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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#<a href=\"http://www.datascience-paris-saclay.fr\">Paris Saclay Center for Data Science (CDS)</a>\n",
"#Test <a href=http://www.ramp.studio/events/iris_test>RAMP on iris</a> \n",
"\n",
"<i> Adapted for Chalab by Isabelle Guyon from original code of Balázs Kégl (LAL/CNRS)</i>\n",
"\n",
"ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED \"AS-IS\". The CDS, CHALEARN, AND/OR OTHER ORGANIZERS OR CODE AUTHORS DISCLAIM ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE, AND THE WARRANTY OF NON-INFRIGEMENT OF ANY THIRD PARTY'S INTELLECTUAL PROPERTY RIGHTS. IN NO EVENT SHALL AUTHORS AND ORGANIZERS BE LIABLE FOR ANY SPECIAL, \n",
"INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS, MATERIALS, PUBLICATIONS, OR INFORMATION MADE AVAILABLE FOR THE CHALLENGE. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"Iris is a small standard multi-class classification data set from the <a href=\"http://archive.ics.uci.edu/ml/datasets/Iris\">UCI Machine Learning Repository</a>, formatted in the AutoML format."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"codedir = 'sample_code/' # Change this to the directory where you put the code\n",
"from sys import path; path.append(codedir)\n",
"%matplotlib inline\n",
"import seaborn as sns; sns.set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fetch the data and load it"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"public_data/iris_feat.name public_data/iris_train.data\r\n",
"public_data/iris_label.name public_data/iris_train.solution\r\n",
"public_data/iris_test.data public_data/iris_valid.data\r\n"
]
}
],
"source": [
"datadir = 'public_data/' # Change this to the directory where you put the input data\n",
"dataname = 'iris'\n",
"basename = datadir + dataname\n",
"!ls $basename*"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading public_data/iris_train from AutoML format\n",
"Number of examples = 105\n",
"Number of features = 4\n",
"Number of classes = 3\n"
]
}
],
"source": [
"import data_io\n",
"reload(data_io)\n",
"data = data_io.read_as_df(basename) # The data are loaded as a Pandas Data Frame\n",
"#data.to_csv(basename + '_train.csv', index=False) # This allows saving the data in csv format"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.4</td>\n",
" <td>3.0</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.6</td>\n",
" <td>0.2</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6.1</td>\n",
" <td>2.6</td>\n",
" <td>5.6</td>\n",
" <td>1.4</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6.4</td>\n",
" <td>3.1</td>\n",
" <td>5.5</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.8</td>\n",
" <td>4.0</td>\n",
" <td>1.2</td>\n",
" <td>0.2</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width target\n",
"0 4.4 3.0 1.3 0.2 versicolor\n",
"1 4.7 3.2 1.6 0.2 versicolor\n",
"2 6.1 2.6 5.6 1.4 virginica\n",
"3 6.4 3.1 5.5 1.8 virginica\n",
"4 5.8 4.0 1.2 0.2 versicolor"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>105.000000</td>\n",
" <td>105.000000</td>\n",
" <td>105.000000</td>\n",
" <td>105.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5.774286</td>\n",
" <td>3.063810</td>\n",
" <td>3.596190</td>\n",
" <td>1.130476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.795423</td>\n",
" <td>0.472132</td>\n",
" <td>1.781201</td>\n",
" <td>0.763484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.300000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5.100000</td>\n",
" <td>2.800000</td>\n",
" <td>1.500000</td>\n",
" <td>0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.700000</td>\n",
" <td>3.000000</td>\n",
" <td>4.200000</td>\n",
" <td>1.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.400000</td>\n",
" <td>3.400000</td>\n",
" <td>5.100000</td>\n",
" <td>1.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>7.700000</td>\n",
" <td>4.400000</td>\n",
" <td>6.700000</td>\n",
" <td>2.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width\n",
"count 105.000000 105.000000 105.000000 105.000000\n",
"mean 5.774286 3.063810 3.596190 1.130476\n",
"std 0.795423 0.472132 1.781201 0.763484\n",
"min 4.300000 2.000000 1.000000 0.100000\n",
"25% 5.100000 2.800000 1.500000 0.300000\n",
"50% 5.700000 3.000000 4.200000 1.300000\n",
"75% 6.400000 3.400000 5.100000 1.800000\n",
"max 7.700000 4.400000 6.700000 2.500000"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe() "
