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Created July 22, 2014 18:30
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{
"metadata": {
"name": "",
"signature": "sha256:50dec51e2d2443c96fa76c4b031cb21c3753b9100182a59e6b2105e0c2c2fc79"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"PyMADlib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PyMADlib (https://github.com/gopivotal/pymadlib) is a Python wrapper to MADlib - the popular library of parallel in-database algorithms.\n",
"It currently has wrappers for 5 of MADlib's algorithms including:\n",
"1. Linear regression\n",
"1. Logistic Regression\n",
"1. SVM (regression & classification)\n",
"1. K-Means \n",
"1. LDA \n",
"\n",
"Refer [MADlib User Docs](http://doc.madlib.net/v0.5/ ) for MADlib's user documentation.\n",
"\n",
"We can employ it to push the heavy number crunching to MADlib, while allowing us to work with awesomeness of Python in the front end."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pymadlib.pymadlib import *\n",
"from pymadlib import example\n",
"from pymadlib.example import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"conn_str = '''host='{hostname}' port ='{port}' dbname='{database}' user='{username}' password='{password}' '''\n",
"#conn_str = conn_str.format(hostname='localhost',database='pivotal_test',port='5433',username='gpadmin',password='gpadmin')\n",
"conn_str = conn_str.format(hostname='192.168.241.161',database='pivotal_test',port='5433',username='gpadmin',password='gpadmin')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#PyMADlib is compatible with only MADlib v0.5, so we need to explicitly specify the MADlib schema.\n",
"#We have installed both MADlib 1.3 and MADlib 0.5 in this VM.\n",
"conn = DBConnect(conn_str=conn_str,madlib_schema='madlib_v05')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Linear Regression"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#View Documentation\n",
"mdl = LinearRegression(conn)\n",
"print(mdl.train.__doc__)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \n",
" Given train a linear regression model on the specified table \n",
" for the given set of independent and dependent variables \n",
" Inputs :\n",
" ========\n",
" table_name : (String) input table name\n",
" indep : (list of strings) the independent variables to be used to build the model on\n",
" dep : (string) the class label\n",
" \n",
" Output :\n",
" ========\n",
" The Model coefficients, r2, p_values and t_stats\n",
" The function also returns the model object.\n",
" \n",
" \n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Train Model and Score\n",
"lreg = LinearRegression(conn)\n",
"mdl_dict, mdl_params = lreg.train('public.wine_training_set',['1','alcohol','proline','hue','color_intensity','flavanoids'],'quality')\n",
"#Show model params\n",
"mdl_params\n",
"#Now do prediction\n",
"predictions = lreg.predict('public.wine_test_set','quality')\n",
"#Show prediction results\n",
"predictions.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>id</th>\n",
" <th>alcohol</th>\n",
" <th>mmalic_acid</th>\n",
" <th>ash</th>\n",
" <th>alcalinity_of_ash</th>\n",
" <th>magnesium</th>\n",
" <th>total_phenols</th>\n",
" <th>flavanoids</th>\n",
" <th>nonflavanoid_phenols</th>\n",
" <th>proanthocyanins</th>\n",
" <th>color_intensity</th>\n",
" <th>hue</th>\n",
" <th>od280</th>\n",
" <th>proline</th>\n",
" <th>quality</th>\n",
" <th>prediction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 32</td>\n",
" <td> 2</td>\n",
" <td> 12.08</td>\n",
" <td> 1.13</td>\n",
" <td> 2.51</td>\n",
" <td> 24.0</td>\n",
" <td> 78</td>\n",
" <td> 2.00</td>\n",
" <td> 1.58</td>\n",
" <td> 0.40</td>\n",
" <td> 1.40</td>\n",
" <td> 2.20</td>\n",
" <td> 1.31</td>\n",
" <td> 2.72</td>\n",
" <td> 630</td>\n",
" <td> 550.955896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 46</td>\n",
" <td> 2</td>\n",
" <td> 13.03</td>\n",
" <td> 0.90</td>\n",
" <td> 1.71</td>\n",
" <td> 16.0</td>\n",
" <td> 86</td>\n",
" <td> 1.95</td>\n",
" <td> 2.03</td>\n",
" <td> 0.24</td>\n",
" <td> 1.46</td>\n",
" <td> 4.60</td>\n",
" <td> 1.19</td>\n",
" <td> 2.48</td>\n",
" <td> 392</td>\n",
" <td> 711.149598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 40</td>\n",
" <td> 3</td>\n",
" <td> 13.62</td>\n",
" <td> 4.95</td>\n",
" <td> 2.35</td>\n",
" <td> 20.0</td>\n",
" <td> 92</td>\n",
" <td> 2.00</td>\n",
" <td> 0.80</td>\n",
" <td> 0.47</td>\n",
" <td> 1.02</td>\n",
" <td> 4.40</td>\n",
" <td> 0.91</td>\n",
" <td> 2.05</td>\n",
" <td> 550</td>\n",
" <td> 386.660786</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 12.17</td>\n",
" <td> 1.45</td>\n",
" <td> 2.53</td>\n",
" <td> 19.0</td>\n",
" <td> 104</td>\n",
" <td> 1.89</td>\n",
" <td> 1.75</td>\n",
