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Possible bug in sklearn StandardScaler with Pandas DataFrame's
{
"metadata": {
"name": "test_sklearn_scaling_problem_dummyexample"
},
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"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Possible bug in sklearn StandardScaler with Pandas DataFrame's"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import datetime as dt\n",
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sklearn\n",
"#from sklearn import svm\n",
"from sklearn import preprocessing"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": [
"'0.10.0'"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sklearn.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": [
"'0.13.1'"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset = pd.DataFrame({'A':np.random.rand(100)*1000, 'B':np.random.rand(100)})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>0</strong></td>\n",
" <td> 978.463783</td>\n",
" <td> 0.831525</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 970.419872</td>\n",
" <td> 0.362665</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 931.932628</td>\n",
" <td> 0.307650</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 748.586876</td>\n",
" <td> 0.947412</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\n",
" <td> 548.387315</td>\n",
" <td> 0.995330</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 6,
"text": [
" A B\n",
"0 978.463783 0.831525\n",
"1 970.419872 0.362665\n",
"2 931.932628 0.307650\n",
"3 748.586876 0.947412\n",
"4 548.387315 0.995330"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Using a pandas column (Series): problems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data in consideration (column `A` in my dataset). Remark the range of the y-axis between 0 and 1000)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = dataset.ix[:,'A']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 8,
"text": [
"[<matplotlib.lines.Line2D at 0xa32612c>]"
]
},
{
"output_type": "display_data",
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VghKg7QwjF7APkB955BFfx/P0CL/55pv42Mc+hunTpyOZTOKzn/0sXnvtNdTV1aG3txcA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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa22f78c>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I fit a standard scaler from sklearn with these data."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scaler = preprocessing.StandardScaler().fit(data)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 10,
"text": [
"[<matplotlib.lines.Line2D at 0xa4c12cc>]"
]
},
{
"output_type": "display_data",
"png": 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pIcdQvaF0K4GREfK7dpvHXiIK8ElAdXtJQPwghqUEWDtIRQmIvN+ogmEnEghz\n2Qgvk8WAcKpqWHjNBHbuzJ0caZSAIvyQQFglopQEVGwYlWDYTyYAuAuHgyABIPf3VZWA0/aSKkqA\nDYaDrA6idlBFBRndsw+xSPHxHvawgmF7JuBUHRTnTWWiVgLUdlUtWvnmN4EdO7JfMySgCK9LRwSx\nnwAgJwGVTiTo6iDAXS4QFAnYR8YiEnCTCbixg8JSAnPmkBG+PRwWZT9+lIDfYDjI6iAdmYDbGcPs\n32GBfq+SEnJ9VVaeHRsjO8bZnwnWDjIkIEFUdpDbTABQ60SCDoYB/UrAafkFFvR37LmAl0zAqeJE\nxQ6SKQG/JaLUDgJyLSER2UdlB3mZLEaJmOZL4+PkPrA/F8WoBAD1XOC998jvyQZGsSaBgYEB3Hrr\nrZg/fz5aWlqwe/du9Pf3o7W1FfPmzcOKFSswwHy7jRs3oqmpCc3Nzdhh1z8eEFV1kJdMQNUOkgXD\nfktEAXdloiok4LT8Agt6fr2SgGx7Sfs1UQmGg1ICZ8+Sc3feeeT/U6bkkkAQdlCQSsBeHZRIZHfO\n1EKyzy6PojooDiSg+ky88QZRDnlLAvfddx9uuukm7Nu3D3/4wx/Q3NyMtrY2tLa2Yv/+/Vi+fDna\n2toAAJ2dndi6dSs6OzvR0dGB9evXY9znNDidSkAUuvI2pxDdxJMmkQeR/VrHjpFFoKK0g9hj6raD\nAPckwLvh7d9blgn4sYPczBj2SgLd3UB9PXm4Ae92kGXx71UedJaIiqqD2Np1+j56HXhWEGCUgBPe\neAO46qo8tYNOnjyJV155BXfddRcAIJlMYsqUKdi+fTvWrVsHAFi3bh1eeOEFAMC2bduwdu1alJWV\nobGxEXPnzsWePXt8NVx3iSjvprnmGuDgwezXRCRQUkIeEvbiHzsGNDbqC4bDsoPo74hWNGURphJQ\nJQGZHRS0EmCtICDbDhofz+1MKewPO+1YSxSeUFkbUynyh/eZgLMdxAuGgWwFwasMAqJTApMmRVMd\npLKSLYs33gBaW+XPRBgk4IKTM+jq6sLMmTNx55134ve//z2uvPJKPP744+jr60NtbS0AoLa2Fn19\nfQCAI0eOYCldEB1AQ0MDDh8+nHPchx9++Ny/ly1bhmXLlgnbEEYmcOJE7mhRNuORXvzqanIhUymg\nrk6PEvBbIgqo20GqKgBQ31NAlAmolojS864yY3jy5Ny9fQF3M4bpNabHVe28ZCQwPEzutdLS3PfZ\n70FVKwiZmihbAAAgAElEQVSQk/vJk8SSEi0E6LR2EC8TALKvUZyUQCpFrm3UdpDTM5FKAX/8I9DW\nBrz0UvbP6LIRAJ8Edu3ahV27dmlpN+CRBNLpNPbu3Yvvf//7uOqqq3D//fefs34oEokEEpIlKHk/\nY0nACU7VQYcOkYsxfXr2624ygcHB3AsgW/uEHQH85S/ArFnqI0mVYDisElG3JOBVCdDVEu2Kw2me\ngIoS0DVjGMhcQzrhinasPHzwAakMopgyJfOdZURvf9jdkoBoCRVZHgB4C4bp++KqBKImAZVJlH/4\nA3DhhcQ65NlBrBKwP7P2AfIjjzziq+2e7KCGhgY0NDTgqquuAgDceuut2Lt3L+rq6tDb2wsAOHr0\nKGbNmgUAqK+vR3d397n39/T0oL6+3lfDqRIQzYD9x38Efvzj3NdVZwxbFp8EZDcx27EcO5YhAR3B\ncJglokGSAHvD02thtzzc2EGWlVuZItoDQHXGsJ3o2c3m02li8Yk6XZkSkF3joJSAEwl4KREFcpUA\njwSiygSmTIm/EqB5AG9CYV4Ew3V1dZg9ezb2798PANi5cycWLFiAlStXor29HQDQ3t6O1atXAwBW\nrVqFLVu2IJVKoaurCwcOHMCSJUt8Nbyigtx4opPd38+v1VW1g+gSrvaHS8UOAjIkoGrBhJUJBGEH\nqZLA9OnZSoCXBwDOk8XYjiWdJvYKKyxF6suvEgDIbmsDA+L3ykhApvb8koDouupQAvbqIPo+Jzso\nqhnD+aAEKAnQ6jF2MOtkB+mGJzsIAL73ve/hC1/4AlKpFC666CL86Ec/wtjYGNasWYPNmzejsbER\nzz33HACgpaUFa9asQUtLC5LJJDZt2iS1ilRB1YB931WAdGR2EhgbIyfb3onzOlj6Xq92EKsEdGUC\n+WwHpVLkfKiQgGhZAp4S4HUqMhKgAwB2joN9FCsjgTfeIH+LzqPdDrIrAVU7SLUyiLZPlxJQWTuI\nvs9eIso7dhTbS86aFS4J2NWoqhJYv55c42Qy2wIKexVRzyRw2WWX4Q36RDDYuXMn9/c3bNiADRs2\neP04LigJNDbm/oxHAvTBsvOPGxKQjWTYjWUoCZSXR1cdZA9do7aDZs3Klr688lDAXSbghgTYYJid\n48DmRpYlJwFa1MY7j+PjJJCWZQJR2EGi/ALIkIBlkXOiWh2kYgeJlEDQdlDYSoB+J9qvVFXxy8sp\nBgdJ1eGll5L/UzVAz6/dDvK6gZYq8nbGMCAPh0+cyO0IeHkAoI8E/NhBYc0YjtIOUlUCMjuopIR0\nWHQ+hioJWFZuZ8XLBcbG5PbSG2+Iif3oUXLe2M5b1Q7yGwyLSGB4WH5flZZm9nAA9FYHRbWpTHV1\nuCWivOo0mRLYuxe45JLMebbnAmHbQXlPAiKWlCkBO3gdLH3o3WYCtFPRHQzrKhGNWgmwJMArDwXk\nJGCfrcoLGnkkQK8bG0Lz2s67vvR4w8Nkqv+iRfzz2N0NzJ6d/VrUwfDwsPNx7DX/djtoaCi3qk6l\nOkhUyVWISkClOo1izx6AjUTtJGA2lXEB0dIR6TS5CDwS4D0QvA5WVyagMvpOp8kfmQfstkSU+t3s\nMaO0g9xkArwF5Oy1+6wfraIEeJYFTwnwri+tNvrd74D588lDyzuPdI4IC3bZiCCVgOgeUzkOe755\nJHDiRGa7Q/Y9KnaQ05wOJ+RDdZBbJUBDYQr7rPK8qA6KC0RKgJ7QqO2g2lq1YJjmAbKs3K0dREcT\n7DGDUAKqG8uIMgE3SoCed7ZzcUMC9msvUgKi49GHV3QeebOB2Qdclvv4UQIycj971nnmt5MS6O+X\nr/Qap8li+aAEeCQgsoPMpjIOEJEAreH2YwfpygRURt+8CVOiNqruDMbrYN1kArKKEhZu7KCpU8n5\now+1l0wAUFcC7LliQ2EKnhJwIoElS8TXlPd9qqrI6+m0sx0UVCbgRgmIqoPs3yuuy0ZEMWNYde0q\ngGRZf/4zMG9e5jVjB/mAjARKSnI7PJEdpDMTOH2adD5/+Qswc6Zax6tCAjSsVN2sQkQCUWYC5eXk\nAWWVWhAkUFaWqXSh4FkWbpXAnj3OSsB+HUtKMhVCTnZQEJmAynFkSoAOmmRLe+SjEqDPqA6orl0F\nkM6+qir7HPCUgCEBRcyYwa8Ookv52pWAyA5KJjO1vhR+MgFa7lVerk8JAO4sIV4HG0QmoLqBBrVj\nWI/cTYkoS76sHSTqVOyWEE8FulECR46QP/PniztdEanR7xxFMKxiB8lCXtoGJxKIgxKg+xpUVck7\nTssCHnoIuPxy9XbI4EYJ9PfnLmVjX248L1YRjQtmzOCz+YkTZE0OVTuIt8fr4CC5mF7sIGoFAWqZ\ngOoD75cEgigRtY9gRaDnnvXIvWYCTkoAyD3vbpQArzrolVdIp5FMuicB+p2DXDtIhx1kWbmDHBUl\n4HaymBsl4GbGMLX8ZM/J+DjwX/8r2dLRy86EPLgJhvv7yfLyLIwS8IGZM/kkMDAANDTw7SBRBQ6P\nBKZP59tBbkhAlx0EuJs17NUOOnuWjKZESw/boUpM9Nyzk6eCsoMAPgn4UQJ792bCPNE1FV1H+pAH\ntWwE7Sh4eZEbO4i3BAcdIHmxg0pKSKfL7rHhRQmozhim95hoYDI2BtxzD7mWdBFOXgf7058Cf//3\n6m0UBcO86+FEAvaqPkMCDqAkYD/ZJ04QElAtEQVyH8KhIUICPCXglAnYlUA+2UFUBaiu6qF6k9JR\nmn0GrVcSkFUHAXw7SEUJ8Oy+iRNJR0Zru70oASc7yI8SKCkh3413HdyUiIrOZUUFPxh2WjaCzumw\nr/XkVgnQ5V6c4KQEfvQjsvbTSy+R+3DKFP4iggcPAu+/r95G+3krKyPt5rXh+HE5CVD7jj5/hgQc\nUFlJLrr9QT5xgqzjn0pl+/yiTAAQKwEvmYBbJRAnO8iNFeSmTXR0w97wbpSAPRMIWwkA2UpARAI8\nMqeeb1BKgLZJRAKqJaKiET2PBFQyASDXznGrBBIJdUuIKgHRPdnbCyxfniFidkDCQrZAIA+87yQq\nE+VlAjKL1JCAAniWEO3IVMJBClUS8JIJxEUJqNhBbknAjRJQtYN4C8iJMgERKasoAV6oLSKBadPI\n+u+Au3kCgFom4KdE1KlNfpWAVzsI8K8E6DF0kID9fmMr1VicPOmfBES5gJMdZB9IGBJQgIwE7A+5\nGzvISyZQXp5ZRMxNMBynTMCrEnCS67ozAdYOUqkO4o1WeeeDd33nzAFuvjkj0YOyg/wqAV6b3GQC\nIlvHSQmI3gf4VwK0fW5JgNdx2q+PyA7SQQIyJSAjAftAwpCAAtwoAZkdZF86QpQJyEYydGXKgwcz\nJEBHWbL6/rDsIDeZgCpKSvgjdzvYTEBHiagOO4hnj/GOd8UVwA9+kP0+1XkCQHh2kIgEVEpE3WYC\nqnZQmEqAXmOZEmDPv8gOiloJGBJwCR4JDAyIlUCQdhCQSwKJhHPnG5YdVFZGyEhWbeGWBFTbxWYC\nUVQH8ewg3nVRWeAsbsGwrE2qdhBVAl5JQPRMRKEERNVBqnaQ20yAZ0mKlMDx47mZQEUFUdJ0tVa2\nL6AkqjpJ1AsKkgS82kFsx8MjAVpHLeskqqvJapKUBADnQDYsOyiRcLaEvJCAymjFrR1kf+h1zBPg\nKQH7uVBZ6tjNshGAeibgRwmIroFbO0hndRD9vbAzAVpRxJamAnw7KEgloGoHAZmBgt0OSiRyr206\nrXdZjIIlgZoad3aQSiaQThP7w74nLovJkwlZsCTglAuEpQRoW3STgEq7eCTgtUTUaQE5QE0JqGYC\ndri1g2pqyD06NibuLINSArrsIDt5hVUdxDsGQCp97DkUu2mUaD0w1UxgaEh99M37TqLVDJxIgPfc\n2u+N3bsBZp953yg4EhgfJxexpsafHcTLBFRGiZMnE5JgL7RTxxtWJgA4q5IglMDYWGb7PXsm4KVE\n1F4dFHQwbIeXZSMOHyYWgWj+RVCZgJv9BES2ju7qIC92kN3C/NSnyN4OLNjnW7QemKodlEg4bxFJ\nwbtn6uoIUdmhQgJ20rY/Xx9+SPo9XSgIEjh2LPP/U6dI559M6s8EVDqIyZNJm1i1oMsO0kUCMkKi\neYobOLWLWjGJhLdMwG7D6bSDVIJhO9zOE6ipIesOyXb4CkIJpNP8PbXtYJUAb0S/eDEwd27ue9xW\nB9GRtUxJ88Czg06ezO3A3ZIAzw4aHyed/6xZ/kjgvPPITnP2Y4ueL5EdBPBJYMYMtbapoCBIgFUC\n7EnmWQJuSkSnTcsuf1QlAdYKou3QEQz7zQR4bfmP/8iW1UEoAfbh9FIiOj6ebcPptoPY7+/XDhJl\nAqOj7rYP1UEC9tmnIjhlAv/wD8DHPpb9mhcl4MUKosewk8DQkHxZGF6ZqEqJ6OnT5DpNmaKeC6gq\ngZMnc1cQpaCDIxU7yJCADXYSYDsxuxJQzQQsi1yMqqrs0lGVyhERCciUQFR20KlTwDXXkB2zKILI\nBOwkMDCQOccqJGC3fHQpgbIyQix2z1p3dRDdbUy2fah9voWXYNjeJhUrCHDOBHiwLz8tUwJOK76q\nfJadBIaH+cvC0HbwKoRU7KCTJ8k9qrpZEqBOAiIrCHBnBx0/bkggC25IQNUOOnuW3EylpdkXQDUT\nsJNAXO2gV18lo+yOjszPg1IC7MNZUkIIqLSU3ynYScDeMesqEQVyO3RVJcC7niI7KJkknYrKhu/0\ne+lYNkL1GE5KgAf7RjSy6iCna6XSPpYExsfJNXNSAjwScJonMDBAOmTVfTIAMQnY7SAZCdDBEU9N\n2q+tUQI2TJpERk+0s2d3xfJqB7GlfOwFULmJp00Dzj8/+7U42UFsp/fyy2Q9nJ//PPNzrySgkglQ\nTJlCHhDRSqU8JcCedy/7CYg6KjsJqBA9/b721TEB8XtrauRKAMgdiOiwg1SVgBcSUJ0noFsJ0OfR\nrgTY+0w1E7DbQVQJ6CCBvr5sq5G3eByFm+ogQwI2JBLZasCrHcTaPmwpGSuzVR6S9euBr389+zUn\nJRCmHcQS0ssvkyVz33qL3Py0I5CNWEXtUs0EAPKQHTnijgSCsIN4v6dyjUtKcr8zPd8i/51Wq8nA\nPuw6MwEn0FG9rDPnvcdtMKxLCdDrJdsvxP6sUPXAnlOZHeSXBOjcCrrVLcBfPI7CbXWQ6DhekPck\nAIhJwOsCcqwSYC+Aql88eXL2a1HNGOZ1OpSQhodJ53/ddcDHPw788pcZFaW6jDSFkxKIAwnotIN4\n73O6hlOmOJMAmwvIBiwq7aFtcmsHiTpzO7wsG6FLCdDr6sYOoqTKVibx7CCvJMD7XnZLSCUTMNVB\nHsGSAFsd5DYToDc124G6zQR4iNNkMdpZ7N4NLFxIvucNN5BcwIsVRNulmgkA5IaX2UH2tYjsD5mX\n6iCREtBFAqLzTaFiB9F7jX7f0lLndojaA7izg9wGw16WjQhbCfCUGouqqtxJYQMDepQAQMpE2XBY\nVzBsSIADVTsojEyAh7gsGwFkVMkrrwCf/CR5zS8JhJ0JxEEJ2NWdKBSmULGD6D3o1goC+OG8qh3k\nJRj2smyEVyXAkj7gTQnwnoeSktwqIDrRVAcJ2CuEVDIBJyWQTpMcg+aeOlDQJOB12Qg/mQAPYc8Y\npuWXvA6AEtLLL2dI4OKLyQPxm98EpwR4JCDqFGnHQUM1mR2kup+ALBi2ZwIqHRXPDvKrBOh59EIC\nfuwgHUogzEyAfk+ZErAPTGSzuVlLSFcmAOSSgGomICOB/n7yjLpRiU4oCBKYNSubBChLei0RFWUC\nXkcyYdtBZ86Q4/HaWllJRhKvv06yAIBkADfcADz7bDBKwH7e6QxaUadJtyUULQ1ht4NUq4NEwbDb\n6iDAvR10881Aa6v8mPQ86iKBoEtE3U4WizITkJEAWyGkkwTss4ad7CA6WUxmB+meIwAUCAnotoNE\nmYAfOyjMElHqa/IwcSIZ8V90UXaHf+ONZAPuMJWArNO0V5/EPRh2soOWLwf+5m/kx/RjB/mtDpKt\nHSR7D5Af1UGiQgm7EtCZCfCUgJd5AmwfpDsPAAqcBPzYQWwmoMMOCrNElI5meKisJKEwtYIorruO\nPLBBZQJsJ+FUHQRkdzJeMgGa5VBLKehg2MkOUgF92KO0g7xWB4WtBGioy8KLErCXiQaZCchIYOJE\ncpwTJwwJeIKu6iBRJhBkMEw3eVF5+NyQgCg4qqwkNdN2Epg8Gbj66vCUgGgZaQo7CdiVgFN1kL2W\nPwwl4JcE/AbDUdpBYSuBmTPly0bw1gKLIhNg7SDehjIUdHHFo0fldlAQJOCBl+MHSgKW5byAXFSZ\ngMgOolaQSm2+LiUAAJ/4RO7PNm70tkStl0wA8E4CbMci8/Dp9a+sFHdUXiaLAe7nCajArxLwWh3k\nde2gsKqDeCQwY0buwMo+Y9ipRBTIzQSoHTQ6qrdEVLaCKEVNDenknZSAzoliQIEpgcFBcjHoDckq\nAbqmvegmZ2cM684EZHaQmwdeVyZw8cVAbW3uz5YuJVmBW3hRArQtIrjJBEQdC3ve3dhBXqqDolYC\nuiaL5cs8gRkz9FQH8ewgqgT8LCUNEOvn9GnSDnaJexFEgyNW5Rk7SIDqanKie3uzbZDKSvL62Fj2\nrkM8BJkJyOwgNyNIHXbQ1VcD//Ivap+nCi+ZACCvm/ebCQDZJKBqB7mpDmKvqVMwrALdJaJBThaj\n18CywlcCw8POJOCmOiioTKCkhFQu9vXJ8wAK+swaO8gDEglyYg4cyJZb7J66o6PyafjsjGFZJhCU\nHaQCHXZQTU1uHuAXQSsBpxJRFRKQKQF2U6Kog+EoS0TdVAfRMl6n9wWZCegqEaW+fTpNzllVFanD\n90sCQMYSSiScbZyaGnIcex8T22B4bGwMixYtwsqVKwEA/f39aG1txbx587BixQoM0D0EAWzcuBFN\nTU1obm7Gjh07/Leag5kzyXZzds+NWkKy8lBAPROIUgnosIOCQNiZgG4loCMTiKMdFGQmQN83NEQ6\nTNFuYUHNGNalBFg76