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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0aJjNZkybNg0dO3aUnY2IiIhINxQVVZ06dcLKlStlZyEiIiLSLQ7+SURERCQBiyoiIiIi\nCVhUEREREUnAooqIiIhIAhZVRERERBKwqCIiIiKSgEUVERERkQQsqoiIiIgkUDT4Z3tRX1+PoqKT\n1p+Li083el9YLDe8Fh5+Gzw9PVslHxEREWkHiyoHiopOIuWFregU0BUAUH7mWwTfeqf1/ctVZXjx\n3fPoFHAWAFB78RxenjEUkZG3q5KXiIiI1MOiyolOAV3hFxQKAKi9WOrwfSIiImq/eE8VERERkQQs\nqoiIiIgk4Nd/RERE7VTTB7IAPnDVEiyqiIiI2qmmD2TxgauWYVFFRETUjvGBK3l4TxURERGRBCyq\niIiIiCRQXFTl5OTgqaeeQnx8PDZu3CgzExGRagoKCpCYmAgAKC4uRkJCAsaMGYO5c+eqnIyItE5R\nUXXw4EF89dVXyMvLQ25uLs6ePSs7FxFRq1u9ejUyMzNhMpkAAIsXL8bUqVOxfv16WCwW7NixQ+WE\nRKRlioqqPXv2ICoqChMnTsSECRMwcOBA2bmIiFpdWFgYsrKyrD8fOXIE999/PwCgf//+2Ldvn1rR\niEgHFD39V1lZiR9//BHZ2dn44YcfMGHCBHz00UeysxFZcSwVag2xsbEoKSmx/iyEsP6/r68vqqqq\n1IhFRDqhqKgKDAxEZGQkjEYjIiIi4O3tjYqKCnTp0sXuOiEh/opDyuZqlspKv2Zvu0sXv2b1VSv7\nRSs5ANtZCgsLbxhLJXdxAqKioqS3b+u4N/e4yqal49OeeHj8cjG/pqYGnTt3dmk9GcfL3vmnNT6L\nbeHzpvc+tFZ+d57v9H4MlFBUVEVHRyM3Nxfjxo1DaWkprly5gqCgIIfrlJVp4y+8kBB/l7NUVFQ3\ne/sVFdUub785WdxJKzkA+1kqKqpvGEulOfu6OWwdd3e15QqtHZ/2pGfPnsjPz8cDDzyATz/9FH36\n9HFpPRnHy975x92fRS193pTSex9aM7+7zndt4RgooaioGjBgAA4dOoQRI0ZACIHZs2fDYDAoCkBE\npFWpqal4/vnnYTKZEBkZibi4OLUjEZGGKR5Rffr06TJzEBFpQmhoKPLy8gAA4eHhyM3NVTkREekF\nB/8kIiIikoBFFREREZEELKqIiIiIJGBRRURERCQBiyoiIiIiCVhUEREREUnAooqIiIhIAhZVRERE\nRBIoHvyzLWo6aW9x8WkV01BbwcmgiYjaBxZVDRQVnWw0aW/5mW8RfOudKqcivWv6uaq9eA4vzxiK\nyMjbVU5GREQysahqouGkvbUXS1VOQ21F08mgiYio7eE9VUREREQSsKgiIiIikoBFFREREZEELKqI\niIiIJGBRRURERCQBiyoiIiIiCVhUEREREUnQonGqysvLER8fjzVr1iAiIkJWJiIicoGt0fqBxiP2\nu7IMtYzM41BfX4/CwkJUVFTbXYa0S3FRZTabMXv2bHTs2FFmHiIiclHT0fqBG0fsd2UZahmZx4Ez\nMOib4qJq6dKlGDVqFLKzs2XmISKiZnBltH6O6O9+Mo8Dj5d+KSqqNm3ahODgYPTr1w9vvPGG7Ey6\nJSyWGyZh5mVb7Wl6Gb6+vh6AAZ6e124x5ETaRESkhOKiymAwYO/evTh27BhSU1Px+uuvIzg42O46\nISH+ikPKZi9LZaVfi7Z7uaoML757Hp0CzgK4dtk2d3ECoqKimp2ltWklB2A7i61j06WLn6LchYWF\nN0yc7eMf7HAibaVtAXKya+n4EBGRbYqKqvXr11v/PzExEfPmzXNYUAFAWVmVkqakCwnxt5ul4Y2B\nSjW9bFtRUW23PUdZWpNWcgD2s9g6No72rSMVFdU3TJztbCJtpW1dX7cl29Pa8SEiIttaPKSCwWCQ\nkYOIiIhI11o0pAIArFu3TkYOIiIiIl1rcVFFRESkFlvjP1VW+qFz5658SIhaHYsqIiLSLY7DRVrC\nooqIiHSN4zqRVnDuPyIiIiIJWFQRERERScCv/4iInHjyySfh53dtENdbb70VixYtUjkREWkRiyoi\nIgfq6uoAcPgYInKOX/8RETlw7Ngx1NbWIjk5GePGjUNBQYHakYhIo3ilijTh+lgzlZV+1mldHE1G\n3XTy6qaTIjtbX0tsjbOjl+ztQceOHZGcnIyRI0eiqKgIf/zjH7F9+3Z4eCj/m/S9v3+AEz+cb/Ta\ngD53oe8D0S2NS6SKpuex6+fy9nYuY1FFmtB0rBln48w0nby66aTIehqnprl9p9YVHh6OsLAw6/8H\nBgairKwMN998s911nM2ReObcBXxfc2uj1+746Wyj9exN8N5wMm5ZyzQ3v5Yo6Z8aGVzNKXPyeFfI\naq/pRPXAtXNZ7uIEREVFtTinXrCoIs1o7lgzjiZF1hs9Z2/rNm7ciMLCQsyePRulpaWoqalBSEiI\nw3WcTYB99ar5hteqq682Ws/eBO8NJ+OWtUxDWprA2xXN7Z9aGVzNKXPyeFfIaq/pRPUt2ZYWKC1i\nWVQRETkwYsQIpKWlISEhAR4eHli0aFGLvvojoraLRRURkQNeXl5Yvny52jGISAf45xYRERGRBCyq\niIiIiCTg139ERERkl61hXwB1h37RYiaARRURERE50HTYF0D9oV+0mAlgUUVEREROaHHYFy1mUlRU\nmc1mpKeno6SkBCaTCePHj8egQYNkZyMiIiLSDUVF1datWxEUFIRly5bh4sWLGDZsGIsqIiIiatcU\nFVWPPfYY4uLiAAAWiwVGI79FtKXp/HSA+jfRyeLu+eqa7rum+7G5GuaVua3rGva96ftN22tu3+rr\n63HixPd225Odn4iIlFFUDfn4+AAAqqurkZKSgilTpjhdRwtzSV29ehXPTF0AH78gAEDd1Sv48/8+\njjt7XJuXyN7cTEo1nZ/O1jxIWtgvQPNzNJ3nqaVzPDXd97bm9gu+9c5mbbPh/FUN88rcFnBj35u+\n37Q9V/rWnPaaS/b2iIjoGsWXmM6ePYtJkyZhzJgxGDx4sNPltTD3T01NDYoqvNDBcG0i06s1F1D4\nfTFuCv5/AOzPzdQSTW+kazgPklbm2FKSw9Y8Ty2Z48nWvm86t5+SbTacd+v69mRuy5X3bbXn7P2m\n+1L2vla6Pa38EUBEpEWKiqrz588jOTkZs2bNQp8+fWRnIiIiItIdRSOqZ2dn49KlS1i1ahUSExOR\nlJSEuro62dmIiIiIdEPRlaqMjAxkZGTIzkJERESkW5z7j4iIiEgCFlVEREREErCoIiIiIpKARRUR\nERGRBCyqiIiIiCTg/DJERESwPYUToL1pnFzJqZe+2CIru6vbsbVcSMh9zUj8CxZVREREAIqKTjaa\nwgm4No3TyzOGIjLydhWTNeZKTr30xRZZ2V3dTtPlai+ew4GNLKqaTVgsKCk5gxMnugBo+US7etKw\nMq+s9ENFRbXDvwKcTRLsbHmgeZMOt5TMCZllT+7c3PYuXixzuDwnSCaSp+kUTlrlSk699MUWWdld\n3Y6s9tp1UVV76Rz+75PL+NvBWgDKJu3VK1uVuaO/Apou72xfOdt+c7fXXDImZHbHttzRXnOPJRER\nuUe7LqqAlk/aq2fNrcybu6+cbd/d+17m9lv7cyJ7XxMRkfvx6T8iIiIiCVhUEREREUnAooqIiIhI\ngnZ/TxUREf3yFOn1p4EB18bzsbWcFvEpWW1o68eBRRUREbn0FGlbGvtIL7nbmrZ+HFhUERERAI59\nRK2jLR8H3lNFREREJAGLKiIiIiIJFH39J4TAnDlz8N1336FDhw5YuHAhunXrJjsbEZHqeL4jIlcp\nulK1Y8cO1NXVIS8vD9OmTcPixYtl5yIi0gSe74jIVYquVH3xxReIiYkBAPTq1QuHDx+WGqo11V48\nZ/3/y1UVAAxu+7n24rlGE+U2fHS5tRUXn27U96bZnC3vrG/Otu9se+782d3H1d19a+6+bsrW8mRf\na53vys79hBMnvrf+3PQ4Ac6PvdJlbC3nyjL2lmtNWuifFo8D22tZJqUMQgjR3JUyMzPx6KOPWk80\ngwYNwo4dO+DhwVu0iKht4fmOiFyl6Kzg5+eHmpoa688Wi4UnGCJqk3i+IyJXKToz3Hfffdi9ezcA\n4Ouvv0ZUVJTUUEREWsHzHRG5StHXfw2fhgGAxYsXIyIiQno4IiK18XxHRK5SVFQRERERUWO8MYCI\niIhIAhZVRERERBKwqCIiIiKSQNHgn67S2vQOBQUFWL58OXJzc1XLYDabkZ6ejpKSEphMJowfPx6D\nBg1SJYvFYkFmZiZOnToFDw8PzJ07F927d