" <td> 0.45</td>\n",
" <td> 1.03</td>\n",
" <td> 2.95</td>\n",
" <td> 1.45</td>\n",
" <td> 2.23</td>\n",
" <td> 355</td>\n",
" <td> 646.727206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 34</td>\n",
" <td> 3</td>\n",
" <td> 13.73</td>\n",
" <td> 4.36</td>\n",
" <td> 2.26</td>\n",
" <td> 22.5</td>\n",
" <td> 88</td>\n",
" <td> 1.28</td>\n",
" <td> 0.47</td>\n",
" <td> 0.52</td>\n",
" <td> 1.15</td>\n",
" <td> 6.62</td>\n",
" <td> 0.78</td>\n",
" <td> 1.75</td>\n",
" <td> 520</td>\n",
" <td> 539.247126</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 16 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" id alcohol mmalic_acid ash alcalinity_of_ash magnesium \\\n",
"0 32 2 12.08 1.13 2.51 24.0 \n",
"1 46 2 13.03 0.90 1.71 16.0 \n",
"2 40 3 13.62 4.95 2.35 20.0 \n",
"3 2 2 12.17 1.45 2.53 19.0 \n",
"4 34 3 13.73 4.36 2.26 22.5 \n",
"\n",
" total_phenols flavanoids nonflavanoid_phenols proanthocyanins \\\n",
"0 78 2.00 1.58 0.40 \n",
"1 86 1.95 2.03 0.24 \n",
"2 92 2.00 0.80 0.47 \n",
"3 104 1.89 1.75 0.45 \n",
"4 88 1.28 0.47 0.52 \n",
"\n",
" color_intensity hue od280 proline quality prediction \n",
"0 1.40 2.20 1.31 2.72 630 550.955896 \n",
"1 1.46 4.60 1.19 2.48 392 711.149598 \n",
"2 1.02 4.40 0.91 2.05 550 386.660786 \n",
"3 1.03 2.95 1.45 2.23 355 646.727206 \n",
"4 1.15 6.62 0.78 1.75 520 539.247126 \n",
"\n",
"[5 rows x 16 columns]"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Show Scatter Matrix of Actual Vs Predicted\n",
"from pandas.tools.plotting import scatter_matrix\n",
"import matplotlib.pyplot as plt\n",
"smat = scatter_matrix(predictions.get(['quality','prediction']), diagonal='kde')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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Rgb//e6SmDkKt3s/69f2pWbMmmZmZ+S6bvX37NikpKTRp0sTijkmCUs5ZDBkyBICP9ThV\nb/z48YSFhdGqVSsWLlyY+3hMTAyDBw8mOzubGTNm0Llz59yDBBMTExkxYgRDhgxBo9Hw9ttvExUV\nRVBQEJMmTQJg7dq1rF27Fp1Oxy+//EK1atVKHaNgGG3awMGD0L07xMfDhAlKRyQIpZeSksL27Qdw\ncrLDzc2ehw9DcXaOx93dvcDfNzdv3mTmzN9Rq1155ZVbvP562drlW2CyaNOmDQD+/v6lavjJsqqj\nRo0iNDQ0t83HZVWbN29OUFAQnTt3zi2r2r9/fwICAhgwYAA7duzAy8uLdevW0bt3b9566y00Gg3B\nwcHs37+/VHEJxtO4sXzMe9eukJ0Nn3+udESCUDrbtu1j3z4HtNqHjBnTj/j4eJo160P9+vULfE98\nfDw5OZ7Y29fn7t2ycbrFkwpMFs2aNSvwTSqVinPnzhXacH5lVR8ni8dlVYE8ZVWXLFnyVFnVfv36\nAf8rq/rw4UO0Wi1dunTBy8uLhQsX5immJCirVi35DsPfX15SW9aOexeeDc7ODmi1SVhZpVOtWjVe\neumlIt/TvHlzunaNIjb2In37Fv16S1NgstixY4deDSclJVGvXj1ArpoXGRmZ+5w+ZVVjY2NRq9Xs\n37+fzz77jD///JNXX31Vr1gFw6pWTS661KmTXExq7FilIxKEkunduyuenqexslIRF/eQiIgztGzZ\notCjPezs7Bg2rK8JozStApNFnTp19Gq4tGVVPTw8Ciyr+txzz5GWloafn7wCITAwkNDQ0HyTxfTp\n03P/7e/vX+rhNKF0atSQE4a/Pzg4wIgRSkckCMVnZ2dHx44dWLZsA8HBjlhbR/J//+dMgwYNlA5N\nMUWO35w4cYK2bdvi7OyMra0tVlZWuXcAhTFGWdV27drRvn373CGwiIiI3LuXf3uyrKpIFMqoXVuu\nlTFjBvz+u9LRCELJZWdrsLZ2AmzRaDRKh6OoIpPFRx99xIYNG2jYsCFZWVmsXLmSUaNGFdmwt7c3\nDg4O+Pn5YWNjQ5s2bRgzZgwAEydOZMqUKXTt2pUpU6YAMHz4cNavX4+fnx/vvvsutra29O7dm/Pn\nz+Pr60v79u3x9PSkRYsWODo6EhAQQFhYGH37lt3bvrKgfn35kMaRI+W5DEGwJEOG9KZnzyzefrsp\njRs3VjocRRW5Ka9169aEhYXRvHnz3L/oW7ZsyZkzZ0wSYGmIdezm59AheOMN+cBGb2+lo1GWJfbP\ntLQ0Vq36nYyMHN55p49FbzwTCqZXWVVnZ2eys7Np0aIFEydOzHNOlCAUl7+/XLI1KEguriRYllOn\nQgkJqcClS8+xe/cRpcNRRE5ODtu2/ZctW3aSmZmpdDgmV2SyeLz57fvvv8fJyYno6Gh+FwPQQim8\n9hpMmyYfRHj/vtLRCCVRpUplbG1votNFUqtWGS9UX4AjR47z66+J/PFHFnv2HFI6HJMrcDXUY49X\nRTk6OuZZYSQIpfH++3Ki6NlTHpoqxloJwQx4eXkxY0Y5cnJyCt2YVpbZ29shSWlkZqZjbd1E6XBM\nrsg5i7p16z79JpWKGzduGC0ofVnimPCzRJJg1Ci58NSuXWBvr3REplUW++exYyFs334SH59G9OnT\nvchSo2q1mqSkJNzc3CxmU61Go2H8+C+4dk3ixRc9mTp1lEWe/1SYUp0N9djp06dz/52VlcWWLVuI\nj483XHTCM0elgu+/lye8hw6V63xbyO8LIR86nY5Vq/bg4vIuW7dupGPHNri7uxf4+pycHL7++idu\n3MjGz68G777b34TRlp5WqyUlxY6WLcdx48YPpKam5tk/VtYV+RV1d3fP/alRowbjxo3jr7/+MkVs\nQhlmbQ3r10NsrLzDu4z9of1MUalUNGjgSXz8HipXVuWeulCQ+Ph4btzQUL36CI4fv4xOpyMjI8Ps\n77bs7e159dW2xMd/R48ejXBxcVE6JJMqchgqLCws95ZSq9USFhbG0qVLOXv2rEkCLI2yeJtfViUl\nyceC9O8PkycrHY1plMX+mZWVRVRUFNWrVy8yWWi1WpYt28Dp0zd4/fX2JCQks3//Jdq0qcFHHw21\nmGGpskivGtwBAQG5/7axsaFOnTp88sknNGrUyLBRGlBZ/DKWZTEx0KEDTJ0K776rdDTGJ/qnTKfT\nodVqGT78K2rW/Iw7dxYxf/47uLm5KR3aM0uvOYugoKCnHtu5cyc7d+5EpVIxQRQuEPRUrZq8Wa9T\nJ/DwgJdfVjoiwRSsrKxQqVR06tSEw4fn07Kl5zM1B2BpiryzGDhwIKdPn+blR9/gHTt20LZtWxo2\nbAjAtGnTjB9lCYm/3CzTqVPQq5dcz7uDZZcrLpTon3lJkkRaWhrOzs5iCEpheg1D+fr6smvXrtxx\nyNTUVHr27MmRI+a7i1N8GS3X7t1yWdb9+6GQkioWTfRP85eSkoJarX7mhsT0Ou4jLi4OW1vb3P9t\na2tLXFxcsS48fvx4/Pz8GDduXJ7HY2JiCAwMpEOHDrmnyqamptK7d286duzIunXrAHld85AhQ/D1\n9WX27Nl52liwYAG+vr7FikOwHD16wHffybu8z59XOhrhWXTr1i0mTlzKp5+u4vTpcKXDMRtFJouh\nQ4fSrl07pk+fzrRp03jhhRcYNmxYkQ0/WVY1JyeH0NDQ3Ocel1Xdu3cvX375JUBuWdXg4GBWrFiB\nWq1m+/bteHl5ceTIEY4ePUpsbCwA2dnZnD17tsiNP4JlGjAAvv1WThhP1MwSzFROTg5r1/7OnDkr\niYmJUTocvd24cZO0tGZYW/tz9mzZK49aWkUmiylTprB69WpcXV2pVKkSa9asYXIx1jjmV1b1scdl\nVZ2dnfOUVe3atetTZVW7du0K/K+sKsDKlSsZNmyYuJUvwwYOhLlz5XreFy4oHY1QmH/++Yd9+zK4\nfLkJv/66L89z2dnZFvc9bd68GbVqXcHR8QCdO7fN81xYWARz567m1KkwhaJTTpGroUA+prx169Yl\nathYZVXVajWHDx8uVk0NwbINGiRv1uvSRa6J8awfbW6uKlWqhK1tLDk5OZQv78DNmzepU6cOv/++\nix07wvH2rsHo0UMt5mgMNzc3Zs2SawE/OXqRlZXFDz/swtHxdX788Q+aNm2Ck5OTUmGaXLGSRWkY\nq6zqunXrGDhwYJHXF2VVy4bBg8HJCbp3h02bIDBQ6YiEf6tbty7Tp7/B5cuX2bw5jODgv+jfvzG7\ndoVSo8anRET8xIMHD6hSpYrSoRZbfkPcNjY2VKxoT1zcBdzdbfPM5T4LjLZOzRhlVdu2bcuVK1dY\nunQpL730EpGRkSxZsiTf64uyqmXHa6/Br7/KcxlbtigdjZCfWrVq4eLiQlZWHeztX+Tq1Rj8/Z8n\nOnoxjRs7lIlVRTY2Nkye/A4fflidKVPeeeaSBZIRjR07VvL19ZXGjBkjSZIkjR49WpIkSYqOjpYC\nAwMlHx8fad++fZIkSVJKSooUFBQktW/fXvr5558lSZIktVotDRo0SOrYsaP09ddfP9W+r69vvtc1\n8scSFBIRIUnVqknSnDmSpNMpHU3pmXP/1Gg00ooVm6SxY+dIJ0+eLtF709PTpYULV0uTJ38n3bx5\nU9LpdFJiYqKk0WgMEtvdu3elbdt2SVevXjVIe8LTCuubRe6zsERiHXvZdfs2vPoqNGkCy5eDo6PS\nEZWcOffPqKgopk3bRaVKQUjSZhYu/FTpkAB5496ECXNJSGiLg8Mp5s0bVeQZVELJ6bXPQhDMSa1a\ncOQI6HTg6ysnD8Fw3N3dcXPLICHhL5o1q6V0OLkkSUKr1WFtbYdOh9km27JM3FkIFkmS5L0Yc+bA\n0qXw+utKR1R85t4/U1NTefjwIbVq1XpqBVN6ejrnz5+nRo0aVK9e3aRx3b59m+PHI2jevCFeXs9e\npTpT0Ou4D0tk7l9GwXBCQuQ9GV26wIIF8sopc2fJ/XP27J84f94VZ+ebfPPNCHHwXxkjhqGEMuuF\nFyAiAtLToUULOHhQ6YjKttjYFJydG5GZaU96errS4QgmJO4shDJj+3a5tnfPnvLwlLn+0WvJ/fPa\ntWts336Mpk1r07VrJ3HkThkjhqGEZ0ZyMkyaBDt2wOzZ8i5wc/t9JvqnYK5EshCeOcePw5gxYGcH\nixZBmzZKR/Q/on8K5kokC+GZpNPBmjUwZYo8NDVrFpjDiRPG7p+XL1/hl1/20aBBFQYO7IONjdFO\n9TGaBw8esHLlNuztbXn33ddyz40TjEtMcAvPJCsreOcduHQJKlaEpk3h668hM1PpyIxr3bq9xMd3\nYv/+BK5ds8wjtvftO8bFi/UIDa3EyZOnlQ5HQCQL4Rng4gLz5sHJkxAaKu/+3rRJ3qtRFtWv70la\n2kmcnRNxd3c3SJvp6eksW7aed975lAULVhIfH2+QdgtSs6YncAFb2+tUruzO2bNnuXr1qlGvKRTO\nqMNQ48ePJywsjFatWrFw4cLcx2NiYhg8eDDZ2dnMmDGDzp07k5qaysCBA0lMTGTEiBEMGTIEjUbD\n22+/TVRUFEFBQUyaNImQkBAmTJiAlZUVbdu2Zf78+U9/KDEMJRTi8GGYMEGez1iwAF580bTXN3b/\n1Gg0XL16FXd3dzw8PIr9Pp1OV2AN7F279rFw4WmuXLGlbl13hg4tx9tv9ytRXDqdDpVKVawVVJIk\ncePGDWxtbTl79hKbN9/G2jqNSZO64eXlVaLrCsWnyDCUsSrl1alTh4MHD3LkyBHi4uI4L2pvCiXU\nqROcPg0jR0LfvvDmm3DrltJRGY6NjQ1NmjQpUaLYvftv3n13JosX/4xGo3nq+UqVXLC3z8DKKgob\nm/u4u7uUKKaoqCjGjp3DJ5/My614WRiVSkX9+vWpVasWDx4kYW1dF7Xak6SkpBJdVzAcoyULY1XK\n8/T0xM7ODpDrgVvi5J2gPCsrGDoULl+GRo2gVSuYPBkelU955mzdegJPz7GEhqZy7969p55/4YW2\nfP31ABYu7M6cOT3o2bNzoe09+ddpeno6P/+8hfj4djx44E1Y2NkSxfbyy4G0aRNDjx52eYqwZWdn\n8+23Kxk58itCQyNK1KZQckZLFvlVuXtMn0p5j507d44HDx7QuHFjY30E4Rng7AzTp8O5cxATIyeO\n5cvhiS76TOjQoTH37q2kdm0dlStXfup5lUpF06ZNefnll/H29i6w6p0kSWzevJ13353Bhg3bkCSJ\nRYt+4fRpiYiIFdjYnKBJkwYlis3d3Z3Ro4cwdOjr2Nvb5z5+/fp1zp2zxt5+IH/8cbRkH1goMaMl\ni9JWyvv3Y09WynvcRkJCAqNHj2bVqlXGCl94xlSvLi+z3bkTfvlFLuG6f7/SUZnOkCGvMW/eUKZO\n/SDPL+TH1Gp1seZZMjMz2bXrHFWrfsqePRdIS0vj3r0katXqScuWTfnss/7UrVvXIDFXq1aNihUf\nkJi4jTZt6hukTaFgFlUpr127dmg0GgYPHsy8efPy/QvosScr5R06dMhYH1MoY1q3hkOH5LuNESMg\nKAguXlQ6KuNTqVR4eHjkDvE+6dixED744BtmzFhCRkZGoe04OjrSrFkVoqN/wsvLHWdnZ0aO7EOd\nOiG89ZYPDRo8fVeRnZ1NZGQkiYmJJYrZ1dWVWbNG8fXXb/Dqqy+V6L1CKehRVKlIxqiUt2HDBsnD\nw0Py9/eX/P39pRMnTjx1XSN/LOEZkZUlSfPmSZK7uyR99JEkPXhgmHYtrX9OnrxYGjfutjR06M/S\nP//8U+Tr1Wq1FBMTI6nV6jyPx8XFSQsXrpFWr/5NysrKyn183rwV0pAhq6WxY+dIqampBo9fKL7C\n+qbYwS0IRXj4EL74Qt6b8fnn8NFH8rLb0jL3/ilJErt2HeDQofO89FJrcnLUbNoUSuXK1kyd+l6p\nd1MvX76ZY8cqk5Nzn9Gj6+SONowa9TW2tv1JStrO11+/QbVq1Qz5cYQSEDu4BUEP7u6weDEEB8O+\nffJR6Pv2KR2V8aSkpPDrr6HodP1Zt+4QgYEdWbjwfb78cjQVKlRAq9Vy8eJF7t+/X6J2q1SpiFZ7\nHVvb+1SsWDH38ffe64md3VasrO6yadMesTzWTIk7C0EoAUmST7QdN06eBJ8/H2rXLlkb5t4/1Wo1\n06Yt4e5P5kB9AAAgAElEQVTdCjz3XCZTp47Ks5Fu06Y/2bUrDgeHRKZPH1zsOwGtVktkZCTOzs7U\nr593QvrPP//Lli3yf5O+fVX06SPmIJRQWN8UmxQEoQRUKnj5ZejaFebOhe+/l/9vWWJra8uUKe8R\nHR1NrVq1ntpxffNmHI6OrcnIOMfDhw8LTRaSJKHRaEhMTESSJJo3b57v66pW9UClOv7o3+0N92EE\ngxF3FoJgYpbeP2/evMnatbupWdONIUNexdbWlnv37hEWdg4vrwbUq1cPgKysLObPX82pU1fIylJT\ntWodxo3rRosWTycM6dHxHgD16tUTRZUUIo4oFwQzUtb6p06n4+OP55GQ0BpHx1DmzfuQcuXKcfny\nZb766ihJSU24enUz7dq9QlBQOn37BikdslAAMcEtCEKp3bt3j2PHjhW6D0Kj0WFj44RGIycPgBo1\nalCtWgqOjvtp1kxHjRr/4Ovb1lRhCwYm7iwEwcQsqX9mZGTw6aeLSUlpTM2aN5g1a1y+Q0S3bt3i\nyJFwvL0b8fzz/zsVNicnh/T0dFxdXcXQkgUQE9yCIJRKdnY2GRkqypWrR0LCeSRJyveXfu3atamd\nz7IwOzu7fHeFC5ZH3FkIgolZWv88efI0ISGX6NatLU2aiIM7yzIxwS0IZkT0T8FciQluQRAEQS9G\nTRbjx4/Hz8+PcePG5Xk8JiaGDh06UK5cOezt7dHpdKSmptK7d2+aNGmCm5sbIJeH7NixIxUqVKBJ\nkyakpqYC0LdvXypUqEDNmjW5e/euMT+CIAiCgIJlVb/66isuX76Mk5MT8L+yqi1btkSj0aBWq/nj\njz+4e/cuSUlJODk5MW/ePMLDwzl+/Dj37t2jXbt2jB8/3lgfQRAEQXhEsbKqnTp1onr16lhbW+eW\nVQXo1q0bzs7OXLp0iT179tCmTRusrKx49dVX2bdvH0eOHKFKlSo4OzszaNAgIiJEOUVBEARjU7ys\nqo2NTW4J1a1btzJ48ODcxx4+fJh7OmXlypVJTk7m/v37ecqvqtVqY30EQRAE4RGj7bMobllVjUaD\nq6sr2dnZtGjRAltbW7RaLa6urri5ueXuGo2Li8PFxQVPT8887dra2j517RYtWogNQILZ6tSpk+if\ngllycXEp8DmjJQsfHx+WLVtGv379OHDgAG+//Xbuc4/LqjZr1gytVku5cuWoWLEiGzdu5MiRI8TF\nxbFp0yZ69uzJxIkT0el0bNu2jV69etGpUyfmzp1Leno6GzZswNvb+6lrnz17VixNFMyWWDormKvC\n/ogx2jCUt7c3Dg4O+Pn5YWNjQ5s2bRgzZgwAEydO5PPPP6d69epotVq6d+/O2LFjqVu3LqmpqdSr\nV49Zs2bxyiuvULVqVVxdXUlPT+eTTz7B29ubF154gapVqxISEsL8+fON9REEQRCER8SmPEEwMdE/\nBXMlNuUJgiAIehHJQhAEQSiSSBaCIAhCkUSyEARBEIokkoUgCIJQJJEsBEEolZQUuHAB7t4Fsbir\n7BPJQhBKQa1Ws3nzdpYu3cDDhw+VDsek/vtf8PeHatXgtdfA2xvq1oWvv4b0dKWjE4xFJAvBbEiS\nZDH7DyIiIti+PZmTJz35/fd9SodjEhkZMGgQjB0Lo0ZBfDxcugSxsbB1K5w5Ay1awOnTSkcqGINI\nFoJZSEtLY+bMH/jgg1mcPfuP0uEUqUKFCtjYxKPR3MHNrbzS4RhdcjIEBIBKBWfPwhtvgL29/JxK\nJd9dbN4Mc+ZAz56wY4ey8QqGJ3ZwC2YhLCyM7767iotLK6pWDWby5OFKh1Sky5cvk5aWRosWLbCx\nKf4xa5bWP7OyoHt3aNoUvv9eTg6FOX0agoJg/Xro0sU0MQqGYZY7uO/du0erVq1wdHREp9Nx8+ZN\n/Pz86NSpE4MGDUKn0wGwfv16OnToQO/evXMr5f3999+0b9+ewMBAUSmvjKhduzaurrdJTd1Ou3YN\nlA6nWBo1akTr1q1LlCgs0dix4OEBixcXnSgA2raFLVtg4EB5AlwoIySFZGVlSYmJiZK/v7+k1Wql\nxMREKSUlRZIkSZoyZYq0Y8cOKScnR/L19ZW0Wq20efNmae7cuZIkSVJAQICUlpYmhYSESB9++OFT\nbSv4sQQ9pKenS3FxcUqHYXSW1D83bJCkBg0kKTm55O9dtUqSGjeWpNRUw8clGEdhfVOxOwt7e/s8\nNS5cXV1ziyXZ2tpiY2PDtWvXaNasGVZWVrnV9jIzM3F0dMTZ2Zl27doRGRmp1EcQDMzJyQkPDw+l\nwxAeiY2V7yo2b4ZH9cZK5O23wccHHh02LVg4s5vgjomJYd++fXTr1o3ExMTcqngVKlTIrahX4Yme\n+2TVPUEQDGfCBPkXfj4lY4rtu+/g779hzx7DxSUow6wGW7Ozs3nrrbdYsWIFVlZWuLq65lbFS0lJ\nwdXVNU8FPgBra2ulwhWEMuvAAThxAs6f16+d8uVh+XIYPlxuq3zZXzhWZplFspAezb6///77fPjh\nhzRu3BiABg0acP78eXQ6Hfv378fHxwcnJycyMzNJT08nMjKS559/Pt82p0+fnvtvf39//P39jf0x\nBKFM0Ong00/lZbBOTvq317UrdOoEX30lb9wTLJTppk7yUqvVUufOnaWKFStKXbp0kQ4fPiyVL19e\n8vf3l/z9/aVt27ZJkiRJ69atk9q3by8FBQXlToDv379f8vHxkQIDA6U7d+481baCH0swUzqdTtJo\nNLn/+/79+1JSUpIisZh7/9y4UZLatpUknc5wbd69K0lubpJ07Zrh2hQMr7C+KfZZCGVeWloa8+at\n5u7dJD74IIiMjCxWrjyOg4OGKVMGUrNmTZPGY879U62GJk3gp58gMNCwbX/1lbwHY+tWw7YrGI5Z\n7rMQBFO5du0aN2644+g4kF27TnHu3A3s7QNJS2vI7du3lQ7PrGzcCLVqGT5RgDxhHhEBR48avm3B\n+ESyEMq8WrVq4eYWTUrKH7z4YmN69HgRR8d9NGgQQ9OmTZUOz2zodPI8xWefGad9BweYNg2mThWn\n1FoiMQwlPBOysrLIyMigUqVKSoditv1z5074z38gLKx4O7VLQ6MBLy9YuhQ6dzbONYTSK6xvimQh\nCCZmrv3T1xc+/BAGDDDudTZuhEWL4Phx4yUloXTEnIVgMqmpqURERJCYmKh0KEIJhITIRYz69jX+\ntfr3h7Q02L3b+NcSDEckC8FgdDods2evYsGCy8ycuYLs7GylQxKKackSuUaFKc5EtLKCKVPk1VGC\n5RDJQjAYrVbL/fupuLq2JTFRR1ZWltIhCcXw8CFs3y4f7WEqfftCTIxYGWVJRLIQSkSSJBISEsjJ\nyXnqOVtbW0aM6ImHxwGGDeuAi4uLAhEKJbV6NfTpA25uprumjQ1MnAjffGO6awr6ERPcQols3fpf\n/vzzPFWr2jB16vs4OzsrHVKJhYSEcvFiFF26vEiNGjVMfn1z6p86HTRsCL/8Ai++aNprZ2VBvXry\n3EXz5qa9tpA/McEtGMzRoxdxdx9CTEz5PIWnYmNjmTbte2bM+IGHDx8qGGHh7t+/z48/HubIkWos\nXvyb0uEobt8++fjxF14w/bUdHGDcOHF3YSkMMp119+5doqKi0Gq1SJKESqXCz8/PEE0LZiYoqB1r\n1/5E48aVqV27du7jwcGniIryQpI0nDgRSu/ePRSMsmC2trZYW2vJykrEyclO6XAU99NPMGKEcktY\nP/hAvru4fh3q11cmBqF49B6GmjRpEps3b8bLyyvPceE7iqjYfu/ePXr16sXFixdJT0/HysqKuXPn\nsn37dmrXrs2aNWuwsbFh/fr1/PDDD1SqVIkNGzZQvnx5/v77b6ZOnYqDgwPr1q2jevXqeT+UGd3m\nl0UajQZra2tUT/yGCQ8/w+LFfwM6Pv74JZo2zf80YEmSyMnJwd7e3kTRPu3atWvcvHmL1q29Fdmk\nZy79Mz5e/gV96xYoOb00eTIkJ8srsgRlFdo39T2lsEGDBlJWVlaJ3/fvsqqxsbFSz549JUmSpNmz\nZ0u//fabKKuaD51OJ2VnZ5f6/Tk5OZJarTZgRJKk0Wik8PBw6eDBg1JMTEyBr1Or1dL8+SuloUNn\nSFu27CzwdVqtVrp8+bJ0//59g8ZpLsylfy5eLElvvql0FJJ0754kubpK0jNQUdfsFdY39Z6zqF+/\nfr4rY4ryZFlVSZIIDQ3NrTnxuISqKKuaV2ZmJjNmLOGDD2YTHHy8xO+/fv06Y8bMY8KEb4mOjubq\n1at89906Dh8+pldc+/Yd5ttvw1m5MpL79+8X+LoHDx5w5kwq1atPYNeusAL/gtm2bTeTJ++kV69J\nfPXVIjIzM/WKT8jfmjXw1ltKRwFVqsAbb8D33ysdiVAYvecsHB0dadmyJZ07d84dWlCpVCxatKhE\n7SQnJxdaQlWUVYWoqChu3CiHm9ur7Nr1O35+7Uv0/hMnzqFWB5CRkcGZM5Hs23eG7OweRETsp3Hj\n5/D09Cx2W5IkERUVRcWKFUlMTEGlqopWm0pSUkqB73F3d6dRI3suX15M165N8wxjPen69fvcu1eN\nxEQ4ejSdwMCzvGjqpTpl3PnzcP+++ZzP9PHH0KGDvJzWAhfYPRP0ThYvv/wyL7/8cu4XX3o0wV0S\nKpUKFxcXoqOjgfxLqJa0rGpZrJRXs2ZNqlb9L/fv/0L//m1K/P62bb0IDv4DJycVzZq9SUTEda5c\nuU65chocHR3zvDYnJ4fDh49ha2uDr2/7p/47z5+/lB9/DMPFJYsVKz4lPf0yDg52+PgUvKzG1taW\nSZPeIy0tLU/C/7d+/QIJD1+EVptO5cp1qVy5cok/q1C4n3+GIUPAXKoSN2wIfn6wciWMGaN0NEK+\nDDHOlZWVJZ07d046d+6clJOTU6L3+vv7SxqNRoqNjZV69eolSdL/5izUarXk5+cn5iyekJOTIyUl\nJUm6UpYxy8jIkDIzMyVJkqTk5GTp+PHjUnR09FOv+/PP3dKbb26Q3nxzlXTkyLHca1++fFlKSkqS\nOnd+V2rW7JhUt+4MaePGjaX/QAXQarXSpUuX8o3N0indP9VqSapaVZIuXlQ0jKecPClJtWtLUgl/\nhQgGVFjf1PvO4tChQwwbNix3GeXt27f5+eef6dSpU6Hv02g09OjRg7Nnz9KjRw9mzZqFn58fvr6+\n1K5dmwkTJmBjY8N7772Hr69v7moogClTptC1a1ccHR35+eef9f0IFsXW1jZ3Z3RiYiIbN+7C0dGO\nAQOCnro7yM+Tr6lQoQI+Pj75vk6n06FSWSNJ1uh0OgCWL99MSEgmFSsm8frrL7J48UJq1rTDz+9d\nA3yyvKysrGjUqJHB2xXkvRW1asGjUvdm44UXoE4d+PVXGDRI6WiEf9N76WyrVq3YuHFj7hf7ypUr\nDBgwgPDwcIMEWBrmsjTR2Nav38qePU5otSkMH16ZgIDCE3RJZGVlsX9/MLa2NnTu7IeNjQ1jxsxB\npepHUtJfzJz5Mi4uLjg4OCi6DNYSKd0/hwyRfzF/9JFiIRRo1y74/HM4c8Zyji+/dg1WrIC//4Y7\nd+TNhi1bwptvwuuvm89QX3EYdQe3RqPJ8xdgw4YN0Wg0+jYrFIOHhys63W2sre9TqZKrQdt2cHAg\nKKgb3bsHYvPoKNKhQ7tib/8n3bvXoEaNGri4uIhEYWEyM+UiR6Y4irw0XnpJPoJk716lIylaejqM\nHw8+PnLM8+fLhaP27oVXX5Vrdnh7y3XHywK97yzefvttrK2tGTx4MJIksX79enQ6HatWrTJUjCWm\n9F9upqLT6Th//jx2dnY0atSoxAsLBGUo2T//+AN++AH271fk8sWybp18uOHffysdScFu3JATQtOm\nsHAheHg8/RpJkofURo+Wj2MfPtz0cZaUUSvlZWVlsWTJEo4dk9fq+/r6MmrUKEX/4nxWkoVgmZTs\nn2+8AV27wnvvKXL5YlGr5Z3lv/8ObdsqHc3TLlyQ/