ORJogDo4DGVyt3WkgfZeaMVQipKYMoUfhtjO0/giSeeQEtLCxJ/9Vfa2trQ\n2tqK/fv3Y/ny5WhrawMAdHZ2YuvWrejs7ERHRwfWr1+PcXYNXk2YORPYv19MAk4LconmCehYNkKm\nBNw88DrsoCDgVgnQTVZ0ZAIqwTAgD4bDWDZCBX6CYUrE7NLFbkpE3S4gB5DzNDgof4+qanP6HF4m\nYN8Vzq8dxCroREJ9YxldJFBTw7+HYqkEenp68OKLL+JLX/oSrL9ehe3bt2PdunUAgHXr1uGFF14A\nAGzbtg1r165FWVkZGhsbMXfuXOzZs0dT8zOYOTPXDgIyHYGsMgiQK4Eg5wmEbQcFAbeZQFkZOScy\nEmAXkZPtJxBEMByVHeRHCZSUkHZ72aLSSzAMkGvqRAKqqs2pfXYSmDyZfC57DWQLyLHPNAu2Osg+\neFLNBZzsoKNH1UlAtOhj7DKBBx54AN/5zndwiqmt6uvrQ+1fS05qa2vR19cHADhy5AiWLl167vca\nGhpw+PBh7nEffvjhc/9etmwZli1bptymmTPJTNgFC7Jfp0qgstKdHaQzE6CWg2XlBtNBkEDYdpBb\nJQCIb3iKMO2guMwTqKggHZGTdenUJvreMOygM2fk7wliUxl6ridNyj7vfquD7IMnHSRQVwe8/bZ6\nJiBTAqOjpG96661d+PWvdzk3TBGuL8nPfvYzzJo1C4sWLcKuXfyGJBKJczaR6Oc8sCTgFjNnAh98\nkGuD0BslkfCuBPxmAnRZYJ7cDqJENGw7yG0mAIj9TwqdJGBZ6juL+VlATkd1kFclwLaJquEgS0SB\naJVAZWVmgEdHxn7tIPvgSRcJ/OIXpD0f+Yj8OKKBEe2D6Cqk1167DNdeu+zczx955BHnRkrgmgRe\nffVVbN++HS+++CLOnj2LU6dO4fbbb0dtbS16e3tRV1eHo0ePYtZfN9qtr69Hd3f3uff39PSgvr7e\nV6N5oJOceHbQ4CB5sL1mAn7tINqOoaHcB6YQ7CAvSmDDhlzVxkKWCbitDqLv54WXdqsuqAXkVMCW\niLrd3Y3XpiBLROn7zpxRzwR0lohSO1G0UqyXBeSCUgJHj5JjLVokP46TEgjCCgI8ZAKPPvoouru7\n0dXVhS1btuC6667D008/jVWrVqG9vR0A0N7ejtWrVwMAVq1ahS1btiCVSqGrqwsHDhzAkiVL9H4L\niEmArQ7yqwT8kIBoETk3JMDb25aHKKqDZCTAG4X/7d9mAmIe2I3M/c4TEOUBQPzsIB1KgMLtfgJu\nSkQBNTsoKCVA7SDRsjBuq4MsK7hMQDUY/uQngW9+M/f1oEnA9zwBau089NBDWLNmDTZv3ozGxkY8\n99xzAICWlhasWbMGLS0tSCaT2LRpk9Qq8gqZEqAje9mDlUxmZhXL7CA3W/7x2mGHmwe+tJTcmCdP\nyqefR1Ed5NYOcoKqHaRSHSRb2yas7SVV4NcOspNxkAvIAe6rg3RnAuwzNT6e3X5VEpgwgbRpeDgY\nJVBbS0jgvPOcSaC6mkzmtCPWJHDNNdfgmmuuAQBMmzYNO3fu5P7ehg0bsGHDBj8f5QgnJVBdLe+I\naGYwPJw9grIrAaeNQUQQzRVw23lMm5bZWIKHdJoc02s7vcBJCfglASc7SFYddOyYOBQG4qcEvJaI\n8trkxg7ykwnEQQlQoqfjS1USADKW0MAAsW8oVEnAaSBSXg4cPOh9zR82E4iFHRRX+LWDAPLzkyez\nN6TWmQn4tYMA8v1OnBD//NSpzISXsBClEtBhB+maLBa1EvBrBwURDOtQArzJYlQJUBKw32OqC8gB\nGUsoCCUAEBXwl784KwERYpcJxBV0yzW7DcLOE3B6sMrLSQfLhnK6MgGRHeRVCYgQdigMZDovduIO\nC5kdI4LOtYNkSoA3WSyI7SVVoFsJqNpBJSXk2RkeDiYYDlIJsCXALAnYK9ZE8wSATIVQEJkAkFEX\nXpZpBzL3Y2+vIQEpSkqAxx/PXR1TdcYwQH7e3y8mAa8jGUAcDLt94KdOdSaBMPMAgHQgtASWB91K\nwG11kJMSYDef8bOAnM7JYl6yJ692EJDx94MIhnVmAuPjmQGdkxJwYwedPBlMiShASGDyZO8DSIB8\n354eQwKO+MpXcm8yt3ZQf3/2zRJHJSCzg8KuDKKQ5QK6MwG3wbBMCdhn2npZNsKy9AXDfpQAew0s\ny/0cFLckoBoM61QC9DslEnIlwJKAZcntuupqYqPy7CCV5aRVSMDvHgCGBHxAddkIgK8EdGUCuoNh\nEaKwgwB5LqBDCfDsILqMsdOyEU52lJdrzL6HjtxFi6ipQmeJ6OgoaY/qyLusjD+PRQbVyWI6lQA7\noldVAqkU+VzRZ7N2UFCZgNc8gIKSgO4NZYAiIAHWDvKSCdAlpmmHozsY9mIHyZRAFHYQIFcCOjIB\n3vaSY2OkoxN1vip2EPt7gDsSoDaSDisI0BsMuz2GFyWgumyETiXAnmtWCdivsX3yp+z6+MkExsaI\nKpENAOrq9JBAX18MS0TzAfRGGRlxnoXJUwKJRGbXMT+ZgE47aN8+8c+jsoOCVgK87SWdOhUVOwjw\npgRYG0mHFQToDYa9kMDIiLfqINmWpEErgSNHyL/t91gySQg6nXYmAWoHeckEVO6X667zPzCrqCDf\nx5CAB1DJODLizMa8TADI3pjG2EF86M4E2FVERZmAKgk4KQF6fWmnodpR0ffFVQm4ua/oefSiBGSr\nwISlBHhET+01FSXwpz+RUT17znSRwOzZ5I8fVFSQz5k82d9xeDB2EIMJE3KVAJC9ip+xg/iIIhNw\nquRxqwTSaVLlpDrHgiWBuCkB1fJQCnp+4l4dxJ5rdrIY7x6j96QKCXR3k7/Za6+yn4CfPsENKiqI\nCghi/k/Bk4DbYNieCQDZJBAHO0imBOJYHaQ7E6Alok7Xw00wPDTkfpVY2unqmCMA6F02wu0xvCiB\nKKqD7HaQqDoIyJxP2RwBgDwvvBWIdSkBHaAkEAQKngTclojKSMDrUtKAngXkgPjaQSIlMDZGarvd\nkqd9ATn2/TQMPntWjx1EVZrbB1q3HURHzYOD4dtBtCP3Uh2kqgS8DqLYeSEs4epSAtXVhATsz40q\nCXgdGLpBZaUhAc/wO08A0JMJyBaQ07lsRNyqg+h5dytjZfMEgExJo+x6TJiQCQZV7CA/JKDDDkok\nMj6238libu0gP5mAm2UjdCsBXXbQiRO5JFBeTnIi2ZIoRgnkAWjnq1oiGlQmIAuG3TysEyeS0bVo\nz+K4VQd57dBkmQBAOhcnEkgkyPkaGFALhr2SgC47CCDtlNW0q7QH8FYdBJBMxM17hofD3VRGFgz7\nIQH2b4pEwlkNGBLIA9B67uFh9QXkZHaQn0xAhx2USMhnDcetOsjJihFBlgkA5MFTWetm4kRyroJW\nArpIoKLCmxXEtgfwZgeVlblTbCphsu6lpN0oAXpPqthBAF9Bx4kEgpgoBhQBCZSUkIfBaTQIZORf\nUNVBOoJhQG4Jxa06yKkyRwSdJDAwIG+DfQcyVVBi1zVPACDn0SsJsETsxQ5ye2/T3y9UJQDEhwRm\nzHDentIrCn6eAEBugP5+NTuI/j4LXfME7EqA+o1uH3pZOBy36iAddhCvc6Z2kFOnTUngvPPEv8OW\niMZBCTjtHOfUHsCbHeT23lYJk4NQApRwWSXAU5yqJDBpEhksxpkEHn00uGMXvBIAyEXu71dTAvT3\nWQSlBOgMTbdrzojsILr1pNeRpB+INpsPKhOIix00NKQvGAb8KQG/k8W8lPHS98qOq0MJpNO5S3RQ\nJUAHU7ISURkJJBLEEoozCSQSwe0RUjQkoGoH0d9noSMT4AXDXm0E0XLS1AoKc0MZCtFm80FmAk7B\nMJB/wbCuTMDLZLG4KoFEggTWdNc8eq5phpFK8W1HVSUAEAKIcyYQJIqCBOgNoDJjGOCTgI4SUbsd\n5BZgyJkAABShSURBVHa0RiGyg6KyggC5EggiE6D7wkatBHTbQTqVQNCZgAoJ6FAC7HHs55pdG0xm\nBzmtGzZlSryVQJAoikyA3gBelQCtMNJtB7kdrVGIguGoKoMAcu6oP8siqEwgqGA4ynkCgLdzxbbH\n64xhP0og6Oog+hm8xeDYtcF4JKBSHQSQRfBmzcp93WlPAUMCeQJ6A6iSgP2G0TVPwK4EvNpBopVE\no6oMAsg54qkTryTALiAnygTcBMOqdpCbTqqykowSddtBXu08qlgBcr+6qSv3QgIq1UGlpWRei9+l\n2OnyE26UgOoCcgDw/PP8xdmKQQkUhR3kVwnoyAToMdjKj0Kyg8LOBFQmiwGZyXXFYgfRPQ7CKBFV\nsYMSiYwa8KsERCQgUwKqJFBdzSdfQwIFAtqpq5aI8uwgv5lASUluGWUh2UFhZwJu7CBATQn4KRHV\nZQf5CYbZPQ682EFBVAfRn6ss/a1yDPu5drKDVElABEMCBQK2mkAGusaN/eHRYQcBuZaQHztIVB0U\nNyXgJxNgA0U/1UH0eLLf8ZMJ6F42wk+JL22TlxLRIJQAELwSUAmGDQmIURQkMGmS2iJm5eXkZrH/\nHvVa/dzEQG447IcEeEpgYCDaTCDstYPcKIF8WEAO8KcE2DaFWSIaphKwEy5VArLJYk7zBGRw2lPA\nkECeYNIktQeivJxfSlZRQW4k2X62KrDPFfC6brxsnkDclIDXTIAGw6LdvqgdpBIM0/aJ4JUE2GUj\n4qIEvG5ME9SyEYBRAnFHUZDAxIlqHZGIBGgViN+LbZ8r4FUJ1NSQDt++vEAhZQIlJfLOQ6cdVCjB\nMNsmrwvIuYEbO0hnJqBaIsouIOc0T0AEQwIFAmoHOUGmBE6d0kMCbC29VxJIJolMPXUq+/Uo7SDd\nmQCQsYR0ZAJBlYjqtoOmTMmsaukFXu0gP5mAih2kshOc0zGMEggGRTNPQKUjWrgQ+Na3cl+vqCAT\nRvzuIDRzJnDsWOb/XktEgYwlxHb6cVUCfknA634CgLtgOOrtJQHggQf8vZ9VAmFVB7mxg/wogZGR\n3AUXWSXgZ9kIEZxIwM93iguKRgmoPBCVlcBNN+W+XlGhxw6aPZtsaE3htUQU4FcIFVImAGSTQBgl\non4WkIvD2kG0TXGzg2i+MzbmfSCVTJJn0D6ZzkkJDA4S29Trs2uUQIFA1Q4SobKSWC9evG0Wc+aQ\nvUwp/KxDz6sQimt1UFWVt2M62UG6qoMqKsjnpFLuSeD0aUJSfu8NXWA3UXJDJkuXurehVO2gZJK0\nqbTU+2zosjLyDNrJduJE4MMPxSRw4gS/4k8V1dXkGo+P84tCCoEEikIJVFd7D4aA4JSADjuIRRyV\ngC47yM9+AvRYIiQSpI1uc5/Kysye1FGs3MoDLWd2awc1NwOf/ay7z1KtDlKt5HI6xsmTuSTgpAR4\ne4a7QTJJPkOkBgwJ5Ak+/nHg2We9v5+Ocv1mAkHaQZZVXJmATjuI/p4XEhgZ0RcK64DXTMAL3GQC\nKtdKBhEJOM0Y9ksCAFHXAwP8nxkSyBOUlADnn+/9/fRhCiIT8KMEWDtocJDc9FHdkEFkAtRLDrpE\nFMhYfm6rg9jPiAO8ZgJeQM+9SnVQUCRAlQDvPquoyNhBfmBIwODcw+T3Yp9/PtDXl1la128mwCqB\nKFUAoH+eAEDeR+dV2P1YWnuuSwlQEnBzjZNJ8iduJDA0REjATw6mgkRCLW+jSiAIO8hJCZw+7c8K\nBgwJcNHd3Y1rr70WCxYswCWXXILvfve7AID+/n60trZi3rx5WLFiBQaYM7dx40Y0NTWhubkZO3bs\n0NP6kKBLCZSVkTXLjxwh//cj2e3BcNQkQJWAZWW/7tcOEvn+qqNQ1XWjvE4IrKyMnx108iQ5Z6Wl\nwX9eZ6fzPRy0EjhzhnTGvBJRwCgBJ3gigbKyMjz22GN455138Prrr+PJJ5/Evn370NbWhtbWVuzf\nvx/Lly9HW1sbAKCzsxNbt25FZ2cnOjo6sH79eox73U07AtCbyW8mAGRbQn7tIFYJRFkZBJAOh24B\nyMIvCYg6D/qaSjB8223Oy33QztPtAz1xYvyUwMBAeMQ0Z47z7+hUAvbvRbeOLSvLDecNCajBEwnU\n1dXh8ssvBwBUVVVh/vz5OHz4MLZv345169YBANatW4cXXngBALBt2zasXbsWZWVlaGxsxNy5c7Fn\nzx5NXyF40JVFdVxsXSQQNzsI4OcCfucJiJQAfc3pmpSWqhUFeAmGAXL94kQC1AcPOhR2A11KQFQi\neuIE/x4zJKAG32PbQ4cO4a233sJHP/pR9PX1oba2FgBQW1uLvr4+AMCRI0ewdOnSc+9paGjA4cOH\nc4718MMPn/v3smXLsGzZMr/N0wadJEDnCvgJ7+x20OuvA01N/tvnBzQXYHdo8psJDA76s4NU4SUT\noO+Lmx3U3x8vEtClBE6dAi66KPv1SZPI9+XNcShUEti1axd27dql7Xi+SODMmTO45ZZb8MQTT2Cy\nbW+2RCKBhKR4mvczlgTiBl0kMGcO8P775N9+SkRZO+jsWeCf/xn4f//Pf/v8gKcEdGQCMjsoDiQQ\nJyVQWUkGB3EiJh1KIJkUB8OpFH+gQZ8tHSTAVvWxiIIE7APkRx55xNfxPFcHjY6O4pZbbsHtt9+O\n1atXAyCj/97eXgDA0aNHMeuvOzfX19ejmzmLPT09qK+v99Pu0FFREa9MgLWDtmwBLr8cmD/ff/v8\ngFchFFQwrGoHqcJLiSh9X5w6XEoChagERMEwYOwgP/BEApZl4e6770ZLSwvuv//+c6+vWrUK7e3t\nAID29vZz5LBq1Sps2bIFqVQKXV1dOHDgAJYsWaKh+eGhslJ/JuDHDqJ75549Czz+OMBchsgQZiag\nGgyrgp7PQlECcSKBIKuD6GZRUZKArnswKnhq/m9+8xs888wzuPTSS7Fo0SIApAT0oYcewpo1a7B5\n82Y0NjbiueeeAwC0tLRgzZo1aGlpQTKZxKZNm6RWURwRRCbgxw5KJIgl9MILZLS9YoX/tvmFfQ9l\nIL8yAS/HixsJ0GA4alXIQpcSSKdzB02yuQp0TwqjBOTwdFmuvvpqYYnnzp07ua9v2LABGzZs8PJx\nsYAuEpg1i9Sj0yWI/VgJ06YBjzwC3Hefvx3PdIEu3csiqEwgCDvIy/HiaAelUvFTAl7Kb+3HAPgd\numypeNEeIW5Q6CQQg64jP6ArEygpAerrgZ4ePSTQ2wvcfrv/dumASAlEOVlMFYWiBOj3iBMJqC72\nJ4OMBGSzlum+4X5gSMAAgL5MACCWUFcXmV3r58GYNg245x7/Ix1d4CmBoDOBqElg6lTyJy6IIwno\nygQA90