1clCwCUl5cjPj4ea9asUfVG4CeffBJ+fn4AgFtvvRWL\nFi1SLUtOTg527twJk8mEhIQExMfHq5Jj8+bN2LRpEwwGA65evYpjx45h79691v3U3jg7t+3cuROr\nVq2C0WhEfHw8Ro4cqWJa25z1Ye3atXj//ffRpUsXAMC8efMQHh6uUlr77J3X9XAMrrPXB60fA2e/\nz/RwDJz1odnHQLjRxx9/LGbOnCmEEOLrr78WEyZMcGdzDv31r38VQ4YMEb///e9VyyCEEBs3bhSL\nFi0SQghx4cIFMWDAANWyfPLJJyI9PV0IIcSBAwdUPT4mk0k8++yz4tFHHxUnT55ULcfVq1fF8OHD\nVWu/oQMHDojx48cLIYSoqakRr776qsqJrpk7d65477331I6hKkfnNpPJJGJjY0VVVZWoq6sT8fHx\nory8XK2odjk7P0+fPl0cOXJEjWgus3de18sxEMLx7yatHwNHv8/0cgyc/U5u7jFw69d/WprOJiws\nDFlZWaq1f91jjz2GlJQUANeuFBmNbr1Y6NDDDz+M+fPnAwBKSkoQEBCgWpalS5di1KhR6Nq1q2oZ\nAODYsWOora1FcnIyxo0bh4KCAtWy7NmzB1FRUZg4cSImTJiAgQMHqpblum+++QbHjx/X5F+crcnR\nue3EiRMICwuDn58fvLy8EB0djfz8fLWi2uXs/HzkyBFkZ2cjISEBOTk5akR0yt55XS/HAHD8u0nr\nx8DR7zO9HANnv5ObewzcWlRVV1fD39/f+rPRaITFYnFnk3bFxsbC09NTlbYb8vHxQadOnVBdXY2U\nlBRMmTJF1TweHh6YOXMmFi5ciMcff1yVDJs2bUJwcDD69esHofIIHx07dkRycjLefPNNzJkzB9On\nT1ftM1tZWYnDhw/jlVdewZw5czBt2jRVcjSUk5ODSZMmqR1DdY7ObU3f8/X1RVVVVatndMbZ+fm3\nv/0t5s6di3Xr1uGLL76wDoCqJfbO63o5BoDj301aPwaOfp/p5Rg4+53c3GPg1qKK0zvYdvbsWYwd\nOxbDhw/H4MGD1Y6DJUuWYPv27cjMzMSVK1davf1NmzZh7969SExMxLFjx5Camory8vJWzwEA4eHh\nGDp0qPX/AwMDUVZWpkqWwMBAxMTEwGg0IiIiAt7e3qioqFAlCwBUVVWhqKgIvXv3Vi2DVjg6t/n5\n+aG6+peJ0mtqatC5c+dWz+iMs/Pz2LFjERgYCKPRiIceeghHjx5VI6YiejkGzujhGNj7faanY+Do\nd3Jzj4FbKxwtTu+g9pWQ8+fPIzk5GTNmzMDw4cNVzbJlyxbr5Uxvb294eHioUvSuX78eubm5yM3N\nRY8ePbB06VIEBwe3eg4A2LhxI5YsWQIAKC0tRU1NDUJCQlTJEh0djc8++8ya5cqVKwgKClIlCwDk\n5+ejT58+qrWvJY7ObZGRkTh9+jQuXbqEuro65Ofn45577lErql2O+lBdXY0hQ4bg8uXLEEJg//79\nuOuuu9SK6lTT87pejkFDTfugh2Pg6PeZXo6Boz4oOQZuvaEnNjYWe/fuxVNPPQXg2vQOajMYDKq2\nn52djUuXLmHVqlXIysqCwWDA6tWr0aFDh1bP8sgjjyAtLQ1jxoyB2WxGRkaGKjkaUvv4jBgxAmlp\naUhISICHhwcWLVqk2tXVAQMG4NChQxgxYgSEEJg9e7aq++fUqVOqPr2rJbbObf/4xz9w+fJljBw5\nEmlpaXj66achhMDIkSNVv1fQFmd9mDp1KhITE+Ht7Y2+ffuif//+Kie27/q/C70dg4Zs9UHrx8DW\n77Pf/e53ujoGzvrQ3GPAaWqIiIiIJOANTkREREQSsKgiIiIikoBFFREREZEELKqIiIiIJGBRRURE\nRCQBiyqcR/9fAAAgAElEQVQiIiIiCVhUEREREUnAooqIiIhIAhZVRERERBKwqCIiIiKSgEUVERER\nkQQsqoiIiIgkYFFFREREJAGLKiIiIiIJWFQRERERScCiioiIiEgCFlVEREREErCoIiIiIpKARRUR\nERGRBCyqyCUHDx7E448/7nS5Hj164MKFC9Lbr66uxtixY93eDhHRda6e95zZuXMnFi5caPO9xx9/\nHPn5+QCA559/HkePHgUAJCYm4uOPP25x29S6WFSRVAaDwS3bvXDhAr755hu3t0NEJNugQYOQkZHh\ndLm9e/dCCNEKichdjGoHoJarra1FWloaiouLYTAY8Ktf/Qrz5s3Dzp078cYbb8BsNqNjx45ITU1F\nr1698Nprr+H777/H+fPncf78efTs2RMLFiyAr68v/v3vfyM7OxtmsxkVFRV44oknkJKS4nKWhieE\n999/H++88w4AIDAwEM8//zwiIiKQlpYGX19fFBYW4qeffsJtt92GFStWwMfHB7t378by5cthNBrR\no0cPfP7559iwYQPS09Nx5coVDB8+HBs3boQQAq+88gq+/vprXLx4EU8//TRGjx4tfd8SkTZp5by3\nePFi+Pj4YPLkySgrK0P//v2xdu1a/PrXv8a2bdvwr3/9Cw899BC2b9+ON954A8ePH0dGRgauXLmC\niIgIXL58GQCwYsUKnDt3DtOnT8fSpUsBADt27MBf//pXlJeXo2/fvnavdpGGCNK9v//97+IPf/iD\nEEKI+vp68fzzz4uioiIxZMgQceHCBSGEEN9//73o16+fuHz5snj11VfFgAEDRHl5uRBCiKlTp4ql\nS5cKIYRISkoSp0+fFkIIUVpaKnr27CkqKyvFgQMHxJAhQ5xmueOOO0RlZaU4ePCgGD16tLhy5YoQ\nQog9e/aIwYMHCyGEmDlzphg1apQwmUzCZDKJ4cOHi02bNonKykrRu3dv8d133wkhhNi8ebPo0aOH\nKCkpEWfOnBH33ntvo3bWrFkjhBDi6NGj4u677xZms7mlu5KIdEIr5738/HwRHx8vhBBi48aN4sEH\nHxQvvfSSEEKIlJQU8eGHH4pNmzaJZ555RgghxLBhw8TGjRuFEEJ88cUX4s477xQHDx4UQggxcOBA\nceTIESGEEGPGjBHPPvusEEKIy5cviwcffFAcOnRIwp4jd+LXf21AdHQ0jh8/jsTEROTk5CApKQl7\n9+7F+fPnMW7cOAwbNgzTp0+H0WjE6dOnAQBxcXHo0qULAGDEiBHYs2cPAOD111/H4cOH8dprr2HJ\nkiUAYP1LyhXXv5bbtWsXiouL8dRTT2HYsGF44YUXcOnSJVy6dAkAEBMTA6PRCKPRiKioKFy8eBGH\nDh3C7bffjqioKADAsGHD4Ovra7etIUOGAADuvPNOmEwmVFdXN2e3EZGOaeW8Fx0djZ9++gkVFRXY\ns2cPJkyYgM8//xwmkwn5+fl46KGHrMteuHAB3333HZ544gkAwH333Yfu3bs32p5ocLV/8ODBAICO\nHTsiPDwcFRUVSnYVtSJ+/dcG3Hrrrfj4449x8OBB7N+/H+PGjUNCQgL69u2Ll156ybrcTz/9hK5d\nu+KTTz6Bp6en9XUhBDw9PXH58mUMGzYMjzzyCO6//36MGDECO3bsUPQdv8ViwRNPPIFp06ZZXyst\nLUXnzp0BXDtJXGcwGKwZLBZLo+14eNiv+43Gxh9fJTmJSJ+0ct4zGAwYNGgQdu3ahYKCAixbtgzZ\n2dn46KOPcO+998LHx6fRstfPd9c1PY81xHOc/vBKVRuwYcMGzJw5E/369cO0adMQExODwsJC7N27\nFydPngQA7N69G0888QTq6uoAAP/6179QXV0Ni8WC9957D4MGDcLp06dRW1uLyZMnY8CAAThw4ABM\nJhPq6+tdznL9H32/fv3wz3/+E2VlZQCAt99+G+PGjXO47n333YfTp0+jsLAQALB9+3ZUVVXBYDDA\naDTeUHDZapeI2gctnfd+85vfYPXq1YiKioLRaESfPn3w0ksv4dFHH220XEBAAO666y787W9/AwAc\nOXLEer4DrhVRZrO5pbuGVMQrVW3AsGHDkJ+fj8GDB8PHxwehoaFYuHAhPv/8c0ydOhUA4Onpiddf\nf916heimm27Cn/70J1RWVuKBBx7AM888Ay8vLwwYMABxcXHo3LkzwsLC0L17dxQXF8PLy8ulLNe/\n/nvwwQfxhz/8AU8//TQ8PDzg5+eH1157zeG6AQEBWL58OZ577jl4eHjgV7/6FTw9PdGxY0cEBATg\nzjvvxODBg/HOO+/c8PQfnwYkal+0dN7r27cvzp07Z31Y5sEHH8SHH36IgQMH3rDsiy++iLS0NGzY\nsAFhYWGIjIy0vveb3/wGU6ZMwYIFC3iO0ymDUPAnvtlsRmpqKkpKSmA0GjF//nxERES4Ix+5wWuv\nvYYLFy4gMzNT7SiNVFdX4/XXX8df/vIXeHt74+jRo3jmmWfw2WefqR2N2pGCggIsX74cubm5+Pbb\nbzFnzhwYjUaEh4fz6Ssd0+p5j9oWRVeqdu/eDYvFgry8PHz++edYsWIFXnnlFdnZSIPefPNNbNu2\nrdFfTUIIGAwGJCcnW28eV8LPzw9eXl6Ij4+H0WiEl5cXXn75ZRmxiVyyevVqbNmyxfqARFZWFiZN\nmoSYmBhMnz4du3btwoABA9QNSa3Onec9alsUFVXh4eGor6+HEAJVVVUuXyIlbZg0aZLidZOTk5Gc\nnCwxTWOTJ0/G5MmT3bZ9IkfCwsKQlZWF5557DsC1J0srKyshhEBNTY3Dm4pJ27R83qO2Q9EZwtfX\nF2fOnEFcXBwuXLiA7Oxs2bmIiFpdbGwsSkpKrD+Hh4dj3rx5eOONN+Dv74/evXurmI6ItE5RUbV2\n7VrExMRgypQpKC0tRVJSErZt24YOHTrYXP76ZdL2oLCwEIlp76BTQFcAQPmZb+HjH2z9ufbiOeQu\nTrCOxURE2rVw4UK88847iIyMxNtvv40lS5Zg1qxZDtdpT+c7ImpMUVEVEBBgvQzu7+8Ps9ns8HF3\ng8GAsrIqZQlbKCTEv1XbrqioRqeArvALCgUA1F4sbfTz9WXclam1+6uFtttbu2q2HRLi3+ptqikw\nMBB+fn4AgJtvvhlfffWV03XUPN/JoObnWha990Hv+QH990HpuU5RUTV27Fikp6dj9OjRMJvNmDZt\nWqPBHImI2oL58+dj8uTJMBqN6NChA+bPn692JCLSMEVFVadOnbBy5UrZWYiIVBcaGoq8vDwA16Yg\n2bBhg8qJiEgvOKI6ERERkQQsqoiIiIgkYFFFREREJAFHsiMiIt2qr69HUdHJRq9VVvqhc+eu8PT0\nVCkVtVcsqoiISLeKik4i5YWt1rEAgWvjAb48YygiI29XMRm1RyyqiIhI15qOBUikFt5TRURERCQB\niyoiIiIiCVhUEREREUnAooqIqIGCggIkJiYCACoqKjBx4kQkJiYiISEBP/zwg8rpiEjLeKM6EdHP\nVq9ejS1btsDX1xcA8MILL2Do0KGIi4vDgQMHcPLkSXTr1k3llESkVbxSRUT0s7CwMGRlZVl//vLL\nL/HTTz/hf//3f/GPf/wDv/71r1VMR0Rap6io2rx5MxITE5GUlITf//736NWrF6qrq2VnIyJqVbGx\nsY0GjCwpKUFgYCDWrFmD//qv/0JOTo6K6YhI6xR9/Td8+HAMHz4cADBv3jyMGDECfn5+UoMREakt\nMDAQAwcOBAAMGjQIK1eudGm9kBB/d8ZyOz3lr6y0/bunSxc/XfWjKT1nv64t9KG5WnRP1TfffIPj\nx49j1qxZsvIQEWlGdHQ0du/ejaFDhyI/Px/du3d3ab2ysio3J3OfkBB/XeWvqLD9LUlFRbWu+tGQ\n3o6BLXrvg9KCsEVFVU5ODiZNmuTSsmpWrK3Ztr2/mhpy919Q7WVft+d21W67vUhNTUVmZiY2bNgA\nf39/vPjii2pHIiINU1xUVVVVoaioCL1793ZpebUq1taulu391dR0GXdlUvOvA7Xabm/tqtl2eyjk\nQkNDkZeXBwC45ZZb8NZbb6mciIj0QvHTf/n5+ejTp4/MLERERES6pbioOnXqFMdrISIiIvqZ4q//\nkpOTZeYgIiIi0jUO/klEREQkAYsqIiIiIglYVBERERFJwKKKiIiISAIWVUREREQSsKgiIiIikoBF\nFREREZEELKqIiBooKChAYmJio9e2bduGp556SqVERKQXLZpQmYioLVm9ejW2bNkCX19f62tHjx7F\nxo0bVUxFRHrBK1VERD8LCwtDVlaW9efKykqsXLkSGRkZKqYiIr3glSoiop/FxsaipKQEAGCxWJCZ\nmYmZM2eiQ4cOEEK4vJ2QEH93RWwVespfWeln8/UuXfx01Y+m9Jz9urbQh+ZSXFTl5ORg586dMJlM\nSEhIQHx8vMxcRESqOnLkCIqLizFnzhxcvXoVJ06cwOLFi5GWluZ03bKyqlZI6B4hIf66yl9RUW33\ndT31oyG9HQNb9N4HpQWhoqLq4MGD+Oqrr5CXl4fa2lq89dZbihonItIiIQTuvvtubNu2DQBQUlKC\nadOmuVRQEVH7paio2rNnD6KiojBx4kTU1NTgueeek52LiEg1BoNB7QhEpEOKiqrKykr8+OOPyM7O\nxg8//IAJEybgo48+kp2N3KS+vh5FRScbvRYefhs8PT1VSnRjpvr6egAGeHo2fpaiJTm12G/SntDQ\nUOTl5Tl9jYioKUVFVWBgICIjI2E0GhEREQFvb29UVFSgS5cudtdR84a11mzb3k2TDbn7Bkpn2y4s\nLETKC1vRKaArAKD24jnkLk5AVFSU29t2NVP5mW/h4x9s/dlZTlfadUe/28vnmoiInFNUVEVHRyM3\nNxfjxo1DaWkprly5gqCgIIfrqHXDWmvfLGfvpsmmy7grkyv9raioRqeArvALCpWaqSX7ummm2oul\nN2S0l9PVdmX3W80bMdVqm4UcEZF9ioqqAQMG4NChQxgxYgSEEJg9ezbvQSAiIqJ2TfGQCtOnT5eZ\ng4iIiEjXOKI6ERERkQQsqoiIiIgkYFFFREREJAGLKiIiIiIJWFQRERERScCiioiogYKCAiQmJgIA\nvv32W4wePRpJSUn4wx/+gIqKCpXTEZGWsagiIvrZ6tWrkZmZCZPJBABYtGgRZs2ahXXr1iE2NhY5\nOTkqJyQiLWNRRUT0s7CwMGRlZVl/XrFiBe644w4AgNlshre3t1rRiEgHFA/+SUTU1sTGxqKkpMT6\n80033QQA+PLLL/HOO+9g/fr1Lm1H79P56Cm/vflW3T3HqrvpOft1baEPzcWiiojIgQ8++ADZ2dnI\nyclxOsfpdWrNCSmDmnNaKmFvvlV3zrHqbno7BrbovQ9KC0IWVUREdmzZsgXvvfcecnNz0blzZ7Xj\nEJHGKS6qnnzySfj5Xbvseuutt2LRokXSQhERqc1isWDRokW45ZZb8Oyzz8JgMKB3796YNGmS2tGI\nSKMUFVV1dXUAgHXr1kkNQ0SkttDQUOTl5QEADhw4oHIaItITRU//HTt2DLW1tUhOTsa4ceNQUFAg\nOxcRERGRrii6UtWxY0ckJydj5MiRKCoqwh//+Eds374dHh5tb4SG+vp6FBWdbPRaePht8PT0VCmR\n+9nqM9D2+01ERNQSioqq8PBwhIWFWf8/MDAQZWVluPnmm+2uo+ajlS1pu7CwECkvbEWngK4AgNqL\n55C7OAFRUVE2l7f3eG9D7n7U19m2bWVsmKlpnwHn/Xa17eZkssXevnOlXWf9VkKvn2siIpJPUVG1\nceNGFBYWYvbs2SgtLUVNTQ1CQkIcrqPWo5UtfayzoqIanQK6wi8otNFr9rZp7/Hepsu4a3+40l9b\nGRtmstXnpssobbs5mewt17QNV9t11u/mUvORYbXaZiFHRGSfoqJqxIgRSEtLQ0JCAjw8PLBo0aI2\n+dUfERERkasUFVVeXl5Yvny57CxEREREusXLS0REREQSsKgiIiIikoBFFREREZEELKqIiBooKChA\nYmIiAKC4uBgJCQkYM2YM5s6dq3IyItI6FlVERD9bvXo1MjMzYTKZAACLFy/G1KlTsX79elgsFuzY\nsUPlhESkZSyqiIh+FhYWhqysLOvPR44cwf333w8A6N+/P/bt26dWNCLSARZVREQ/i42NbTQVkxDC\n+v++vr6oqlJnsFci0gdF41QREbUHDQc1rqmpQefOnV1aT+8jz+spv70prtw9HZi76Tn7dW2hD83F\nooqIyI6ePXsiPz8fDzzwAD799FP06dPHpfXUmr5IBjWnX1LC3hRX7pwOzN30dgxs0XsflBaELKqI\niOxITU3F888/D5PJhMjISMTFxakdiYg0jEUVEVEDoaGhyMvLAwCEh4cjNzdX5UREpBe8UZ2IiIhI\nghYVVeXl5RgwYABOnTolKw8RERGRLikuqsxmM2bPno2OHTvKzENERESkS4rvqVq6dClGjRqF7Oxs\nmXncqr6+HkVFJxu9Fh5+W6NxafSgaT/q6+sBGODp6YHKSj9UVFQ36lfT5YuLT7slU2FhofVJnIaZ\nrtPjviYi+WydiwH3nSNauz1qvxQVVZs2bUJwcDD69euHN954w6V11Byv4nrbhYWFSHlhKzoFdAUA\n1F48h9zFCYiKirK7rq0xUByNf2JvzBRX13dF036Un/kWPv7Bdvtla/ngW++0m0nJuC+FhYVITHvH\n5UxNubLfHGVwZX8291i6QgufayK9KSo62eicBFw7R7w8YygiI2/XfXvUfikuqgwGA/bu3Ytjx44h\nNTUVr7/+OoKDg+2uo9Z4FQ3HyqioqEangK7wCwq1vu9sLBNbY6A4WsfemCmuru+Kpv2ovVjqsF+2\nlneUScm4L83NZGt9V9jahqvjoTT3WDqj5jgsarXNQo5kaXp+aGvtUfukqKhav3699f8TExMxb948\nhwUVERERUVvX4iEVDAaDjBxEREREutbiwT/XrVsnIwcRERGRrnFEdSIiB8xmM1JTU1FSUgKj0Yj5\n8+cjIiJC7VhEpEEcUZ2IyIHdu3fDYrEgLy8PEydOxIoVK9SOREQaxaKKiMiB8PBw1NfXQwiBqqoq\neHl5qR2JiDSKX/8RETng6+uLM2fOIC4uDhcuXNDVgMfUPO4cIJoDkLYPLKqIiBxYu3YtYmJiMGXK\nFJSWliIpKQnbtm1Dhw4d7K6j9/G83J1fyQDDLdmWq+0pGSDaVU237Wz7ev8MAW2jD83FooqIyIGA\ngAAYjddOlf7+/jCbzbBYLA7XUWtQWBlaY2BZJQMMt2RbrranZIDo5uS0NQBpSwY01jK990FpQcii\niojIgbFjxyI9PR2jR4+G2WzGtGnTOJE8EdnEooqIyIFOnTph5cqVascgIh3g039EREREErCoIiIi\nIpKARRURERGRBIruqbJYLMjMzMSpU6fg4eGBuXPnonv37rKzEREREemGoqJq586dMBgM2LBhAw4e\nPIiXXnoJq1atkp2NiIh0hoNcUnumqKh6+OGHMWjQIABASUkJAgICpIYiIiJ9Kio6aXOQy5dnDEVk\n5O0qJiNyP8VDKnh4eGDmzJnYsWMHXnnlFZmZNE1YLCguPt3otZb8BebOaRG0RPZ+A37Zd5WVftbB\n/bS279rL8SVqyNYgl0TtQYvGqVqyZAnKy8sxcuRIfPDBBw4HxFNzuHpHUxU4mxah6TqXq8rw4rvn\n0SngLIAbpxmwNx2CvTaVTIvQ3DZkLe9oXzlrQ8Z+a5qhuftOyfF3xtm67pz2oj1OAUFEpGWKiqot\nW7agtLQUf/rTn+Dt7Q0PDw94eDh+kFCt4eobDpVva6oCZ1MQ2FrH0TQG9qZDsNemkmkRlLQhY3lH\nuVxpo6X7zdY6zdl3So6/I65Mw+CuaS/UmgKChRwRkX2KiqpHHnkEaWlpGDNmDMxmMzIyMhxOLkpE\nRETU1ikqqnx8fDhtAxG1Gzk5Odi5cydMJhMSEhIQHx+vdiQi0iDO/UdE5MDBgwfx1VdfIS8vD7W1\ntXjrrbfUjkREGsWiiojIgT179iAqKgoTJ05ETU0NnnvuObUjSdfwKVUtPU3Lp2dJb1hUERE5UFlZ\niR9//BHZ2dn44YcfMGHCBHz00UcO19HbDf1Nn1IFlD+p6srTw64+YezK07My23PHE8LNydmQ3j5D\ntrSFPjQXiyoiIgcCAwMRGRkJo9GIiIgIeHt7o6KiAl26dLG7jlpPOytl6ynV6683ty+uPD3s6hPG\nrjw9K7s9Z8so1ZynqtV6ulcmvfdBaUHICZWJiByIjo7GZ599BgAoLS3FlStXEBQUpHIqItIiXqki\nInJgwIABOHToEEaMGAEhBGbPng2DwaB2LCLSIBZVREROTJ8+Xe0IRKQD/PqPiIiISAIWVUREREQS\nsKgiIiIikoD3VBEREWnA9cFOGw7ACrhvwFNbg6u6s732QFFRZTabkZ6ejpKSEphMJowfPx6DBg2S\nnY2IiKjdKCo6aXMQ1pdnDEVk5O26b689UFRUbd26FUFBQVi2bBkuXryIYcOGsagiIiJqIVuDsLal\n9to6RUXVY489hri4OACAxWKB0chvEYmIiKh9U1QN+fj4AACqq6uRkpKCKVOmSA1F2iMsFhQXn270\nmt6/d3fWJ71O5qrX3EREeqf4EtPZs2cxadIkjBkzBoMHD3a6vJoTKzqaVNPZZJn2JsG0tw0Zy2sx\n0+WqMrz47nl0CjgL4MaJTV1po6UZXVnH0b5ruryzPrkymauzz7U7J2i1tw1XchMRkXyKiqrz588j\nOTkZs2bNQp8+fVxaR62JFRtO6qhkskx7k2Da24aM5bWYCbjxu/fmttHSjK6s42jf2VreWZ8cve/K\nhKHumqDVUduuTELbknbbo/LycsTHx2PNmjWIiIhQOw4RaZSicaqys7Nx6dIlrFq1ComJiUhKSkJd\nXZ3sbEREqjObzZg9ezY6duyodhQi0jhFV6oyMjKQkZEhOwsRkeYsXboUo0aNQnZ2ttpRbsD754i0\nhY/tERHZsWnTJgQHB6Nfv3544403XF6vtb4mlXX/nL17GpXc/+fKtlxtz5V7Elu7PaVk5pTF3e21\nx9sFWFQREdmxadMmGAwG7N27F8eOHUNqaipef/11BAcHO1yvte4hlXX/nL17Gt21LVfbc+WexNZu\nTymZOWVxZ3uu3HOqZUoLQhZVRER2rF+/3vr/iYmJmDdvntOCiojaL06oTETkAoPBoHYEItI4Xqki\nInLBunXr1I5ARBrHK1VEREREErCoIiIiIpKARRURERGRBLyniohIY2wN6gmoP7AnBxu1jfuFrmNR\nRUSkMUVFJxsN6glcG9jz5RlDERl5u2ZyaSGTFnC/0HUsqoiINKjpoJ5aodVcauN+IYD3VBERERFJ\n0aKiqqCgAImJibKyEBEREemW4q//Vq9ejS1btsDX11dmHiIiIiJdUlxUhYWFISsrC88995zMPM3S\n9ImL+vp6AAZ4ev5yAa5Ll1521xcWC4qLTzd6Te0nNmxlAtTPpQdaPJ7OuPIZ1nof2jqz2Yz09HSU\nlJTAZDJh/PjxGDRokNqxiEiDFBdVsbGxKCkpcXl5pTM+N7T27U3IP3zG+nPluVM4cSHA+sRF+Zlv\n4eMf3OgJjNzFfoiKirq2fKVfo+1drirDi++eR6eAsw2WT7Aub2sdW7p08bP2r6XLN81kK1drZ5K5\njjvbdHY8ZeyHhu8Dzj/XzrZRWFjY6Kkh25/hxp9JZ227kptct3XrVgQFBWHZsmW4ePEihg0bxqKK\niGxqtaf/ysqqWryN46fLUFwXZv259mJRoycuai+W2nwC43rbFRXVN2yz6fIVFdWNstpap6mG68hY\n3lYfZLfR0uWVruPuNh0dTxn7oeH7ISH+Tj/XzrZRUVHt9DPc9DPprG1nbbZEeyzMHnvsMcTFxQEA\nLBYLjEY+NE1EtrX47CCEkJGDiEiTfHx8AADV1dVISUnBlClTVE5E7Z0rg43KHECWg5u6rsVFlcFg\nkJGDiEizzp49i0mTJmHMmDEYPHiw0+VbekXP3lfVTb/GlfVVr8z2XNmWntuzpbVzNr1twNZtAk2X\nsbWczPZsaY9XtltUVIWGhiIvL09WFiIizTl//jySk5Mxa9Ys9OnTx6V1WvpVq72vql25PUHJV70y\n23NlW3puz952WjunK7euKL2VREl7Tblye4SWKS0IOfgnEZED2dnZuHTpElatWoXExEQkJSWhrq5O\n7VhEpEG845KIyIGMjAxkZGSoHYOIdIBXqoiIiIgkYFFFREREJAGLKiIiIiIJeE8VEVEbJnO8IiJ7\nmn7OKiv90Llz12aPneXu8bWUbstVLKqIiCR6/OmFqK//ZVDksJsMmDfzL6rlKSo6aXO8opdnDEVk\n5O2q5aK2pennzNZnzJXPoszPqxqffRZVREQyBf0KDf8G9uxw4wTprc3WeEVEsrnyOZO1jMxMMvGe\nKiIiIiIJWFQRERERScCiioiIiEgCRfdUCSEwZ84cfPfdd+jQoQMWLlyIbt26yc5GRKQ6nu+IyFWK\nrlTt2LEDdXV1yMvLw7Rp07B48WLZuYiINIHnOyJylaKi6osvvkBMTAwAoFevXjh8+LDUUEREWsHz\nHRG5StHXf9XV1fD39/9lI0YjLBYLPDzce4uWhzDBUv6N9WdTzTlcFZ2tP1+uqgBgsP5ce/EcTp06\nhYqKagBAcfFp1F4853D54uLGjz83dx3Zy7dGG86Wl9FGW9gPTd+vrPSzfrbscbYNJZ9JZ23bapOU\na+75znDxCOrNFuvP5R7lOHHi+2a1aevfoCvnJ1eWsbUc23O9PVu0mFPt9tydyRX2tuVOBiGEcL5Y\nY0uWLME999yDuLg4AMCAAQOwa9cu2dmIiFTH8x0RuUrRpaX77rsPu3fvBgB8/fXXiIqKkhqKiEgr\neL4jIlcpulLV8GkYAFi8eDEiIiKkhyMiUhvPd0TkKkVFFRERERE1xsE/iYiIiCRgUUVEREQkAYsq\nItF0Y5QAAAaBSURBVCIiIglYVBERERFJoGjwT2fKy8sRHx+PNWvWNHpKZufOnVi1ahWMRiPi4+Mx\ncuTIVml37dq1eP/999GlSxcAwLx58xAeHi6t3SeffBJ+fn4AgFtvvRWLFi2yvufOPjtq1519zsnJ\nwc6dO2EymZCQkID4+Hjre+7sr6N23dnfzZs3Y9OmTTAYDLh69SqOHTuGvXv3Wve9u/rsrF139dls\nNiM1NRUlJSUwGo2YP39+q/471gOz2Yz09HSUlJTAZDJh/PjxGDRokPV9PewjZ31w93mzpSwWCzIz\nM3Hq1Cl4eHhg7ty56N69u/V9PRwDZ33Q+jG4Tq3f+TJJqx+EZCaTSTz77LPi0UcfFSdPnmz0emxs\nrKiqqhJ1dXUiPj5elJeXu71dIYSYPn26OHLkiLS2Grp69aoYPny43Uzu6rOjdoVwX58PHDggxo8f\nL4QQoqamRrz66qvW99zZX0ftCuHeY9zQ3LlzxXvvvWf92d2fa3vtCuG+Pu/YsUNMnjxZCCHE3r17\nxZ///Gfre63VX63buHGjWLRokRBCiAsXLogBAwZY39PLPnLUByFa79+UUp988olIT08XQlw7P0yY\nMMH6nl6OgaM+CKH9YyCEer/zZZJZP0j/+m/p0qUYNWoUunbt2uj1EydOICwsDH5+fvDy8kJ0dDTy\n8/Pd3i4AHDlyBNnZ2UhISEBOTo60NgHg2LFjqK2tRXJyMsaNG4eCggLre+7ss6N2Aff1ec+ePYiK\nisLEiRMxYcIEDBw40PqeO/vrqF3Avcf4um+++QbHjx9v9NeWuz/X9toF3Nfn8PBw1NfXQwiBqqoq\neHl5Wd9rjf7qwWOPPYaUlBQA1642GI2/XPTXyz5y1Aegdf5NtcTDDz+M+fPnAwBKSkoQEBBgfU8v\nx8BRHwDtHwNAvd/5MsmsH6QWVZs2bUJwcDD69esH0WT4q6bzZ/n6+qKqqsrt7QLAb3/7W8ydOxfr\n1q3DF198YR0dWYaOHTsiOTkZb775JubMmYPp06fDYrk275c7++yoXcB9fa6srMThw4fxyiuvYM6c\nOZg2bZr1PXf211G7gHuP8XU5OTmYNGlSo9fc2WdH7QLu67Ovry/OnDmDuLg4zJo1C4mJidb3WqO/\neuDj44NOnTqhuroaKSkpmDJlivU9vewjR30AWuffVEt5eHhg5syZWLhwIR5//HHr63o5BoD9PgDa\nPwZq/c6XSXb9IL2o2rt3LxITE3Hs2DGkpqaivLwcAODn54fq6l8mgK2pqUHnzp3tbUpauwAwduxY\nBAYGwmg04qGHHsLRo0eltAtc+6t+6NCh1v8PDAxEWVkZAPf22VG7gPv6HBgYiJiYGBiNRkRERMDb\n2xsVFRUA3NtfR+0C7j3GAFBVVYWioiL07t270evu7LOjdgH39Xnt2rWIiYnB9u3bsXXrVqSmpqKu\nrg6A+/urJ2fPnsXYsWMxfPhwDB482Pq6nvaRvT4A7v83JcuSJUuwfft2ZGZm4sqVKwD0dQwA230A\ntH8M1PqdL5Ps+kFqUbV+/Xrk5uYiNzcXPXr0wNKlSxEcHAwAiIyMxOnTp3Hp0iXU1dUhPz8f99xz\nj9vbra6uxpAhQ3D58mUIIbB//37cddddUtoFgI0bN2LJkiUAgNLSUtTU1CAkJASAe/vsqF139jk6\nOhqfffaZtd0rV64gKCgIgHv766hddx9jAMjPz0efPn1ueN2dfXbUrjv7HBAQYL0Z3t/fH2az2XoV\n1N391Yvz588jOTkZM2bMwPDhwxu9p5d95KgPrfFvqqW2bNli/TrG29sbHh4e8PC49itNL8fAUR/0\ncAzU+p0vk+z6wW3T1CQlJWHu3Lk4cuQILl++jJEjR2LXrl147bXXIITAiBEjMGrUqFZpd+vWrVi3\nbh28vb3Rt29fm1+lKGUymZCWloYff/wRHh4emD59Os6cOeP2Pjtr1519Xr58Ofbv3w8hBKZOnYrK\nyspWOcaO2nVnfwHgzTffhJeXF5KSkgAA//jHP1qlz47adVefa2trkZ6ejrKyMpjNZiQlJUEI0ar/\njrVu4cKF+PDDD3HbbbdBCAGDwYDf/e53utpHzvrg7n9TLXX58mWkpaXh/PnzMJvN+NOf/oTa2lpd\nHQNnfdD6MWhIrd/5MsmoHzj3HxEREZEEHPyTiIiISAIWVUREREQSsKgiIiIikoBFFREREZEELKqI\niIiIJGBRRURERCQBiyoiIiIiCf4/EsBwU2+2nNIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112181710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.hist(figsize=(10, 10), bins=50, layout=(3, 2));"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x115263090>"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JtLXVaMinuVMcAuWxHbhyaROv176NVz//CGgEyuYsxiftn2FJz2UB5efaFr2J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\nnnvuwQsvvICnn34aEydOxO7du1FVFdkc4RSaeEyv7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2KUG1ISOxwIPeb7bIvD+0NCaqx2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\ntVNUHgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112333b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.pairplot(data, hue=\"target\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building a predictive model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data matrices for training and making predictions."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimensions X_train= (105, 4) y_train= (105,)\n",
"Reading public_data/iris_valid from AutoML format\n",
"Number of examples = 15\n",
"Number of features = 4\n",
"Reading public_data/iris_test from AutoML format\n",
"Number of examples = 30\n",
"Number of features = 4\n"
]
}
],
"source": [
"X_train = data.drop('target', axis=1).values # This is the data matrix you already loaded (training data)\n",
"y_train = data['target'].values # These are the target values encoded as categorical variables\n",
"print 'Dimensions X_train=', X_train.shape, 'y_train=', y_train.shape\n",
"X_valid = data_io.read_as_df(basename, 'valid').values\n",
"X_test = data_io.read_as_df(basename, 'test').values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The initial classifier in your starting kit (in the sample_code directory)."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import classifier\n",
"reload(classifier) # If you make changes to your code you have to reload it\n",
"from classifier import Classifier\n",
"Classifier??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Train, run, and save your classifier and your predictions. If you saved a trained model and/or prediction results, the evaluation script will look for those and use those in priority [(1) use saved predictions; (2) if no predictions, use saved model, do not retrain, just test; (3) if neither, train and test model from scratch]. Compute the predictions with predict_proba, this is more versatile."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"res/iris_model.pickle res/iris_test.predict res/iris_valid.predict\r\n"
