From 637074b18305ed7a6a87ccaf2df2f437287f9e82 Mon Sep 17 00:00:00 2001 From: jakob1111996 Date: Mon, 8 Apr 2024 15:19:47 +0200 Subject: [PATCH] finish notebook for msr banzhaf --- notebooks/msr_banzhaf_digits.ipynb | 663 ++++++++++++++++++++++++++--- tox.ini | 2 +- 2 files changed, 597 insertions(+), 68 deletions(-) diff --git a/notebooks/msr_banzhaf_digits.ipynb b/notebooks/msr_banzhaf_digits.ipynb index 87f8647a5..ff653d7c1 100644 --- a/notebooks/msr_banzhaf_digits.ipynb +++ b/notebooks/msr_banzhaf_digits.ipynb @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "hide" @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "tags": [ "hide" @@ -71,7 +71,7 @@ "plt.ioff() # Prevent jupyter from automatically plotting\n", "plt.rcParams[\"figure.figsize\"] = (20, 6)\n", "plt.rcParams[\"font.size\"] = 12\n", - "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"xtick.labelsize\"] = 10\n", "plt.rcParams[\"ytick.labelsize\"] = 10\n", "plt.rcParams[\"axes.facecolor\"] = (1, 1, 1, 0)\n", "plt.rcParams[\"figure.facecolor\"] = (1, 1, 1, 0)\n", @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -108,12 +108,12 @@ "source": [ "## Loading and grouping the dataset\n", "\n", - "pyDVL provides a support function for this notebook, `load_digits_dataset()`, which downloads the data and prepares it for usage. The data consists of greyscale images of shape 8x8 pixels with 16 shades of grey. These images contain handwritten digits from 0 to 9." + "pyDVL provides a support function for this notebook, `load_digits_dataset()`, which downloads the data and prepares it for usage. The data consists of grayscale images of shape 8x8 pixels with 16 shades of gray. These images contain handwritten digits from 0 to 9." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "editable": true, "slideshow": { @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "editable": true, "slideshow": { @@ -156,15 +156,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Visualize some of the data\n", "fig, axes = plt.subplots(2, 2, figsize=(4, 4))\n", "for i in range(4):\n", " ax = axes[i % 2, i // 2]\n", - " ax.imshow(np.reshape(training_data[0][i], (8, 8)), cmap=\"grey\")\n", + " ax.imshow(np.reshape(training_data[0][i], (8, 8)), cmap=\"gray\")\n", " ax.set_xlabel(f\"Label: {training_data[1][i]}\")\n", "plt.suptitle(\"Example images from the dataset\")\n", "plt.tight_layout()\n", @@ -180,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -204,9 +217,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy: 0.665\n", + "Test Accuracy: 0.619\n" + ] + } + ], "source": [ "import torch\n", "from torch import nn, optim\n", @@ -280,9 +302,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n", + " 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
data_valuedata_value_stderr
66-1.0485240.029862
152-0.9945730.008960
64-0.7608250.018543
3-0.7181650.642347
14-0.7075690.323538
\n", + "" + ], + "text/plain": [ + " data_value data_value_stderr\n", + "66 -1.048524 0.029862\n", + "152 -0.994573 0.008960\n", + "64 -0.760825 0.018543\n", + "3 -0.718165 0.642347\n", + "14 -0.707569 0.323538" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.head()" ] @@ -322,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "editable": true, "slideshow": { @@ -333,7 +435,20 @@ "invertible-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "low_dvl = df.iloc[:30].copy()\n", "low_dvl.index = low_dvl.index.map(str)\n", @@ -360,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "editable": true, "slideshow": { @@ -371,7 +486,27 @@ "invertible-output" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average value of first 10 data points: 0.6300257540520159\n", + "Exact values:\n", + "39 0.392304\n", + "167 0.461181\n", + "33 0.468827\n", + "169 0.519828\n", + "135 0.551593\n", + "19 0.594768\n", + "51 0.636773\n", + "140 0.741426\n", + "71 0.880786\n", + "121 1.052771\n", + "Name: data_value, dtype: float64\n" + ] + } + ], "source": [ "high_dvl = df.iloc[-10:].copy()\n", "print(f\"Average value of first 10 data points: {high_dvl['data_value'].mean()}\")\n", @@ -387,9 +522,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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ii1W95u2o52GT3L9Z1+MGUh3quxhYHBFbkvcwLyTvTduaSU7iMdN1zoSIeCq5kTsDeEHnoa2Rz9r6hq5ZBtm+TayH/0spHT6dumrmaMJvufd7uWuMHxE3kV/LDyAfObCQnIOmXErei/+oiJjVuW4rj65+Xoyk1vA7c1K7nED+z3SP6tDAyfwj+btyv6X3oX39+A35e1JPqg4b67ZTzfHXZhNyk/CjHo3cRtx7+GId25L3Wi3tcd/ODYwP+cQjk423E117I4fgPPL2/G973Pe31fJ7neFxUimlS1JKHyc/p5X0bnon9qS9mrxXDuDD/SynckL184CI2AF4EvnEN91N4U+qn1M1LwOpvr/55ZTSXuS9uduQG9TJHn8z+Q3wwyPi0T0esmv1s691P0WNV1TfLXs++YQ7O1Xf8Vqb86qfO0Xvy0A0XucQbVv9/GqPZuPp5EbnHtWhyr8n/4227jFer+3bOeQ99wPlrN8cdZj4nukaj60OPd6E/FocZE//lKrvA/6IfDbWXs994lIu3+txn6QRsZmTWiSl9Hvg7eTDir7a60QO1fck3ktu+g5J9z1l9yDLXE0+bGkeeS9W57K2J590Ylj+RD6kcsfOE3pU34l5L/nNS13LyXutntQ5MSIOYs0TJQzqhOrnf3SeyKM61ffRDS1jbT5R/Tw6IjboWP4G5EspQD4z4KQi4pER8agedz2QfNjaGidGIR+2ejF5T9pewG9Tnxexr3yZvAflFdy7p+GEHo87nrzX4siIeHr3nZGvtbfLdBca+fpwf9Nj+mzynkbI+VybT5BPhvHuao/5xBibkM+iOPGYgUTEQyJiux53bUg+zO9OprjsRXUCoO+Q9+It6hr/r8mHb95A78P72mZ59XOXzonV3rDjJpnnk+T3O0dXl+CYmGdLutYH3HPympOAp0a+7uIaH8ZExDbV5R6ayhHknNwKvLbztVgt/93Vr1/o0cQ26YPVz/+ptl8TNTyNfLjvn8mXkJHUEh5mKbXPYvIbtcOB8yPiW8CvyA3es8jXLbsN2HfAN869vIl87aA3VG/ufkQ+9Gov8nc1FtDjTHp1pZTujohjq+VfEBGnkq/Jtiv5TdCZ3LvXYFBLyE3b2dWJCVaQT7SyE/n6bbUvgJtS+mFEvI98AoQLq+8yTVxn7gZqnLxlmsv/TETsTv57/SoiTuHe7xw+knzoY/eZArttTz7Rws+Ai8jfxXwI+TnM5t7v0HUuN0XEh4BjqkkfGbD+2yLiC+RTyx9KPs3713s87vqI2JPcdPwkIr5Lfm0k8ndIn0n+vlP3CW4m8wByLi4hn4Dj8mre55G/u/rVlNJFU4zxHvIei93Jr9fTyXs2XkY+ZO5dKaVeJ9mYrocD50XEBcAvySdI2Zh8DbKHAcdOcuKibq8hH5L97oj4f+STkUxcZ+5u4MBpjjNqPyM/j5dExI/Ih6ZvSv4b/Jbe3yF+F/m1sA/w2Ij4NvnDq73I19VcwJrbt38mH1b4X8D+EXE2+ft1m5Oz8TTyWYAvo5kcTRxyfCj5Q4tlEfEV8iUbdiF/v/di1jyMdK0i4j3c+6HYxF7If4+IV1T/PqXrhCmfI5/VdE9y7r5Gfk3tTd7Df/AkJ2aSNCqpBddH8ObN25o38iFDJ5LfLNxGPrzmQvKbxzWum1bNs5Ae13jrekzPa0eR3zSeSP7k9TbymQ0P4N5rii3qevxSJr/O3OJJlr2cNa9BNYvcuP66Wu415NOVP4J7r4u0dcfjt6aP68xV87yIfIjezeQ9O9/m3u8drbG+etXZcd/iap5duqYH+Q3gReSTMVxN3lMwb23j9fM3qu5bY51U09chN0Lnkj/dv5X8xvK1wDpTLYN8xsC3k98oT5xQ4kryhYJfsJZaH0jeS3wb07zm2STj7MS919R63xSP3Rp4P/A74HbyXr3fVLlZMJ31Vd03m/zm+BvkE+/cTs7/T8jNz3rTyQX5jfubya/P26qcnU3+wKVX7ZPml67XFfkw5P8kH9p2VfV3+WP1uH3puG7aNNbxw8l7Xi4n7827jnyyjKf1m8O1LGONdcRatkusZZsx2boif9DzgWpZt5MPdX07uYme7G80HziW/LpcVeXlX8nb2QQs6THPeuTX9I/IHwKtqnLyXfIevQcPkqNprMNdyNeyu6H6O11CbkjnD/C6Ws59r1fXfeu13meRvzt7QZXnG8gf6j2r3+V78+Zt+LdIqZ/zJEi6v4mIt5HfqO6W8nWwpHtUhzWeCXw6pbT/2h8ttUtEHEzeo/yalNIg3/eUpJGymZMEQERsnlK6umvaduRPpVcDD0/5C/LSParDCl8APCOl9NNR1yP1Msn2bSvyHtTNgEd03y9JJfA7c5ImnFt95+NC4Bby90VeSD5879U2cppQNfkvAnYkN3Kn2cip5b5UnZDk5+RDrbcmZ3gD8jUUbeQkFck9c5IAiIgjyScC2BqYS37D8xPgPan3af11PxURC8knabgJ+BZwaEppKKdLl5pQnVhkf/KHVPPI30E+D3h/SunLo6xNkuqwmZMkSZKkAnmdOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALNmukFRkQAmwM3z/SyVaS5wNUppTTqQsD8aiCtybD51QBak18wwxpIazJsfjWAKfM7480cOcRXjmC5KtcWwFWjLqJifjWItmTY/GoQbckvmGENpi0ZNr8axFrzO4pmrlWfRrzpTW+qPcazn/3sBippxn777dfIOCtWrGhknIa0KTNtqoXtttuu9hhf//rXG6ikmVpalrsmtSU3jdRxyCGHNDEML3rRi2qPsdVWWzVQSTPjHH300Q1UAu94xzsaGadBbckvNFTLvHnzmhiGI444ovYYTf2//Yc//KH2GE28JwI4++yzGxmnQW3JcFvqAGCnnXZqxRjQzGtp3333baASOP300xsZp0Frzc0omrlWWX/99WuPseGGGzZQSTPyHnzdX6y77rq1x9h4440bqMTs3Z/MmTOnkXGa2HbOnTu3gUqaeR008f+JZkZT26sm/uZNbYM32mij2mPMmnW/f1t4v9LE37up7V4Tr4PZs2c3UEl5PAGKJEmSJBVooGYuIl4bEcsj4vaI+GlEPL3pwqRhMb8qnRlWycyvSmeG1SZ9N3MRsTdwDHAU8BTgfOBbEfHQhmuTGmd+VTozrJKZX5XODKttBtkzdzjw0ZTS8SmlXwOvAW4F/rHRyqThML8qnRlWycyvSmeG1Sp9NXMRsR6wI3DGxLSU0t3V789stjSpWeZXpTPDKpn5VenMsNqo39PYbAKsC1zbNf1a4HG9ZoiIOUDnqc+aOfWY1D/zq9L1lWHzq5ZxG6zSuQ1W68zE2SyPAFZ03LxYokpiflUy86vSmWGVzPxq6Ppt5q4D7gI27Zq+KXDNJPMcDczruG3R5zKlpphfla7fDJtftYnbYJXObbBap69mLqW0Gvg58JyJaRGxTvX7jyeZZ1VK6aaJG1NcxVwaFvOr0vWbYfOrNnEbrNK5DVYbDXLp92OAEyPiXOAcYBGwIXB8g3VJw2J+VTozrJKZX5XODKtV+m7mUkonR8RDgP8CHgYsA3ZLKXV/GVRqHfOr0plhlcz8qnRmWG0zyJ45UkrvB97fcC3SjDC/Kp0ZVsnMr0pnhtUmM3E2S0mSJElSw2zmJEmSJKlAAx1m2QY77LBDI+MceeSRjYzThMsvv7z2GIsXL65fCLBo0aJGxtFwLVmypBVjANx4442NjKP2mz9/fiPj7LzzzrXHaGK7CbBixYraYzT1/5KGr6n/45r4my9YsKD2GAC77LJLK8YAWLp0aSPjaLiayN5hhx1WvxDgqKOOqj3GgQce2EAlcOqppzYyzkxxz5wkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFWjWqAsY1A477NDIOCtWrKg9xgknnFC/kIbGWbJkSe0xALbeeuvaYyxfvrz2GOOqqfzOnz+/9hiLFy+uPYbuX5ra5jXxOli0aFHtMQAuu+yy2mOccsop9QvRjGhi2wlw44031h5j6dKltccAmDdvXu0xmspwE+MsW7as9hhauzPPPLP2GE29lpp4L9JUZkp7D+yeOUmSJEkqkM2cJEmSJBXIZk6SJEmSCtRXMxcRR0TEzyLi5oj4U0ScEhGPHVZxUtPMsEpmflUy86vSmWG1Ub975nYGjgOeATwPmA18OyI2bLowaUjMsEpmflUy86vSmWG1Tl9ns0wp7db5e0QsBP4E7Ah8v7mypOEwwyqZ+VXJzK9KZ4bVRnW/MzdxHty/1C1EGhEzrJKZX5XM/Kp0ZlgjN/B15iJiHWAJ8MOU0oVredwcYE7HpLmDLlNq0nQybH7VVuZXJfM9hErnNlhtUWfP3HHAXwH7TPG4I4AVHbcrayxTatJ0Mmx+1VbmVyXzPYRK5zZYrTBQMxcR7wdeBOyaUpoqmEeTd0NP3LYYZJlSk/rIsPlV65hflcz3ECqd22C1SV+HWUZEAO8D9gB2SSldNtU8KaVVwKqOMfqtUWpMvxk2v2oT86uS+R5CpXMbrDbq9ztzxwH7AbsDN0fEw6rpK1JKtzVamTQcZlglM78qmflV6cywWqffwywPIe8mXgr8seO2d7NlSUNjhlUy86uSmV+Vzgyrdfq9zpz7h1U0M6ySmV+VzPyqdGZYbVT3OnOSJEmSpBGwmZMkSZKkAg180fBRmz9/fiPjnHDCCbXHWLRoUe0xmrJs2bJGxlmwYEHtMZYsWVJ7jHHVVH5vvPHGRsaR+rF8+fJGxhm37czWW2/dyDi777577TFOPfXUBioZX6ecckoj4zSRv8WLF9ceA+DII49sZJwm7LDDDrXHaOr9jCbXxHbiqKOOaqAS2GWXXWqP0dQ2uDTumZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAs0adQGjduONN466hEYtX768kXHmz5/fyDjqbd68eY2M08Tf+5RTTqk9BsDuu+9ee4zLL7+8gUpghx12qD3GuG0bmrR48eJGxlmwYEHtMbbffvv6hTRkl112aWSc8847r5FxNLmlS5c2Ms6iRYtqj9FUbh75yEfWHmPJkiX1C6GZbbDK0NRr6cwzz6w9xlFHHdVAJc29l54p7pmTJEmSpALZzEmSJElSgWzmJEmSJKlAtZq5iHhTRKSIWNJQPdKMMb8qnRlWycyvSmZ+1RYDN3MR8TTg1cAvmytHmhnmV6UzwyqZ+VXJzK/aZKBmLiI2Ak4CDgZuaLQiacjMr0pnhlUy86uSmV+1zaB75o4Dvp5SOqPJYqQZYn5VOjOskplflcz8qlX6vs5cROwDPAV42jQfPweY0zFpbr/LlJpiflW6fjJsftU2boNVMvOrNuprz1xEbAm8F3h5Sun2ac52BLCi43ZlXxVKDTG/Kt0AGTa/ag23wSqZ+VVb9XuY5Y7AQ4FfRMSdEXEnsDPwL9Xv6/aY52hgXsdtizoFSzWYX5Wu3wybX7WJ22CVzPyqlfo9zPK7wHZd044HfgO8M6V0V/cMKaVVwKqJ3yOi3xqlpphfla6vDJtftYzbYJXM/KqV+mrmUko3Axd2TouIW4DrU0oX9p5Lagfzq9KZYZXM/Kpk5ldtVeui4ZIkSZKk0ej7bJbdUkq7NFCHNBLmV6UzwyqZ+VXJzK/awD1zkiRJklQgmzlJkiRJKlDtwyxH5bLLLmtknD322KORccbN/PnzR13CWDv//PMbGeeUU06pPcaJJ55YvxBg1113rT3GDjvsUL8QYMmSJbXHWLhwYe0xxtWNN97YyDjbb7997THOOuusBiqBRYsW1R5j2bJltcdQWZYuXdqKMZrS1Gu7qXE0XIsXL649RlP/V15++eW1xzjvvPMaqKQ87pmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALNGnUBgzr11FMbGee9731v7TGWLVtWv5CGxjnggAPqFwIceOCBjYyj3pYvX97IOCtWrKg9RlO1NDHO4sWLa48BsHTp0kbGUW9NbfOa0FRm2vScVI5FixbVHmPJkiW1xwDYYYcdao+xyy671B4DmntdariOPPLI2mMsWLCgfiE08368ifdEJXLPnCRJkiQVyGZOkiRJkgpkMydJkiRJBeq7mYuIh0fEpyPi+oi4LSIuiIinDqM4qWnmV6UzwyqZ+VXpzLDapq8ToETEA4EfAmcCLwD+DDwauKH50qRmmV+VzgyrZOZXpTPDaqN+z2b5RuCKlFLnqQ4va7AeaZjMr0pnhlUy86vSmWG1Tr+HWf4DcG5EfCEi/hQR50XEwcMoTBoC86vSmWGVzPyqdGZYrdNvM/co4BDgd8DzgQ8Cx0bEpBc3i4g5EbHxxA2YO3C1Uj3mV6XrK8PmVy3jNlilcxus1un3MMt1gHNTSm+ufj8vIv4KeA1w4iTzHAHUvyqhVJ/5Ven6zbD5VZu4DVbp3AardfrdM/dH4Ndd0y4CtlrLPEcD8zpuW/S5TKkp5lel6zfD5ldt4jZYpXMbrNbpd8/cD4HHdk17DHD5ZDOklFYBqyZ+j4g+Fyk1xvyqdH1l2PyqZdwGq3Rug9U6/e6Z+z/gGRHx5ojYNiL2A/4JOK750qTGmV+VzgyrZOZXpTPDap2+mrmU0s+APYB9gQuBtwKLUkonDaE2qVHmV6UzwyqZ+VXpzLDaqN/DLEkpnQacNoRapKEzvyqdGVbJzK9KZ4bVNv0eZilJkiRJagGbOUmSJEkqUN+HWY6bww47rPYYRx11VAOVwPz582uPseuuu9YvBFi6dGkj42i4dtlll9pjLFmypPYYAEceWf9SOk29lhYvXtzIOOptwYIFjYxz1lln1R7DbZVKl1JqZJzLL5/0pKDTdsIJJ9QvpMFxNFwHHnhg7TFOOeWU+oXQzP8Hy5Ytq19IgdwzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCmQzJ0mSJEkFspmTJEmSpALZzEmSJElSgWzmJEmSJKlANnOSJEmSVCCbOUmSJEkqkM2cJEmSJBXIZk6SJEmSCjRr1AWM2h133FF7jJUrVzZQCdx66621x7jzzjsbqESluOuuu2qPccsttzRQCdx00021x7j99tsbqETD1tTfqansSaOyatWq2mM0se0EuPnmm2uP4Tb4/mX16tW1x2gqv038f5BSaqCS8sRMP/GIeDhw5YwuVKXbIqV01aiLAPOrgbUiw+ZXA2pFfsEMa2CtyLD51YDWmt9RNHMBbA5M9hHSXHLQt1jLYzS40tbvXODq1JKPW6aRXyhvHZekxHXbmgyb31Yobf22Jr9ghlugxHXbmgyb35Ercd1Omd8ZP8yyKmby7jJi4p83p5Sa2XerexS4fltV41T5hSLXcTEKXbetqdP8jl6B67dVNZrh0Sp03bamTvM7WoWu2ynr9AQokiRJklQgmzlJkiRJKlAbm7lVwFHVTzXP9Tt8ruPhcd0On+t4uFy/w+c6Hh7X7fC5jodnLNftjJ8ARZIkSZJUXxv3zEmSJEmSpmAzJ0mSJEkFspmTJEmSpALZzEmSJElSgVrXzEXEayNieUTcHhE/jYinj7qm0kXE4ohIXbffjLqucWR+h8MMzxwz3DzzO3PM73CY4Zlhfodj3PPbqmYuIvYGjiGfNvQpwPnAtyLioSMtbDz8Ctis47bTaMsZP+Z36MzwkJnhoTK/Q2Z+h84MD5H5HbqxzW+rmjngcOCjKaXjU0q/Bl4D3Ar842jLGgt3ppSu6bhdN+qCxpD5HS4zPHxmeHjM7/CZ3+Eyw8NlfodrbPPbmmYuItYDdgTOmJiWUrq7+v2Zo6prjDw6Iq6OiN9HxEkRsdWoCxon5ndGmOEhMsNDZ36HyPzOCDM8JOZ3RoxtflvTzAGbAOsC13ZNvxZ42MyXM1Z+CiwEdgMOAR4J/CAi5o6yqDFjfofLDA+fGR4e8zt85ne4zPBwmd/hGuv8zhp1ARq+lNI3On79ZUT8FLgc2Av4+GiqkqbPDKtk5lelM8Mq2bjnt0175q4D7gI27Zq+KXDNzJczvlJKNwIXA9uOuJRxYn5nkBkeCjM8Q8zvUJjfGWSGG2d+Z9C45bc1zVxKaTXwc+A5E9MiYp3q9x+Pqq5xFBEbAdsAfxx1LePC/M4sM9w8MzxzzG/zzO/MMsPNMr8za9zy27bDLI8BToyIc4FzgEXAhsDxoyyqdBHxHuBr5F3Km5NPe3sX8NlR1jWGzO+QmOEZY4aHwPzOGPM7JGZ4RpjfIRn3/LaqmUspnRwRDwH+i/yFz2XAbiml7i+Eqj9bkAP7YODPwNnAM1JKfx5pVWPG/A6VGZ4BZnhozO8MML9DZYaHzPwO1VjnN1JKo65BkiRJktSn1nxnTpIkSZI0fTZzkiRJklQgmzlJkiRJKpDNnCRJkiQVyGZOkiRJkgpkMydJkiRJBbKZkyRJkqQC2cz1KSIWR8RAF+eLiIURkSJi64bL6lzG1tUyFg5rGSqbGVbJzK9KZ4ZVMvPbPvebZi4inhgRn46IqyJiVURcHREnRcQTR11b20TE/Ij4SET8OSJuiYgzI+Ipo67r/s4M9ycinhsR34uIFRFxc0T8PCL2HnVd91fmd3oiYmn1RqTX7Y5R13d/ZoanLyKeFxFnR8StEXFDRHxxmG/gNTXzO30RsWNEnBYR10TEyoj4ZUT8S0SsO+raeomUBmquixIRLwE+C/wF+DhwGbA1cBDwYGCflNJXpjnWLGBWSun2AepYF5gNrEpDWvHVxvIy4MCU0gkDzL8O8ANge+DdwHXAocCWwI4ppd81VqymzQz3PcaB5PX0HeCrwF3AY4GrUkrvaaZSTZf57Wv+5wGbdk3eEPgQcHpK6YV1a1T/zHBf878IOBX4BfApYGPgMGAV8OSU0p8bK1bTYn77mn9H4EfA78jr6lbgBcDuwLEppcMaK7YpKaWxvgHbALcAFwEP6bpvk2r6SuBRU4yz4aifyzSf79ZAAhYOOP9e1fx7dkx7CHAD8JlRP7/7480MDzT/rcB7R/1cvJnfhsZ8RTXmfqN+fvfHmxnue/5fkd8Ir9cxbXvyh2r/O+rnd3+7md++5/8I+YOHB3VNPwtYMern1+t2fzjM8t+BDYB/Sl2fBqWUrgNeTf7U8w0T0yeOB46IJ0TEZyLiBuDszvs6x4mIB0TEsRFxXXU411cj4uHVGIs7HrfGscIRsbzalbtTRJwTEbdHxO8j4pVdy3hQRLwnIi6odvneFBHfiIjtp1oBETE7Ih4XEZtNY33tCVwLfLljPf0Z+Dywe0TMmcYYapYZ7i/DrwHWBf6zmnejiIhpzKfhML/95beX/chvxk4dcH7VY4anmeGIeBDwBOArKaXVHevpfHLTsM9Uy1LjzG9/2+CNgduBG7um/xG4bRrzz7j7QzP3YmB5SukHve5MKX0fWA70OnTlC+QXwJuBj65lGScArwNOB95I/mN/vY8atwW+SD4k7F/Je8FOiPsex/woYAFwGnA4+RDI7YCzImLzKcZ/OHkjevQ0anky8IuU0t1d088hr4vHTGMMNcsM95fh5wK/Af4+Iq4Ebgauj4j/jnwYsWaW+e0vv/cREQ8BngecklK6pd/51QgzPP0MT3zg2+tN763A5hHxsCnGULPMb3/b4KXkhu7DEfH4iHhERLwGeMk0559xs0ZdwDBFxDxgc6b+NPOXwD9ExNyU0s0d089PKe03xTKeQj40cUlK6fXV5A9ExPHkwwqm47HA30680CLi88AVwIHAv1WPuQB4TGeTFRGfIr9pPQj472kuayqbAd/vMf2P1c/Nq1o0A8zwQB5NPpzneOBdwPnkjfBbyNu8IxpajqZgfhuxNzm3Jw1pfK2FGe7bteQ9Gn/TOTEiHkzeYwf5jfU1DSxLUzC/A/ko8ETyHstXVdPuAv45pfShhpbRqHH/lHpu9fPmtT7q3vs37po+nT/abtXPD3RNf9805p3w685PTKrd4L8lfwoxMW3VRIAjYt1qw7iyetxazzSZUlqeUoqU0sJp1PIA8rHC3W7vuF8zxwzTd4Y3Ah4IHJlS+s+U0pdSSi8HvgkcFhFz1z67GmR+6Tu/3fYD/kz+xFozzwwz/QxX438YeE5EHB0Rj458QonPA+tVD/N9xMwxv/S3DU4p3QVcCnwLOID8gdrXgPdFxII+ntOMGfdmbiKcU715myzsl01jGY8A7u7x2EumMe+EP/SYdgP5DSmQzzIZEa+PiN+Rm63ryP/BPwmY18eypnIb9x4m0Wn9jvs1c8xw/yYy+tmu6Z8lv4l4coPL0tqZ3xoi4lHAM4GTU0p3DmMZmpIZ7t9/ks8C+AbgYuBc4M5qGuQ34JoZ5rdPEfEm8qGi+6aUPplS+nxKaQ/ydwaPi3w2z1YZ62YupbSCfHjgk6Z46JPIpyy/qWv6TDUud00yvfOkDW8GjiEfAvkK4Pnk71H8imb/jn8kH2rZbWLa1Q0uS1MwwwOZyOi1XdP/VP18IJoR5re2icObPMRyRMxw/1JKq1NKryIf3ve3wGNTSs8nv+G+m/7e5KsG8zuQQ4HvpZS6P3T4KjnTWze4rEa0rrscgtOAgyNip5TS2d13RsSzyX+YDw84/uXkED2SfCreCdsOON5k9gTOTCkd1DkxIuaTP51oyjLg2RGxTtdJUP6a/OXlixtclqbHDPfn5+TvzT0c+H3H9IkvSHuNo5llfge3H3BpSuknQxpf02OGB5BSupbqQ7XI1xfbBfhpjzfJGi7z259NyWfE7ja7+tm63mms98xV3k3+ZOHD1fG194h8Ct0PkZuUdw84/reqn4d2TX/dgONN5i7u+wkFEfEy8hvWterzlKxfJAf5JR3zbwK8DPhaSqnX9+k0XGa4vwyfXP28Z4NfncXyQPIFU38+7YrVBPM7wKUJIuLJwOOBz/RbqBpnhutfXuPfyEf4/O+A82tw5re//F4MPK9zXVUfRuxFPgz10r6qngGt6y6bllL6XUQcQD5M5YKI+Dj5uN6tyW/2NiEfFzvQHyel9POI+BKwqPrD/wTYmXtP4d/UFe5PA/6zOjvQj8inY305993zMJmJU7KeCCyc4rFfJD+H4yPiCeRPOw4lf0px5ECVqxYzDPSX4VOB7wJHVB9EnE8+nfFOwKv9QGJmmV+gv/xOeHn100MsR8wMA31kOCJeAbyUfDjcSvLlYvYCPpZS+tKgxWsw5hfobxv8DuDTwE8j4iPkRnhfYEfgLSmlOwaqfojGvpkDSCl9ISJ+Qz4l+URwrwfOBN6eUrqw5iJeST7N7r7AHsAZ5LPf/JZ7zwJZ19vJF3Xcrxr7F+RrgryjofGBfBafiPh78ic0/0I+YcTPgIUppd82uSxNnxmevpRSqs449T/VchaSn8crUkq+MR4B89ufak/yPuRrfrrdbQEz3JeLgQcBbyW/h/gt8BrgIw0vR9NkfqcvpXRSRFxHXlf/Tj7D52+B16SUBj0UdagipaYaZnWKiB2A8/ANpAplhlUy86vSmWGVzPzOnPvDd+aGLiJ6XTNlEfmsTb0uwC21ihlWycyvSmeGVTLzO1r3i8MsZ8Abqotinkm+lsoLqttHUkpXjLQyaXrMsEpmflU6M6ySmd8R8jDLBkTE88gnB3kCsBH54oefAt7mhV5VAjOskplflc4Mq2Tmd7Rs5iRJkiSpQH5nTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBZs30AiMigM2Bm2d62SrSXODqlFIadSFgfjWQ1mTY/GoArckvmGENpDUZNr8awJT5nfFmjhziK0ewXJVrC+CqURdRMb8aRFsybH41iLbkF8ywBtOWDJtfDWKt+R1FM3czwBVXXMHGG2888CDz5s1rpJi//OUvtcd45jOf2UAl8PjHP772GLvttlsDleS/T11ve9vbGqgEaNcnWDcDHHLIIcyZM2fgQY466qhGitl6661rj7F8+fLaYwA861nPqj1GE88H4Nhjj609xjbbbNNAJfdoS4ZvBthjjz2YPXv2wIPss88+jRTzkpe8pPYYe+21VwOVwEYbbVR7jE984hMNVNKMgw8+uPYYq1ev5sQTT4T25BcaqmW//fZrYhg+85nP1B5jxYoVDVTSjCa2nQBvfetbGxmnQW3JcCN1PPGJT2xiGH71q1/VHuMBD3hAA5XANddcU3uMpnqD888/v/YY22+/fQOV3GOtuRlFMwfAxhtvXKuZa7KOutZdd90GKqHWm6sJTb2o1l9//UbGGVdz5syp1cw1lf18xEY9TdXSxOugidcAwNy5cxsZZ1zNnj271rrecMMNG6ymnvXWW69V47TFuD2fprVp/bThvdAE/+8vQ1PvO5vQxPsQaNfroLT3EJ4ARZIkSZIKNFAzFxGvjYjlEXF7RPw0Ip7edGHSsJhflc4Mq2TmV6Uzw2qTvpu5iNgbOAY4CngKcD7wrYh4aMO1SY0zvyqdGVbJzK9KZ4bVNoPsmTsc+GhK6fiU0q+B1wC3Av/YaGXScJhflc4Mq2TmV6Uzw2qVvpq5iFgP2BE4Y2JaSunu6vdmTukoDYn5VenMsEpmflU6M6w26vdslpsA6wLXdk2/FnhcrxkiYg7Qedq/sk4Ro3FiflW6vjJsftUyboNVOrfBap2ZOJvlEcCKjpsXS1RJzK9KZn5VOjOskplfDV2/zdx1wF3Apl3TNwUmu9rf0cC8jtsWfS5Taor5Ven6zbD5VZu4DVbp3Aardfpq5lJKq4GfA8+ZmBYR61S//3iSeVallG6auDHFVcylYTG/Kl2/GTa/ahO3wSqd22C1Ub/fmYN8OtYTI+Jc4BxgEbAhcHyDdUnDYn5VOjOskplflc4Mq1X6buZSSidHxEOA/wIeBiwDdkspdX8ZVGod86vSmWGVzPyqdGZYbTPInjlSSu8H3t9wLdKMML8qnRlWycyvSmeG1SYzcTZLSZIkSVLDbOYkSZIkqUCRUprZBUZsDKzYc889mT179sDjPP/5z2+knuuvv772GIcffngDlTTj+OOb+f7tiSeeWHuMjTfeuNb8d9xxB9/85jcB5lVngRq5ifw+8pGPZJ11Bv8s5NJLL22kniZevz/60Y8aqASWLVtWe4xtttmmfiHAYYcdVnuMK664ovYYKSVuu+02aEmGJ/K7wQYbEBEDj3Psscc2Us9VV11Ve4zvfve7DVQCS5curT3GaaedVr8Q4LWvfW3tMb797W/XHmPlypU89alPhZbkF+7N8KjrmPCGN7yh9hj77LNPA5XAhhtuWHuMJUuW1C8E+OAHP9jIOA1qRYYn8hsRtbbBd999dyP1rLvuurXHWLx4cf1CgLlz619P/YADDmigEnjZy15We4wzzjijgUrusdb8umdOkiRJkgpkMydJkiRJBbKZkyRJkqQC2cxJkiRJUoFs5iRJkiSpQDZzkiRJklQgmzlJkiRJKpDNnCRJkiQVyGZOkiRJkgpkMydJkiRJBbKZkyRJkqQC2cxJkiRJUoFs5iRJkiSpQDZzkiRJklQgmzlJkiRJKpDNnCRJkiQVyGZOkiRJkgoUKaWZXWDExsCKuuP84Q9/aKAa2HLLLWuP8eQnP7mBSuDwww+vPcYPf/jDBiqBBz3oQbXHOProoxuoBIB5KaWbmhqsjqbyu2DBgvrFAF/5yldqj7HVVls1UAk84hGPqD3Gy172sgYqgdmzZ9ce4wMf+EDtMe666y4uuugiaEmGJ/L7jne8g/XXX3/gcRYtWtRIPU38/zN37twGKoFbb7219hj7779/A5XArFmzao9x99131x5j9erVnHTSSdCS/EJz2+ALLriggWpgu+22qz1GE9tOgOXLlzcyTltERJPDtSLDTeW3KZtttlntMa6++uoGKmnm773jjjs2UAkceuihtcc46KCDGqjkHmvNr3vmJEmSJKlANnOSJEmSVCCbOUmSJEkqUF/NXEQcERE/i4ibI+JPEXFKRDx2WMVJTTPDKpn