qgosKQgBPyPNIID7rsIICQwP795IH1E41s3Ag0NOhpkw4EoQREmYBuO4h2FG5J+dFH4xUM\nUhKIk0VFMwE/kxmjVAJTpoj3FCgEEjBKQBG67CCAzBWgJOAHCxZEP0uYhSgT8BoMy1YK1V0d5McO\nilMnEFclcPZsdJmAXxIoLRXvKWBIoIgQlBIoJNiVAF1HyGtHnQ92UNxAO/84kYCu6iAgGiUAiC0h\nQwJFBN2ZwP798XpQdcCuBPzkAUBmATlDAuqIsx3kd8YwwP9ekyaJ1eYddwAXX+z9cylqanK3cwUM\nCRQVdCuBP/85Xg+qDtiVgJ88AIgmE8j3BzqudlCQSkBmB331q8DMmd4/l6KQlUCMIq14o7JSXy3+\n7NkkZCo0ErArAT95ABD+2kE6jxcVSkrIeYsTCejaXhIQ20FBz5MxJGCA9ev1HaumBqiqiteDqgNB\nKIGwlo0oFBIA4jeBLYwSUftmRrphSMAA552n71iJBFEDcXpQdaC8PLMdJKAnEwhzATn2uPmMiop4\nDTCSSTIgCMoOmjGDdMZBwpCAgXYUIgnQdWsodCgBUedh7CAxKivjRQI6rhV9L+97ffWrRgn4gQmG\nI8Ls2fF6UHUgiEwACCcTKJRgGIifHURJ3K8SmDiRP7kymQz+uhkSMNCOOXPi9aDqQBCZABCuHZTv\nDzRQuEogyueFRwKWRZYfz3cLMc+bn7+48UbgyJGoW6EX9k1ldGQCgAmG3WLixHgNMHQqgajAIwG6\nl0CerYqfA0MCEeGqq6JugX7YN5UJUgmUlZGHr7TU+/Htn5VIFAYJbNoEzJsXdSsy0KEEdOwL4Ac8\nEkinC+N+MSRgoA12JRBkJqDbB04kgK1b42WjeMXChVG3IBs6lMAFFwB/+7d62uMFIiVgSMDAgIFu\nJSCzfMrK9D+An/uc3uMZEOhQAtOnA//jf+hpjxcUMgmYYNhAG8LMBKZMAfJ4o7qigg4lEDUMCRgY\nKCDMTCCZNCSQL9BdzhsFqqszewpQGBIwMLAhzEzAIH9QCEqgtJQs9XLqVOY1QwIGBjaEqQQM8geF\noASAXEvIkICBgQ1hZgIG+YNCUAIA2UvakICBgQR2JTA0ZEjAwCiBuMOQgIE22JXAv/878PGPez+e\nyQQKA4WiBAwJGBg4gFUCb74J9PcDK1Z4P57upSEMooFRAvGGIQEDbaCriFoW8L//N3DPPf52fCot\nJX8MCeQ3jBKIN/L8shjECXQxrf5+4Kc/Bfbt83/MCRMK40ErZhglEG8YJWCgFRUVwObNwKc+BdTV\n+T/ehAn5P4IsdhSSEmA3TSoUEsjzy2IQN5SXA08+SYhABwwJ5D907/0QFXhKoBDuTaMEDLSiooI8\n7Nddp+d4hgTyH8YOijcMCRhoRXm5/0CYRRCrhRqEi0Kyg1gSGB72tyxKXGBIwEArvvIV4O679R3P\nKIH8R6Eqge3bgU98Irr26IJ5vAy04qtf1Xs8QwL5j0JUAr29wC9/Cfzwh9G2SQeMEjCINQwJ5D8K\nMRj+4Q+BW28lS0znO8zjZRBrmHkC+Q+6F3S+k3l1NXDmDAmEn3oK+Nd/jbpFemCUQAyxa9euqJsQ\nG5x33i40NETdinggn+8L3QF/FOeipASYPBn4yU+AadOAxYtDb0IgCI0EOjo60NzcjKamJnzrW98K\n62PzEvn8sOvGpZfuwrx5UbciHsjn+yKZ1KsEojoXNTXAt78N/Of/HMnHB4JQSGBsbAxf+cpX0NHR\ngc7OTjz77LPYp2NNAQMDg7xAeXlhlFPW1ADvvw+sXRt1S/QhFBLYs2cP5s6di8bGRpSVleG2227D\ntm3bwvhoAwODGODXvwZmzIi6Ff5RUwN84QvEFioUJCzLsoL+kJ/85Cd46aWX8NRTTwEAnnnmGeze\nvRvf+973Mg1JJIJuhoGBgUFBwk83Hkper9LBh8BFBgYGBgY2hGIH1dfXo7u7+9z/u7u70WBKPgwM\nDAwiRygksHjxYhw4cACHDh1CKpXC1q1bsWrVqjA+2sDAwMBAglDsoGQyie9///u4/vrrMTY2hrvv\nvhvz588P46MNDAwMDCQIbZ7AjTfeiPfeew/vv/8+/u7v/i7rZ8U8h6C7uxvXXnstFixYgEsuuQTf\n/e53AQD9/f1obW3FvHnzsGLFCgywK1cVMMbGxrBo0SKsXLkSQPGeBwAYGBjArbfeivnz56OlpQW7\nd+8uyvOxceNGLFiwAAsXLsTnP/95jIyMFNV5uOuuu1BbW4uFCxeee032/Tdu3IimpiY0Nzdjx44d\njsePfMZwsc8hKCsrw2OPPYZ33nkHr7/+Op588kns27cPbW1taG1txf79+7F8+XK0tbVF3dRQ8MQT\nT6ClpeVcMUGxngcAuO+++3DTTTdh3759+MMf/oDm5uaiOx+HDh3CU089hb179+Ltt9/G2NgYtmzZ\nUlTn4c4770RHR0fWa6Lv39nZia1bt6KzsxMdHR1Yv349xsfH5R9gRYxXX33Vuv7668/9f+PGjdbG\njRsjbFG0+MxnPmP94he/sC6++GKrt7fXsizLOnr0qHXxxRdH3LLg0d3dbS1fvtz61a9+ZX3605+2\nLMsqyvNgWZY1MDBgXXDBBTmvF9v5OH78uDVv3jyrv7/fGh0dtT796U9bO3bsKLrz0NXVZV1yySXn\n/i/6/o8++qjV1tZ27veuv/5667XXXpMeO3IlcPjwYcyePfvc/xsaGnD48OEIWxQdDh06hLfeegsf\n/ehH0dfXh9raWgBAbW0t+vr6Im5d8HjggQfwne98ByXMjjTFeB4AoKurCzNnzsSdd96JK664Al/+\n8pcxODhYdOdj2rRp+NrXvoY5c+bg/PPPR01NDVpbW4vuPNgh+v5HjhzJqrxU6U8jJwEzSYzgzJkz\nuOWWW/DEE09gsm06YiKRKPjz9LOf/QyzZs3CokWLhHNGiuE8UKTTaezduxfr16/H3r17MWnSpBzL\noxjOx8GDB/H444/j0KFDOHLkCM6cOYNnnnkm63eK4TzI4PT9nc5N5CRg5hAAo6OjuOWWW3D77bdj\n9erVAAi79/b2AgCOHj2KWbNmRdnEwPHqq69i+/btuOCCC7B27Vr86le/wu23315054GioaEBDQ0N\nuOqqqwAAt956K/bu3Yu6urqiOh9vvvkmPvaxj2H69OlIJpP47Gc/i9dee63ozoMdoufC3p/29PSg\nvr5eeqzISaDY5xBYloW7774bLS0tuP/++8+9vmrVKrS3twMA2tvbz5FDoeLRRx9Fd3c3urq6sGXL\nFlx33XV4+umni+48UNTV1WH27NnYv38/AGDnzp1YsGABVq5cWVTno7m5Ga+//jqGh4dhWRZ27tyJ\nlpaWojsPdoiei1WrVmHLli1IpVLo6urCgQMHsGTJEvnBdAcYXvDiiy9a8+bNsy666CLr0Ucfjbo5\noeKVV16xEomEddlll1mXX365dfnll1s///nPrePHj1vLly+3mpqarNbWVuvEiRNRNzU07Nq1y1q5\ncqVlWVZRn4ff/e531uLFi61LL73Uuvnmm62BgYGiPB/f+ta3rJaWFuuSSy6x7rjjDiuVShXVebjt\nttus8847zyorK7MaGhqsH/7wh9Lv/81vftO66KKLrIsvvtjq6OhwPH4oC8gZGBgYGMQTkdtBBgYG\nBgbRwZCAgYGBQRHDkICBgYFBEcOQgIGBgUERw5CAgYGBQRHDkICBgYFBEeP/A2bpM52Z8q1+AAAA\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa2d578c>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I apply the scaler on the data (the default value for '`copy`' is True, so this should not perform this operation in place). This seems to also have scaled my original data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_scaled = scaler.transform(data)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 12,