]
}
],
"source": [
"result_dir = 'res/'\n",
"outname = result_dir + dataname\n",
"clf = Classifier()\n",
"clf.fit(X_train, y_train)\n",
"Y_valid = clf.predict_proba(X_valid) \n",
"Y_test = clf.predict_proba(X_train) \n",
"clf.save(outname)\n",
"#clf.load(outname) # Uncomment to check reloading works\n",
"data_io.write(outname + '_valid.predict', Y_valid)\n",
"data_io.write(outname + '_test.predict', Y_test)\n",
"!ls $outname*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the training accuracy."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training accuracy = 0.980952380952\n",
"Class labels= ['setosa' 'versicolor' 'virginica']\n",
"Confusion matrix [known in lines, predicted in columns]=\n",
"[[30 0 0]\n",
" [ 0 40 0]\n",
" [ 2 0 33]]\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import confusion_matrix\n",
"# Directly predicts the (categorical) class labels\n",
"y_predict = clf.predict(X_train) \n",
"print 'Training accuracy = ', accuracy_score(y_train, y_predict)\n",
"class_labels = clf.get_classes() \n",
"print 'Class labels=', class_labels\n",
"print 'Confusion matrix [known in lines, predicted in columns]=\\n',confusion_matrix(y_train, y_predict, class_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute cross-validation accuracy. This is usually worse than the training accuracy. Notice that we internally split the training data into training and validation set (this is because we do NOT have the labels of X_valid and X_test)."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fold 1 validation accuracy = 0.867924528302\n",
"Fold 2 validation accuracy = 0.905660377358\n"
]
}
],
"source": [
"from sklearn.cross_validation import StratifiedShuffleSplit\n",
"# This is just an example of 2-fold cross-validation\n",
"skf = StratifiedShuffleSplit(y_train, n_iter=2, test_size=0.5, random_state=61)\n",
"i=0\n",
"for idx_t, idx_v in skf:\n",
" i=i+1\n",
" Xtr = X_train[idx_t]\n",
" Ytr = y_train[idx_t]\n",
" Xva = X_train[idx_v]\n",
" Yva = y_train[idx_v]\n",
" clf = Classifier()\n",
" clf.fit(Xtr, Ytr)\n",
" Y_predict = clf.predict(Xva)\n",
" print 'Fold', i, 'validation accuracy = ', accuracy_score(Y_predict, Yva)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ADVANCED: Sklearn does not have multi-class metrics, this shows how libscore metrics work."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimensions Y_train= (105, 3) Class labels= ['setosa' 'versicolor' 'virginica']\n",
"Training balanced accuracy = 0.916666666667\n",
"Training probabilistic accuracy = -0.11618174813\n"
]
}
],
"source": [
"import libscores\n",
" \n",
"# To evaluate results with multi-class metrics, the targets must be encoded as one vs. the rest\n",
"Y_train, C = libscores.onehot(y_train) \n",
"print 'Dimensions Y_train=', Y_train.shape, 'Class labels=', C\n",
"assert((class_labels==C).all()) # Just to make sure the labels of the classifier are in the right order\n",
"# Note: if all went well, you should recover public_data/iris_train.solution\n",
"# You had it all along, but to show you some nice plots we loaded the data as a data frame so we lost it!\n",
"\n",
"from libscores import bac_metric \n",
"from libscores import pac_metric \n",
"# Predicts probabilities, a matrix patnum x classnum \n",
"# As solution, you must use Y_train, not y_train\n",
"y_predict_proba = clf.predict_proba(X_train) \n",
"print 'Training balanced accuracy = ', bac_metric(Y_train, y_predict_proba, task='multiclass.classification')\n",
"print 'Training probabilistic accuracy = ', pac_metric(Y_train, y_predict_proba, task='multiclass.classification')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unit testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is <b><span style=\"color:red\">important that you test your submission files before submitting them</span></b>. All you have to do to make a submission is modify the file <code>classifier.py</code> in the <code>sample_code/</code> directory, then run this test to make sure everything works fine. This is the actual program that will be run on the server to test your submission. The program looks for saved results and saved models in the subdirectory <code>res/</code>. If it finds them, it will use them: (1) If results are found, then are copied to the output directory; (2) If no results but a trained model is found, it is reloaded and no training occurs; (3) If nothing is found a fresh model is trained and tested."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"outdir = '../outputs' # If you use result_dir as output directory, your submission will include your results"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using input_dir: public_data/\n",