5VcnMr0pnhtVG/e6Z2xk4DngG8DxgNvDtiNiw6cKkITHDKpn5VcnMr0pnhtU6fZ0yK6W0W+fvEbEQ+BOwI/D95sqShsMMq2TmVyUzvyqdGVYb1f3O3Lzq51/qFiKNiBlWycyvSmZ+VTozrJEb+GI2EbEOsAT4YUrpwrU8bg4wp2NSMxcFkmqaTobNr9rK/KpkvodQ6dwGqy3q7Jk7DvgrYJ8pHncE+QKJE7crayxTatJ0Mmx+1VbmVyXzPYRK5zZYrTBQMxcR7wdeBOyaUpoqmEeTd0NP3LYYZJlSk/rIsPlV65hflcz3ECqd22C1SV+HWUZEAO8D9gB2SSldNtU8KaVVwKqOMfqtUWpMvxk2v2oT86uS+R5CpXMbrDbq9ztzxwH7AbsDN0fEw6rpK1JKtzVamTQcZlglM78qmflV6cywWqffwywPIe8mXgr8seO2d7NlSUNjhlUy86uSmV+Vzgyrdfq9zpz7h1U0M6ySmV+VzPyqdGZYbVT3OnOSJEmSpBGwmZMkSZKkAg180fC6VqxYwcYbbzzw/N/5zncaqWPLLbesPcbll1/eQCXwyle+svYYBx10UAOVwOMf//jaY+y555615r/jjjs49dRTa9cxDM997nOZPXv2wPMfeOCBjdTx5je/ufYYV1xxRQOVwKabblp7jP/7v/9roBLYdttta49x/fXX1x7j7rvvrj3GMFx99dXMmTNn6gdO4g1veEMjdTRxZrd3vvOdDVQCb3zjG2uPccIJJ9QvpCGeNW/ttttuu0bGOeSQQ2qP8YEPfKCBSuArX/lK7TE222yzBiqBM888s5FxNFy33Vb/nC0f/ehHG6gE9t67/tcODz300AYqgZ133rn2GOecc07tMVauXMnf/d3fTfk498xJkiRJUoFs5iRJkiSpQDZzkiRJklQgmzlJkiRJKpDNnCRJkiQVyGZOkiRJkgpkMydJkiRJBbKZkyRJkqQC2cxJkiRJUoFs5iRJkiSpQDZzkiRJklQgmzlJkiRJKpDNnCRJkiQVyGZOkiRJkgpkMydJkiRJBbKZkyRJkqQC2cxJkiRJUoEipTSzC4zYGFhx+OGHM2fOnBlddi8Pe9jDao9x2GGHNVBJM6644opRl3CPLbfcsqmh5qWUbmpqsDom8vvqV7+6Vn6PPfbY5oqqqaltQETUHmP+/Pn1CwG22mqr2mNss802tce44447OO2006AlGZ7Ib91xZvr/jVKcfvrpjYzzwhe+sJFxGtSK/EJzGW7KD37wg9pj7LTTTg1U0sw2uKnX9t577117jM9//vMNVHKPVmS4qfw+4xnPaKAa+MlPftLIOE1oInuHHHJIA5XAhz70oUbGadBa8+ueOUmSJEkqkM2cJEmSJBXIZk6SJEmSClSrmYuIN0VEioglDdUjzRjzq9KZYZXM/Kpk5ldtMXAzFxFPA14N/LK5cqSZYX5VOjOskplflcz8qk0GauYiYiPgJOBg4IZGK5KGzPyqdGZYJTO/Kpn5VdsMumfuOODrKaUzmixGmiHmV6UzwyqZ+VXJzK9aZVa/M0TEPsBTgKdN8/FzgM4Lcs3td5lSU8yvStdPhs2v2sZtsEpmftVGfe2Zi4gtgfcCL08p3T7N2Y4gXyBx4nZlXxVKDTG/Kt0AGTa/ag23wSqZ+VVb9XuY5Y7AQ4FfRMSdEXEnsDPwL9Xv6/aY52hgXsdtizoFSzWYX5Wu3wybX7WJ22CVzPyqlfo9zPK7wHZd044HfgO8M6V0V/cMKaVVwKqJ3yOi3xqlpphfla6vDJtftYzbYJXM/KqV+mrmUko3Axd2TouIW4DrU0oX9p5Lagfzq9KZYZXM/Kpk5ldtVeui4ZIkSZKk0ej7bJbdUkq7NFCHNBLmV6UzwyqZ+VXJzK/awD1zkiRJklQgmzlJkiRJKlDtwywHdcwxx9Saf5tttmmkjve85z21x9hkk00aqASOPvro2mPcddcaJ1MayPe///1GxhlXH/7wh2vNf9BBBzVSx4477lh7jKbOrjV//vzaYzTxGgDYaKONao+x//77N1DJeJo3b14j46xYsaL2GJ/61KcaqKSZv/emm27aQCXN+Ld/+7faY6xatYr3ve99DVTTPimlRsZpYvv50Ic+tIFKYMMNN2xknCZccskltce46KKLao+xcuVKnva0aV3fuyg/+clPGhmnidfBxz72sQYqydubuj70oQ81UAlsueWWtcdo4v+Du+66i/POO2/Kx7lnTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKNGvUBQzq0ksvbWScBQsW1B5j3rx59QsBjjvuuNpj7Lbbbg1UAhtssEEj44yr2bNnExEDz/+d73ynkTo+9rGP1R5j7ty5DVQC+++/f+0xtt122wYqgdNPP72RcdTbTTfdNOoS7vGpT32qkXFe+cpXNjJOEzbeeOPaYzTxf+Qdd9xRe4y2qrP97pRSqj1GU7U04fzzz29knEMPPbT2GI95zGNqj9GmbVWnuu8hVq9e3WA19fzgBz9oZJxXvepVjYzThLe97W21x5jJ/1PcMydJkiRJBbKZkyRJkqQC2cxJkiRJUoH6buYi4uER8emIuD4ibouICyLiqcMoTmqa+VXpzLBKZn5VOjOstunrBCgR8UDgh8CZwAuAPwOPBm5ovjSpWeZXpTPDKpn5VenMsNqo37NZvhG4IqV0YMe0yxqsRxom86vSmWGVzPyqdGZYrdPvYZb/AJwbEV+IiD9FxHkRcfAwCpOGwPyqdGZYJTO/Kp0ZVuv028w9CjgE+B3wfOCDwLERccBkM0TEnIjYeOIGNHNRK6l/5lel6yvD5lct4zZYpXMbrNbp9zDLdYBzU0pvrn4/LyL+CngNcOIk8xwBHDlgfVKTzK9K12+Gza/axG2wSuc2WK3T7565PwK/7pp2EbDVWuY5GpjXcduiz2VKTTG/Kl2/GTa/ahO3wSqd22C1Tr975n4IPLZr2mOAyyebIaW0Clg18XtE9LlIqTHmV6XrK8PmVy3jNlilcxus1ul3z9z/Ac+IiDdHxLYRsR/wT8BxzZcmNc78qnRmWCUzvyqdGVbr9NXMpZR+BuwB7AtcCLwVWJRSOmkItUmNMr8qnRlWycyvSmeG1Ub9HmZJSuk04LQh1CINnflV6cywSmZ+VTozrLbp9zBLSZIkSVIL2MxJkiRJUoEipTSzC8wXTVxRd5yjjz66gWpg9erVtcd4xzve0UAlcOutt9Yeo6kzJd1xxx21x5g9e3YDlQAwL6V0U1OD1TGR3+c85znMmtX3Ucr3+Na3vtVIPU1k741vfGMDlcDKlStrj3HFFVc0UAk84QlPqD3GIYccUnuM1atX8/GPfxxakuGJ/H7zm99kww03HHicZz/72Y3Uc8kll9Qe4zOf+UwDlcD73//+2mMsXLiwfiHAu971rkbGaVAr8gv3Znjbbbdl3XXXHXic3/72t43U8+IXv7j2GL/73e8aqAQuuuii2mNcfPHFDVQCy5Ytqz3G3nvvXb+Qe7Uiw029B57p9+4zYf/99689xgYbbNBAJfC5z32u9hi333577TFSShPvx9eaX/fMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAs0adQGDuv322xsZZ/Xq1bXHSCk1UAncdNNNjYzThDbV0kZ33nnnqEsAmnkdNPW3XrlyZSvGaEoT24YmxhiGW265ZdQlAHDzzTfXHqOp/wvuvvvu2mOsWrWqgUo0HU38vZpwxx131B7jrrvuaqCSZrblTW2Db7311kbGUW/j+B6tif8vZ81qpq1p4n39TI4RTTUi0xURDweunNGFqnRbpJSuGnURYH41sFZk2PxqQK3IL5hhDawVGTa/GtBa8zuKZi6AzYHJPpKdSw76Fmt5jAZX2vqdC1ydZjqok5hGfqG8dVySEtdtazJsfluhtPXbmvyCGW6BEtdtazJsfkeuxHU7ZX5n/DDLqpjJu8uIiX/enFIav/3II1bg+m1VjVPlF4pcx8UodN22pk7zO3oFrt9W1WiGR6vQdduaOs3vaBW6bqes0xOgSJIkSVKBbOYkSZIkqUBtbOZWAUdVP9U81+/wuY6Hx3U7fK7j4XL9Dp/reHhct8PnOh6esVy3M34CFEmSJElSfW3cMydJkiRJmoLNnCRJkiQVyGZOkiRJkgpkMydJkiRJBWpdMxcRr42I5RFxe0T8NCKePuqaShcRiyMidd1+M+q6xpH5HQ4zPHPMcPPM78wxv8NhhmeG+R2Occ9vq5q5iNgbOIZ82tCnAOcD34qIh460sPHwK2CzjttOoy1n/JjfoTPDQ2aGh8r8Dpn5HTozPETmd+jGNr+tauaAw4GPppSOTyn9GngNcCvwj6MtayzcmVK6puN23agLGkPmd7jM8PCZ4eExv8NnfofLDA+X+R2usc1va5q5iFgP2BE4Y2JaSunu6vdnjqquMfLoiLg6In4fESdFxFajLmicmN8ZYYaHyAwPnfkdIvM7I8zwkJjfGTG2+W1NMwdsAqwLXNs1/VrgYTNfzlj5KbAQ2A04BHgk8IOImDvKosaM+R0uMzx8Znh4zO/wmd/hMsPDZX6Ha6zzO2vUBWj4Ukrf6Pj1lxHxU+ByYC/g46OpSpo+M6ySmV+VzgyrZOOe3zbtmbsOuAvYtGv6psA1M1/O+Eop3QhcDGw74lLGifmdQWZ4KMzwDDG/Q2F+Z5AZbpz5nUHjlt/WNHMppdXAz4HnTEyLiHWq3388qrrGUURsBGwD/HHUtYwL8zuzzHDzzPDMMb/NM78zyww3y/zOrHHLb9sOszwGODEizgXOARYBGwLHj7Ko0kXEe4CvkXcpb04+7e1dwGdHWdcYMr9DYoZnjBkeAvM7Y8zvkJjhGWF+h2Tc89uqZi6ldHJEPAT4L/IXPpcBu6WUur8Qqv5sQQ7sg4E/A2cDz0gp/XmkVY0Z8ztUZngGmOGhMb8zwPwOlRkeMvM7VGOd30gpjboGSZIkSVKfWvOdOUmSJEnS9NnMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDM3AyJiaUQsHXUd0qDMsEpmflU6M6ySmd/hspmrRMTCiEgRcXtEPLzH/Usj4sJR1NaUiHh8RHwzIlZGxF8i4lMR8ZBR16Vm3E8yvE5EHBIRyyLitoi4PiK+FxHbj7o21TPu+a2e22S374y6PtU37hkGiIh/joiLImJVRFwVEcdExIajrkv13U/yu1dE/CQibqzeP5wVES8cdV11zRp1AS00B3gT8LoGx/x/DY41kIjYAvg+sAJ4M7AR8G/AdhHx9JTS6lHWp0aNZYYrnwBeDnwSeD+wIfBk4KGjLEqNGtf87t9j2lOBw4Bvz3AtGq6xzHBEvBN4A/BF4L3AE8jP8YnA80dYmpo1rvl9HXAs8HXy81sfWAicFhEvTSl9eYTl1WIzt6ZlwMERcXRK6eomBmxJo/Rm8hvfHVNKfwCIiHOA75DD/JHRlaaGLWMMMxwRewEHAC9JKX1l1PVoaJYxhvlNKX26e1pE7AIk4LMzXY+GahljluGI2Aw4HPhUSumVHdMvBt4XES9OKX1tZAWqScsYs/xWXgf8DHhxSikBRMQngKvI7y2KbeY8zHJNbwfWJXftaxURsyLirRFxaXXIwfKIeHtEzOl63BrHCkfE6yLiVxFxa0TcEBHnRsR+1X27Vru69+ixzP2q+54ZEfMi4nERMW8az+ulwGkTjRxASukM4GJgr2nMr3KMa4YPB85JKX2lOtzSQ3vG07jmt3ucOeTt8lkppSv7nV+tNo4ZfiZ5B8DnuqZP/L7PVM9VxRjH/AJsDPxpopEDSCndBKwEbpvG/K1lM7emy8iHcB0cEZtP8diPAf8F/AJ4PXAWcARrbuzuIyIOJu/q/TWwCDiS/EnIX1cPWQpcQT6crNvLgUtTSj8G9gAuqn6ubXkPJx+Gdm6Pu88hH6am8TGOGd4YeDrws4h4O/lw4ZUR8fvIe+w0PsYuv5P4e2A+cNIA86rdxjHDE2/Ou9/03lr93HGK+VWOcczvxJi7VU3k1lUTeBwwj3zYcLlSSt5yk76QfLjLU4FHAXcA7+24fylwYcfv21eP/2jXOO+upu/aNe/Sjt9P6RxrknreDtwOzOuY9pCqrsVdNS+cYqynVo/bv8d976rumzPqv4G3ercxz/CTq8ddB1wDHALsB/wUuBvYbdTr31u92zjnd5Lxv1iNP3/U695bM7dxzjDwlOpxb+ma/vxq+s2jXv/e6t3GOb/VYx8KnFE9fuL2Z+CZo173dW/umeshpfR74FPAP0U+TryXv69+HtM1/X+rn2s7O86NwBYR8bS1POaT5E/C9uyYtjf5MIdPV3WekFKKlNIJaxkH4AHVz1U97ru96zEaA2OY4Y2qnw8Gdk8pfTCl9BngOcD1wFummF8FGcP83ke1p/mFwOkppRv7mVdlGLcMp5R+Qf7w7I0RcWC1Z+MFwIfJb659DzFGxi2/lVuB3wInAi8D/hH4I/DliNh2GvO3ls3c5P6HHJjJjhl+BHmPwCWdE1NK15BD+oi1jP1O8jG650TE7yLiuIj4m65xfkP+ombnLuaXAz9JKd1nmdMwcVjEnB73rd/1GI2PcczwZSmln3YsYyXwNeDpEeEJncbLOOW320vJ214PsRxv45bhlwLnk88qfBl52/t54LyqFo2XccvvF4CtUkoLU0pfTCkdD+wCrAe8bYDxWsNmbhLVpxKfZu2fSkDeTdvv2BcBjyV/Yfhs8gby7Ig4quuhnwR2jogtImIb4BlVTf36Y/Wz1/PYDPhLSqnXXjsVbMwyPHFGrWt73PcnYDb5bK0aE2OW324vJ3/v87QGxlJLjVuGU0pXpZR2Ah4D/C2wRUrpDcCW5JOpaYyMU34j4lHAbsBXu+r4S7X8v+k1Xyls5tZu4lOJN/a473Ly+nt058SI2JT8pfbL1zZwSumWlNLJKaUDga3I1734j4hYv+NhnwPuAvYl/+d/B3Byv08ipXQV+bjgp/a4++nkL51qPI1Lhq8mf1dujQuZApuTDxe+ud9x1Xpjkd+u+jYDdgW+5Ido9wtjl+GU0u9SSj9IKV0TEU8gfyh8Rp0x1Vrjkt9Nq5/r9rhvNoVfqs1mbi1SSpeSPwF4NfCwrrtPr34u6pp+ePXz65ONGxEP7lrOavIZfYIcqonp1wHfAF5BDvE3q2kT4/RzStYvAS+KiC075n8O+RO2L0xjfhVozDJ8MrBlRDyvY/5NgN2B76WU7p7GGCrImOV3wj7k/3s9xPJ+YEwzPDHvOuSTqN0KfKjf+dV+Y5TfS8iHhO4dEdEx/xbAs8mHCher6E50hrwN2J+8O/hXExNTSudHxInk3c/zyadjfTr5woOnpJTOXMuY346Ia4Afkg8bezzwz8DXU0rdexc+ST7rGcBbu+7bAzgeOBA4YYrn8XbyFz7PjIj3kk8o8e/ABdUYGl/jkuGjyddE/FJEHEM+TO015A3/m6eYV+Ual/xOeDn5sOGl03y8yjcWGa7eO6xPPppnNvmMwk8HDkgd17DV2Ck+vymlP0e+QPirgO9GxJeBucCh5JP3HL2WWlvPZm4KKaVLIuLT5HB2exXwe/KpUfcgHwZ2NNB9zG+3D5P/Qz+c3FRdSb7exv/0eOzXgBvIn+R+tcf905JSuiIidiafdegdwGrypyb/6qE+422MMnxtROwEvId8PZvZwI+BV6SUzh90XLXbuOQXICIeS74e1zHuSb7/GKMMn0feC/Ny8l6Oc4DnTPGmXYUbo/weQj6Bz0Hc27z9DHhlSun7NcYduUip7+8tagZVZ+i7GvhaSumgUdcj9csMq2TmV6UzwyqZ+Z2a35lrvwXkiyR+csR1SINagBlWuRZgflW2BZhhlWsB5net3DPXUhHx18CTyMcHX5dSesqIS5L6YoZVMvOr0plhlcz8Tp975trrEOCD5GtovXLEtUiDMMMqmflV6cywSmZ+p8k9c5IkSZJUIPfMSZIkSVKBbOYkSZIkqUA2c5IkSZJUIJs5SZIkSSqQzZwkSZIkFchmTpIkSZIKZDMnSZIkSQWymZMkSZKkAtnMSZIkSVKB/j+ZXF3l1YwsSwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "x_train_anomalous = training_data[0].copy()\n", "y_train_anomalous = training_data[1].copy()\n", @@ -408,8 +556,8 @@ "\n", "fig, axes = plt.subplots(2, 5, figsize=(9, 5))\n", "for i in range(5):\n", - " axes[0, i].imshow(np.reshape(current_images[i], (8, 8)), cmap=\"grey\")\n", - " axes[1, i].imshow(np.reshape(noisy_images[i], (8, 8)), cmap=\"grey\")\n", + " axes[0, i].imshow(np.reshape(current_images[i], (8, 8)), cmap=\"gray\")\n", + " axes[1, i].imshow(np.reshape(noisy_images[i], (8, 8)), cmap=\"gray\")\n", " axes[0, i].set_xlabel(f\"Original: {training_data[1][indices[i]]}\")\n", " axes[1, i].set_xlabel(f\"Noisy: {training_data[1][indices[i]]}\")\n", "plt.suptitle(\"Original and noisy versions of images 6-10\")\n", @@ -419,9 +567,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n", + " 0%| | 0/100 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from scipy.stats import norm\n", "\n", @@ -511,9 +683,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average value of original data points: 0.6300257540520159\n", + "Average value of modified, anomalous data points: 0.07210118735126189\n", + "For reference, these are the average data values of all data points used for training (anomalous):\n", + "data_value 0.022450\n", + "data_value_stderr 0.083306\n", + "dtype: float64\n", + "These are the average data values of all points (original data):\n", + "data_value -0.016296\n", + "data_value_stderr 0.118257\n", + "dtype: float64\n" + ] + } + ], "source": [ "print(\n", " f\"Average value of original data points: {plot_data['original_data_value'].mean()}\"\n", @@ -542,9 +731,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
data_valuedata_value_stderr
136-0.0955750.049190
142-0.0872620.043481
187-0.0853640.046185
22-0.0812510.039468
5-0.0803460.041551
\n", + "" + ], + "text/plain": [ + " data_value data_value_stderr\n", + "136 -0.095575 0.049190\n", + "142 -0.087262 0.043481\n", + "187 -0.085364 0.046185\n", + "22 -0.081251 0.039468\n", + "5 -0.080346 0.041551" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "msr_df.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "high_dvl = df.iloc[-30:]\n", "high_dvl.index = high_dvl.index.map(str)\n", @@ -590,9 +873,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "low_dvl = df.iloc[:30]\n", "low_dvl.index = low_dvl.index.map(str)\n", @@ -611,12 +907,14 @@ "source": [ "### Compare convergence speed of Banzhaf and MSR Banzhaf Values\n", "\n", - "While the conventional Banzhaf values algorithm evaluates the utility twice to do one update of the Banzhaf values, the Maximum Samples Reuse (MSR) algorithm promises higher sample efficiency because it updates multiple samples per one evaluation of the utility. This part of the notebook takes a look at the convergence speed of the algorithms compared with each other." + "While the conventional Banzhaf values algorithm evaluates the utility twice to do one update of the Banzhaf values, the Maximum Samples Reuse (MSR) algorithm promises higher sample efficiency because it updates multiple samples per one evaluation of the utility. This part of the notebook takes a look at the convergence speed of the algorithms compared with each other. \n", + "\n", + "To analyze this, we will compute the semivalues using different samplers and use a high number of iterations to make sure that the values are converged." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -630,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -652,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -680,9 +978,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PermutationSampler requires caching to be enabled or computation will be doubled wrt. a 'direct' implementation of permutation MC\n", + " 0%| | 0/100 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Compare convergence speed of both methods\n", "import numpy as np\n", @@ -787,19 +1146,124 @@ "After 1000 iterations (subsets tried), Monte Carlo Banzhaf has evaluated the marginal function about 5 times per data point (we are using 200 data points). \n", "For maximum sample reuse, the semivalue of each data point was updated 1000 times. Due to this, the values converge much quicker, which is one of the advantages of MSR sampling. \n", "\n", - "MSR sampling does come at a cost, however, which we will visualize in the next plot. The semivalues that MSR converges to are less consistent." + "MSR sampling does come at a cost, however, which is that the updates to the semivalues are more noisy than in other methods. \n", + "We will analyze the impact of this tradeoff in the next sections. \n", + "First, let us analyze how similar all the computed semivalues are. They are all Banzhaf values, so in a perfect world, all samplers should result in the exact same semivalues. However, due to randomness in the utility (it is a neural network) and randomness in the samplers, the resulting values are likely never exactly the same. \n", + "Another quality measure is that a good sampler would lead to very consistent values, a bad one to less consistent values. \n", + "Let us analyze how similar the samplers are first, then look at consistency." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Similarity of the semivalues computed using different samplers" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "from scipy.stats import spearmanr\n", + "names = [\"Permutation\", \"MSR\", \"Uniform\", \"Antithetic\", \"RandomHierarchical\"]\n", + "values = [\n", + " permutation_values,\n", + " msr_values,\n", + " uniform_values,\n", + " antithetic_values,\n", + " random_values,\n", + "]\n", + "top_consistency = np.zeros((len(names), len(names)))\n", + "low_consistency = np.zeros((len(names), len(names)))\n", + "twenty_percent = training_data[0].shape[0] // 5\n", + "\n", + "for sampler1_id, sampler1_values in enumerate(values):\n", + " for sampler2_id, sampler2_values in enumerate(values):\n", + " sampler1_values.sort(key=\"value\")\n", + " sampler2_values.sort(key=\"value\")\n", + " top_20_1 = set(sampler1_values.indices[-twenty_percent:].tolist())\n", + " lower_20_1 = set(sampler1_values.indices[:twenty_percent].tolist())\n", + " top_20_2 = set(sampler2_values.indices[-twenty_percent:].tolist())\n", + " lower_20_2 = set(sampler2_values.indices[:twenty_percent].tolist())\n", + " top_consistency[sampler1_id, sampler2_id] = len(top_20_1.intersection(top_20_2))\n", + " low_consistency[sampler1_id, sampler2_id] = len(\n", + " lower_20_1.intersection(lower_20_2)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fix, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + "axes[0].matshow(top_consistency, vmin=0, vmax=twenty_percent)\n", + "mat2 = axes[1].matshow(low_consistency, vmin=0, vmax=twenty_percent)\n", + "axes[0].set_xticks(np.arange(len(names)), names, rotation=90)\n", + "axes[1].set_xticks(np.arange(len(names)), names, rotation=90)\n", + "for (i, j), z in np.ndenumerate(top_consistency):\n", + " axes[0].text(j, i, f\"{int(z)}\", ha=\"center\", va=\"center\", c=\"white\")\n", + "for (i, j), z in np.ndenumerate(low_consistency):\n", + " axes[1].text(j, i, f\"{int(z)}\", ha=\"center\", va=\"center\", c=\"white\")\n", + "\n", + "axes[0].set_yticks(np.arange(len(names)), names)\n", + "axes[1].set_yticks(np.arange(len(names)), names)\n", + "axes[0].set_title(\"Top 20% of points\")\n", + "axes[1].set_title(\"Low 20% of points\")\n", + "fig.colorbar(mat2)\n", + "plt.suptitle(\"Overlapping high and low value points between samplers\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot shows that the samplers lead to quite different Banzhaf semivalues, however, all of them have some points in common. \n", + "The MSR Sampler does not seem to be significantly worse than any others. \n", "\n", - "print(\"Similarity of the ranking of Permutation Sampler and MSR Sampler semivalues:\")\n", - "print(spearmanr(permutation_values.values, msr_values.values))" + "In an ideal setting without randomness, the overlap of points would be higher, however, the stochastic nature of the CNN model that we use together with the\n", + "fact that we use only 200 data points for training, might overshadow these results. " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of top 20 points that all samplers have in common: 1\n" + ] + } + ], + "source": [ + "all_in_common = set(permutation_values.indices.tolist())\n", + "for sampler_id, sampler_values in enumerate(values):\n", + " sampler_values.sort(key=\"value\")\n", + " top_20 = set(sampler_values.indices[-twenty_percent:].tolist())\n", + " all_in_common = all_in_common.intersection(top_20)\n", + "print(\n", + " f\"Total number of top 20 points that all samplers have in common: {len(all_in_common)}\"\n", + ")" ] }, { @@ -814,7 +1278,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -826,9 +1290,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/45 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot results\n", "\n", @@ -927,7 +1452,11 @@ "metadata": {}, "source": [ "**Conclusion:**\n", - "MSR" + "MSR sampling updates the semivalue estimates a lot more frequently than any other sampler available, which leads to a lot **faster convergence**. \n", + "Additionally, the sampler is more consistent with its value estimates than the other samplers, which might be caused by the higher number of value updates. \n", + "\n", + "In general, the recommendation is to try different samplers when computing semivalues and test, which one is best suited for your use case. The MSR sampler\n", + "seems like a more efficient sampler which may bring fast results and is well-suited for stochastic models." ] } ], diff --git a/tox.ini b/tox.ini index 499bd1996..600c8dc14 100644 --- a/tox.ini +++ b/tox.ini @@ -24,7 +24,7 @@ deps = {[testenv]deps} -r requirements-notebooks.txt commands = - pytest --nbmake --nbmake-timeout=60 -n 0 --cov "{envsitepackagesdir}/pydvl" notebooks/ {posargs} + pytest --nbmake --nbmake-timeout=90 -n 0 --cov "{envsitepackagesdir}/pydvl" notebooks/ {posargs} [testenv:linting] skip_install = true