"text": [
"[<matplotlib.lines.Line2D at 0xa50f8ac>]"
]
},
{
"output_type": "display_data",
"png": 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5/YrmAWLbmMosoNOnxbYBhcrD/t//DXz5y85tBJwtIJk6gNJSsk/e/D1sJ+XU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\nhgAMDAwMshT/H19KMwBH3BZsAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa4ac54c>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data_scaled)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 13,
"text": [
"[<matplotlib.lines.Line2D at 0xa81adcc>]"
]
},
{
"output_type": "display_data",
"png": 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5/YrmAWLbmMosoNOnxbYBhcrD/t//DXz5y85tBJwtIJk6gNJSsk/e/D1sJ+XU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\nhgAMDAwMshT/H19KMwBH3BZsAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa4fbdec>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td><strong>0</strong></td>\n",
" <td> 1.520276</td>\n",
" <td> 0.831525</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>1</strong></td>\n",
" <td> 1.492172</td>\n",
" <td> 0.362665</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>2</strong></td>\n",
" <td> 1.357706</td>\n",
" <td> 0.307650</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>3</strong></td>\n",
" <td> 0.717132</td>\n",
" <td> 0.947412</td>\n",
" </tr>\n",
" <tr>\n",
" <td><strong>4</strong></td>\n",
" <td> 0.017675</td>\n",
" <td> 0.995330</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 14,
"text": [
" A B\n",
"0 1.520276 0.831525\n",
"1 1.492172 0.362665\n",
"2 1.357706 0.307650\n",
"3 0.717132 0.947412\n",
"4 0.017675 0.995330"
]
}
],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Using numpy array: no problem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Defining data again"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset = pd.DataFrame({'A':np.random.rand(100)*1000, 'B':np.random.rand(100)})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now using numpy array from the pandas column:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = dataset.ix[:,'A'].values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 17,
"text": [
"array([ 969.82578354, 688.34865906, 835.42796273, 170.42231511,\n",
" 834.28653805, 644.50690432, 549.72274704, 910.18005832,\n",
" 868.35273129, 895.08607268, 931.22249141, 231.64433855,\n",
" 599.66616901, 211.32403034, 866.90746006, 33.72112595,\n",
" 968.14778516, 271.6604869 , 496.7796319 , 594.95957877,\n",
" 945.64610754, 759.65270186, 271.14290193, 286.95185972,\n",
" 579.781777 , 116.46599882, 992.389742 , 211.89977388,\n",
" 640.91062665, 278.45550984, 644.75757005, 824.71813304,\n",
" 80.72723087, 913.13370181, 642.29068392, 395.96973499,\n",
" 38.8569027 , 962.70335143, 904.88286268, 587.83528604,\n",
" 266.42849131, 254.24216292, 99.41943847, 690.84863267,\n",
" 806.38304505, 893.88010358, 253.21659874, 271.99006208,\n",
" 811.96170996, 879.02398544, 157.66153335, 100.5451206 ,\n",
" 360.05970207, 798.77638985, 345.51810914, 734.05025638,\n",
" 604.64113281, 971.98390733, 123.47018328, 299.96177011,\n",
" 473.41613391, 500.8465362 , 143.60814259, 411.73241935,\n",
" 964.041076 , 351.04423596, 488.85083644, 392.9642887 ,\n",
" 544.0322614 , 456.79519888, 512.16464041, 619.24346044,\n",