"Using output_dir: /Users/isabelleguyon/Documents/Projects/ParisSaclay/Projects/ChaLab/Examples/iris/outputs\n",
"************************************************************************\n",
"****** Attempting to copy files (from res/) for RESULT submission ******\n",
"************************************************************************\n",
"[-] Missing 'test' result files for iris\n",
"[-] Missing 'valid' result files for iris\n",
"======== Some missing results on current datasets!\n",
"======== Proceeding to train/test:\n",
"\n",
"************************************************\n",
"******** Processing dataset Iris ********\n",
"************************************************\n",
"========= Reading and converting data ==========\n",
"Info file NOT found : /Users/isabelleguyon/Documents/Projects/ParisSaclay/Projects/ChaLab/Examples/iris/starting_kit/public_data/iris_public.info\n",
"========= Reading public_data/iris_feat.type\n",
"[+] Success in 0.00 sec\n",
"========= Reading public_data/iris_train.data\n",
"[+] Success in 0.00 sec\n",
"========= Reading public_data/iris_train.solution\n",
"[+] Success in 0.00 sec\n",
"========= Reading public_data/iris_valid.data\n",
"[+] Success in 0.00 sec\n",
"========= Reading public_data/iris_test.data\n",
"[+] Success in 0.00 sec\n",
"DataManager : iris\n",
"info:\n",
"\ttask = multiclass.classification\n",
"\tvalid_num = 15\n",
"\thas_categorical = 0\n",
"\tfeat_type = Mixed\n",
"\tformat = dense\n",
"\tmetric = auc_metric\n",
"\ttarget_type = Binary\n",
"\ttest_num = 30\n",
"\tlabel_num = 3\n",
"\ttarget_num = 3\n",
"\ttrain_num = 105\n",
"\thas_missing = 0\n",
"\tusage = No Info File\n",
"\tfeat_num = 4\n",
"\ttime_budget = 600\n",
"\tis_sparse = 0\n",
"\tname = iris\n",
"data:\n",
"\tX_train = array(105, 4)\n",
"\tY_train = array(105,)\n",
"\tX_valid = array(15, 4)\n",
"\tX_test = array(30, 4)\n",
"feat_type:\tarray(4,)\n",
"feat_idx:\tarray(4,)\n",
"\n",
"[+] Size of uploaded data 72.00 bytes\n",
"[+] Cumulated time budget (all tasks so far) 600.00 sec\n",
"[+] Time budget for this task 600.00 sec\n",
"[+] Remaining time after reading data 600.00 sec\n",
"======== Creating model ==========\n",
"**********************************************************************\n",
"****** Attempting to reload model (from res/) to avoid training ******\n",
"**********************************************************************\n",
"======== Trained model not found, proceeding to train!\n",
"[+] Fitting success, time spent so far 0.01 sec\n",
"======== Saving model to: /Users/isabelleguyon/Documents/Projects/ParisSaclay/Projects/ChaLab/Examples/iris/outputs\n",
"[+] Success!\n",
"[+] Prediction success, time spent so far 0.02 sec\n",
"======== Saving results to: /Users/isabelleguyon/Documents/Projects/ParisSaclay/Projects/ChaLab/Examples/iris/outputs\n",
"[+] Results saved, time spent so far 0.02 sec\n",
"[+] End cycle, time left 599.98 sec\n",
"========= Zipping this directory to prepare for submit ==============\n",
"See: ../sample_submission_16-11-01-21-34.zip\n",
"[+] Done\n",
"[+] Overall time spent 1.03 sec :: Overall time budget 600.00 sec\n"
]
}
],
"source": [
"!python run.py $datadir $outdir"
]
},
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"cell_type": "markdown",
"metadata": {
"collapsed": true
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"source": [
"## Making your submission\n",
"\n",
"The test program <code>run.py</code> prepares your <code>zip</code> file, ready to go. You find it in the directory above where you ran your program. For large datasets, we recommend that <b><span style=\"color:red\">you do NOT bundle the data with your submission</span></b>. The data directory is passed as an argument to run.py, and it is already there on the test server."
]
},
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"metadata": {
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"outputs": [],
"source": []
}
],
"metadata": {
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"display_name": "Python [default]",
"language": "python",
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Sample code for mini project L2
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