xt+/rlcWbCov9GuXoVu3eRzsMaONU2MpSXK\nqgqCGVGqf6alQfXq8l/FpjyOvDQWLpTLrv76q9KR5HXpEgQGwty5JZuEv3ULAgJg+nQYOtRo4enN\nKMmiX79+/PbbbzRt+vTmKpVKxblz50rTrEGIZCGYM6X658aN8hDPrl0mv3SJpaVB3bpw4gQ895zS\n0cgePJCPcZ86tXSFoi5cAH9/2LpV3oBojoySLGJiYqhWrRq3bt16qnGVSpXnRFJTE8lCMGdK9c8+\nfeTVOeb8l+2Tpk6VDztculTpSOTaG507y7/sZ80qfTu7d8O778qrvfKb51CaUYehJk2axOzZs4t8\nzJREshDMmRL9MykJateG27eVPWG2JOLioFEjeeinBCfRGMW4cfJ/uy1b5Bri+pg0Sf5M27aZ3/Jg\noy6d3ZvPGrddlnCfKwjPkG3b5LF2S0kUAJUrw4ABsHixsnHs3i2vIlu5Uv9EATBzJkRHw/Ll+rdl\nSqW+s1i6dCk//PAD169fp/4TVUtSU1Pp0KED69evN1iQJSXuLARzpkT/7NFDHmfv39+kl9XbtWvy\nPoabN6FcOdNfPy5O3mC3YYM8BGUokZFye+fOQdWqhmtXX0YZhkpOTiYxMZHPPvuM2bNn516gfPny\nuCm81EIkC8Gcmbp/PnwoTxLfvWuZJ7q+8Qa0by8PBZmSJMHLL8t7Kb7+2vDtf/65nAQ3bTJ826Vl\n1DmLEydO8Pzzz+eeIpqSksLFixd5QYmivo+IZCGYM1P3z2XL4OBB8/qlVBKnT8sT89evg62t6a67\nZIlc8+PYMbAzQgXejAw5ES1dCt27G7790jDqnMXIkSMp98T9obOzMx988EGp2srOzqZPnz4EBATw\nyiuvkJOTw9y5c/H19WXw4MG5x4isX7+eDh060Lt3b1JTU/X9CIJQpm3aJI/9W6q2baFBA3nZr6lE\nRsp7IjZsME6iAHBykudjxoyRNyKaOwNM18gnhD5mbW1d6oJEu3fvpm3bthw8eJB27dqxceNGDh06\nxJEjR2jevDnbtm1DrVazbNkyjhw5wpAhQ1i2bJkhPoJetFot0dHRZGdnKx2KIORx7x6cPSvPWViy\nmTPlX96mKJqYlQUDB8I338hJyph69pRPAP7pJ+NexxD0ThZ169Zl0aJFqNVqcnJy+O6776hXr16p\n2nJ3dycpKQmQj9++ffs2AQEBQOGlVpX2448bmDJlK7NmLStViVlBMJbffoPeveWTUC1Z+/bQpo1p\nVkZ9/rmcJN55x/jXUqlg3jyYMQOSk41/PX3onSx+/PFHjh07RvXq1alRowYnT57kp1KmSR8fH8LD\nw2natClhYWE899xzlC9fHii81KqSJEkiLOwGVasO4dYtDYmJiYrGIwhPsvQhqCd9/bV8zEZCgvGu\nsWePfCbVTz+Zbg9EixbQq5dxJtENSe/iR56enmzevNkQsbBu3Tp69erFxx9/zLfffotarS60rOrj\nx/JjqrKqKpWK/v39+P337wkIaISHgbZlPh7SEkeAC6V16xZcuQJduigdiWE0aiRPdM+cCQsWGL79\n2Fh5efH69VCpkuHbL8zMmdC8OYwaJQ9LmaNSJ4vZs2czadIkRo8e/dRzKpWKRYsWlbjNlJQUKlas\nCICbmxtRUVGcOnWKTz/9lP379+Pj40PDhg05f/48Op0u97H8PJksjK179wC6dw8wWHs3btxg7tzN\nWFmp+PTTAdSpU8dgbQvPjl9/lY/RNuUKImObOVNeQTR0qFwrwlB0Ohg2TB56CjDcV7nYqleHESPk\nz2eum/VKnSy8vLwAaN269VPPlbauwuDBg+nfvz/r1q3Dzs6OzZs389NPPxWr1Kq5yMrK4vvvf+HG\njTiGD+9Jq1YtS9xGaGgk2dm+SJKO8PBIkSyEUtm8GRQ8dccoPDzk4ZoRI+RDBg1VhW7BAkhJkSfR\nlfLJJ9CwoXwciLkcnvgkcUS5gUVGRjJ79mkqVuxEhQq7mDnzw0f1rFXFTqLizqJsM0X/vHYNOnaU\nN+JZUlnP4pAk6NRJHpIyRH2IEyfkQxZPnZJrgCvpiy/k/SRr1ypzfaNsyuvdu3eBF1CpVGzfvr00\nzRqEkskiMTGRL75YQVKSLa+80phmzRowf/6vlCtnx6efDsPd3b1Y7Yg5i7LLFP1z1ix52ez33xv1\nMoq5elVeIXXggDzWX1rR0XI98uXL5WWsSktOlldiHT4MTZqY/vpGSRaHDh0CYOvWrdy/fz+3rOrG\njRvx9PRk4cKFpQ5YX0rv4E5PTyclJYUqVaqwYsWvnD79HJmZcbzzjiMBAf6KxSWYB1P0z2bN5J3B\nHTsa9TKKWrtW3gtx6lTpzo1KT5fPZ+rbVx76MRfffAMREfIwoqkZ9biP1q1bExYWVuRjpmTIL+PD\nhw+Jjo6mYcOGODk5lfj94eFn+P77Pdjbw+efv0ktc13qIJiMsZNFZKS8Ce/WLcOckmrOhg+XVzFt\n3Qo2JZiBzcqS95/UrCmfJmtOR4Wnp8tlZffu1e+uqTSMmiyaNGnCzp07c0+evXHjBr169eLixYv6\nNKsXQ30Z09LSmDz5B5KSquPllcakSe9z6dIlUlJSaNWqFbbFXGaSlJSEjY1NnmNRTOHOnTvExcXR\ntGlTMZxlRoydLP7zH/kXzrffGu0SZkOthqAgeTXR8uXFm59JT5cPJ3R2lqsHmuOczoIF8lDUtm2m\nva5Rz4ZasGABAQEBdOrUiU6dOhEQEKDoEJQhpaWlkZJii4tLO27fjufy5ct8881eFi26wh9//LfY\n7bi6upo8Udy7d48ZMzaxcOFF1q7datJrC8qRJHn4wtKOIi8tW1t5E93t2/JnLuqouFu35MlxT095\nP4U5JgqADz6QD1BUcIDmKXonix49enDlyhUWLVrEokWLuHLlCt3N5QhFPXl6evLGG82oXPlvRozo\nTVpaGhqNC9bWVUlMTFM6vEIlJyeTnV0BB4dGxMQkkpKSwuXLl8VxJGXc2bPyX9tt2yodiemUKwd/\n/QWurvLei5075aT5pMxMWLhQPjJkwAB56Mmc9584OsrHjii5lPff9B6GSk9PZ/78+dy+fZvly5dz\n9epVLl++TFBQkKFiLDFj3eZrNBq2b99LfHwKr73WVfG6HYXR6XRs27abmzdj6d27A8uWbefhw4o0\nbw4ff/yu0uE904w5DPXZZ/L4u7kfHWEsO3fC5MnyUFOnTnJlwFu35CPa/f3hyy/h+eeVjrJ4srLk\nlVG//w7t2pnmmkads3jjjTdo3bo1a9euJTIykvT0dNq3b8/Zs2f1aVYvxvoynjoVxrVrd+jc2QfP\nfIoCS5KEJEl5TuE1B/fv32fSpI24ufUjLW01y5ZNLfXGSUF/xuqfkgT16snj3C1aGLx5iyFJcgW6\nkBB5o1316nKiMKeKdMX144/y/z937zbN9Yw6Z3H9+nUmTZqE3aND350tsRTXE5KTk4mIiHjqgMLo\n6Gh++CGYvXtdWbp0y1PvefDgAf/3f4sYNeprIiMvmDLkInl6ehIU1AAbmy28/XYPkSjKqFOnwN7e\n9CtozI1KJSfL99+Xd0W/+aZlJgqQjx+5dEkuwKQ0vQ8StLe3J/OJQ+avX79usStvtFot06d/z507\nFalb9wBffz0Om0fr8WxsbLCy0qJWZ2Br+79ZsT17DrJp0ynU6rtoNO1xc+vK7t2neP55L6U+xlNU\nKhX9+gXRr5/SkQjG9HhiW/wtUHbY2cHUqTBtGuzfr2wset9ZTJ8+nR49ehAdHc3AgQMJDAxkth4H\n0qxdu5YuXboQGBhITEyMSSvlRUdHs3v3WSIjHxIRcSlPMaMqVarwySc9efNNG0aOfCP38eDgSFxd\nB5KT0xArq3DS03fSrl0jg8X0mFar5bffdjJ//hqio6MN3r5g2XQ6+eDAZ2UV1LNk2DC5Vvfhw8rG\noVey0Ol0JCYm8vvvv7N69WoGDhxIaGhobsGikrp79y7BwcHs37+fv//+GxsbG5NWyouPj6d27Y6U\nL9+E+vWrPTWk5uXlRc+e3aj0xPnFLVtWIypqAU2a6Fi0aCLz5w/H1zf/k3D1cfHiRbZvv8+FC41Z\nt674y3aFZ8OhQ/Ihe17mc0MrGIitrbx35j//eXqVlynplSysrKyYM2cO7u7uBAUFERQUpFc9hz17\n9qDVaunSpQtjxowhNDQ0tw6FKSrlNW7cmM6dnfDxucm4cQOLfP2VK1f4668onJwa0Lp1I9zd3fMk\nEkORJIm7d++SnHyOjIyLVKtW0eDXECzbL7/AkCFKRyEYy6BB8llfBw8qF4Pew1Bdu3Zl3rx53Llz\nh4SEhNyf0oiNjUWtVrN//36cnJxITk5+qiqeMSvlZWRk0KlTc7744n2aNGlc5Ovj4+NRq6tiZ+fF\nvXvGK98VFhbOunU3UKka8sILWbz55sskJSXlmSsqjgcPHhASEkKyuddvFEokI0M+7uLNN5WORDAW\nGxt53kLJuwu9J7g3bdqESqViyZIleR6/efNmidtydXXFz88PgMDAQEJDQ3OP1ChppbySys7OZtas\n1cTH16NataN89dW4IpfAent707nzHRITb/Dqq70MEkd+0tMz0OlcKF++PJ6eOk6dCmflymDKl4ep\nU9+icuXKRbaRmZnJzJmrSUqqT40ax5k1a5xYFVVGbN8ur8O31BU/QvEMGCDvE9m3D7p1M/319U4W\nFy9eZMmSJRw9ehQrKys6duzIyJEjS9VW+/btWf6oTFRERAQ1a9Zk8+bNelfKK05Z1ZycHJKTtVSo\n8Dzx8ZfQaDS5y4EL4uDgwDvvvFHoawzhxRfbEReXTGZmNr16dePHH7fg5NSbxMQL3Lx5s1jJIjs7\nm9RUifLlvXjw4CI6nQ5rcz3rQCiRdevEENSzwNpa3tH9n/9A166mX/Wm96a8fv36UaFChdwjyjds\n2EBycjK//fZbqdr79NNPCQ0NxcPDg/Xr17NgwQJ27NhB7dq1WbNmDTY2Nvzyyy8sXbo0t1Je+fLl\n81o+slEAABVpSURBVH6oUm56On78FMeORdKlSyu8vVug1WqJiYnBzc2tVCfOGss//5xn8eJteHiU\nY+LEt3FxcSnW+44ePcmJExfp0aMtzZo1NXKUQkEMuSkvLk6urhYdXbpjugXLotPJ+2jmzDFO/Q2j\n7uD28vLiwoULRT5mSob6Mq5cuZng4Ad4euYwffoHJUoYaWlp2NvbF+tk2uvXr3Pv3j28vb2LvalR\nq9ViZWUlhpIskCGTxaJF8oFz69YZpDnBAvz+u3ycy+nThr+7MOoO7latWuVZkXTy5Ml863JbovDw\nm3h49CU21o6HDx8W+30HDhxm0KAvGDNmVu78SkHu3bvHV19tZenS+yxfXvy7MWtra4MlitjYWE6d\nOmXQPSuCaYghqGfPq6+CRgM7dpj2unoni9DQUDp06EDt2rWpU6cO7du3JzQ0lGbNmtHcAs4dSEhI\n4OLFi6jV6qeeGzCgExkZq+nQwY3q1asD8jLW8PAI9u37m/T09Hzb/P77LVy+HMD+/TmEhIQUev3M\nzEw0GgccHKqRnJyh/wcqobS0NL788mcWL77F/Pk/m/z6QulduCDX2A4MVDoSwZSsrGDmTPnAxEf7\nlE1C7wnu3aY64coIkpOTmTZtBcnJbvj4hDNy5KA8z/v6+jy1we7q1assXHgUrbY6UVE7eO+9AU+1\n6+FRjgsX9uLg8L9lvgWpW7cuQ4Y04/r1WwQF9dH/Q5VQVlYWaWlWVKjwPPfvX0GSJDG0ZSFWroS3\n3ipZhTihbAgKkotbrVoln4FlCnp3szp16hggDGUkJiaSkuKMi4sfV69uL/B1kiRx7do1bGxsUKvV\nSJIt1tZOZGfnf2cxdepIatfeRd26lWnTpk2hMahUKrp06USXLnp9lFJzd3dn2LAXOX06hJ49XxWJ\nwkJkZ8tDUMePKx2JoASVSk4WQUHy/pp/rfExzjX1neA2R8WdQNTpdGzevIPz52/Tv38AzZvnv0Io\nOPg4K1dGAGrGjw8gMTGF2NgEunf3o2JF4+ymvnnzJqGh52nd+nnq1atnlGsIyjDEBPdvv8HSpfD3\n3wYKSrBIQ4dCrVry/gtDMOpqKHNk6HoBGzdu47//LYdOl87QoeXo1q2zwdrOT05ODuPHzyczsyN2\ndkdZuHAcDg4ORr2mYDqG6J/du8sHzA0s+lQaoQy7cwdatoQzZ6BmTf3bM+pqqGdBt26+NG9+m3bt\nkvHxyb9kVU5ODj//vIV581Zx7949va73eChIknTiuGnhKVFRcm3m115TOhJBaTVrwsiRMGWK8a8l\n7iwM5PTp03z33WXs7Org7X2T0aP1W89469YtwsLO4+3tRd26dQ0UpWAO9O2f06ZBYqK8x0IQUlOh\nSRO5nkmHDvq1VVjfFOsoDKRSpUrY2cWSk5NF9erV9G6vdu3a1K5d2wCRCWWJWi2vgtq1S+lIBHNR\nvrw82T1yJISHG291nLizMKBbt26RkpKCl5eXOHdJKJA+/fPXX2HJEuUL4QjmRZLkwwVfegkmTCh9\nO2KCWxDMiD79s2NHGDcO+vY1cFCCxbtyBdq3lye7a9QoXRsWN8G9YMECfH19AUxaVlUQzFlEBNy+\nDa+8onQkgjlq2BBGjZL/mDAGs0sW2dnZnD17FpVKxYMHD0xaVlUQzNnixfK4tNixLRTk88/hn3/k\nwwYNzeySxcqVKxk2bBiSJJm8rKogmKuHD+VqeO+9p3QkgjlzdIQ1a+Cjj+DBA8O2bVbJQq1Wc/jw\nYQICAgDyLaFqzLKqxhIfH1/k6bOCUJjr1+UhBnd3pSMRzJ2Pj3wS8YcfGrZds7qhXbduHQOf2JLq\n4uJCdHQ0UPKyqiWtlGcsp0+Hs3TpAWxtdUyePEAshxVK5YUX5B9BKI4ZM8DbW14994aBinmaVbK4\ncuUKZ86c4ccffyQyMpLQ0FBOnTqld1lVJZ09ew0bmwDS02O5ceOmSBaCIBidgwOsXSsfNPjii/L5\nUfoy26Wzfn5+BAcHM2fOHJOVVTWGmzdvsmDBZsqXd2DChCG4ubkpHZKgMHPqn0LZNncubNsGhw5B\nMYp2in0WSnscizj+WwDz659C2aXTyXcXLVrIpViLIpKFIJgR0T8FU3rwQJ6/WLECevQo/LUWtylP\nEARBMAwPD9iwQT7S/tq10rcjkoUgCEIZ5+cHX3wBL78MpV3FL4ahBMHERP8UlDJyJERHy5Pe+Z11\nKoahBEEQBBYtkutfTJxY8veKZCEIgvCMsLWFP/6A//4XFi4s2XvFMJQgmJjon4LSbt2Sq+pt2iQf\ne/+YWDorCGZE9E/BHNy+Lde9sHpifEkkC0EwI6J/CuZKTHALgiAIejGrZBESEkKHDh3w9fVlwqNC\nsqJSniAIgvLMKlnUqVOHgwcPcuTIEeLi4ggODjabSnmHDh0SbYo2zYK5xGQOcZhDDGAecRg7BrNK\nFp6entjZ2QFga2tLZGSk2VTKs5RfRKJN829TX+YSkznEYQ4xgHnEYewYzKqexWPnzp3jwYMHuLq6\nYvVoqt6SK+UJgiBYOrO6swBISEhg9OjRrFq1Kt+qeMWtlCcIgiAYkGRG1Gq19NJLL0mnTp2SJEmS\nYmNjpV69ekmSJEmzZ8+WfvvtN0mtVkt+fn6SVquVNm/eLM2dO/epdurXry8B4kf8mOWPs7Oz4jGI\nH/GT38//t3f3MU3cfxzA3xUQn2CgTPbERNyAgRRKmVsLVArqZFifiKKluumYOhZxcw+QLDF7cHFk\nU2cyxjDZFBgigwUdkclQCkyHbDQMZiXQKYVMDSoBCo6HPtzvD8P9QNGu155c4ftKjHi2734OPse3\nd9/r3eLFi+/7+5lTn7PIz8/Hrl27EBgYCADYt28fqqurLb5THkEQBGFbnBosCIIgCG7i3JwFQRAE\nwT1ksCAIgiDM4uSps5bo7e1FVlYWampq0N3dDTc3N4hEImzfvp3xXMZkzbx69Sr27dsHtVoNo9EI\nBwcHBAYGIi0tDU899RSjGkmmdZls9I2luFADV+pgo0/spg6bn9L0kK1YsYIqKCigOjs7Kb1eT3V2\ndlIFBQXUihUrSKaFpFIpVVtbO2pZbW0tFR0dzbhGkmldJht9Yyku1MCVOtjoE3upw+4HC7FYTBmN\nxlHLjEYjJRaLSaaFRCIR1dfXN2pZX18fJRKJGNdIMq3LZKNvLMWFGrhSBxt9Yi912P1hqOTkZERF\nRSEoKAiurq7Q6XS4ePEi3njjDZJpob1790Imk2H69Ol0Xn9/Pz755BPGNZJM6zLZ6BtLcaEGrtTB\nRp/YSx0T4tRZvV6Pv//+G93d3XjkkUfg6+sLR0frxsHJnNnf30/nzZgxw6oskml9Jhs/Y3usgUt1\nsNEnXK/D7vcsDAYDTp48ec+k1+rVqxk30WTNtIdJ+MmWyUbfWIoLNXClDi5Mso9XHXa/Z6FQKMDn\n87FkyRJ6d+zMmTNobGzE999/TzItIJPJsGnTpnvycnNzUVJSwqhGkmldJht9Yyku1MCVOtjoE7up\ng7XZkIckIiLCouUk8/7sYRJ+smWy0TeW4kINXKmDC5Ps41WH3R+GWrlyJeLi4hAVFUWPsFVVVZDJ\nZJMmUyqVwsXFxepMe5iEn2yZbPSNpbhQw8g6bNXvTHBhkn286rD7w1AAUF1dDbVaTV/CPCwsDFeu\nXMGLL77IOPPmzZuoq6ujJ4/q6uqwZ88exnnXrl2Do6Mj6urq0NPTg9bWVnh5eWHDhg1wcnJilDk0\nNIT8/Hy0t7fjmWeegV6vh1arRUpKCuNLt7M5Cd/V1QU3Nzc8++yzjNd5ZKZGo6GP19qqTo1Gg56e\nHputu8FggEajodedSaate9FSbPQuE2z0OxNcmWRnY7t6ELsfLHbv3o0bN27AyckJN2/exHfffYe5\nc+dCKpVCqVQyyoyMjASPx8PIb41arcbChQtRXV3NKDM6OhoVFRVISUnBjBkzEB0djfr6eqhUKvzw\nww+MMlevXo1Fixahq6sLKpUKcXFxmDNnDvLz81FWVmZxnsFgwIkTJ2w6gWgymVBaWgpHR0csW7aM\nvpnVyZMnsWrVKkaZY9mzZw8+/vhjxs+vr6+HQCDAv//+i6ysLDQ1NcHHxwc7duxg/Iuos7MTeXl5\n8PDwwJo1a/DFF19Ap9MhOTkZ8+fP/08ZbPSipdjoXSZs3e9MsLGNMPGwtqtRWDvA9ZCMPF7Z0NBA\nSSQS6vfff6eioqIYZx44cIB65ZVXqIqKCnrZ8uXLraozJiZm1N/DrKlz5HMDAwOtzkxMTKTS09Mp\nlUpFaTQaSqVSUenp6VRiYiLjGuVyOfXBBx9QH374IRUeHk41NTVZVSNFUZSXlxcVHh5ORURE0H/c\n3d2pyMhIxplSqZSiKIratGkTlZmZSTU3N1PHjx+nYmNjGWcuWbKEysvLo7766isqKCiIKioqopRK\n5QPvGXA3NnrRUmz0LhO27ncm2NhGmGBjuzLH7ucsTCYThoaGMHXqVPD5fBQXF0OhUECtVjPOfPvt\ntzE4OIhvv/0W33zzDeRy+ah3dkxs3rwZSUlJ8PLygkKhgEQiQWNjI8LCwhhnurq6Yu/eveju7sbc\nuXOxf/9+uLu7M94VbWtru+esktDQUERGRjKu8Z9//kFeXh4A4PXXX8err76KN998k3EeAHz55Zco\nKirC0qVLoVAo4OTkhNjYWPz888+MM3k8HkwmEzo6OrB9+3bweDz4+vri66+/Zpyp1+shl8sBABkZ\nGYiPj7c4g41etBQbvcuErfudCTa2ESbY2K7MsfvDULW1tfD29oanpye9zGAwoLCwEBs3brQ6X6/X\nIzc3Fy0tLfjss8+syrp69SrKysrQ0dEBNzc3iMViBAcHM84bHBzE6dOn4eHhAZFIhNzcXOh0OigU\nCri7u1uc9/nnn6OysvKeiUyJRIL333+fUY0SiQSnTp2iz/0eHBzEtm3bUFxcTN8el6nS0lLk5ORA\nJBLhp59+wtmzZxlnlZeX4/Dhw5gyZQquX7+OiIgINDU1YfHixXjrrbcYZW7duhU8Hg99fX2YNm0a\nXFxcMHv2bKjVavz4448W59myFy1l695lwtb9zgQb2wgTbG5X92P3gwVhWzdu3KAnModPFtBqtVi0\naBGjvNbWVri7u99z3L+6uhoSicQWJaOiogIXL16ESCTC888/zzinv78fNTU19C/EsLAwtLa2Ml53\nAGhoaIC7uzuefvppVFVVQafTwdPT06pMYnzZehth4mFsV3cjgwVBM5lMAEAf5hieWF2+fDnKy8ut\nyhyJrcyXXnoJZ86csSqT6+tOjC82+sSaOkZiuw67n7MgbGfmzJljnm7c0NBAMjmSSYwvrvxMx6UO\n1qbOCbsjEAiorq6ue5bffRYMyRy/TGJ8ceVnOh51kMNQBO369euYPXs2nJ2dRy03GAyMzyEnmbbN\nJMYXV36m41EHGSwIgiAIs6aMdwEEQRAE95HBgiAIgjCLDBYEQRCEWWSwIP4TrVaLoKAgAEBdXR12\n7doFAKiqqkJNTc14lkZMEpWVlfTlyEtKSpCenn7fx/b09CAzM5P+97Vr17Bu3TrWa5zIyGBBWCws\nLAyHDh0CACiVSvz222/jXBFhz8b6gJk5MpkMqamp9/3/rq6uUdf1euKJJ1BYWMioPuIOMlhMAp9+\n+in8/PwQGRkJuVyO/fv3QyqVQqVSAQBu3bpFXzJbq9VCIpFAKBRCKBSOudcw/A6vra0NWVlZOHjw\nIEJDQ3Hu3Dn4+PjAYDAAAHQ6HXx8fGA0Gh/eyhKcotVq4e/vD4VCgYCAAKxbtw79/f3w9vZGWloa\nhEIhCgsL8csvv0AsFkMoFGL9+vW4ffs2AOD06dN47rnnIBQKUVxcTOcePXoUO3fuBAB0dHRgzZo1\nCAkJQUhICGpqapCWlobLly9DIBAgNTUVbW1tWLhwIQBgYGAAW7ZsAZ/PR2hoKCorK+nMtWvXIjY2\nFr6+vg8cjCYjcrL3BKdSqVBQUICGhgbo9XqEhoZCKBQCuHOpgrt5enqivLwczs7O0Gg0kMvl+OOP\nP8bMnjdvHnbs2AEXFxfs3r0bABAVFYVTp05h1apVOH78OOLj4+Hg4MDeChKc19LSgiNHjkAkEuG1\n115DRkYGeDwePDw8oFKpcOvWLcTHx+Ps2bOYPn060tPTceDAAbz33nvYtm0blEolFixYgISEhDF7\nNiUlBVKpFMXFxTCZTOjr60N6ejrUajXq6+sB3Bm0hp+bkZEBBwcHNDY2orm5GcuWLUNLSwuAO5+A\n/vPPPzF16lT4+fkhJSUFTz755MP7ZnEY2bOY4H799VesXbuWvurpypUrH/j4oaEhJCUlgc/nY/36\n9bh06ZLZ1xj5UZ2kpCQcOXIEwJ13alu2bLFuBQi75+XlBZFIBABQKBQ4d+4cACAhIQEAcOHCBVy6\ndAlisRgCgQA5OTlob29Hc3Mz5s+fjwULFtDPHetjYUqlkr6d6JQpU+Dq6vrAy7ifP38eCoUCAODn\n54d58+ahpaUFPB4PMTExcHFxgbOzMwICAqDVam32fbB3ZM9igrv7LmvDHB0d6cNDAwMD9PKDBw/i\n8ccfR25uLoxGI6ZNm2bR64nFYmi1WlRWVsJoNCIgIMC6FSDs3si9AYqi6Lu6zZw5k16+dOlSHDt2\nbNTz7r7O0YMGAEs/W3y/x4/8RLSDgwM5hDoC2bOY4CQSCU6cOIGBgQH09vaipKQEAODt7U3PWRQV\nFdGP1+l0eOyxxwAAOTk5ZjcWFxcX9Pb2jlq2efNmJCYmYuvWrbZcFcJOtbe348KFCwCAY8eOISIi\nYtT/v/DCCzh//jwuX74MALh9+zY0Gg38/f2h1Wpx5coVAEB+fv6Y+TExMfSZT0ajETqdbsy+HBYZ\nGUnfOKilpQXt7e3w9/cfcwAhF7j4PzJYTHACgQAJCQkIDg7Gyy+/TN/v4d1330VmZiZCQ0PR2dlJ\nv/tLTk5GdnY2QkJC0NzcjFmzZtFZI98hDn8tk8lQXFwMgUBAH16Qy+Xo6uqyyc2nCPvn5+eHjIwM\nBAQEoKenhz5kNOzRRx/F0aNHsXHjRgQHB0MsFqO5uRnOzs44fPgw4uLiIBQK4enpSfcdj8ejvz50\n6BCUSiX4fD7CwsLQ1NSEOXPmIDw8HEFBQUhNTR31+OTkZJhMJvD5fGzYsAHZ2dlwcnIa9ZhhY82R\nTFbk2lCTzEcffYRZs2bhnXfeYe01ioqKUFJSguzsbNZeg7APWq0WMpkMf/3113iXQliJzFlMQmy+\nW9q5cyfKyspQWlrK2msQ9oW8O58YyJ4FQRAEYRaZsyAIgiDMIoMFQRAEYRYZLAiCIAizyGBBEARB\nmEUGC4IgCMIsMlgQBEEQZv0PfBjAOjMtQwUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109518110>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Support for Categorical Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PyMADlib has utility function to perform Pivoting of categorical variables when used in the context of algorithms which require a nominal value."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Train Linear Regression Model on a mixture of Numeric and Categorical Variables\n",
"mdl_dict, mdl_params = lreg.train('public.auto_mpg_train',['1','height','width','length','highway_mpg','engine_size','make','fuel_type','fuel_system'],'price')\n",
"predictions = lreg.predict('public.auto_mpg_test','price')\n",
"#Show sample predictions\n",
"predictions.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>id</th>\n",
" <th>height</th>\n",
" <th>width</th>\n",
" <th>length</th>\n",
" <th>highway_mpg</th>\n",
" <th>engine_size</th>\n",
" <th>make_val_0</th>\n",
" <th>make_val_1</th>\n",
" <th>make_val_2</th>\n",
" <th>make_val_3</th>\n",
" <th>make_val_4</th>\n",
" <th>make_val_5</th>\n",
" <th>make_val_6</th>\n",
" <th>make_val_7</th>\n",
" <th>make_val_8</th>\n",
" <th>make_val_9</th>\n",
" <th>make_val_10</th>\n",
" <th>make_val_11</th>\n",
" <th>make_val_12</th>\n",
" <th>make_val_13</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 31</td>\n",
" <td> 54.3</td>\n",
" <td> 64.8</td>\n",
" <td> 176.8</td>\n",
" <td> 28</td>\n",
" <td> 164</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 25</td>\n",
" <td> 50.8</td>\n",
" <td> 63.9</td>\n",