" 684.09466817, 654.78488641, 88.18028921, 907.17883057,\n",
" 156.34539802, 417.73022613, 394.43277461, 457.97758662,\n",
" 718.80408005, 904.00115726, 617.46367868, 167.63005695,\n",
" 689.78018946, 418.16335327, 308.6790114 , 457.85376206,\n",
" 49.26946377, 683.54997313, 200.08382084, 948.27213613,\n",
" 68.40674799, 57.93246442, 477.03266546, 604.00243972,\n",
" 304.73214896, 375.24680464, 480.4266416 , 683.81340467])"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 18,
"text": [
"[<matplotlib.lines.Line2D at 0xa83754c>]"
]
},
{
"output_type": "display_data",
"png": 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pdPxiAnT7VhXtoTUCQPR2UJgxgd27d2P37t3KjidFAh9++CGeeOIJHD58GOPHj8ff/u3f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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa7ea80c>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scaler = preprocessing.StandardScaler().fit(data)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_scaled = scaler.transform(data)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 21,
"text": [
"[<matplotlib.lines.Line2D at 0xa99fc0c>]"
]
},
{
"output_type": "display_data",
"png": 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2qgzyhgScA6moEtAZE6A3n7XpikolIEsCtA28TWHoQ9rb690e+nn74MaygyZM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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa6cf8cc>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data_scaled)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 22,
"text": [
"[<matplotlib.lines.Line2D at 0xab0826c>]"
]
},
{
"output_type": "display_data",
"png": 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IHD+eCHibj5WOBPDWW8Att6QG9fPy2ArArwXU1ZVYaIkFJwuop4f0B15toCgQ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\nABoaGhoZCk0AGhoaGhmK/w+ytwfzRijwtwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa992c8c>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Possible reason: check_arrays()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the code of chack_arrays(): https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py#L215\n",
"\n",
"you have something like this:\n",
" \n",
" array_orig = array\n",
" array = np.asarray(array, dtype=dtype)\n",
" if copy and array is array_orig:\n",
" array = array.copy()\n",
"\n",
"If `copy=True`, it will make a copy if this is not already done (`array is array_orig`).\n",
"\n",
"However with Pandas dataframes, `array is array_orig` will return False even though `array` is a view of the data and no copy:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset = pd.DataFrame({'A':np.random.rand(100)*1000, 'B':np.random.rand(100)})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.asarray(dataset) is dataset"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 24,
"text": [
"False"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset_numpy = np.asarray(dataset)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset_numpy.base is dataset"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 26,
"text": [
"False"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset_numpy.base is dataset.values"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 27,
"text": [
"False"
]
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**BUT:**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.may_share_memory(dataset.values, dataset_numpy)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 28,
"text": [
"True"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset_numpy.flags['OWNDATA']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 29,
"text": [
"False"
]
}
],
"prompt_number": 29
}
],
"metadata": {}
}
]
}
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