaGsnnzZnbu3Mn06dMN+2i1Wvz8/Jg5cya+vr4MHjyYQYMGsWzZs3PZjxs3jpSUFMPH/fv3i/pWCkX68eNoUlKQOziwIVe/JqtpJUe23NxCrjaXynaVqWJf5bW2SSKR0NtHP4O3/vp6cjW5SCQS2lZ1IdilCtnGpqgfRpERXPAijgDZ166R8Ch1pfPkScjM83ZwdDodk4Mmk6XOws/Rj07luzBwzRnO3E3CwljOL4H+eDk/e9FiSWBrbMuCpgswkhlxPPK4IRV5YXOzNeWPTwLoWssNnQ6+3neDgT+fISXzxSrMS6RSjDw9DXVlHrtwP5kt5/TZ3ya29cnzhrfq8ioO3z+MUqrkmybfYGVkVWj3828q2VZicPXBABg7b2PjuctP3c9+sH7mOeXPP/91Ma8mPcOQaMTqvSfZ6H69sB+pURRolXzi16ewml8i2Zgp6efviypeH2L4zZlvDOnolW5uhix+8UuW5jlOnZBAwuo1ADh89hkSuRydTsecU3PQoUOdWg1NljujmpecdMOFaXSt0bRyb4Vaq2bEXyMISyy+DE/nYs/RfWd3pgZPJSkniXJW5fih+Q8saLrA0Cn6O4lEQnnr8vTy6cWSZks40e0EK1uuZFDVQVS1r4pUIuV2ym0upu3A1G0N8nKT6fZnbxaFLuVi3MU8iSckSiWlv1uIiZ8f2tRU7gUORBURUaD2a1UqMk+fJu77RUT07EWYf13uDxpEwoqf0Fy7hhQdUVZOGH3QhVILF1Ih6ATlNm/G6YvPMW/c+LV2iv7OwcKI/vU92DakPofHNH3qQOC80/OIy4rD3dKdT2t8WgytLBr+HrZUdrUkO1fLulP36FC+A2d9lMRYgyYpqUjKhhS25E2b0MTHoyhVCssOHVjxKLqid72yGCuePgganRHNd6H6EO8RNUfgYFpyE874unrQwGoUOp2UkLj9/Hrt1+Ju0ksrUMfI3t4emUxGTExMntdjYmJwdn56dfKJEyfSu3dvBg4cSNWqVenUqRMzZ85k1qxZaB9VvHZxccHHJ29More3N/fuPbs4ppGREZaWlnk+SoLHtYtymzTnYZoKY4WUOu7W/HZDX7vodc8WPdbKvRWOJo7EZcWxJ2IPoC/2qpIpOFRKX2cpZfMfBT6vTqMhauIk0GiwaNnSsGbj736/+TshUSEYyYyY4D+ZT9eeIyg8ATOljDUD6lCl1Ot50C5qPnY+TA3Qz3KuuLTC8HUubMYKGXM6V2POB1VRyqUcuh5Lu0XHuBz5cjVTdLon6bnf9y1F1dJPvh+no0/z3Tn9G/s4/3FUtqv86jdQAAOrDsTTqhISWTaXclYQmZQ/W5ZJ9eqY+vuDWk3CqtXPPFfavn3osrNRurtj/LdEDZtu6TM2llE0fa0hrm+qgQ09UKY3RauyIy4rjmUXngxg2X80GKRS0g8fJuvKFcPrCT8uR5eZiXHlyli00CeuOHDvAGdiziBFQXZMG96p5Ijvc2p8vK2kEikzGsygllMtQwHYqPSo19qG2MxYxh0bR5/dfbiWeA1zhTmf1/6c3zv8XqAMqQqZgtrOtRnupw8bPtr1KPObzOfDih/iYlYKiUSLShHOD5eW0HNXTxpubMjIv0ayKWwT91PvIzUxwW3ZUowqVUITH8+9AYHkPmMdJOj/xmRdukT88uXcCxzIjTr+3O3dh/jFi8k6exZyc5G5uHDGuz5za3ZnxAfT8dy1i3IzpmLZskWJWb9yPPI4W29tRYKE6fWnv1Uz1xKJhIEN9Wup1gRFYCq35F2PFvxZR/+ImrhqlT5k8Q2lzc4mfoW+GK/dR4MJfZjOhQcpKOVSetUt+8zjZp+aTaY6k+oO1elcsfPram6Rmdy8PZq4dug0xmRn5C/6XlIUqGOkVCqpWbNmnkQKWq2WgwcPUq9evacek5mZifQfdWpkj8KxHud9qF+/PmFheUfIbty4Qdmyz/6BKok0aWmkHzoEwCnP2gA08LTnbNxJItMjsVRa0sq9VbG0TSFT0N27O6BP3a3T6ahWWh9Ot8tN39a0/QdQJxWsiFfiL7+QffkyUgsLnL4cn297VHoU35z5BoAh1Yfy9c5EDofFYayQsrJf7ecWQytp2pZrS7/K/QCYdGJSkY4Md61dhs2fBOBma8L9xCzeXxrEptMFn1ndeyWaUxGJGCuk/O9vhQ9jM2MZc2QMWp2WDuU78EGFDwqz+S9EIVUwr/EsJDo5cvMwZh//+an72T2aNUr+7TfUiU9PKZqyXR9GZ9XxPcOM2JX4K0SrLqPTSfnAs0cR3EHJY22qpH99T7Kj9eFwv179ldvJ+tFRpbs7lu30oXXxS/WzRrlRUSStXw+Aw8iRSCQScjQ5ht/77PhG6NTWjGz235wtekwpU7LwnYV4WnsSmxX72grAqjQqVlxaQbst7dhxewcSJHxQ4QN2dNpBb5/er5xu38rIiuZlmzOp3iT2dd7DFN9fyI7qSG5qZUxk5qSp0jhw7wDTT06nzZY2tPqjFV9d+Zbbk3siK+NG7sOH3AscaPjbo9PpyL5xg8Sff+H+kKHcqFuPiA+7EPfNfDJOnECXnY3Mzg7LNm1wnjaV0rt2MbHrV0z06sR5r7osGt6SMnYla61quiqdKUFTAOjp3ZMajjWKtT1FoW1VVxwtjIh9FFL4YcUP+auahFRTCbmRkaTuLpqBxMKQvOk3NHHxyF3163d/elSXqVONUtib58/WCHDo3iEO3juIXCJncr3JryULcVFzsTLhwwrdyLw9GlV6yX0/L/B3YtSoUSxfvpw1a9Zw7do1PvnkEzIyMujfX59Np0+fPnmSM7Rv356lS5eyYcMG7ty5w/79+5k4cSLt27c3dJBGjhzJyZMnmTlzJrdu3WLdunX8+OOPDCkhC+5eVNq+fehUKpSe5dmerp8mb1rJkQ3X9UkXOnl2KtZRoA8rfoiJ3ITridc5E3PGUOw13LoUcc7u6HJzDWnGX4TqQSRxC/WzCY5j/ofiH+ncdTodU4OnkpGbQTWH6oReqsreKzEoZVKW96mFf7mSO+Lwbz7z+4x6LvXIUmfx2V+fkZydXGTXqlLKih1DG/JOJUdUai2f/3GRL36/SHbui9VNUam1zNqtXz84qGE5Q6a7XG0uY46MISE7gYo2FZlQd0KxxRN72njyjrM+FPRI/E88TMsfLmcWEICxjw+67GwSf/kl3/bcqCgyQ/QZtyzbtTe8/sMF/SigOqU6bX3enEw7xS2wgQcm6iqo07xR69TMOjXLMNBl//HHIJGQfuAg2devE79kCTqVCtPatTGrr595+PnKz0SmR6LEhpz4xrTwccozE/lf9bgArKOpI7dTbjP80HByNIVbYPsxnU7HkftH6LitIwtDF5KlzqK6Q3XWt13PlIAp2JkUzfvvB9VqMKB6D7Ije5N1axLfNvyJoTWGUtOpJnKJnMj0SH6/8TsjL07lk/bRpFgpUIWHc713dx6MGsXNBg250+E9YmbOJP3gQbRpaUgtLDB/912cxo/HY/s2Khw/Rqn532D2fmeGH4kjJCIJCyM5Pw/wz7e2qST45uw3xGTGUNq8NMN8hxV3c4qEUi41FHz96fgd/Bz9KGXnwc5ajwq+rliRr+bTm0Cbk0PCo9ki+8EfcT9Nzd4r+hnOwIZPT9GdkZvBzBD9etd+VfpRwabC62nsazD03QrsHdaWIU3z1zgsKQrcMeratStff/01kyZNokaNGpw/f549e/YYEjLcu3ePqKgnIQATJkxg9OjRTJgwAR8fHwIDA2nZsiU//PCk4GXt2rXZsmUL69evp0qVKkyfPp0FCxbQs2fPQrjFN8fjMDply7ace6AfCfR2y+V4pD5zSRevLsXWNtCP7HUor18j8PMV/ch7m6ouAGx10ddbSv7jjxd6c9LpdERPmYIuKwvTWrUMxTb/buutrZx4eAKlVIllWk/+vBiNXCphSU8/GlYoubG2zyOXypnXeB6lzUsTmR7J/47+D7W26MIErEwVrOhTi/+1qIhEAhvP3OeDpUHcS3h+kcafgyO4m5CJg4VRnqrdC88uJDQ2FHOFOfObzMdEXrwLY6c0+hRtVll00mxG//Vlvp9RiUSC3WD9eqSktevQpGfk2Z7y5w7Q6TCtXduQlv5e6j0OP9DP8LpIWuFk+faErrwq/ayRO9kx7UAn52TUSQ7c06/PMipXDss2bQCInjKV5M36QryPZ4tiM2NZfkmfnS4lsgXolIz8j64tehpnM2eWNluKhcKC0NjQIikAeyflDp8e/JShh4ZyP+0+9ib2zGwwk59b/0xl+6IPh/1fCy+qu1mTlq1l2V41gVUGsbrVao53P86idxbR07sn5azKEWupY3JXLakmILt1l7Rdu9EkJCAxNsasfn0cRo/C/bdNVDwZjNviRdj26Y1xRX0NLI1Wx8hN5/nrUQTCT/1ql8jO98mok/x+Q1/4dlr9aZgqStZsV0H0qFMGY4WUKw9TORWRROeKndnnJyHHSEpOWBgZr1DSpagk//Y76thY5C4uWL/fiVVBd9A+p6DronOLDB3dj6p99JpbXLQcLYxL5ODD373U3N3QoUO5e/cuOTk5hISE4O/vb9h2+PBhVq9ebfhcLpczefJkbt26RVZWFvfu3WPx4sVYW1vnOWe7du24dOkS2dnZXLt27V9TdZdEuVFRZJ46BcC5inXQ6cDbxZLDD7ejQ0d91/qUscxf3fp16+Wtr0R/5MERIlIiqF7ailLWJuxzqY5OoSAnLIzsy1eecxZI3bGDjOPHkSiVOE+bhuQf4ZQxGTHMO62vdO+p/IDd57RIJfBdd1+a+RRuUdA3kZWRFQvfWYiJ3ISQqBBDRr6iIpVKGPpOBX4eUAdbMyVXHqbS7vtjHLoe88xjkjJUfHfwJgD/a1HRkAr7wN0DrLmqX0g/vf50yloWf8irtakxAZafotMquJx0hk1hm/LtY9G8GUp3d7SpqSRv3Gh4XafTkbJ9G5A36cKaK2vQoUWd7kUTj+pFfxMlTGADD8ykTuTENwZg7um5ZKmzAH1WJiQSss6fB40G8yZNDKmXH89OWFAedWoN2lZzwdulZKwRfV0q2lRk4TsLUUgV7L+7n3ln5hXKaHm6Kp1vznzD+9ve53jkceRSOf2r9GdHpx20L9/+tYXzKGRSvu/mi4WRnDN3k1j46H3GTGFGY7fGjK0zlm0dt7G/834+6fAVh//XmNM+Cn5rIGFyTxkTJ5UlbFJXbAcGYlK1ar5sqVqtjnGbL7LzYhQKmYQfetcqkdkOM3MzDSF0Xb26Utu5dvE2qIjZmCn5wE+f4GPFsTt0KN8BlZmSfdX1P/sJy1cUZ/Py0ebk6Iuko0/yk6aVGMLVA59R0PVK/BXWXV8HwMS6E9+qtWJvi5If1FhCpO7cqR+RrlWLfQn6qeFGXpZsvqXPtlJcSRf+yd3KnSalm6BDx6/Xfn0UTudMutKUW176N+Xk5yRhUCclETNTXxjU/pOPMSqX9w1Cp9Mx/eR00nLTsJWXJ+RcFSQS+KZLdcMM1X9BRZuKzGgwA4Bfrv7Cn+F/Fvk1G1ZwYMewBlR3syY1W82A1Wf4Zl8YGm3+h67vDt0kNVtNJWcLOtd0A+Bu6l0mntAXAe7r05dmZZsVeZtfVK9atcmJbQnoQ0/up+VdTyWRybAbNBCAxNWr0apUAGRfuYrqVjgSIyMsWuqPj8+KZ+utrQCo4hvTwDN/Paj/usezRqqExsi0tkRnRLP8ov4hwahCBcPXEsBhxGcAXIq7xPZw/Vqu6DutkEgkjGz29oSRFKbazrUN7w9rr61lzZU1L30urU7LtlvbaLelHauvrEatU9OodCO2dNjCqJqjMFO8/kxsZexMmfG+PsnJor9uEXQrPt8+zmbOdPTsyBc9l9Fxw3Hshw7lfnkLrqeFM+rwKD7880MO3D2AVqc1HKPT6Zi+8yqbzjzQD7Z186VxxZIZgbAgdAGR6ZG4mrkysmb+MhdvowGPOhQHr8eQkq6kednm7KwtRSOTknn6NFkXLhRzC59I/v131DExyJ2dsfrgAzaeuk+GSkMFR3MaPaWGoFqrZmrwVLQ6LW082hBQ6sWTmgivj+gYvSaPw+jM2rXjyI04/f9trpKSk4KLmQsNS705Rb0ep+7eHr6dlJwUQ2dlnZ1+1Dx1x0602dnPPD529hw0SUkYVaiAXWD+Qp87bu/gyIMjSJFz/0Z7QMasTlXp5Js/FezbrnnZ5gyupg/xmhI0hSvxz5+Ne1Wu1iZs+qguferpZ3q+P3SLfqtOkZihMuxzOy6dX4LvAjChrQ8yqYQsdRYjD48kPTcdP0c/Pqv5WZG3tSAaeNpjrW6KOsODLHUWE09MzPPABGDVvj1yJyfUcXGkbNkKYJgtsnj3HWQW+hCAddfWodKq0GS5QU65t3a926sKbOCBudKU9If60LnVV1ZzL1WfTdRh+DBk9vbY9O6NcaVK+vTcp+cAYKcLQJvtxnvVXfF0LNlhF0WptUdr/lfrf4C+s7/r9q4Cn+Ny/GV67+rNhBMTSMhOoKxlWRa/u5jF7y7G3cq9kFtcMB2quxpKC4zYeJ6E9Gevp7JUWvJpjU/Z88EePq7+MeYKc24k3WDk4ZF0+bMLB+8eRKfT8e2Bm6w6EQHA3M7VaV1CB9vORJ9h/XV90pLJAZOLpfNaHMo7mNPUywGdDladuEPnip1JtJQQVEU/K/h4PU9x06pUhhksu8GD0MrkrA6KAPSZO5+25nbttbVcS7yGpdKSz2t//jqbKxSA6Bi9BtlhYeTcuIFEoeCWdx3SstXYmik5Ga/vLHXx6lLoxT5fRW3n2lSyrUSWOovfbvxGDTdrXK2MCbH2QO3ogjYtjbR9+556bPqJE6Rs2wYSCS5fTTcUyXwsPiue2admA5AV2xRtjjNT2vvQrU7xhxEWlyE1htC4dGNUWhWf/fUZ8Vn5R04Lm5FcxrT3qrCgaw2MFVKO3Yyn3XfHOHdPn/lp1u7rqLU6mno50KCCPTqdjq9OfsXNpJvYGdvxdeOvXzlbVWGTSSW87+tGdlRnpBhxNuYsa6+tzbOPRKnEtn8/ABJ++gltTg6pO/UPm5aPavBk5mayIUyfEEWV0BhfNxvMjQpUC/s/4/GskTqtMka53uRqcw2dH6Ny5ah4/BjOj7JR7rqziwtxFzCSmXD3VhNkUgmf/ccz0b2IvpX7GkKcvzzxJSFRIS90XHxWPBNPTKT7zu5cjL+IqdyUkTVHsrnDZhqVblSUTS6QyR18KO9gRmxaDmN+v/jckEErIyuG1BjCng/28FG1jzBTmBGWFMaIwyN4Z8N7LAnZCuiY2qEynWuWzMG2LHUWk4MmA/BBhQ8KlC79bTCwYTkANp15QAWL6rhburO5jn6QK+3AQXJu3y7O5gGQ8scfqKOjkTs5Yd25M3uuRBOZnPXMgq4P0x+y+PxiQF+3rKiSmwivTnSMXoPUP/UdIPMmTfgrUj/T4uuZzqX4S8ilcjp5dirO5uWTp+DrtfWotWpaV3VBJ5ESWrk+AMm/5w+n02ZlET1FX6fHpmdPTKrnXZfx+OE6VZWKJtsVVUITxrauRL/6T4/F/a+QSqTMajgLd0t3YjJjGH14NLmaFyvI+qo6+pZi65D6eNib8TAlmy4/BDNp22X2X41BJpUwvo03AH/c/IPt4duRSqTMazzvjS1E975faXS5duTE6GcwFoYu5E7KnTz72Hz4ITIrK3Lv3SN60iQ0CQnIbG0xr6//2f79xu+kqdIwwRl1mg/1RRjdvwps4IG5kYKEe22QSeQcfXCUI/eP5NknMzfTsI7OVtUKndqS931L4WH/3xgFf1Vjao+hpXvLFyoAm6vJZc2VNbTf0t4QDtqhfAd2dNrBgCoDUMqUzzy2OJgq5Szq4Weoubby0WzP81gZWTHUdyh7P9jL4GqDUUpNiFfdwcTtFzyqLcet9O03MovZi1h0bhH30u7hZOrE6Fqji7s5r11AeTsqOVuQlathw5n7dK7YmUh7CdcrW4JOR8JPPxVr+7QqFfE/6sOG7QYNQqJQsPyY/u9Mr7r5C7rqdDpmhsw0FLHv6NnxdTdZKADRMSpiOq1Wn/EKsOzQnoPXYwHQmJ8AoEXZFm/kyEFr99Y4mDgQmxXL3rt7DeF0q8wqg0RC5qlTqP5RgDdu0SJy799H7uKCw4gR+c65N2IvB+8dRKeTkv2wMyOaVcqT6ey/zEJpwXfvfIe5wpzQ2FDDqPvrUMnZku1D69OqsjO5Gh0/Pwqh617HjQpOFlxJuGJILTrcd/gbvQDYy9mCKqUsyU6sQ1nTGuRocphwYkKerF5SMzNseus7/inb9OtdLNu1RaJQkKvN5eer+oyMqoRGgJQGT4kVF554PGukUzlgmqUv4Dz71Ow8aaZXXVlFbGYs9kbO3Ljph1wqYfi7Ym3Ri3pcALamU03Sc9OfWQA2KDKID/78gK/PfE16bjo+dj780voXZjSY8cYOZoA+EdGEtvpBmNm7r3HpwYvXb7IysqKM5H0Sr/+PnPimyCXGxOfe5rO/PqPrjq4cvn+4xHSQdDodQQ+D+OWqvqTA5HqTsVD+90JNJRKJYa3RmqAIWru3QyFV8IufPptoyvY/yY15duKgopayeQvqqCjkjo5Yf9iZ0HtJXLif/MyCrvvv7ufIgyPIpW9PzaK3mfjuFLHMU6dRx8QgtbQkqUptbsWmI5NncSnlMPDmJF34J4VMQfdKjwq+XvmZGqWtcLEyJkJuQVb1WgAkb95s2D/76lUSV+sXBztPmojMPO9IcGJ2IpNPTAdAFd+UQXUb8Jl4MMrDw8qD2Q1nI0HCxrCNhhStr4OFsYKlvfwY36YSMqkEG1MFI5pVJCUnRT+Dpc2liVsTBlQZ8Nra9LLe9y0NSNDFdcFcYc7FuIusvrI6zz62vXoiMX2S9taqw3sA7L6zm5jMGKyVdiTHVcVMKaOGm/Xra3wJpZ81kvMwoj6WCjsepD9g1eVVgL6I8+P/G6e/BzoFXWq74Wb79qYdLgpGMiMWNn1SAPaTA58YCsDeT73PsEPD+OjAR9xJuYOtsS1TA6ayvu36ElMMtHfdsrTwcSJXo2PY+lDSc16shMGBqzGM2nQBncaMLuUHcejDfQysOhBTuSnXEq8x7NAwuu3sxpH7R97IDlJ8Vjx/hv/Jl8e/pNnvzfho/0fo0NGhfAcaln5z1h6/bh2qu2JvriQqJZuTt3JoVrYZN0tLiKvoALm5JK55ejHvoqZTqYj/UV9uxm7gQKRGRoaCrh1ruOJgkbega0pOCjNC9ElUBlYdSDnrcq+3wUKBiY5REUv589GIdMuW/HVbv37Dw/0qKk0OXjZe1HCoUYyt+3cfVvwQY5kx1xKvcS4ulNZV9LNGxyvUAyBly1Z0Gg06tZqoCRNBo8GidSssmjbNd66heyeSqUlFk+1Mtwr9GduqUrEVBH2TNXZrzJAa+sLGM0JmcD72/Gu7tkQiYXCj8hwZ04Q9Ixpha6Zg/PHxRKZHUtq8NDMazCgR37MONVyRSSVcuSelXyV9gojF5xdzM+mmYR+ZtTU2XfR1w5Tly2Nc2QedTmd4gPcxaws6Bf7l7FDIxNvk8zyeNUJnhDxZ38lccWkFkemRzD87nxxNDhUtq3PtljtKmZShJbj4X3GyMrIyFIANTwnns78+47vQ73hv23scvn8YmURGb5/e/NnpT96v8H6JGpmWSCTM7VwNVytjIhIymbT18nOPCboVz6frQtFodbzvW4ppHapgY2LDZ36fseeDPQRWCcREbsLVhKsMPTSU7ju7c/TB0WLtIGXmZnLswTHmnp7L+9vfp+mmpow/Pp7t4duJzYzFSGZE87LN//OL840VMnrXdQfgp2O36VxBXwvxV790AJI3bECT8uIzi4UleetW1A+jkDnYY93lQ+4nZrLn8qOCrg3yd3rmnp5LYnYi5a3KM6jq21WG5m1Vct41SyBtdjZpe/VJCqw6tOdQWBygJdtEH0bXtVLXN/pB09rY+knB16s/07aaMwArdWWQWlujjokh4/hxEtf8TPbVq0gtLXEePz7feb4+/huXko+i00lpbDOUKe2rvdH3XdwGVxtM87LNUWvVjDw8koN3D3Ih7gL30+6Tmfv8oqyvqrSNKU6Wxvx06SeOPjiKkcyIb5t+i6WyZNSasTc3osmj9LzJMdVpXLoxudpcvjz+JbnaJ2u37D/9BJvevXGZPh2JRMKxyGPcSr6FmcKMtHh9uKBYX/TiHs8a3b1XgfLm1cnR5DDirxHsidiDBAnZMe0BCd3ruOFqXbwFgUuyxwVgzRXmnI05y/JLy8nV5lLXpS5/dPiDz2t/XmJ+V//J2lTJgm6+SCWw+Vwkf5x98Mx9Q+8lMfDnM6jUWlr4ODG3czWk0id/V2yMbRhRcwR7PtjDgCoDMJGbcCXhCkMODqHHzh6vrYOk0Wq4GHeRHy/+SP89/am/oT6fHvyUX67+ws2km0iQ4GPnw4AqA1jeYjknup9gfpP5WBmVvGK0ha1n3TIo5VIuPEhBkl0ed0t3gt1VZJd1RJuZSdL6Da+1PTqVioQffgTAfuBApMbGrDoRgVYHDSvY4+WcN+zxROQJtodvR4KEqfWnvnHr+4SnE6mWilD64cNo09ORu7qgrVKdk1sPIjMLJ0X9EHOFOW092hZ3E5+rl08vNt3YxOH7hxnll4azpTHRqdmkNGiGxY7fiV/2A9nXrgHg9PkY5A5549h3Xr7J6rD5SOTgIW/Los4d8vzxEvKTSCR8Vf8r7qTc4VbyLUYcHpFnu4ncBFtjW+yM7bA1efSvsS12JnZ5/m9rbIuVkdVLjRqfjDrJovOLAPjS/0sq2VYqjFt7bT6oWZqD12PZev4hW4dN4lxsJ64lXmPFxRV8UuMTAGSWloaMaQArL68E4H3Pzqz8U1+oVNQvenGPZ42+P3SL9IftkVld5nridQDqObZh7xFzjORShojZoldW0aYiC5suZOihodga2zKm9hjecXvnrRhwquNhy2fvVuTbAzeYuO0yvmWsKedgnmefqw9T6bfyFJkqDQ0r2PN9D1/kz5jZtTW2ZWTNkfSt3JfVV1az4foGLidcZsjBIVS1r8on1T+hQakGhfa10+l03E+7T/DDYIKjgjkVfYo0VVqefUqZl6KuS13qutbF39kfG2ObQrn228be3IhONUqx8cx9Vp6IoLNvZ74+8zW7Aox5/y4k/vILtv36IjV+PUVSk7dtIzcyEpm9PdZdu5KancumM08v6JqRm8HUYH0yqp7ePanuIIqElxSiY1SEHtcusmrXnqDbiag0WmwdT5GLPkuQqeLNj7H3sPKgUelGHH1wlHVha2ld9T1WnYhgT5nafMjvZJ07B4Cpvz9WH3yQ59ig8HjG/DUdmWU6JriyoctEZKJT9EJMFaYsfncxC0MXci/1HonZiSRkJ5CjySFLnUVkeiSR6ZHPPY9MIsPG2OaZHal/dqiUMiUxGTF8cfQLtDotnTw70anCm5U18UW8U8kRS2M5USnZhEdLGe8/nrHHxvLjxR9p7NYYHzufPPtfiLvA2ZizyKVyKpu3JTv3Ng4WRlR0Mn/GFYSnCWzgwaoTEdyKNKeN53sci92MucKcyNuNAB2965bF0VJUei8MdVzqcLjLYYxkRm9UuYfCMPQdT4LC4wm5k8iw9efY/GkARnL9Pd6OS6fPyhBSs9XULGvDD71rGrb9G1tjW0bVHEVfnycdpEvxl/j04KdUs6/GJzU+ob5r/ZfqICVlJxESFcLJqJMEPwzmYcbDPNstlBb4O/tTz7Ue9VzqUdqi9FvRiX0dBjTwYOOZ++y9Es2Qd5ujkC7kN7dIOjnZo4mJJ2XrVmy6Ff1abV1uLgnLHq8tCkRqbMymY7dJz1FTwdE8XxHhhaELicqIopR5KYb5Divy9gmFR3SMiog6KYn0o0cBfRjdXxdjkciTyTXWx0139epanM0rkD4+fTj64Chbb21lXp2erDoBvyUY0atKFXIuX0aiVOIydUqeN/qzdxMZ+NvPyFzOARKWtpyDmVKEzxSEq7krcxo9yU6n0+nIVGeSkJWg7yhlJZCQ/eT/jztPjz9PVaWi0WmIz4p/4dpIFgoLJBIJqapUKtlWYrx//tDIksBYIaNddVfWhdzjj9BIvv6wDQfuHuDAvQN8efxLNrbbmCes4fHaovbl2nP1vv7nuIGnvXh4KaC/zxqF32hAj7rGmGuq8M1FHSYKGR83EVkoC1NJGFx7GTKphAXdatBm4TGuPExlzu4wJrX34UFSJr1WhBCfrqKyqyUr+9XGVFmwxxg7EztG1xqtn0G6vJqNYRu5GH+RTw58QjWHanxa/VMCXAP+9Xc/W53NudhzBEcFc/LhSa4lXsuzXS6V4+voSz2XetR1qYuPnc9b13l9XbycLWhYwZ5jN+PZfCaZZmWasTtiN+ffLYPvungSVq7C+sMPkciK9uubsn27YbbIpmtX1BqtoZBwYIO8BV1DY0LZcF0f5je53uS39vf0bSU6RkUkbc8eUKsx8vFGWb48hzYdRGETAuio41ynRGUmqeNch4o2FbmRdINb2QdwtnQnOjWbqLZdsb12DcfPP0fp7m7Y/+KDZPqtPoKklD6rWi/v3tR0rlE8jX+LSCQSzBRmmCnMKGP5/IK4uZpcErMTDR8J2QkkZiXm70w9+letU5OWqw/5sFBaML/xfIzlJXd0/wO/UqwLucfuy1FMe68yE+pOIDQ2lFvJt1h6YSmf+ekTM9xJucOhe4cA6Fe5HyPX6tMgi/VFL+fxrFFYlIoRpv34/tAtIJW+Ae7Ymxs993hBAHCxMmFe5+oM/PkMK0/cwcvZnKWHw3mYkk15BzN+HlAHK5OXLzJtb2LP/2r/j35V+rHq8ip9BynuIh8f+JjqDtX5tPqn1HOth0QiQavTcj3xumFG6FzsuTzp6EEf3ljXpS71XOvh5+gnHoYLUWADD47djGfTmfssDuzE7ojdLHW7yQprfT26tH37sGzdusiur8vNJf7xbNGAAUhNTNh9MYrI5CxszZR09H1S0DVHk8PkoMno0NHJsxP1XOsVWbuEoiE6RkXEEEbXvgNXHqYSk5qBeYXTwJubovtZJBIJfXz6MOHEBNZdX0eLyvP5OfgBf5h58s3FC3lGaq5FpdL7p1PkWm9DoUijjEVZPvMT08jFQSFT4GTmhJOZ03P31el0pKpSDZ2n0halcTZzfg2tLDp+ZWxwtzMlIiGTvVeied+vNBPrTmTk4ZGsvLySpm5NqeZQjTVX1qBDR1O3ptgp3bj0QD/6W9/zzasvVhL8fdboiz8ukpqtxtxIzkeNSs5gkPBmaObjRL8Ad1YHRfDFH5cAKG1jwq8D/bErpE62vYk9Y2qPoX+V/qy8vJJNYZu4EHeBjw58RA2HGjiZORESFUJyTnKe4xxNHfUzQq51qetSF3sTMZBSVBpXdMDT0ZxbsenciHCkrGVZ7qbeJbp1IxzXHyJh+QosWrUqshn+lD93kHv/PjJbW2y66aN9Vhy/DeQv6PrDhR+ISI3AwcSB/9X+X5G0RyhaIitdEVDdv69feyOVYtmmDX9dj0VueQWJPB1HE0eauDUp7iYWWGuP1tgZ2xGbGYuDs35B9f6rMaj+ltTnVmw6vVaEkC69jML6LBIkfNVgeomedfivkEgkWBlZUc6qHLWca5X4ThHo7+l9v9IAbA7Vr8dqVrYZbTzaoNVp+fL4l9xPu8/2cH1K/QFVBhB8OwGtDso7mOFiJUI/X9bjDHWp2fpaNAPqu2NjJjIyCQU3rk0lfFz0WfYcLYxYO9C/SH437U3s+bz25+x+fze9vHthJDPifNx59kbsJTknGTOFGU1KN2FsnbFse28bBzof4KsGX9GuXDvRKSpiEomEAfX1yQ1WB93lfU/9eubVlWKQmJiQffUqGUFBRXJtnVpN/LJlANgFDkBqasrZu0mcu5eMUial998Kul5PvG5I4vNl3S9LbHbI/zrRMSoCqTt2AGBWty4KJ0cOXo9FYRMMQOeKnVFIX376v7goZUpDwdcTcVtwsFCSlq3mxC392pW7CRn0XHGShKxULEpvAfSZWHwdfYutzYLQ6VGIw4nweB4m6zPNjfcfj4OJAxGpEfTd3ZdcbS5+jn7UcKxh+HkW2ehejaGuEWBpLCewoZgtEl6OkVzGT/1qMewdTzZ+VI+ydmbPP+gVOJg68EWdL9j9/m4+rf4pn1b/lJ9b/8yxbsf4/t3v6endk3LW5cT6w9fsfb9S2JgqiEzOwjy3LgqpgjPZYWjb6esmJqxYUSTXTdmxg9x795DZ2GDTXf8MtPJxQVffJwVd1Vo1k05MQqPT0KJsC94t826RtEcoeqJjVMh0Op0hjM6yQ3sS0nO4FHcduWkEUomMDyp+8JwzvLm6eHXBSGbElYQr1KmkL6y282I0kclZ9FgeQkxqDo5l96GRJuNm4cZwv+HF3GLhv87N1hR/D1t0Oth6Xj9rZGVkxZSAKQDEZcUB0L9KfwBDx0isL3p1HzUuT++6Zfm2a41XWgsiCC5WJoxu4YWHfdF2iv7OwdSBT2p8wic1PsHX0bdEDmi+TYwVMno9mp3ZGJJEszLNANhdRwEyGZnBJ8m6fKVQr6lTq0lYqp8tsh3QH6mpKfcTM9l9Wb8OdcDfUnSvvrKaa4nXsFRaMs5/XKG2Q3i9RMeokGVfvoLqzh0kxsZYNGvO4bA45NYnAXi3zDs4mjoWcwtfno2xDe3LtwcgzUi/WH3f1Wh6LD9JZHIWpV3uk2Wsn86eGjAVE7kIRRKK3wd/C6d7XNCxUelGdPTsCEB5q/I0Kt2IyOQsbsdnIJVA3fJifdGrMjeSM71jFd71fv4aN0EQhOfpXbcsCpmEs3eTqG7dEoDf0g5j1lr//8KeNUrduRPV3bvIrK2x7dEDgNVBTwq6VnLWh8rdSbnD0vNLAfiizhcitLKEE8kXioBZo4bIrK2RmZux79p5FJahAIZQtJKst3dvfr/xO+cTjmNn3YiEZAvSstWUtpNg7LKZlCzo5tWN2s61i7upggBA66rOTNx2mVux6VyKTKFaaWsAxtUZR1nLsjRybYQqR8WpW9GUspDh7WKJEg3Z2ZribbggCHkolUqkUjGe+1/laGlMh+ql+CP0AUGXrSljUYZ7afe42KIc5XdA2r59qCIi8mTJfVk6jYb4x7NF/fsjNTMjLTuXjafzFnTV6rRMCZqCSquivmt92pdr/8rXFoqX6BgVMpOqVSjz44/otFpyNVqCYvYisVfhalqWWk61irt5r6ycdTkalGrA8cjjuJU9S0JyE1ysjGlQ+xg77+qLmY2sObK4mykIBhbGClpWdmb7hYf8cfaBoWNkIjehrX1bkmOTucMdHFExpam+