" <td> 144.6</td>\n",
" <td> 54</td>\n",
" <td> 92</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 19</td>\n",
" <td> 53.5</td>\n",
" <td> 63.8</td>\n",
" <td> 170.2</td>\n",
" <td> 37</td>\n",
" <td> 97</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 13</td>\n",
" <td> 50.2</td>\n",
" <td> 68.3</td>\n",
" <td> 168.9</td>\n",
" <td> 27</td>\n",
" <td> 151</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 37</td>\n",
" <td> 54.3</td>\n",
" <td> 64.8</td>\n",
" <td> 176.8</td>\n",
" <td> 28</td>\n",
" <td> 164</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 28 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
" id height width length highway_mpg engine_size make_val_0 \\\n",
"0 31 54.3 64.8 176.8 28 164 0 \n",
"1 25 50.8 63.9 144.6 54 92 0 \n",
"2 19 53.5 63.8 170.2 37 97 0 \n",
"3 13 50.2 68.3 168.9 27 151 0 \n",
"4 37 54.3 64.8 176.8 28 164 0 \n",
"\n",
" make_val_1 make_val_2 make_val_3 make_val_4 make_val_5 make_val_6 \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 1 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" make_val_7 make_val_8 make_val_9 make_val_10 make_val_11 make_val_12 \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 1 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" make_val_13 \n",
"0 0 ... \n",
"1 0 ... \n",
"2 0 ... \n",
"3 0 ... \n",
"4 0 ... \n",
"\n",
"[5 rows x 28 columns]"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Display Scatter Plot of Actual Vs Predicted Values\n",
"smat = scatter_matrix(predictions.get(['price','prediction']), diagonal='kde')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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5vp6Xl0d2djahoRHY2toQGNgZGxsbtm/fzuuvr8TKyoEpU/wZP35Ege0risLF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\nif8B02pnsLr0SRwAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10b29ec10>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Logistic Regression"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#1) Logistic Regression with Numeric Variables Alone\n",
"log_reg = LogisticRegression(conn)\n",
"#Train Model\n",
"mdl_dict, mdl_params = log_reg.train('public.wine_bool_training_set','indep','quality_label')\n",
"#Show Model Parameters\n",
"mdl_params.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>coef</th>\n",
" <th>log_likelihood</th>\n",
" <th>std_err</th>\n",
" <th>z_stats</th>\n",
" <th>p_values</th>\n",
" <th>odds_ratios</th>\n",
" <th>condition_no</th>\n",
" <th>num_iterations</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> [3.67131782851, -2.3556530177, -0.36640132682,...</td>\n",
" <td>-59.664051</td>\n",
" <td> [2.62370220266, 0.645203905536, 0.583811938681...</td>\n",
" <td> [1.3992890751, -3.65102101443, -0.627601634265...</td>\n",
" <td> [0.161726314396, 0.000261199864726, 0.53026493...</td>\n",
" <td> [39.3036672396, 0.0948315596232, 0.69322453577...</td>\n",
" <td> 6198.907232</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
" coef log_likelihood \\\n",
"0 [3.67131782851, -2.3556530177, -0.36640132682,... -59.664051 \n",
"\n",
" std_err \\\n",
"0 [2.62370220266, 0.645203905536, 0.583811938681... \n",
"\n",
" z_stats \\\n",
"0 [1.3992890751, -3.65102101443, -0.627601634265... \n",
"\n",
" p_values \\\n",
"0 [0.161726314396, 0.000261199864726, 0.53026493... \n",
"\n",
" odds_ratios condition_no \\\n",
"0 [39.3036672396, 0.0948315596232, 0.69322453577... 6198.907232 \n",
"\n",
" num_iterations \n",
"0 6 \n",
"\n",
"[1 rows x 8 columns]"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#2) Logistic Regression Prediction \n",
"predictions = log_reg.predict('wine_bool_test_set','',None)\n",
"predictions.head()\n",
"\n",
"#Display ROC Curve\n",
"actual = predictions.get('quality_label')\n",
"predicted = predictions.get('prediction')\n",
"ROCPlot('ROC curve Logistic Reg. on Continuous Features ',['Logistic Regression'],actual,predicted) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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ghUpkFfDHkdqJygysoq4QLS+p1UWDNbBTp05pDSm9dOlSWrp0qd7yISEhtH79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\nsQlIa4Tw8QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10b73e1d0>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"K-Means"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Demonstrate K-Means\n",
"example.kmeansDemo(conn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
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g9OVPbZnIY9zKKDoSSOnb+x5hLjlY+YJ7/aYzkM9oSFXG0cu7rRg7XZjEHn0m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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10b290690>"
]
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
}
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