MOydO3eKt9GCIOQjlUrx8PBAqRSJPP6rAht48EfoA/ZciWFYp/f46er3rFUdZ27jxqQfOULCylW4TJv6ytdJ3bULVUQEMisrbHr2BGDj6fuk56jx/FtB101hmwiNDcVUbsqkepPE2rO3gOgYFRGJVMrpW/FoLE4gA/pU7vHW/ML08enD8cjjPNQc4dN3euLjnsq44D8AmBIwRdRvEN44H9QszfYLD9l+4SFftvVBKZcSHR1NcnIyjo6OmJiYoInPxFyrpbStKWYFLBopCELR0mq1PHz4kKioKMqUKfPW/D0VCsbH1ZJ65ewIvp1AUkx15FI5lxMuk95lChw5QsrWrTgMG4rcweGlr6HTaIhfog+Ns+3fH5m52VMLukalR/Ht2W8BGFFzBK7mrq96e8IbQPz1L0IbLx9GZhSLDCM6eL4906t1XepSwaYCN5NuYu4YxOJL+mQTnSt2pq5L3WJunSDkV7+8HY4WRsSm5XA4LJZ3KzkYOkV2dnZkqTRopSpkMgk2FmZIxUOXILxxHBwcePjwIWq1GoVCJEP4rwps4EHw7QQ2n0mmWeN3OHBvH7+bXKF3jRpknT9P4s+/4Dh61EufP3X3HlR37iC1ssKml362aO+VGENB106+pdDpdEw7OY1MdSa+jr509epaWLcnFDMRrFuETsTq66PUtHsXC6VFMbem8EgkEnp79wZg6YWlPEh/gLOZM6Nrji7mlgnC08llUkN18j9CH5CbmwuAqal+djM9R19vx8xILjpFgvCGehxCp9GI9X//Ze9UcsTD3oy0bDU26oYA7IzYhXl//XNJ0oYNaNLTX+rc+rVF+tkiu359kZmbA/DT44Ku/mUwVsjYcXsHxyOPo5QqmRowFalEPE6/LcR3soici7xLtvICAJ/W7FPMrSl8bcu1xc74SeauKfWmYK40L8YWCcK/e99P3zE6dD2WlEwVgCEc53HHyNxITKILwptKhM8JAFKphAGP6qQdOG+Jm0UZMnIzOOqeibJ8ebRpaSRv3PhS507buxdVeDhSS0tsevUC4OzdJEIfFXTtVa8s8VnxzDk9B4BPanyCh5XHv51SKGFeqmO0ePFi3N3dMTY2xt/fn1OnTv3r/gsWLMDLywsTExPc3NwYOXIk2dnZT9139uzZSCQSRowY8TJNe2MsC12HRKLFVOtJTZfKxd2cQqeUKelTWd/h+6DCB9QvVb+YWyQI/66SsyWVXS3J1eg4HBZneF2r05EhOkaCIAglxgc1S2NlouBeQhbVrfTpun+/tRm7wEAAElevQatSFeicOq2WuCVLALDt2weZhT7S53FB1/dquOJoYczsU7NJyUmhkm0l+lbuW1i3JLwhCtwx2rhxI6NGjWLy5MmEhoZSvXp1WrZsSWxs7FP3X7duHWPHjmXy5Mlcu3aNn376iY0bNzJ+/Ph8+54+fZoffviBatWqFfxO3iBqrZrTCbsBaOjUoZhbU3T6Ve7H+rbrmVh3YnE3RRBeyPuPahrtuxpjeC1TpUGr0yGXSjFWiEn0fyORSNi6dWtxN0N4w6lUKjw9PQkKCnrhY/bs2UONGjXQarVF2DLhbWGqlNO9ThkAwm55IZfKuRR/iaj6nsidnVHHxZGybVuBzpm2dy+qW+FILSyw7a0Py/t7QdfAhh4cvHeQvRF7kUlkTA2YKgr/voUK/BQwf/58Bg0aRP/+/fHx8WHZsmWYmpqycuXKp+4fFBRE/fr16dGjB+7u7rRo0YLu3bvnm2VKT0+nZ8+eLF++HBsbm5e7mzfEntsHyZUkoVWbE+j7XnE3p8hIJVKq2FdBJpUVd1ME4YV0qO6KTCrhenQquRr9A1h69pPZopIQqtOvXz8kEgkSiQSFQoGHhweff/75M2fhS6LH9/f3jwYNGhR7m/7ZKVy9erWhfVKpFBcXF7p27cq9e/eKp5FviGXLluHh4UFAQIDhNXd393zf09mzZxu2t2rVCoVCwdq1a4ujyUIJ1DegLHKphNA7amo5NALgjzvbsO2rn8VJ/Gkluhdcj6bTaol/PFvUpw8yS33x1jWPCro28LTH1RZmnJwB6AeGfex8CvuWhDdAgTpGKpWKs2fP0qxZsycnkEpp1qwZwcHBTz0mICCAs2fPGjpCt2/fZteuXbRp0ybPfkOGDKFt27Z5zv1vcnJySE1NzfPxplh5Uf/GbpodQCXnkt3JE4S3iYOFEU0e1Z/IVOn/YBrWFxmXnDC6Vq1aERUVxe3bt/n222/54YcfmDx5cnE3q1CtWrWKqKgow8f27dtf+lyPk20UBUtLS6KiooiMjOSPP/4gLCyMDz/8sMiu91hR3tPL0Gg0aLVadDodixYtIvBRSNPfTZs2Lc/3dNiwYXm29+vXj+++++51NVko4VysTGhbzQUAVWIdAHbc3oFRp7ZILS1RRUSQdvDgC50rbd9+cm7e0s8W9dUvE0jLzmXD44KuDT2Yf2Y+cVlxuFu680mNT4rgjoQ3QYE6RvHx8Wg0GpycnPK87uTkRHR09FOP6dGjB9OmTaNBgwYoFArKly9PkyZN8oTSbdiwgdDQUGbNmvXCbZk1axZWVlaGDzc3t4LcSpG5nXKbm2nn0OkkNHFtXyJGoAXhv+RxOF2WSoNaoyXrUQepJK0vMjIywtnZGTc3Nzp27EizZs3Yv38/AAkJCXTv3p1SpUphampK1apVWb9+fZ7jmzRpwvDhw/n888+xtbXF2dmZKVOm5Nnn5s2bNGrUCGNjY3x8fAzn/7tLly7xzjvvYGJigp2dHYMHDyb9b9mg+vXrR8eOHZk5cyZOTk5YW1szbdo01Go1Y8aMwdbWltKlS7Nq1ap857a2tsbZ2dnwYWtrC+jr2UybNo3SpUtjZGREjRo12LNnj+G4iIgIJBIJGzdupHHjxhgbGxtmIVasWIG3tzfGxsZUqlSJJY9GiEE/8Dd06FBcXFwwNjambNmyhr9J7u7uAHTq1AmJRGL4HPQzSc7Ozri4uBAQEEBgYCCnTp3KM1i3bds2/Pz8MDY2ply5ckydOhW1Wm3Yfv36dRo0aGD4Wh84cCDPDFVR3JNOp2PKlCmUKVMGIyMjXF1dGT58uOHYpKQk+vTpg42NDaamprRu3ZqbN28atq9evRpra2u2b9+Oj48PRkZG3Lt3j7NnzxIeHk7btm3zfU8tLCzyfE/NzMzybG/fvj1nzpwhPDw837GC8DSBDfSJD05ctsLVzI2M3Az2xx3HpmcPABJW/IROp/vXc+SZLerd2zBbtOnMA9Jz1JR3MMPYPJw/burrNU4NmIqRzKiobkkoZkUeUH/48GFmzpzJkiVLCA0NZfPmzezcuZPp06cDcP/+fT777DPWrl2LsbHxC5933LhxpKSkGD7u379fVLdQIHGZcUjU9qjTK9G+8tuXdEEQSrp3vR0xM5Kj1upIzFChQ4dSJkWt1ZKpUr/2j+f90X6ey5cvExQUZEhlnJ2dTc2aNdm5cyeXL19m8ODB9O7dO1/48po1azAzMyMkJIS5c+cybdo0Q+dHq9Xy/vvvo1QqCQkJYdmyZXzxxRd5js/IyKBly5bY2Nhw+vRpfvvtNw4cOMDQoUPz7Hfo0CEePnzI0aNHmT9/PpMnT6Zdu3bY2NgQEhLCxx9/zEcffcSDBw9e6H4XLlzIN998w9dff83Fixdp2bIlHTp0yPPQDjB27Fg+++wzrl27RsuWLVm7di2TJk1ixowZXLt2jZkzZzJx4kTWrFkDwHfffcf27dvZtGkTYWFhrF271tABOn36NPBkFuvx5/8UGxvLli1bkMlkyGT6EONjx47Rp08fPvvsM65evcoPP/zA6tWrmTFDH5Kj0Wjo2LEjpqamhISE8OOPP/Lll18+9fyFeU9//PGHYbbx5s2bbN26lapVqxqu1a9fP86cOcP27dsJDg5Gp9PRpk2bPDNVmZmZzJkzhxUrVnDlyhUcHR05duwYFStWxMIif4mK2bNnY2dnh6+vL/PmzcvTOQQoU6YMTk5OHDt27Kn3Lwj/VK20NbXdbVBrJThLGgPw243fsO3VC4mREdkXL5IZ8u8JwtIOHCDnxg2k5uaG2SJ9QVd90oXeAa5MPTkVgG5e3fBz8ivCOxKKW4GGSO3t7ZHJZMTExOR5PSYmBmdn56ceM3HiRHr37s3AgQMBqFq1KhkZGQwePJgvv/ySs2fPEhsbi5/fkx80jUbD0aNHWbRoETk5OYY/MH9nZGSEkdGb12M301Yi9eYoTI1V+JezLe7mCILwD8YKGU0qOgJqkjNVIFcil0rxmbS3WNpzdVpLTJUFm63asWMH5ubmqNVqcnJykEqlLFq0CIBSpUrxv//9z7DvsGHD2Lt3L5s2baJOnTqG16tVq2YIv6tQoQKLFi3i4MGDNG/enAMHDnD9+nX27t2Lq6u+mvvMmTNp3bq14fh169aRnZ3Nzz//bBj5X7RoEe3bt2fOnDmGyAJbW1u+++47pFIpXl5ezJ07l8zMTEPUwLhx45g9ezbHjx+nW7duhvN37949z3v/r7/+SseOHfn666/54osvDPvOmTOHv/76iwULFrB48WLD/iNGjOD99983fD558mS++eYbw2seHh6Gjkrfvn25d+8eFSpUoEGDBkgkEsqWLWs41sFBH375eBbr71JSUjA3N0en05GZmQnA8OHDDV+TqVOnMnbsWPo+WvdQrlw5pk+fzueff87kyZPZv38/4eHhHD582HDuGTNm0Lx583zf98K8p3v37uHs7EyzZs1QKBSUKVPG8PNx8+ZNtm/fzokTJwzrhNauXYubmxtbt241hArm5uayZMkSqlevbjjv3bt3DT8zfzd8+HD8/PywtbUlKCiIcePGERUVxfz58/Ps5+rqyt27d/MdLwjPEtigHKcjznL+mifysvokDLckcVh/8D5J69aTsGIFZnX9n3qsfrZIX7fIpncvZFZWgD5Bz4OkLGxMFURJthCZHomzmTMjao54XbclFJMC/TVWKpXUrFmTgwcP0rFjR0A/snjw4MF8o4SPZWZmIpXmnZh6/MdOp9Px7rvvcunSpTzb+/fvT6VKlfjiiy+e2il6k6m1OhpWcMTSRIGRvGS1XRD+K1pWdiQx5iE6QAKYGZes39WmTZuydOlSMjIy+Pbbb5HL5XzwwQeAfmBp5syZbNq0icjISFQqFTk5OYZito/9M/uni4uLIbvotWvXcHNzy/OAW69evTz7X7t2jerVq+cJh6pfvz5arZawsDBDx6hy5cp5/gY4OTlRpUoVw+cymQw7O7t8mU2//fbbPGtOXVxcSE1N5eHDh9Svn7c8QP369blw4UKe12rVqmX4f0ZGBuHh4QQGBjJo0CDD62q1GqtHD0L9+vWjefPmeHl50apVK9q1a0eLFi14HgsLC0JDQ8nNzWX37t2sXbvWMBsEcOHCBU6cOJHnNY1GQ3Z2NpmZmYSFheHm5panw/X3DmxR3dOHH37IggULKFeuHK1ataJNmza0b98euVzOtWvXkMvl+Ps/eZi0s7PDy8uLa9euGV5TKpX5fo6ysrKeGv0xatQow/+rVauGUqnko48+YtasWXkGOU1MTAwdTEF4Ec19nChja8q9RKhtVo/racf4/cbvfN6/P0kbNpJx/DjZ165h7O2d79j0Q4fIuX4dqZkZdn2fpN7+6VGK7hZ+OawP04etTqo7CTOFWb5zCG+XAgfVjxo1ir59+1KrVi3q1KnDggULyMjIoH///gD06dOHUqVKGeKY27dvz/z58/H19cXf359bt24xceJE2rdvj0wmw8LCIs8fSQAzMzPs7OzyvV4S1HCz5pdA/1cOjxEEoej4uFpxMk6fglUC2JspuTqtZbG0xURR8E6ZmZkZnp6eAKxcuZLq1avz008/ERgYyLx581i4cCELFiygatWqmJmZMWLECFT/qOmhUORNMyuRSIokVfLTrvMi13Z2djbc42MFSbLz9w7b43VPy5cvz/OwD08G6vz8/Lhz5w67d+/mwIEDdOnShWbNmvH777//63WkUqmhnd7e3oSHh/PJJ5/wyy+/GK49derUPDM9jxUkfLyw78nNzY2wsDAOHDjA/v37+fTTT5k3bx5Hjhx54faYmJjkW0drb2+fb7Dzafz9/VGr1URERODl5WV4PTEx0TBDJwgvQiaV0C/AnWk7rhJ1vzpYH2Pn7Z2MqjkKy1atSN21i4QVP1Hqm6/zHKfT6YhbrF9bZNOrFzJrawBC7yVx9m4SSrmWK6rl6NDRvlx7GpZu+LpvTSgGBe4Yde3albi4OCZNmkR0dLRh4evj0cF79+7lGR2cMGECEomECRMmEBkZiYODA+3bt88zevY2EkkXBOHNJZFIMDXSPzyaKOUo5DJKajUKqVTK+PHjGTVqFD169ODEiRO899579HpUtV2r1XLjxg18fF48tay3tzf3798nKioKFxd91qeTJ0/m22f16tVkZGQYHthPnDhhCJkrCpaWlri6unLixAkaN25seP3EiRPPnGUB/SyVq6srt2/fpmfPnv96/q5du9K1a1c6d+5Mq1atSExMxNbWFoVCgeYFUv+OHTuW8uXLM3LkSPz8/PDz8yMsLCxfJ+8xLy8v7t+/T0xMjOHv6LPWMBX2PZmYmNC+fXvat2/PkCFDqFSpEpcuXcLb2xu1Wk1ISIghlC4hIYGwsLDn/hz5+vqydOlSdDrdv/4dPH/+PFKpFEdHR8Nr2dnZhIeH4+vr+9z7F4S/61LbjW/33+BBlCseTq7E5zxkb8ReWg8aSOquXaTu3o3DiM9Q/i1RV/qhQ+Rcu4bU1BTbfvlni3y8TxOedgdbY1s+r/35a78noXi8VBqmoUOHPjN07vDhw3kvIJczefLkAqWS/ec5BEEQCpu5kRwLUyX2VgUbtX8Tffjhh4wZM4bFixdToUIFfv/9d4KCgrCxsWH+/PnExMQUqGPUrFkzKlasSN++fZk3bx6pqan5EgL07NmTyZMn07dvX6ZMmUJcXBzDhg2jd+/e+TKXFqYxY8YwefJkypcvT40aNVi1ahXnz59/bv2bqVOnMnz4cKysrGjVqhU5OTmcOXOGpKQkRo0axfz583FxccHX1xepVMpvv/2Gs7Mz1o9Gkd3d3Tl48CD169fHyMjomfX23Nzc6NSpE5MmTWLHjh1MmjSJdu3aUaZMGTp37oxUKuXChQtcvnyZr776iubNm1O+fHn69u3L3LlzSUtLY8KECcDzB9he5Z5Wr16NRqPB398fU1NTfv31V0xMTChbtix2dna89957DBo0iB9++AELCwvGjh1LqVKleO+9f6/N17RpU9LT07ly5Yoh6iM4OJiQkBCaNm2KhYUFwcHBjBw5kl69euX5Op48eRIjI6N8YZuC8DzmRnK61XFj+bE7yDPrgewPfr/xO53arsWsQQMyjh8ncdUqnCdNAh7PFunXJNr07In80c/hg6RM9lyORmoUTYRGXyJgvP94rI2ti+W+hNdPlHkXBOE/SSqR4GhpjGkJStP9LHK5nKFDhzJ37lxGjx6Nn58fLVu2pEmTJjg7OxvWhL4oqVTKli1byMrKok6dOgwcODDfLL+pqSl79+4lMTGR2rVr07lzZ959911DEoiiMnz4cEaNGsXo0aOpWrUqe/bsYfv27VSoUOFfjxs4cCArVqxg1apVVK1alcaNG7N69Wo8PPTpfi0sLJg7dy61atWidu3aREREsGvXLkMExDfffMP+/ftxc3N77ozGyJEj2blzJ6dOnaJly5bs2LGDffv2Ubt2berWrcu3335rSIQgk8nYunUr6enp1K5dm4EDBxo6oc8LtXuVe7K2tmb58uXUr1+fatWqceDAAf7880/s7OwAfQa+mjVr0q5dO+rVq4dOp2PXrl35wiD/yc7Ojk6dOuXpqBoZGbFhwwYaN25M5cqVmTFjBiNHjuTHH3/Mc+z69evp2bNnvvVwgvAi+ga4I5XArXBvZBI5F+MvEpYYht2j5F/Jf2xGnZAAQPpfh8m5eg2JqSm2A/obzrEmKAKNVo29+xY0Og3vuL1Di7LPX2sovD0kurdkMUxqaipWVlakpKRg+SgHvSAIwtNkZ2dz584dPDw8CrzOQxCK2okTJ2jQoAG3bt2ifPnyxd2cArt48SLNmzcnPDwcc3PzFzomPj4eLy8vzpw5Y+jY/ZP4vRWeZ8jaUHZeisKzymZiNKfo5tWN8f7jiejSlexLl7D75GMchg8novOHZF+5gt2ggTiOHg3oi33Xm3mQbLNDGDvtwkJhwdaOW3E0dXzOVYU31cv0DcSMkSAIgiAUoy1btrB//34iIiI4cOAAgwcPpn79+iWyUwT6rHNz5szhzp07L3xMREQES5YseWanSBBeRGBD/c/P/bv6FPI7bu8gS51lmDVKWreetN27yb5yBYmJCbb9n8wWbTp9n3RtNMaO+npu/6v9P9Ep+g8q+TEkgiAIglCCpaWl8cUXX3Dv3j3s7e1p1qwZ33zzTXE365X069evQPvXqlUrTzpyQXgZfmVs8C1jzbl7HtjJnEnLjWZvxF46NuuA0t0dVUQED8eOA8CmR3fktvp6kxqtjp9O3MbYZTNIcvF39qeTZ6fivBWhmIgZI0EQBEEoRn369OHGjRtkZ2fz4MEDVq9ebVjrIwhCwQQ28ACkZMTXBOD3m78jkcmwDRwAgE6lQmJigt2AAYZj9l2JJlZ3FLnZbYxkxkwOmCyyC/9HiY6RIAiCIAiC8FZoVdmZUtYmpMbVQIqMi3H6JAxW772H/FGNLJtu3ZD/bfBh2YlQjBx3AjDcdxhuFm5PPbfw9hMdI0EQBEEQBOGtIJdJ6Rfgjk5jgVJVDYDfb/yOVKnEdc5srLt1xf6Tjw37h95N5IZ6NRJZDt42Vejp/ey6YMLbT3SMBEEQBEEQhLdG1zpumCllJEb5AU+SMJgFBOAyZQqyv2Uom31sA3KL60iQMbPhdGRSWXE1W3gDiI6RIAiCIAiC8NawNFbwYS03NJnlUeocSM9NZ2/E3nz7XY2J4mrOzwB0LtcPTxvP191U4Q0jOkaCIAiCIAjCW6V/fXckEilpcfokDL/d+C3fPp//NQ2JPAMjbSnGBQx53U0U3kCiYyQIgiAIgiC8VcramdHCx4nc5JpI/paE4bG9d/7ibs5xdDoJQ6qOQyFTFGNrhTeF6BgJgiAIz6RSqfD09CQoKKhQz9uvXz86duxYqOcsTt26dSvxtYcE4W0T2KAcOo0F6jQfAP64+QcA6ap0pgRNA8A0qyl9/RoVWxuFN4voGAmCIJQQL9KZOHfuHF27dsXFxQUjIyPKli1Lu3bt+PPPP9HpdABEREQgkUgMH0qlEk9PT7766ivDPo8tW7YMDw8PAgICDK+5u7vnOV4ikTB79uxCv9+XcfHiRRo2bIixsTFubm7MnTv3ucfcu3ePtm3bYmpqiqOjI2PGjEGtVhu2Hz58ON/9SiQSoqOjDftMmDCBGTNmkJKSUiT3JQhCwdV2t6FqKStykuoAsCNcn4Rh/tn5pKvj0ars+KT6p0ilomaRoCcv7gYIgiAIhWPbtm106dKFZs2asWbNGjw9PcnJySEoKIgJEybQsGFDrK2tDfsfOHCAypUrk5OTw/Hjxxk4cCAuLi4EBgYCoNPpWLRoEdOmTct3rWnTpjFo0CDD5xYWFoV6LyqVCqVSWaBjUlNTadGiBc2aNWPZsmVcunSJAQMGYG1tzeDBg596jEajoW3btjg7OxMUFERUVBR9+vRBoVAwc+bMPPuGhYVh+bdsVo6Ojob/V6lShfLly/Prr78yZIhYqyAIbwKJREJgAw9GbEwCtR1pJDD71Gw239wMgDyxC91qiYQLwhOiYyQIgvAWyMjIIDAwkLZt27J58+Y827y9vQkMDMw3G2RnZ4ezszMAZcuWZdWqVYSGhho6RmfPniU8PJy2bdvmu56FhYXh2MLQpEkTqlSpglwu59dff6Vq1ar89ddfBTrH2rVrUalUrFy5EqVSSeXKlTl//jzz589/Zsdo3759XL16lQMHDuDk5ESNGjWYPn06X3zxBVOmTMnTOXN0dMzTsfyn9u3bs2HDBtExEoQ3SJuqLszebUpiYm2MHPcYOkWqpDoMqvEuJkqRnlt4QoTSCYIg6HSgyiiej390Vl7Wvn37SEhI4PPPP3/mPhLJs8NFzpw5w9mzZ/H39ze8duzYMSpWrPjU2aDZs2djZ2eHr68v8+bNyxN69rLWrFmDUqnkxIkTLFu2DIDWrVtjbm7+zI/KlSsbjg8ODqZRo0Z5OjMtW7YkLCyMpKSkp14zODiYqlWr4uTklOeY1NRUrly5kmffGjVq4OLiQvPmzTlx4kS+c9WpU4dTp06Rk5PzSl8HQRAKj1IupU9AWXKTa4JO/9irzbVEm9CGPvXKFnPrhDeNmDESBEHIzYSZrsVz7fEPQWn2yqe5ceMGAF5eXobXTp8+TdOmTQ2fb9iwgXbt2hk+DwgIQCqVolKpyM3NZfDgwfTp08ew/e7du7i65v+6DB8+HD8/P2xtbQkKCmLcuHFERUUxf/78V7qHChUq5FsTtGLFCrKysp55jELxJJNUdHQ0Hh4eebY/7vBER0djY2OT7/jo6Og8naJ/HgPg4uLCsmXLqFWrFjk5OaxYsYImTZoQEhKCn5+f4ThXV1dUKhXR0dGULSseuAThTdGjThm+P3gLVYofSqtQsqPf572q5XG0NC7upglvGNExEgRBeEtVq1aN8+fPA/pOxz9ndTZu3Ii3tze5ublcvnyZYcOGYWNjY0ikkJWVhbFx/geHUaNG5bmGUqnko48+YtasWRgZGb10e2vWrJnvtVKlSr30+QqLl5dXng5nQEAA4eHhfPvtt/zyyy+G101MTADIzMx87W0UBOHZrE2VdK5Zml9OdiIntjVozAhs4PH8A4X/HNExEgRBUJjqZ26K69qFoEKFCoA+QUDdunUBMDIywtPz2QuL3dzcDNu9vb0JDw9n4sSJTJkyBWNjY+zt7bl06dJzr+3v749arSYiIiJPB6KgzMzyz5y1bt2aY8eOPfOYsmXLGkLenJ2diYmJybP98efPWg/l7OzMqVOnCnQM6MPmjh8/nue1xMREABwcHJ55nCAIxaN/fXd+OXkXNGYElLejsqtVcTdJeAOJjpEgCIJEUijhbMWpRYsW2NraMmfOHLZs2fJS55DJZKjValQqFcbGxvj6+rJ06VJ0Ot2/rk86f/48Uqk0T5a2wlKQULp69erx5Zdfkpuba3h9//79eHl5PTWM7vExM2bMIDY21tD+/fv3Y2lpiY+PzzOve/78eVxcXPK8dvnyZUqXLo29vf0L358gCK9HOQdzOlR3ZcfFhwxpKjLRCU8nOkaCIAglSEpKiiE87jE7Ozvc3NxYsWIFXbt2pW3btgwfPpwKFSqQnp7Onj17AH3H5+8SEhKIjo5GrVZz6dIlFi5cSNOmTQ0pqZs2bUp6ejpXrlyhSpUqgD5ZQUhICE2bNsXCwoLg4GBGjhxJr169ntn5eBUFCaXr0aMHU6dOJTAwkC+++ILLly+zcOFCvv32W8M+W7ZsYdy4cVy/fh3Qdyh9fHzo3bs3c+fOJTo6mgkTJjBkyBBDWOCCBQvw8PCgcuXKZGdns2LFCg4dOsS+ffvyXP/YsWO0aNGiEO5aEISi8PWH1RnXphIuVibF3RThDSU6RoIgCCXI4cOH8fX1zfNaYGAgK1asoFOnTgQFBTFnzhz69OlDYmIiVlZW1KpVK1/iBYBmzZoB+g6Ti4sLbdq0YcaMGYbtdnZ2dOrUibVr1zJr1ixAH563YcMGpkyZQk5ODh4eHowcOTLPuiPQZ8BbtWoV/fr1K4KvwtNZWVmxb98+hgwZQs2aNbG3t2fSpEl5UnWnpKQQFhZm+Fwmk7Fjxw4++eQT6tWrh5mZGX379s1Tu0mlUjF69GgiIyMxNTWlWrVqHDhwIE9ii+zsbLZu3WrohAqC8OZRyqWiUyT8K4nun4UtSqjU1FSsrKxISUnJU4BPEAThn7Kzs7lz5w4eHh5PTS4gPHHx4kWaN29OeHg45ubmL3TMnTt3qFixIlevXjWsfXrbLV26lC1btuSbRRIKj/i9FQShIF6mb/BSdYwWL16Mu7s7xsbG+Pv751u4+k8LFizAy8sLExMT3NzcGDlyJNnZ2Ybts2bNonbt2lhYWODo6EjHjh3zjOgJgiAIxaNatWrMmTOHO3fuvPAxu3btYvDgwf+ZThHo1zp9//33xd0MQRAE4RUUOJRu48aNjBo1imXLluHv78+CBQsMBfSetvB23bp1jB07lpUrVxIQEMCNGzfo168fEonEUPPiyJEjDBkyhNq1a6NWqxk/fjwtWrTg6tWrT81SJAiCILw+BQ2HGzJkSNE05A02cODA4m6CIAiC8IoKHErn7+9P7dq1WbRoEQBarRY3NzeGDRvG2LFj8+0/dOhQrl27xsGDBw2vjR49mpCQkHypTh+Li4vD0dGRI0eO0KhRoxdqlwilEwThRYmQHEEoecTvrSAIBVHkoXQqlYqzZ88aFuwCSKVSmjVrRnBw8FOPCQgI4OzZs4Zwu9u3b7Nr1y7atGnzzOukpKQAYGtr+8x9cnJySE1NzfMhCIIgCIIgCILwMgoUShcfH49Go8HJySnP605OTobUp//Uo0cP4uPjadCgATqdDrVazccff8z48eOfur9Wq2XEiBHUr1/fkB72aWbNmsXUqVML0nxBEARBEARBEISneqnkCwVx+PBhZs6cyZIlSwgNDWXz5s3s3LmT6dOnP3X/IUOGcPnyZTZs2PCv5x03bhwpKSmGj/v37xdF8wVBEARBEARB+A8o0IyRvb09MpmMmJiYPK/HxMTg7Oz81GMmTpxI7969DQtTq1atSkZGBoMHD+bLL79EKn3SNxs6dCg7duzg6NGjlC5d+l/bYmRkZCi+JwiCIAiCIAiC8CoKNGOkVCqpWbNmnkQKWq2WgwcPUq9evacek5mZmafzA0+qrz/O+6DT6Rg6dChbtmzh0KFDeHh4FOgmBEEQBEEQBEEQXkWB03WPGjWKvn37UqtWLerUqcOCBQvIyMigf//+APTp04dSpUoZqqS3b9+e+fPn4+vri7+/P7du3WLixIm0b9/e0EEaMmQI69atY9u2bVhYWBAdHQ3oq5ibmIgKxYIgCIIgCIIgFK0Cd4y6du1KXFwckyZNIjo6mho1arBnzx5DQoZ79+7lmSGaMGECEomECRMmEBkZiYODA+3bt2fGjBmGfZYuXQpAkyZN8lxr1apVBa6fIQiCIBQelUqFj48PP//8MwEBAYV23n79+pGcnMzWrVsL7ZxFZc+ePYwdO5bQ0NB8ERCCIAjC2+Ol3uGHDh3K3bt3ycnJISQkBH9/f8O2w4cPs3r1asPncrmcyZMnc+vWLbKysrh37x6LFy/G2trasI9Op3vqh+gUCYIgPNGvXz86duz4r/ucO3eOrl274uLigpGREWXLlqVdu3b8+eefhvDliIgIJBKJ4UOpVOLp6clXX33FP0vbLVu2DA8PjzydInd39zzHSyQSZs+eXej3+zIuXrxIw4YNMTY2xs3Njblz5z73mOHDh1OzZk2MjIyoUaNGvu2tWrVCoVCwdu3aImixIAiC8KYQQ1+CIAhviW3btlG3bl3S09NZs2YN165dY8+ePXTq1IkJEyYYasQ9duDAAaKiorh58yZTp05lxowZrFy50rBdp9OxaNEiAgMD811r2rRpREVFGT6GDRtWqPeiUqkKfExqaiotWrSgbNmynD17lnnz5jFlyhR+/PHH5x47YMAAunbt+szt/fr147vvvitwmwRBEISSo8ChdIIgCMKbJyMjg8DAQNq2bcvmzZvzbPP29iYwMDDfbJCdnZ0ho2jZsmVZtWoVoaGhho7Q2bNnCQ8Pp23btvmuZ2Fh8cxspC+jSZMmVKlSBblczq+//krVqlX566+/CnSOtWvXolKpWLlyJUqlksqVK3P+/Hnmz5/P4MGDn3nc4w5PXFwcFy9efOo+7du3Z+jQoYSHh1O+fPkCtUsQBEEoGcSMkSAI/3k6nY7M3Mxi+fhnZ+Vl7du3j4SEBD7//PNn7iORSJ657cyZM5w9ezZPaPSxY8eoWLEiFhYW+fafPXs2dnZ2+Pr6Mm/ePNRq9avdALBmzRqUSiUnTpxg2bJlALRu3Rpzc/NnflSuXNlwfHBwMI0aNUKpVBpea9myJWFhYSQlJb1S28qUKYOTkxPHjh17pfMIgiAIby4xYyQIwn9eljoL/3X+z9+xCIT0CMFUYfrK57lx4wYAXl5ehtdOnz5N06ZNDZ9v2LCBdu3aGT4PCAhAKpWiUqnIzc1l8ODB9OnTx7D97t27uLq65rvW8OHD8fPzw9bWlqCgIMaNG0dUVBTz589/pXuoUKFCvjVBK1asICsr65nHKBQKw/+jo6PzlXt4nBgoOjoaGxubV2qfq6srd+/efaVzCIIgCG8u0TESBEF4S1WrVo3z588D+k7HP2d1Nm7ciLe3N7m5uVy+fJlhw4ZhY2NjSKSQlZWFsbFxvvOOGjUqzzWUSiUfffQRs2bNeqXC2zVr1sz3WqlSpV76fIXNxMSEzMzM4m6GIAiCUEREx0gQhP88E7kJIT1Ciu3ahaFChQoAhIWFUbduXQCMjIzw9PR85jFubm6G7d7e3oSHhzNx4kSmTJmCsbEx9vb2XLp06bnX9vf3R61WExERkWfGqqDMzMzyvda6det/DV8rW7YsV65cAcDZ2ZmYmJg82x9/XhjroRITE3FwcHjl8wiCIAhvJtExEgThP08ikRRKOFtxatGiBba2tsyZM4ctW7a81DlkMhlqtRqVSoWxsTG+vr4sXboUnU73r+uTzp8/j1QqxdHR8WWb/0wFCaWrV68eX375Jbm5uYbX9+/fj5eX1yuH0WVnZxMeHo6vr+8rnUcQBEF4c4mOkSAIQgmSkpJiCI97zM7ODjc3N1asWEHXrl1p27Ytw4cPp0KFCqSnp7Nnzx5A3/H5u4SEBKKjo1Gr1Vy6dImFCxfStGlTLC0tAWjatCnp6elcuXKFKlWqAPoEByEhITRt2hQLCwuCg4MZOXIkvXr1euXOx9MUJJSuR48eTJ06lcDAQL744gsuX77MwoUL+fbbbw37bNmyhXHjxnH9+nXDa7du3SI9PZ3o6GiysrIMX18fHx9DIoeTJ09iZGREvXr1CufGBEEQhDeO6BgJgiCUIIcPH843axEYGMiKDuZ/UAAATvtJREFUFSvo1KkTQUFBzJkzhz59+pCYmIiVlRW1atXKl3gBoFmzZoC+w+Ti4kKbNm2YMWOGYbudnR2dOnVi7dq1zJo1C9CH523YsIEpU6aQk5ODh4cHI0eOzLPuCPSzcKtWrXqthbqtrKzYt28fQ4YMoWbNmtjb2zNp0qQ8qbpTUlIICwvLc9zAgQM5cuSI4fPHX987d+7g7u4OwPr16+nZsyempiV7ZlEQBEF4NomusHLFFrPU1FSsrKxISUkxjHYKgiA8TXZ2Nnfu3MHDw+OpyQWEJy5evEjz5s0JDw/H3Nz8hY65c+cOFStW5OrVq4a1TyVZfHw8Xl5enDlzJl/WO+H1Eb+3giAUxMv0DUQdI0EQBOGZqlWrxpw5c7hz584LH7Nr1y4GDx78VnSKACIiIliyZInoFAmCILzlxIyRIAj/OWLkWRBKHvF7KwhCQYgZI0EQBEEQBEEQhJcgOkaCIAiCIAiCIPzniY6RIAiCIAiCIAj/eaJjJAiCIAiCIAjCf57oGAmCIAiCIAiC8J8nOkaCIAiCIAiCIPzniY6RIAiCIAiCIAj/eaJjJAiCIDyTSqXC09OToKCgQj1vv3796NixY6Ges6gsW7aM9u3bF3czBEEQhCImOkaCIAglxIt0Js6dO0fXrl1xcXHByMiIsmXL0q5dO/78808e1/OOiIhAIpEYPpRKJZ6ennz11Vf8s+b3smXL8PDwICAgwPCau7t7nuMlEgmzZ88u9Pt9GRcvXqRhw4YYGxvj5ubG3Llz/3X/Cxcu0L17d9zc3DAxMcHb25uFCxfm2WfAgAGEhoZy7Nixomy6IAiCUMzkxd0AQRAEoXBs27aNLl260KxZM9asWYOnpyc5OTkEBQUxYcIEGjZsiLW1tWH/AwcOULlyZXJycjh+/DgDBw7ExcWFwMBAAHQ6HYsWLWLatGn5rjVt2jQGDRpk+NzCwqJQ70WlUqFUKgt0TGpqKi1atKBZs2YsW7aMS5cuMWDAAKytrRk8ePBTjzl79iyOjo78+uuvuLm5ERQUxODBg5HJZAwdOhQApVJJjx49+O6772jYsOEr35sgCILwZhIdI0EQhLdARkYGgYGBtG3bls2bN+fZ5u3tTWBgYL7ZIDs7O5ydnQEoW7Ysq1atIjQ01NAxOnv2LOHh4bRt2zbf9SwsLAzHFoYmTZpQpUoV5HI5v/76K1WrVuWvv/4q0DnWrl2LSqVi5cqVKJVKKleuzPnz55k/f/4zO0YDBgzI83m5cuUIDg5m8+bNho4RQPv27WnevDlZWVmYmJgU/AYFQRCEN95LhdItXrwYd3d3jI2N8ff359SpU/+6/4IFC/Dy8sLExAQ3NzdGjhxJdnb2K51TEAShsOh0OrSZmcXy8c/Oysvat28fCQkJfP7558/cRyKRPHPbmTNnOHv2LP7+/obXjh07RsWKFZ86GzR79mzs7Ozw9fVl3rx5qNXqV7sBYM2aNSiVSk6cOMGyZcsAaN26Nebm5s/8qFy5suH44OBgGjVqlGemqWXLloSFhZGUlPTC7UhJScHW1jbPa7Vq1UKtVhMSEvKKdykIgiC8qQo8Y7Rx40ZGjRrFsmXL8Pf3Z8GCBYY/PI6Ojvn2X7duHWPHjmXlypUEBARw48YN+vXrh0QiYf78+S91TkEQhMKky8oizK9msVzbK/QsElPTVz7PjRs39Ofz8jK8dvr0aZo2bWr4fMOGDbRr187weUBAAFKpFJVKRW5uLoMHD6ZPnz6G7Xfv3sXV1TXftYYPH46fnx+2trYEBQUxbtw4oqKiDO/pL6tChQr51gStWLGCrKysZx6jUCgM/4+OjsbDwyPPdicnJ8M2Gxub57YhKCiIjRs3snPnzjyvm5qaYmVlxd27d597DkEQBKFkKnDHaP78+QwaNIj+/fsD+oW5O3fuZOXKlYwdOzbf/kFBQdSvX58ePXoA+kW73bt3zzPqVtBzCoIgCM9XrVo1zp8/D+g7Hf+c1dm4cSPe3t7k5uZy+fJlhg0bho2NjSGRQlZWFsbGxvnOO2rUqDzXUCqVfPTRR8yaNQsjI6OXbm/Nmvk7p6VKlXrp8xXU5cuXee+995g8eTItWrTIt93ExITMzMzX1h5BEATh9SpQx0ilUnH27FnGjRtneE0qldKsWTOCg4OfekxAQAC//vorp06dok6dOty+fZtdu3bRu3fvlz6nIAhCYZKYmOAVerbYrl0YKlSoAEBYWBh169YFwMjICM//t3ff4VFVWwOHf5NOgARpCaH33qQEEJESqSJFkd6kiIJcQbmKCshnQWwXC4piFFB6FVEjSFeaggihBIhoEAhVEpJA2pzvj5ViJEASJnNmMut9nnk4mUzOrOjkzOy9116rWrWb/kz58uUzvl+7dm0iIyOZMmUKL730Ej4+PpQsWZKDBw/e9rmDg4NJSUnhjz/+yLJilVuFCxe+4b4uXbrcshpcxYoVOXToEACBgYGcO3cuy/fTv77dfqjDhw/ToUMHRo8ezYsvvpjtYy5fvkypUqVueR6llFLOK1cDo4sXL5KampqRmpAuICCAo0ePZvszAwYM4OLFi7Ru3RrDMEhJSWHMmDE8//zzeT4nQGJiIomJiRlfx8bG5uZXUUqpDBaLxSbpbGbq2LEjxYsXZ+bMmaxevTpP53B3dyclJYWkpCR8fHxo3LgxH330EYZh3HJ/0v79+3Fzc8uX1OfcpNK1bNmSF154geTk5Iz7N2zYQM2aNW+ZRnfo0CHat2/P0KFDefXVV7N9TGRkJNevX6dx48Z5/E2UUko5unyvSrdlyxZee+01PvzwQ4KDgzlx4gT/+c9/ePnll5kyZUqezztjxgymT59uw0iVUsrxxcTEZKTHpStRogTly5fn008/pW/fvnTr1o3x48dTvXp14uLiCAsLA2Tg80+XLl0iOjqalJQUDh48yLvvvku7du3w8/MDoF27dsTFxXHo0CHq1asHSIGD3bt3065dO4oWLcrOnTuZMGECgwYNytEentzKTSrdgAEDmD59OiNGjODZZ58lPDycd999l//9738Zj1m9ejWTJ0/OmHgLDw+nffv2dOrUiYkTJxIdHQ3If6t/rg5t376dKlWqULVqVRv9ZkoppRxNrqrSlSxZEnd392xTFW6WpjBlyhQGDx7MyJEjqV+/Pr169eK1115jxowZWK3WPJ0TYPLkycTExGTcTp06lZtfRSmlnNKWLVto3Lhxllv6JFGvXr3YsWMHvr6+DBkyhJo1a9K+fXs2bdp0Q+EFgJCQEMqUKUOlSpUYPXo0Xbt2ZenSpRnfL1GiBL169WLhwoUZ93l7e7NkyRLuu+8+6taty6uvvsqECRP45JNPspzbYrEwb968/PsPkQ1/f3/Wr1/PyZMnadKkCU8//TRTp07NUqo7JiaGiIiIjK9XrFjBhQsX+PLLLylTpkzGrVmzZlnOvXjx4ix9m5RSShU8FiOXtWKDg4Np3rw577//PgBWq5UKFSowbty4bAslNGnShJCQEGbOnJlx3+LFixkxYgRXr17F3d091+fMTmxsLP7+/sTExGTMdiqlVHauX7/OyZMnqVy5crbFBVSmAwcOcP/99xMZGUmRIkVy9DMnT56kRo0aHD58OGPvkzNLT7U7duwY/v7+ZofjsvTvVimVG3kZG+Q6lW7ixIkMHTqUpk2b0rx5c2bNmkV8fHxGRbkhQ4ZQtmxZZsyYAUhTvHfeeYfGjRtnpNJNmTKF7t27Z6R13O6cSimlzNGgQQNmzpzJyZMnqV+/fo5+5ttvv2X06NEFYlAEcPbsWRYsWKCDIqWUKuByPTDq27cvFy5cYOrUqURHR9OoUSPCwsIyiidERUXh5paZoffiiy9isVh48cUXOX36NKVKlaJ79+5ZNrje7pxKKaXMM2zYsFw9fuzYsfkTiElCQkLMDkEppZQd5DqVzlFpKp1SKqc0JUcp56N/t0qp3MjL2CBXxReUUkoppZRSqiDSgZFSymUVkAVzpVyC/r0qpfKbDoyUUi4nvflnQkKCyZEopXIqKSkJuLEfl1JK2Uq+N3hVSilH4+7uTrFixTh//jwAvr6+WCwWk6NSSt2M1WrlwoUL+Pr64uGhH12UUvlDry5KKZeU3kA6fXCklHJsbm5uVKhQQScxlFL5RgdGSimXZLFYKFOmDKVLlyY5OdnscJRSt+Hl5ZWlHYhSStmaDoyUUi7N3d1d9ywopZRSSosvKKWUUkoppZQOjJRSSimllFIuTwdGSimllFJKKZenAyOllFJKKaWUy9OBkVJKKaWUUsrl6cBIKaWUUkop5fJ0YKSUUkoppZRyeTowUkoppZRSSrk8HRgppZRSSimlXJ4OjJRSSimllFIuTwdGSimllFLKvi4cg5jTZkehVBY6MFLqdpIS4Jtn4LPOEHvW7GiUUkop53XhGCwZCLObwdz2kHzN7IiUyqADI6Vu5VIkhN4PP8+FqJ3w0yyzI1JKKaWcz9Vo+Po/8GELOLpO7ouLhmNh5sal1D/owEipmzm8Fj5pC+fCwauo3LdvASRcNjUspZRSymlcj4WNL8N7jWHvPDBSoWY3aNBXvn9gmanhKfVPOjBS6t9Sk+H7F2DZYEiMhfItYNweCKwPyQnwS6jZESqllFKOLSUJds2B9xrB9rfk/bNccxgeBv0XQeuJ8rjj6yH+kqmhKpVOB0ZK/VPsWZjfHXZ+IF+3HAfD1oFfELT6j9y3+2NIvm5ejEqpm0tJhMQ4s6NQynVZrXBwhewhCnsWEi5BierQ90sYsR4qtpTHla4FgQ3AmgKHV5sbs1JpdGCkVLqT2+Dje2UvkbcfPPIFdHoV3D3l+3V7gn95iL8Avy02NVSlVDYSr8LHbeDtWvDHT2ZHo5Tr+X0rzG0HK0fA339AkQB44H/wxC6o3R0slqyPT0+n+22p3UNVKjt5GhjNnj2bSpUq4ePjQ3BwMHv27LnpY9u2bYvFYrnh1q1bt4zHxMXFMW7cOMqVK0ehQoWoU6cOc+bMyUtoSuWe1Qrb3oIFPWTQE1APRm+BOg9mfZy7J7R4Qo53fiA/p5RyHOunwIWjkHQVFj0Cf+01OyKlXEN0OHz5ECx4EM7uB68i0O4FGP8rNH0U3D2y/7l6D4HFDf7aA5d/t2vISmUn1wOjpUuXMnHiRKZNm8a+ffto2LAhnTp14vz589k+ftWqVZw9ezbjFh4ejru7O3369Ml4zMSJEwkLC+PLL7/kyJEjPPXUU4wbN461a9fm/TdTKicSLsPifrDpZTCs0GggjNgAJapm//i7h4CPP1w6ARHf2jdWpdTNndgIez+X44D6kBQHX/aCswfMjUupguxKFKweA3Naw4kfwM0Dmo+G8fvhvv+CV+Fb/7xfGah8nxwfWJ7v4Sp1O7keGL3zzjuMGjWK4cOHZ6zs+Pr68tlnn2X7+OLFixMYGJhx27BhA76+vlkGRjt27GDo0KG0bduWSpUqMXr0aBo2bHjLlSil7tjpffDJfXD8e3D3hgffh54fgpfvzX/Guwg0HSHHO96zT5xKqVu7dgXWPinHzR+DR8Nkk/f1GPiiJ5w/amZ0ShU8CZelSNH7TdNSyw2o2wvG7oGub0KRUjk/V8N+8u+BpWAY+RKuSzr8lez1UrmSq4FRUlISe/fuJSQkJPMEbm6EhISwc+fOHJ0jNDSUfv36Ubhw5ixCq1atWLt2LadPn8YwDDZv3syxY8fo2LHjTc+TmJhIbGxslptSOWIY8Mtn8Fknme26qxKM3CCrQTkR/Bi4e8Gp3RC1O19DVUrlwPfPQ+xpKF4FQqbJBMagFVCmkWz8XtBDepIppe5M8jX4cZZUmtv5AaQmQqV7YdQm6DPv5tkWt1LrAfD0hcuRMmGp7lzUblg2RPZ6/fK52dE4lVwNjC5evEhqaioBAQFZ7g8ICCA6Ovq2P79nzx7Cw8MZOXJklvvff/996tSpQ7ly5fDy8qJz587Mnj2bNm3a3PRcM2bMwN/fP+NWvnz53PwqylUlxcPqx2DdBEhNkl4Ko7dCmYY5P0fRwMwNo7pqpJS5IsJg/0LAAj0/ykzd8fGHwauhdB1pIrmgB1w5ZWqoSjktayr8ulBWiH6YJquxpevCwBUw9Gso2yTv5/YuArXS9p0f0CIMdyw1Bb6ZmPn1N09D5Gbz4nEydq1KFxoaSv369WnevHmW+99//3127drF2rVr2bt3L2+//TZjx47lhx9+uOm5Jk+eTExMTMbt1Cl9w1O3cfE4zO0gF16LO4RMh34LoVCx3J+rVVraztFv5LxKKftLuAxfj5fjlmOhQous3/ctDkO+ghLVIOaUlOKPPWv/OJVyVoYBx9bDnHvhqycg9i/wKyeTEGO2Q/X7b6w0lxfpk43hK6WXoMq73XOkMX2hu6D2g9JQd9lQuBBhdmROIVcDo5IlS+Lu7s65c+ey3H/u3DkCAwNv+bPx8fEsWbKEESNGZLn/2rVrPP/887zzzjt0796dBg0aMG7cOPr27ctbb7110/N5e3vj5+eX5abUTYWvgk/awoUjUj506Fpo/VTeL+ilakLNroABO963YaBKqRz7dhLEnYOSNaD9i9k/pkhpGLIWilWEv0+mVZ+8aN84lXJGp/fKZMKiPnD+kKzC3v9/8OQv0GgAuLnb7rmqtAPfkpBwESI32e68ribmNGyZIcch0+GhT6VJfWIMLOyj174cyNXAyMvLiyZNmrBx48aM+6xWKxs3bqRly5a3/Nnly5eTmJjIoEGDstyfnJxMcnIybm5ZQ3F3d8eq5ZDVnUpJgu+ehRXDpUpVxdbw2Dao1PrOz90qbab6tyUQl31VRqVUPjn8FYSvkFK/PeeAZ6GbP9a/rEyGFA2CixGwoCdc+9tuoSrlVC5FwvJhMLc9/LFdihO1elIqzd3zn1v/reWVuwfUf1iONZ0u776fLJ91yjWHxoPBw1syY+6qBFf+hCUDtEH9beQ6lW7ixInMnTuX+fPnc+TIER5//HHi4+MZPnw4AEOGDGHy5Mk3/FxoaCg9e/akRIkSWe738/PjvvvuY9KkSWzZsoWTJ08yb948FixYQK9evfL4a7mg6HA4tForuvxTzGmY102WlQHueUrSaoreenUzxyq0gHLNZPPp7o9tc06l1O3FX4R1aTn0rSdAuRzsb7irkgyOCpeCcwel58p1LdqjVIa4C7IKO7u5fJ7AAg37ywpRx1ckNTU/NXhE/j36jf5t5sXxH2TCyOIuTXXTFxwKl4QBy8HbX4pGrR2nnxVvIdcDo/QUt6lTp9KoUSP2799PWFhYRkGGqKgozp7NmsMdERHBjz/+eEMaXbolS5bQrFkzBg4cSJ06dXj99dd59dVXGTNmTB5+JRd07W9Z7l4+DHZ9ZHY0jiFyE3x8rzSN8/aHfovh/uk3bzKXFxZL5qrRz59CYpztzq2Uyp5hSPGUhIuy+fu+Z3P+syWry+RIobskTWhRX0hKyL9YlXIGKUmw9Q2pNLfnE7CmQLUQ2UPUaw4Uq2CfOILuhhLVIeU6HF1nn+csKJKvwbdPy3HwGAisl/X7pWpA3wXSZ+rgctg60/4xOgmLYRSMYWNsbCz+/v7ExMS43n6j756D3WkDIjcPGB4G5ZuZG5NZrFbY9mZajq0BgQ3gkQVQvHI+PV8qfNBUOnZ3ngktdDCv8plhSDNia4q8/qwpcsu4L+Uf30v7vpEKPsWgWAGo3nlwhZSgdfOAUZuhTIPcn+PMrzD/QUiMlb0N/ZeAp4/tY1XK0RkGrBwpaakgJe7v/z+ocp858Wx9Eza/AlXayiSGypnNr8lgp2gQjNsD3kWzf9zeefD1f+S496fQoE/2jysg8jI20IGRs7sQAR+2lA8+ZRrC2d+kYsyY7fm/7O1o4i/BqlEQmbYH7u6h0OWN/P/A83OolMb0rwDjf7XtqpQqWKxW2PIa/L4l+wFMtgOcf3xtpD0ur9q/CG0m2ezXsbur0TA7GK5fgbbPQ9tcrBb9W9Ru+KIXJMdDjS7Q9wtw97RZqEo5ha1vwOZXZaKhx2yo/0hmCpYZLp+UlSssMPEI+JUxLxZncfEEfNRSWpD0mQ91e9768etflKJR7l4wdB1UCLZLmGbIy9jAxFe/sonvn5cPSzW6yAu8eBUpp7n6MflA5Sr+2gsft5FBkYePlBJ98D37zAI3GiDVdGKi4PCa/H8+5bw2vyIrmn/9LKsW0Qfh/GEpCHA5UjbHxpyCq2ch/oKkySbGyIf31MTbD4osbvJm5+kL3n6ySuRbUioxAmx6BXZ/ku+/Zr4wDJnpvH5FJoHunXjbH7mlCsEwYIlcL459J7PmqXcw6FTK2YSvkkERQNe3oGE/cwdFINkd5VsARuYqlro5w5AUutQkSX+s0+P2PxMyXZrqpiZJMYbLJ/M/TieiU9vO7Nh6OPEDuHlCp1fBx0/SxuZ2gOPr4adZd/7hwdEZhuzvCZsM1mQZGD7yxY35tfnJsxAEPyZvMD+9C/Uesk1fB1WwHFgG29+W4w5TIaCelLu1uMtsbcbNLevXFnd5XMZ97pn/Wv51/61ed5tnwNbX4btJUna3YV/7/N62sn8RHAuTgV/PObZZ3ancBvp+CYv7y6SGZyHo8aH5Hw6Vym+n98Kax+W4xRPQdLi58fxTg0fg1C6pTpfeM1Bl79AqyUBw95YMmZx89nBzh96fwOddJMtoUV8YsT5vPR0LIE2lc1YpSbJ0eumEXDg6vpL5vb3zpemhxU06UtuiNLUjSoyT3zN8pXxdu7ukAvj42z+WhMvwv7qQnCB50VXa2j8G5bj++gU+7yqrPvc8JYVA7M0wpHT9no9lQNVvIdTsYv848iLmNHzYQvYEdZhm+wmfI19LA0QjFZo+Ct3e0ckNR3M9xpxre0EU85eU4o47B9U7Qf/Ftu1JdKcSLsNbNWSy8/GdEFDH7Igc0/VY+KAZxEXnLbU49qy8Dq6ekc8sA1cUuHRiTaVzJT/PlUFR4VI37hm4ewg06Cd7FVaMKJg9di5EyB90+Er5kNfxVVkpMuuN07c4NE7r0fXTe+bEoBxTzF+yIpGaKE2BO0wzJw6LBTq/LuV30zuhn9xuTiy5YRhSXjYxFso2zawEaUu1u8sMKhb45TP4/gUtZ+sorsfA8uHweoXMFVeVd4lxsLifDIpK15EGoI40KAJ5P63RSY61p9HNbX5VBkXFq0p/qdzyKyPpxJ6FZdXp20l63UMHRs4p/iJsSSu12H7KjYMBiwUeeAdK1ZI/mpUjZXN3QXFwBXzSTvZlFC0Dw76BVuPMn+FtOVZW6SI3Sl8ppZLi5UNI/HkpLd37E3PTtNzc4MEPZICWmiixnd5nXjw5sXeelN/38JHSwflV3KT+w/Dg+3K8a3bm3gtlnr/2wpx7JV0IpPKWXlvzzmqV/cfRB2XvYf8lkoLviNJ7Gh1c7lr7pXPqzH4prQ7Q7a2876cu0xAeDgUssPdz2DnbVhE6LR0YOaNNr8iG7MAGmasU/+ZVWPYbefrCya1SeaYg2DtPSvUmx8v+gMe2QcWWZkcl7qqUufFxx/umhqIcwL8/hAxYcvMSqvbk7gEPfw6V7pUO6V8+JCuwjujvP6WCEsi+rJLV8/f57h4MXd6U421v6gqFWaxWuYZ+1lEKkhSrABVbS/GRtU8WrIk+e9o4XfoDuXtBv0VwV0WzI7q56p2kB2HsafjzJ7OjcSxWq1TCNaxQtzdUbX9n56vZRfapg1xvj3575zE6MR0YOZvog7Bvvhx3mXnrJfBSNeGBWXK8dabMujqz35bC10/JcfDjMHgNFCltZkQ3Sk/zCV8hKVTKdW15TfauuHnKBn97NUnMCU8f2VcQdDdcuwwLesogxJFYrfDVWBm8VWglf/P2EDxaqjYBbPw/bZptb/EXYXFf+YBmTZHJpse2S8qXtz+c2Qe755gdpfP5daEUZALZi+voJZo9faBu2kSjptNltW+eFM/wKgqdXrPNOVs8AU2GA4ZMPp/Zb5vzOiEdGDkTw5Dqa4YV6vaCiq1u/zMN+0KTYciLfRTEnsnvKPPH4a9gzRjAgGajoPMMx8uLBih7t8zEW1P0A5UrO7hCVhwAur/rOKua/+RdFAatlJTbq2fgi55w9ZzZUWX6eS78sV1WvXvOtm8KYuun4L7n5DjsOfjlc/s9tys7uR3mtJaqqu7eUgSjz3ypluVXBjr+nzxu0yvw9x9mRupc/vgps6lnm0mZaWqOrkFa5czDX0HydXNjcRRxF+CHl+S4/Qu26/NksUDXN6XhdXKCpFk76+fFO6QDI2dyZK18UPDwkc7UOdV5JgTWh4SLsOJR5+vVcex7KSJhWKHRwJyXpDRL+ibIvfPg2hUzI1Fm+GsvrHlCjls9CY0HmhvPrfgWh8GrZTXr8u/wZW/HeM1eioQNaUUq7v8/KcNvb22fy1wBXjcBflti/xhchTVVyskveFB6eJWsAaM2QbMRWa/1jYdISl1ygmQP6Ebx27v8OywdJBXe6vSQ6mXOokIraVifGCul+hVsmCoFSQLryySxLbl7Qp95aZNlZ6WMd2KcbZ/DCejAyFkkX8/MtW81PndpOZ4+MuvmVRSidsKml/Mnxvzw+xZYOlgu6nV7y+ZoR+8xUi1Eqv0kxclmRuU6Yk7DkrQKdDU6Z6ZkOTK/IElLLVwazoXDokekaIRZrKnSXyXlmuwjbDrCnDgsFhmUNRsFGBLToTXmxFKQxZ6B+Q9Kjy3DCo0Gwegt2feic3OTxt3u3vD7Zh2s3s61K7Con6TLBjWW/l+O/v75T25u0KCPHGs6naz8/bYIsMg2ifwoRFOoGAxYKvtiow/AqtEut6fPif5CXNzOD+BKFBQNkjSP3CpRFXp8IMc/zYIIJ5h9idr1jzLH3dIqejlg+ty/WSyZTel2zYGURHPjUfaRFC+DorhzUKo29J7rHK9XkOvD4NVS4fLUbpmMSEkyJ5ZdH0oMXkVlL4SZH+QsFlmhbjRIPrSvHCEr2Mo2jn0PH90Df/4IXkXkb6bnbCkedDMlqspqHsD3kyW1SN0oNQVWDE+r3hoE/RaDl6/ZUeVeg37y7/H1EH/J3FjMlJIkBRdAtkeUa5p/z3VXJSnO4e4NEd/ADya1mDCJDoycQexZ2P6OHN8//dZvGrdStycEj5Hj1Y/JQMtRnd4HC/tIykTV9tDnc+dqPFbvYXkziouWcqOqYLNaZUXh7G/gW0Iq0DlqGdybCawnDf48faXk/KpR9p8pvBABG9NWtDu96hgFK9JXKeo9JHsHlw6GyM1mR+XcUpKkV9SiR2Q1I7CBVBjN6d6XVk9KKtG1v+G7/+ZvrM4q7DkpuOTpK9cjW+1FsbfSteT1YU2Bw6vNjsY8uz6EC0dlJafD1Px/vgrB0PNDOd7xvkvts9SBkTPYOF3KU5drBvX73Nm57n8ZyjaB61dg+TDzZoVv5dwh2euQGAsV74G+C8HD2+yocsfDC1qkDUJ/ek/7MBR0W2fKBuH0CnR3VTI7orwp3xz6LZTf4/AaWPeU/fZxpKbA6jGyQlwtRBpVOwo3d+j1MdR6QOJbMgD+3Gl2VM7p8kn4rJNkQYBM1o38QVaCcsrdU9KqLW7S4yjiu/yJ1VntmSvFS0AyLco0NDeeO5VehOHAMnPjMMuVKHmPAej4suwNtYf6D2fuSfvmaZeZENKBkaP7ay/8tliOO8+886IDHl7Sw8SnmJR73GCHmYfcuHgcFvSQmcCyTSXX1RmX/0GWu72KSirD8fVmR6PyS/gq2R8B8MD/clYt0pFVbS+lkS1usG+B/dIofpolpZi9/aH7e45XYMXdEx7+TAZtyQmyon16r9lROZfwlfBxG/n/7FNM0nW6zMzbxFdQY2g5To6/eRqux9o0VKd14gf47lk57jANanc3Nx5bqPeQXI9O7ZaBtav57jm55lRoBQ372/e57/uvDEyNVFg21HF73tmQDowcmWFAWNoFrmF/KNfENue9q6LMfgLs/khmuh3B33/IJtz4C5ImMWiFYzTEzCsff2g6TI53vGdqKA7NMKSwyDt1YO9856o0dXqfpNCBfEi7e7C58dhK3Z5SZhzgp3czU3nzS3Q4bEkbXHaZCf5l8/f58srDGx75Iq057lX4orf0llO3lpQAa8dLVdTEWCjfAh7/CWp1u7Pztp0Md1WWJqAbnaDQSX47fxSWD5cPsQ0HQOsJZkdkG35loPJ9cuxqqekR38k+HzcPeOAd+08YWSyyOlu+BSTGyIRQ/EX7xmBnOjByZAeXw18/g2dhmfmxpZqdM8tKfzVOyuOaKeY0zO8u/VRK1ZIqWYXuMjcmWwh+XC5of/4kq38qK8OADVMkhzn2NHw9HpYMdI4N1bFnJKUq5TpU75i7EvrO4O4h0PEVOd44HX4OzZ/nSUmSHmXWZKjZFRr2y5/nsRUvX2mOW665pCQv6OkSs6h5dv4IzG2f1pjcAvc+A8O+Af9yd35uL9/MAfzPn7p2emP8JWmMmxgrKwvdZznequudyEinW+pck2d3IikBvk3bQ9dyLJSubU4cHt6SYn1XJbjyp7xHF+C+UjowclRJ8Zl9PO6dmD8bJ9tPgQot5UK6fCgkX7P9c+RE3HnpX3ElSvqVDPkKCpc0JxZb8y8L9dM2FO9419xYHNG2N2VQBPLfyc1TZsc+aunY1b+SEmRQdPWsDOQfCnWeCnS50epJ+SALkq50cIXtn2P7W7LqUuguKUHrDB/mvIvCwOWydyPhoqx0X/7d7Kgci2HICvAn7eDCESkHP3g1dJhi2zLDVe6DxoPk+OvxBfoD202lJEqvor//gGIVZZ+js+3LvZ3aD4BHIbh0QlbqXcG2NyEmSno53fesubEULgkDlkuq86ldsHZcgR2g6sDIUf04S1ZPilXIzKO2tfSced8S8sEk7Ln8eZ5bSbgse4ounQD/8jBkLRQNtH8c+Sm9dPeRr/XD0z/t/BA2vyrHnWbAQ3Nh9GYpdR1/QSpWff2UuT11smMY8NVYOPMrFCoO/Z2wAl1utH8Rmo0EDKlmecyG++XO7Idtb8lxt7ehaIDtzp3fChWDQavl9RoXLYOjmL/MjsoxXI+V0uZfj5d+VFXbS+pc1Xb583wdX5GB18VjMtB2JYYh18moHeDtBwOWQeESZkdle95FZXAErtHT6EJE5qRhl5l5r0ZsS6VqQN8FkgVzcHlmQYgCRgdGjuhKVOaelI6vSIPW/OIXJL0jsMDeefCbHS8412Pgi15w/jAUCZSVomLl7ff89hJQB6rdL31Qds42OxrHsHe+9CABaPcCtHxCjgPrS3PH9MmAvZ/DnNbw1y+mhJmtrW9IJSw3D+j7BRSvbHZE+ctigS5vSkVMawosGwx/7rjz86YkShU6IxXq9JQN1s6mcAm5bhWvCjGnpJmmC3aKz+L0PimwEL4SLO4Q8hIMXAlFSuffcxa6C7q+Kcc//k8qm7qKn2ZJ00+Lm7S1KF3L7IjyT3o6XfhKSE02N5b8ZBiyQm9Nlkbhd7oXz5aqtJVJLIAtM+BAwdvzpQMjR7RhquxbqNgaaj+Y/89XrYNUHgEpz3v+aP4/Z2KcbOI7u19WrIZ8lbtyrc7mnvHy769fFviNi7d1cAV8nba/rdV4aDMp6/c9faSHzZC14FdWVtlCO8LmGea/GR5aDVtek+Nu70Cl1ubGYy9ubtDzI6jeSa5Ni/pKz6Y7sWVGWopVKflv6ayKBsCQNfJ7nDuY1ineBcvzG4asAod2hL9Pgn8FeDRMCgDYo0lvnR5STt2aAmuftH8PLjMcWQc/pBWd6DxTKiYWZFXaSR+fhIsFu3T0gWXwx3ZJHexig2rEttZkWGYmzFdPQNRuU8OxNR0YOZo/d8iHL4sbdJ5hvz+I+56Fym2kJOTyofmbvpR8DZb0l9KbPv5SaKEgz3KBVLEq00g+VO6Za3Y05jn6rXxwxICmj0rBgpu9xqvcJ+k39R6WVYWtr0v/E7MKhZz5FVanVaBr8QQ0GWpOHGZx94RH5ktvscRYqch28XjeznXqZ6l2B1Li3NlTf4pVyNop3tUqpMVfgsX9ZBXYmiwlosdsk75Y9mKxyKqRt5+UUd/9sf2e2wxnf5MmzBiS6ho82uyI8p+7h/TWgYKbTnftb1j/ghy3ecZxe+KFTE/r65Yk+20LUBl1HRg5EmtqZv+Bu4dAmQb2e243d9lAXiRAuiuvm5g/G+tSkmDZEDi5DbyKwKBV9v09zWKxZK4a7flENu+7mt+3SFNhI1VSIrq+ffuBf6G74OFQeW16+8sHnjmt4ZfP7Lvx82o0LB4g+yWqhUijZFfkWUj2VKUXHVjQE66cyt05kq9JiXPDKgU3CkKfFZBBQI+0pqU/zYJfF5oajt388ZP8TR4Lk4Fh17ekpLkZVUX9gjKrQ256WYoRFERXo2Fxf5nIrNJOVotcRYO0YkZHv4HEq+bGkh82vSJ7bEvWkIwKR+Xmntk8OOGiZBFcu2J2VDaRp4HR7NmzqVSpEj4+PgQHB7Nnz56bPrZt27ZYLJYbbt26Zc2ZPHLkCA8++CD+/v4ULlyYZs2aERUVlZfwnNf+hRB9QD4Atp9i/+cvUlqKMVjc4MAS+PUL254/NUU25B5fL0vEA5ZBuaa2fQ5HVruHzCxfuyz/r11J1G55I09NlFmmHh/mLr2m/sPwxA5ZeUtOgHUTZIY67nz+xZwu+ZrEfvWMvFk9/Jltq2o5Gx8/mdAoUR1i/4IveuauvPrGl+HScdlX2PWNfAvTFA0eyUwN/fo/Bbt8tDUVtsyE+Q/I30aJ6jBqIzQfZW7qz91DZVUz/TpR0CpnJSXI9Sj2tFyP+sxzretR0N1QoppMUh1ZZ3Y0tnV6b2ZbhG5vg4eXufHcjldhmSgrGiSN7JcPNT/d3QZyPTBaunQpEydOZNq0aezbt4+GDRvSqVMnzp/P/gPKqlWrOHv2bMYtPDwcd3d3+vTpk/GYyMhIWrduTa1atdiyZQsHDhxgypQp+PjkY9EBR3M9FjamzXTd91/zylVXai1VqAC+nWS75oXWVJklPrIW3L2kJn6le2xzbmfh7gEt0/Jyd852jRx4kMpjC/vIB5WqHfI+sPAvJ/uOOr0mM9PHwuDDFjJzmF8MQ/p8ndknM+D9l0j6p6srXFL21fiXl4qSX/aWYiq38+cO2PWhHD/4fsHoVfZvbZ+XvaHWZFg6sGCuWsSelWqiW16Tlb+GA6RoSmB9syOTCZfu78k1InIT/LbE7Ihsx2qV99Ez+6Qi5oClUh3RlVgs/+hpVJD+36ZKpg6G/H6V25gdUc74BcGAJeDpK1kh305y+smIXA+M3nnnHUaNGsXw4cOpU6cOc+bMwdfXl88++yzbxxcvXpzAwMCM24YNG/D19c0yMHrhhRfo2rUrb7zxBo0bN6Zq1ao8+OCDlC6dj1VsHM22N2X5tEQ1aG5yrvA9E6SKWsp1WDZUBm13wjBk5u7gMqnk1We+FHxwRY0HyofBv09K+e6C7kKEfGhOjJGmg3faX8PNTRrdjd4CAfUg4ZLkN381Ln/SKra/BeEr5HX7yIKCXSAkt/zLyf7AwqVkpXtRv1uniCbGyYc6DOk7U6OjvSK1Lzc36DUnLcXkkvx3udNrqCM5sVFS5/7YLs3He30MvT4C7yJmR5apZDVom5aW/v1k52gYnRNbZsDhNdLvre+X0vfPFdVP+/z4+1YZpBcEP4dKMSpv/8zG2s6iTENJd8cilWSdvPpurgZGSUlJ7N27l5CQzMonbm5uhISEsHNnzlIGQkND6devH4ULS012q9XKN998Q40aNejUqROlS5cmODiYNWvW3PI8iYmJxMbGZrk5rUuRsOsjOe70mvnLp25ukjvqVw4uR0qFn7zOABiG9EfaN19S9Hp/ArW62jZeZ+JVGJqNkuOf3nX6mZVbunxSZpUTLknhiQFLpFO9LQTUgVGb0nKwLZL2Oac1nLp5Wm+uHV4r+d4gm7qdZQbPnkpWk7Q6bz/po3KrVIofXpLVE79ycp0ryNJTTIoESuW9FY8WjBXi8JXSXyzhoqwOPbYNGvYzO6rstRoPAfVlM3uYyc0xbeHActiWlnrafZbrZVz8U/HKUL4FYMjElbO7ek72xIE0QM7P0vb5pVZXqSYLsP5FKbTkpHI1MLp48SKpqakEBGRtwhcQEEB0dPRtf37Pnj2Eh4czcuTIjPvOnz9PXFwcr7/+Op07d2b9+vX06tWL3r17s3Xr1puea8aMGfj7+2fcypd34v4361+UtItqIVDdQWZRfYtLTwQ3D5mh+vnTvJ1n4//B7jly/OAHztmrxNaajwYPH0mH+PMns6PJH7FnZFB09aw0wBy82vYpaB7e0PFlGLZOUrr+/kOq1m165c7znM/+Js1MAZo/JhX0VPbKNJD9gh6FZP/g6jE3DgJ+3wI/p1Vj7PGBa6Qj+gVB/8Xy3+XEBrnOO7N9X8CKEVIOu34fGPGDDIwdlbsnPPieTMiFr4SIMLMjyrtTe6SpNMA9/5EVV1eXXoShIFSnW/+CVPoMauzc7zUtnoAmwyXd8eoZs6PJM7tWpQsNDaV+/fo0b55ZwtOa1u+hR48eTJgwgUaNGvHcc8/xwAMPMGfOnJuea/LkycTExGTcTp3KZWUkRxG5CSK+lUZ4nV5zrHr15ZtnVvgJmywbA3Nj25vwY1p/kq5vSRqZgiKloGF/Of7pPXNjyQ9xF2RQdOVPSfUYskYG2vmlUmsp692gn+x32PYmhN6f91LSV89lVnyq2r7gr27YQsWW0uzWzUNmcP+ZZ349VlIdQd70q7YzL057K3u3pJmB7K365XNz48mr3R/D2nGAIT1Men2Sv43HbaXs3ZJ6C/DNROdMabwSJenCqYlQsxt0eMnsiBxD3V6SUhh9EM4dNjuavPt9CxxcDlikn5ubu9kR5V16yfzhYVJC3knlamBUsmRJ3N3dOXfuXJb7z507R2Bg4C1/Nj4+niVLljBixIgbzunh4UGdOnWy3F+7du1bVqXz9vbGz88vy83ppKZA2PNy3Hw0lKppbjzZafFEWtO8ZCm1fO3vnP3cztmZaUj3vyyVilSmVk8CFjj+vX0a6trLtSvwZS+4eExSpoZ8BUVvfW2wCR9/6P2xVGjyKSY9h+bcKz2jcpOumHxdPoTEnpYqWw9/7loVn+5E9fslVRYL/BKamRqy/gWIOQXFKrpmmfO6vaBdWl+Sb5+RVgXOZPvb8F1aA/AWY+GBWfZp2GorbZ+XXjCxpzMLHDmLxKtSBjn+gqQF9v7Euf7b5yff4pkZNgeXmRtLXqUkwjfPyHGzkTKQd3bunlAh2Owo7kiu/sK8vLxo0qQJGzduzLjParWyceNGWrZsecufXb58OYmJiQwalHUJ2MvLi2bNmhEREZHl/mPHjlGxYsXchOd89n4u+eeFimduFHU0Fgv0mC1vLFeiYM0Tt/+g+ctn8H3agK/t85n9e1SmElWhVlrJ+h3vmxuLrSTGSfW56IOyIX/IV1Ke3J7q9oIndkpvj5Rr8kF04cPS9+N2DEP2053+RQZXrljx6U7Ve0gatoJ8oF45EvYtkK97fuhYG/Ttqc0kaVRsTYGlg81rUpwbhiEDiYxqqc/KHgJHymrICS9f6J7WTPjnTyFql7nx5JQ1VVIXzx+W/oIDlrju38/NZKTTLZeKfc5mx3vSuqBw6cxqwMp0uZ56mDhxInPnzmX+/PkcOXKExx9/nPj4eIYPHw7AkCFDmDx58g0/FxoaSs+ePSlR4sYO55MmTWLp0qXMnTuXEydO8MEHH/D111/zxBNP5OFXchIJl2Fz2ka19i84dtnaQsWkkpy7l6T97fzg5o/dvzit5CSSC33ff+0SolO65yn598BS56+sk3wdlvSHv/bIoGLwGvP2H/gFSUGAzjNlL9eJH+DDllJM4VZ+fEdmHi3uWoHuTjQdDiEvyfHB5fJv8OOS8uiqLBbZW1W2KVy/IgUMcrr6bob0ojnb35av7/8/aPe88w2K0lVpm7YvJ23yIyXR7Ihub8NUySjw8IF+i6UKpMqqRmep4hb7l/Pt1718Era9JcedXtVJOAeS64FR3759eeutt5g6dSqNGjVi//79hIWFZRRkiIqK4uzZrB/yIiIi+PHHH29Io0vXq1cv5syZwxtvvEH9+vX59NNPWblyJa1bF+A30i2vyxtj6Tpw9zCzo7m9oEbQ+XU53jAt+1m3Q6vhqycAQ1IDQ6Y77xupPZRvBhVaSpri7pvvp3N4qWlplie3gVcRGZQE1jM3Jjc3aDEGRm+V6lnXLsOywbLimd0+gyPrMmfGu74BVe6zb7wFTesJmQP/EtWgw1RTw3EInoWg3yJJMb10Qv5mHLEZojVVBg/p16Sub8kkl7Pr+IrMzF88lvmB1FHtnZc5AdnzIyjXxNRwHJanD9TtIcfOVITBMCQ9NeW6VDtNLz+uHILFMApGveDY2Fj8/f2JiYlx/P1G54/AR/eAkSrpRlXamh1RzhiGpMaEr5BOx2O2ZzaijQiTZobWFJmZ6/6+5kLnxNFvZaXF2w8mHAIfB3/t/ps1FVaNkqpPHj4waKXjrQykJEn/jx//BxiS3tfrY6jYSr4ffRBCO0FyvJRS7+bgH5qchWHIBErpWo69Im5vWV5vI6XDvaNITZZqjOErpZpbj9nQaIDZUdnOoTVSUt7NQ0qNB9Q1O6IbHd8Ai/vJe2nb5x03zd5R/PEjzOsm76HPHHeOoiBHvoalg6R4xOM7oFQNsyMqsPIyNtBPrvZmGFLhzUiVogbOMigCWf3pPks2pV89A6tGS15v5GZYNkQu5PUelq7jOijKmRqdoWQNKdW5b77Z0eSOYcC6p+RDVHrDQUcbFIH0BQuZBsO/k0HRlSj4vKv01Yn5SxpwJsfL32L6qqi6cxaLVKvTQVFWgfXhobmARfa87JlrdkQi+bpcx8NXysDh4c8K1qAIoE4PqexmTYG14x2rt9TF47BkoOyJTH8v1VT026vQSlZhE2PhmBOUZE+Mg+/SBrv3jNdBkQPST6/2diwMft8s+3U6OmGFJu+i8Mh86c0RuRHWjMksJVrrAen47szlJu3NzQ1appUy3vWRY6bWZMcwpMDGvgUys/zQp1KVzJFVbAljfoJGAwFDVpDeayz56SWqSUU7rUCn7KFWt8x9WN89Cyc23vLh+S4pXlYpIr4Fd29J+avby9yY8oPFIivC3n5SZGX3x2ZHJO0B1k2A2cFwdJ1cT5sMkz1pmop+e25u0CAtFe2AE1Sn2/q6VEgsVgHufcbsaFQ2dGBkTylJmdXaWjwhPV6cUUDdzPSPA0ul30u1EJlhdPc0NzZn1KCv5L7HnpbZWmew+TXpywKSblO3p6nh5JiPn1RHe2SBVINMTZJS3/2X6sqGsq97/gMNB0j2wPLhcCHi9j+TH67HwJcPyYSdZ2EYuBxqdDInFnvwC4L7p8vxppfh7z/NiSMxDjbPkMmZXz6T10GNLvD4Tqmi51nInLicUYO+8u/x9VLYylGdOww70943u74lFROVw9GBkT3tngOXf5fSm22cfKag8UBolFZ6vdK98MgX4OFtbkzOytMHgh+T45/ey13fHTP89C5se0OOu77lnOk2dXpIWe82k2DIWvMq6CnXlZ6aXKElJMZIvxp7f6hLuAzzH4SonVLda8ga1yg8cvcwqHiPTOqte8q+19zUZEmhfK+xrB4kx0PZJjDsWynJXbqW/WIpKErXlhRVa7IUgXJEVqs0GU7fRlGQJx+cnA6M7CXuPGx7U447TJWUNGf34PswYoNUIdOZjzvTbITM1p4/JCmKjurnUCkjC9BhmnM37i0aKL0jghqZHYlyVR7esjevWAX4+6T0OEpJss9zXz0nm9bP7gffEjDsayjf3D7PbTY3N9kL6+4NkZvsU9HMMKRlwIct4JunIf68ZI30mQ8jN0Kle/I/hoKsQT/511Gr0/22SCYgPH11L6uD04GRvWx6WTYHlmkk6RMFgZubvJF6eJkdifMrdBc0GSrHP71nbiw389tSeUMHuPdpuHeiufEoVRAULgkDloFXUfjzx7RZ5XxewbhyCj7vktY8NFBWK8o0zN/ndDQlq2VWfAt7DuIu5N9zRe2C0I7SMuDSCfAtKavtY/dIGrLuJbpz9R6S/VmndkuPIEcSdz5zQrHtc1CsvLnxqFvSgZE9nP0N9n0hx11masU2lb0Wj0tz0ZNb4cx+s6PJ6sjXsOZxpEfVY9B+itkRKVVwlK4NfT6XD3a/fgE7Z+ffc12KlEHR5UjwrwCPfue66VutxkNAfekpGPac7c9/4RgsHgCfdZLm156+0Oa/MP5XWW3XPbm241cGKqelgaY3lnYE54/CpyGQcAlK1Zb95cqh6Sf0/GYY8N1zgCHlNyu0MDsi5aiKVYB6veV4hwOtGp34QTaHG6lS0a3z6zrDqZStVb8fOr4qx+tflN5wtnb+iAyKYk5JJcZHv3PeIkC24O4JD74nA9LwFXDse9uc92o0fP2UpM1FfJNZaW78r9D+BefrV+cs0oswHFjqGHt1IzfJSuGVP+GuStD3Cx0MOwEdGOW3w2sgaoeUt06vhKPUzbQaL/8eWmNetaR/+nMHLBkkm1rr9JR9ZbriqVT+aPG4fIDGgJUj4Nwh2537zH7p3xV3DkrXlb5e/uVsd35nVfbuzFn8dRMg8Wrez5V4VSp2vtcY9n4uk0k1u8ETu6TSXNFA28Ssslf7AfmsdekEnNlnbiw/fwpfPiyFVSq0hJGboGR1c2NSOaKfcPJT8jVYn5ZX2vopfRNSt1emgTQaNVIzy2Gb5fQ+WPgIpFyD6p2g91ztUaVUfrJYZO9JpXshKU6aD9ti70vUbpjfHa5dhqC7Ydg6KFL6zs9bULR7QWb0Y0/DD3mYwExNlka97zWGrTOl2l25ZjA8DPovglI1bR6yyoZ3UekRBub1NLKmSpbQN0/L+3iDfjDkKyhcwpx4VK7pwCg/7fgAYqKkK3P6SoBSt5P+Wtm3wLyeDOcOw5e9IelqWjn2+VpkQyl7cPeUPlvFq8j7x9KBkJKY9/P9vgW+6CnFfyq0kg9pvsVtFW3B4OULD8yS458/lWIJOWEYsro/Oxi+fQbiL0DxqvL/b8QGaSqt7Cs9ne7gCvs3TE+8Cov7w+6P5Ov2U6TpvbYycSo6MMovsWfgx3fk+P7pWs5a5VzV9rIhODkBfgm1//NfipQPUtf+hrJNof9ibTaolD35FpdKdd7+UmVr7fi87ZmICJNV3+QEua4MWqn7W26maru03nyG/Pe+3WD0zx0Qej8sHyqFLAqXksbnY3dLnzTdh2mOqu2l6l/CRYjcbL/nvXIKQjvB8e/Bw0fKsLd5Rl8HTsjD7AAKrB9ekjej8i2kjKRSOWWxwD3jYdUo2P0xtHxSmsCmMwxITYKkeEnXTE5IO06ApARpGJjl35t9/yb3p6Z9IAioB4NWFIyeW0o5m5LVZaX2y4fgwBJJx8pNifzwVXINsaZIQ8mHP9OZ69vp+DIcXw8XI2D729Du+RsfcyFC3t8jvpWvPX1llb/VOL1WOgJ3D6j/MOyeI0UYanTM/+f86xdZKYo/D0UCZDKxbJP8f16VLyyG4QilO+5cbGws/v7+xMTE4Odn8ozYqZ8hNASwwOjNENTY3HiU80lNhncbQexfmVWj/jmYMVLz9/nLNISBK3QfglJm+/nTzP5hfb+E2t1v/zO/LoS148CwQv0+0PMjrYaVU4dWw/Jh4OYJj22DgDpyf+xZ2DJDyqkbVmmtcPcQ6UujRRUcy+m9MLe9FGKYdDx/B6zhq6SVRcp1mUzsv0T7FDmQvIwNdMXI1qxWCEtrGtdooA6KVN64e8I9/4HvJsHl32/+ODdPSdP0LJz2ry94FU7792b33+b7nr6SyqMpAEqZr9lIWaXY8wmsGg2Pht26GeueubLfBeSD+wOztGhKbtTpCTW7yorQ2icl/XDnB9JbKjlBHlPrAegwDUrVMDVUdRNBd0s5+ksn4Mg6aNTf9s9hGLDtTdicVmK/Rmd46FNdNSwAdMXI1g4sk/QFryLw5D4oGmBeLMq5GQb8sV3S5jIGMP8ayOgssFIFX2oKLOojfVH8ysKoTdmvUvz4P0nzAilB3ek1neDIi9gzUlAhMVZWHVKuyf3lmku6nfYjdHxb35BBS5V2MGSNbc+dkiiD5gNL5esWY+V1oRMQDicvYwMdGNna9RiZRSgSKDnHSiml1J26dkU2+188JvsXhn2TWRTFMORD4LY35es2k6QEtQ6K8u7nUPgmbU9XiWoQ8pKsFOl/U+dw+SS810ia6044DH5lbHPe+IuwZCCc2iXplN3egqaP2ubcyuZ0YOQIAyOllFIqP1z+XfZOXPsb6vaWggoA378Au2bLcchL0HqCaSEWGFYr/DxXVuYb9NXVeWcU2lGqOnZ81TYT1eePwqJH4MqfUjHykflSzVA5LN1jpJRSShVUxatIAYYFPeDQKqlcdzUa9s2X73d9C5qPMjfGgsLNDYIfMzsKdSca9JWB0YEldz4wOrFRinIkxkoz4AHLdY9ZAaV9jJRSSilnUak1PPA/Od46UwZFFjfo8aEOipT6p7q9pEBR9EFpWp5XP38KC/tkNkkeuUkHRQWYDoyUUkopZ3L3EGiZNgPu5gEPhULjgebGpJSj8S0O1dP6GB1clvuft6bCd89JuXwjFRr2l0IOhUvYNEzlWDSVTimllHI29/8flKolt/LNzI5GKcfU4BGI+AYOLIf2UyVFMicSr8KKR6XhL0CHqdB6ohbfcAE6MFJKKaWcjZs73D3Y7CiUcmw1OoO3nzRLj9ohqai3cyUKFvWD84fAwwd6fQx1e+Z7qMox5CmVbvbs2VSqVAkfHx+Cg4PZs2fPTR/btm1bLBbLDbdu3bpl+/gxY8ZgsViYNWtWXkJTSimllFIKPH2gTg85Tu87dCt//QJzO8igqEgADP9WB0UuJtcDo6VLlzJx4kSmTZvGvn37aNiwIZ06deL8+fPZPn7VqlWcPXs24xYeHo67uzt9+vS54bGrV69m165dBAUF5f43UUoppZRS6p8a9pN/D30Fyddv/rjwVTCvG8Sfh4D60ki5bBP7xKgcRq4HRu+88w6jRo1i+PDh1KlThzlz5uDr68tnn32W7eOLFy9OYGBgxm3Dhg34+vreMDA6ffo0Tz75JAsXLsTTU/sFKKWUUkqpO1ShFfiVg8QYOBZ24/cNA7a+ASuGQ8p1Sb97NAz8y9k/VmW6XA2MkpKS2Lt3LyEhIZkncHMjJCSEnTt35ugcoaGh9OvXj8KFC2fcZ7VaGTx4MJMmTaJu3bq5CUkppZRSSqnsublBg7TJ+AP/qk6XfB1WjYbNr8rXLcdBv0XgXcS+MSqHkauB0cWLF0lNTSUgICDL/QEBAURHR9/25/fs2UN4eDgjR47Mcv/MmTPx8PBg/PjxOY4lMTGR2NjYLDellFJKKaWyaNBX/j2+HhIuy3H8RWmWfHCZlL1/YBZ0elUKmyiXZdc+RqGhodSvX5/mzZtn3Ld3717effdd5s2bhyUXZRBnzJiBv79/xq18+fL5EbJSSimllHJmpWtDYH2wJsOh1XD+KMxtD6d2gbc/DFoJTYebHaVyALkaGJUsWRJ3d3fOnTuX5f5z584RGBh4y5+Nj49nyZIljBgxIsv927dv5/z581SoUAEPDw88PDz4888/efrpp6lUqdJNzzd58mRiYmIybqdOncrNr6KUUkoppVxF+qrRjvch9H648ifcVRlG/gBV2poamnIcuRoYeXl50aRJEzZu3Jhxn9VqZePGjbRs2fKWP7t8+XISExMZNGhQlvsHDx7MgQMH2L9/f8YtKCiISZMm8f3339/0fN7e3vj5+WW5KaWUUkopdYN6D4PFDf4+CYmxUpRh5EYoVcPsyJQDyXWD14kTJzJ06FCaNm1K8+bNmTVrFvHx8QwfLkuQQ4YMoWzZssyYMSPLz4WGhtKzZ09KlCiR5f4SJUrccJ+npyeBgYHUrFkzt+EppZRSSimVlV8ZqN5RKtM1HADdZ4GHt9lRKQeT64FR3759uXDhAlOnTiU6OppGjRoRFhaWUZAhKioKN7esC1ERERH8+OOPrF+/3jZRK6WUUkoplRu9PoaLx6FcU8jFvnblOiyGYRhmB2ELsbGx+Pv7ExMTo2l1SimllFJKubC8jA3sWpVOKaWUUkoppRyRDoyUUkoppZRSLk8HRkoppZRSSimXpwMjpZRSSimllMvTgZFSSimllFLK5enASCmllFJKKeXydGCklFJKKaWUcnk6MFJKKaWUUkq5PB0YKaWUUkoppVyeDoyUUkoppZRSLs/D7ABsxTAMAGJjY02ORCmllFJKKWWm9DFB+hghJwrMwOjq1asAlC9f3uRIlFJKKaWUUo7g6tWr+Pv75+ixFiM3wygHZrVaOXPmDEWLFsVisZgaS2xsLOXLl+fUqVP4+fmZGotybvpaUragryNlK/paUrairyVlKzd7LRmGwdWrVwkKCsLNLWe7hwrMipGbmxvlypUzO4ws/Pz89I9d2YS+lpQt6OtI2Yq+lpSt6GtJ2Up2r6WcrhSl0+ILSimllFJKKZenAyOllFJKKaWUy9OBUT7w9vZm2rRpeHt7mx2KcnL6WlK2oK8jZSv6WlK2oq8lZSu2fC0VmOILSimllFJKKZVXumKklFJKKaWUcnk6MFJKKaWUUkq5PB0YKaWUUkoppVyeDoyUUkoppZRSLk8HRjY2e/ZsKlWqhI+PD8HBwezZs8fskJSTeemll7BYLFlutWrVMjss5QS2bdtG9+7dCQoKwmKxsGbNmizfNwyDqVOnUqZMGQoVKkRISAjHjx83J1jl0G73Who2bNgN16nOnTubE6xyWDNmzKBZs2YULVqU0qVL07NnTyIiIrI85vr164wdO5YSJUpQpEgRHnroIc6dO2dSxMpR5eS11LZt2xuuS2PGjMnV8+jAyIaWLl3KxIkTmTZtGvv27aNhw4Z06tSJ8+fPmx2acjJ169bl7NmzGbcff/zR7JCUE4iPj6dhw4bMnj072++/8cYbvPfee8yZM4fdu3dTuHBhOnXqxPXr1+0cqXJ0t3stAXTu3DnLdWrx4sV2jFA5g61btzJ27Fh27drFhg0bSE5OpmPHjsTHx2c8ZsKECXz99dcsX76crVu3cubMGXr37m1i1MoR5eS1BDBq1Kgs16U33ngjV8+j5bptKDg4mGbNmvHBBx8AYLVaKV++PE8++STPPfecydEpZ/HSSy+xZs0a9u/fb3YoyolZLBZWr15Nz549AVktCgoK4umnn+aZZ54BICYmhoCAAObNm0e/fv1MjFY5sn+/lkBWjK5cuXLDSpJSt3LhwgVKly7N1q1badOmDTExMZQqVYpFixbx8MMPA3D06FFq167Nzp07adGihckRK0f179cSyIpRo0aNmDVrVp7PqytGNpKUlMTevXsJCQnJuM/NzY2QkBB27txpYmTKGR0/fpygoCCqVKnCwIEDiYqKMjsk5eROnjxJdHR0lmuUv78/wcHBeo1SebJlyxZKly5NzZo1efzxx7l06ZLZISkHFxMTA0Dx4sUB2Lt3L8nJyVmuS7Vq1aJChQp6XVK39O/XUrqFCxdSsmRJ6tWrx+TJk0lISMjVeT1sFqGLu3jxIqmpqQQEBGS5PyAggKNHj5oUlXJGwcHBzJs3j5o1a3L27FmmT5/OvffeS3h4OEWLFjU7POWkoqOjAbK9RqV/T6mc6ty5M71796Zy5cpERkby/PPP06VLF3bu3Im7u7vZ4SkHZLVaeeqpp7jnnnuoV68eINclLy8vihUrluWxel1St5LdawlgwIABVKxYkaCgIA4cOMCzzz5LREQEq1atyvG5dWCklIPp0qVLxnGDBg0IDg6mYsWKLFu2jBEjRpgYmVJKiX+mXtavX58GDRpQtWpVtmzZQocOHUyMTDmqsWPHEh4erntm1R272Wtp9OjRGcf169enTJkydOjQgcjISKpWrZqjc2sqnY2ULFkSd3f3GyqpnDt3jsDAQJOiUgVBsWLFqFGjBidOnDA7FOXE0q9Deo1S+aFKlSqULFlSr1MqW+PGjWPdunVs3ryZcuXKZdwfGBhIUlISV65cyfJ4vS6pm7nZayk7wcHBALm6LunAyEa8vLxo0qQJGzduzLjParWyceNGWrZsaWJkytnFxcURGRlJmTJlzA5FObHKlSsTGBiY5RoVGxvL7t279Rql7thff/3FpUuX9DqlsjAMg3HjxrF69Wo2bdpE5cqVs3y/SZMmeHp6ZrkuRUREEBUVpdcllcXtXkvZSS9ilZvrkqbS2dDEiRMZOnQoTZs2pXnz5syaNYv4+HiGDx9udmjKiTzzzDN0796dihUrcubMGaZNm4a7uzv9+/c3OzTl4OLi4rLMjJ08eZL9+/dTvHhxKlSowFNPPcUrr7xC9erVqVy5MlOmTCEoKChLtTGl4NavpeLFizN9+nQeeughAgMDiYyM5L///S/VqlWjU6dOJkatHM3YsWNZtGgRX331FUWLFs3YN+Tv70+hQoXw9/dnxIgRTJw4keLFi+Pn58eTTz5Jy5YttSKdyuJ2r6XIyEgWLVpE165dKVGiBAcOHGDChAm0adOGBg0a5PyJDGVT77//vlGhQgXDy8vLaN68ubFr1y6zQ1JOpm/fvkaZMmUMLy8vo2zZskbfvn2NEydOmB2WcgKbN282gBtuQ4cONQzDMKxWqzFlyhQjICDA8Pb2Njp06GBERESYG7RySLd6LSUkJBgdO3Y0SpUqZXh6ehoVK1Y0Ro0aZURHR5sdtnIw2b2GAOPzzz/PeMy1a9eMJ554wrjrrrsMX19fo1evXsbZs2fNC1o5pNu9lqKioow2bdoYxYsXN7y9vY1q1aoZkyZNMmJiYnL1PNrHSCmllFJKKeXydI+RUkoppZRSyuXpwEgppZRSSinl8nRgpJRSSimllHJ5OjBSSimllFJKuTwdGCmllFJKKaVcng6MlFJKKaWUUi5PB0ZKKaWUUkopl6cDI6WUUkoppZTL04GRUkoppZRSyuXpwEgppZRSSinl8nRgpJRSSimllHJ5OjBSSimllFJKubz/Bx+vml/bnlLbAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"RandomForestRegressor(5)\")\n", + "ax.plot([_[1] for _ in scores], label=\"LGB(5, lr=0.05)\")\n", + "ax.plot([_[2] for _ in scores], label=\"LGB(5, lr=0.1)\")\n", + "ax.plot([_[3] for _ in scores], label=\"LGB(5, lr=0.2)\")\n", + "ax.set_title(\n", + " \"Comparaison pour différents learning_rate\\net des arbres de décisions \"\n", + " \"avec LightGBM\"\n", + ")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[LightGBM](https://lightgbm.readthedocs.io/en/latest/) paraît moins sensible au *learning_rate* que [XGBoost](https://xgboost.readthedocs.io/en/latest/index.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CatBoost\n", + "\n", + "[CatBoost](https://catboost.ai/) est une des plus récentes. Elle est sensée être plus efficace pour les catégories ce qui n'est pas le cas ici." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "from catboost import CatBoostRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:11<00:00, 2.26it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.8878458732034202,\n", + " 0.5106308809971148,\n", + " 0.6633876012158881,\n", + " 0.7962100349534728],\n", + " [0.8702333178716275,\n", + " 0.47507766677086194,\n", + " 0.632648027686756,\n", + " 0.7779024827838191]]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = experiment(\n", + " [\n", + " RandomForestRegressor(max_depth=5, n_estimators=20),\n", + " CatBoostRegressor(\n", + " max_depth=1, n_estimators=20, learning_rate=0.05, verbose=False\n", + " ),\n", + " CatBoostRegressor(\n", + " max_depth=1, n_estimators=20, learning_rate=0.1, verbose=False\n", + " ),\n", + " CatBoostRegressor(\n", + " max_depth=1, n_estimators=20, learning_rate=0.2, verbose=False\n", + " ),\n", + " ]\n", + ")\n", + "scores[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"RandomForestRegressor(5)\")\n", + "ax.plot([_[1] for _ in scores], label=\"CAT(1, lr=0.05)\")\n", + "ax.plot([_[2] for _ in scores], label=\"CAT(1, lr=0.1)\")\n", + "ax.plot([_[3] for _ in scores], label=\"CAT(1, lr=0.2)\")\n", + "ax.set_title(\n", + " \"Comparaison pour différents learning_rate\\net des fonctions en escalier \"\n", + " \"avec CatBoost\"\n", + ")\n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:12<00:00, 1.97it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[[0.8605624010619503,\n", + " 0.7022983070352993,\n", + " 0.8366211999791443,\n", + " 0.8636015275401271],\n", + " [0.8456129223719402,\n", + " 0.7027514870740816,\n", + " 0.8333140676555679,\n", + " 0.8596728951071267]]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = experiment(\n", + " [\n", + " RandomForestRegressor(max_depth=5, n_estimators=20),\n", + " CatBoostRegressor(\n", + " max_depth=5, n_estimators=20, learning_rate=0.05, verbose=False\n", + " ),\n", + " CatBoostRegressor(\n", + " max_depth=5, n_estimators=20, learning_rate=0.1, verbose=False\n", + " ),\n", + " CatBoostRegressor(\n", + " max_depth=5, n_estimators=20, learning_rate=0.2, verbose=False\n", + " ),\n", + " ]\n", + ")\n", + "scores[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "ax.plot([_[0] for _ in scores], label=\"RandomForestRegressor(5)\")\n", + "ax.plot([_[1] for _ in scores], label=\"CAT(5, lr=0.05)\")\n", + "ax.plot([_[2] for _ in scores], label=\"CAT(5, lr=0.1)\")\n", + "ax.plot([_[3] for _ in scores], label=\"CAT(5, lr=0.2)\")\n", + "ax.set_title(\n", + " \"Comparaison pour différents learning_rate\\net des fonctions en escalier \"\n", + " \"avec CatBoost\"\n", + ")\n", + "ax.legend();" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/_doc/practice/ml/lasso_rf.pickle b/_doc/practice/ml/lasso_rf.pickle new file mode 100644 index 0000000000000000000000000000000000000000..29a8bc16404f748cf1b2f5eca4ffd836dbc9ee9a GIT binary patch literal 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