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@messefor
Created August 16, 2016 09:48
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# bias vs variance traial\n",
"See how to select best degree of polynomial term\n",
"\n",
"### Todo\n",
"1. create dataset\n",
" - 4degree polynomial term\n",
" - error with gaussian destribution\n",
"1. devide dataset to three part (train:60, cv:20, test:20)\n",
" 1. Create model with training set\n",
" 1. Select model using training error and cv error\n",
" 1. Evaluete model using test data\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create dataset"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 4 3 2\n",
"-0.8 x - 0.5 x + 1.3 x - 0.4 x + 5\n"
]
}
],
"source": [
"\n",
"RANDOM_SEED = 5\n",
"np.random.seed(RANDOM_SEED)\n",
"\n",
"sample_size = 100\n",
"x_range = (-2, 2)\n",
"x_step = 0.01\n",
"\n",
"x_list = np.arange(*x_range, step=x_step)\n",
"\n",
"# create x dataset\n",
"dt_x = np.random.choice(x_list, sample_size) \n",
"\n",
"# def polynomial function\n",
"coef_tpl = ( -0.8, -0.5, 1.3, -0.4, 5) # coef to estimate\n",
"y_func = np.poly1d(coef_tpl) \n",
"print y_func\n",
"\n",
"# create y dataset\n",
"epsilon = np.random.normal(size=sample_size) # epsilon error with gaussian distribution\n",
"\n",
"dt_y_noerr = y_func(dt_x)\n",
"dt_y = y_func(dt_x) + epsilon\n",
"\n",
"dt = pd.DataFrame({'x' :dt_x, 'y':dt_y, 'y_noerr': dt_y_noerr})"
]
},
{
"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>x</th>\n",
" <th>y</th>\n",
" <th>y_noerr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.55</td>\n",
" <td>0.687371</td>\n",
" <td>1.023707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.06</td>\n",
" <td>4.879947</td>\n",
" <td>4.980562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.11</td>\n",
" <td>6.473676</td>\n",
" <td>5.060278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.82</td>\n",
" <td>6.337361</td>\n",
" <td>6.116107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.27</td>\n",
" <td>5.237031</td>\n",
" <td>6.547804</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x y y_noerr\n",
"0 1.55 0.687371 1.023707\n",
"1 0.06 4.879947 4.980562\n",
"2 -0.11 6.473676 5.060278\n",
"3 -0.82 6.337361 6.116107\n",
"4 -1.27 5.237031 6.547804"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dt.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## plot all dataset"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11c5f8510>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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IiEiiPPDoLjr29JFKp9i66yAPPLor6pIipRAQkUTRmMB4CgERSRSNCYyneQIi\nkiirl4cPMuzo7mPZyc9j+UnHRFxRtBQCIpIo6VSKs1YcB8T/yWKFUHeQiEiCKQRERBJMISAikmAK\nARGRBCv7wLCZzQV+DDQBg8Bl7r673HWIiEg0LYHLgTZ3Pxu4GfhYBDWIiAjRhMCjwOiTHOYCQxHU\nICIilLg7yMyuBD4IBEAq99/3AK8zsz8BxwBnlbIGERGZWioIgrIe0Mx+Btzl7t8xs1OBH7j7irIW\nISIiQDTdQXuBA7mfu4HmCGoQERGiWTbi08B/mtk/5Y5/dQQ1iIgIEXQHiYhI5dBkMRGRBFMIiIgk\nmEJARCTBKv55AmbWAPyQcE7BIeAd7t4ZbVXFk1tG4weEE+fqgA+7+4PRVlV8ZnYp8BZ3f2vUtRSD\nmaWAbwArCJc/udrdn4q2quIyszOA/+nur4m6lmIys1rgBmApUA98zt3viLSoIjKzNPAdwIAscI27\nPz7V/tXQEngn8Ad3Pwf4L+DjEddTbB8C7nb3c4ErgK9HW07xmdlXgc8RThiMi0uAWe7+auA64MsR\n11NUZvZRwgvJrKhrKYHLgD25pWsuBK6PuJ5ieyMQuPuZwKeAz0+3c8WHgLt/jfACAnACsC/Cckrh\ny8B/5H6uAwYirKVUWoF/jLqIIjsTuAvA3R8CXhFtOUX3Z+DSqIsokZsJL44QXgOHI6yl6Nz9duBd\nuc2lzHDNrKjuoCmWmbjC3TeY2RrgpcAFEZZ4RGY4v4XA94H3RVjiEZnm/G4xs3MiLa745vLMpEeA\njJml3T0bVUHF5O63mdmJUddRCu7eD2BmzcAtwCeiraj43D1rZjcStljfMt2+FRUC7n4DYV/dZK+d\nb2YG/BJ4QVkLK5Kpzi+3fMYPCccD7i97YUUy3d9fDB1k/Gz32ARAEpjZEuBW4Hp3/0nU9ZSCu19u\nZvOBdWa2zN0n7WWo+O4gM7vWzC7LbfYBmSjrKTYzezFh8/Tv3f03UdcjBWsFLgIws1cSro4bR3Ea\nxwHAzBYAvwY+5u43RV1PsZnZZWZ2bW5zEBghHCCeVEW1BKZwA3CTmV1FGFpXRFxPsX2ecPDta7k7\nTva7e1z7YuPkNuACM2vNbcft3+WoOC4pcB1wNPApM/s04Tle6O6Hoi2raG4Fvmdm9xJe498/3blp\n2QgRkQSr+O4gEREpHYWAiEiCKQRERBJMISAikmAKARGRBFMIiIgkmEJARCTBFAIiIgmmEBB5jszs\nvbnZmJheEt/NAAAAoUlEQVTZmWbWbmaNUdclcjg0Y1jkMORWtf0Z8F7ClVJj9yAgSYZqWDtIpBJd\nBTwGfF0BINVM3UEih2cp4fMEXh5xHSJHRCEg8hyZWRPwbeBNQL+Zxe2paZIgCgGR5+4LwB3uvoFw\nTOBTcX0Kl8SfBoZFRBJMLQERkQRTCIiIJJhCQEQkwRQCIiIJphAQEUkwhYCISIIpBEREEkwhICKS\nYP8fwlDFFRjccRkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bf466d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot dataset\n",
"sns.regplot(x='x', y='y', data=dt, fit_reg=False)\n",
"sns.plt.title('dataset with error')\n",
"sns.plt.show()\n",
"\n",
"sns.regplot(x='x', y='y_noerr', data=dt, fit_reg=False)\n",
"sns.plt.title('dataset with no error')\n",
"sns.plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## holdout dataset"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.cross_validation import train_test_split \n",
"\n",
"# define rate of train/cv/test dataset\n",
"TRAIN_SIZE = 0.6\n",
"CV_SIZE = 0.2 / (1 - TRAIN_SIZE)\n",
"\n",
"X_train, X_hold, y_train, y_hold = \\\n",
"train_test_split(dt.x, dt.y, train_size=TRAIN_SIZE, random_state=RANDOM_SEED)\n",
"\n",
"X_cv, X_test, y_cv, y_test = \\\n",
"train_test_split(X_hold, y_hold, train_size=CV_SIZE , random_state=RANDOM_SEED)\n",
"\n",
"# reshape\n",
"X_train, X_cv, X_test, y_train, y_cv, y_test = [(lambda x: x.reshape(-1, 1))(x) for x in [X_train, X_cv, X_test, y_train, y_cv, y_test]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## create model, estimate coef"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.pipeline import make_pipeline"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model_dict = dict()\n",
"degree_trial_list = range(33)\n",
"\n",
"# create model\n",
"for degree in degree_trial_list:\n",
" model = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n",
" model.fit(X_train, y_train)\n",
" model_dict[str(degree)] = model\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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,
"text/plain": [
"<matplotlib.figure.Figure at 0x11de7cf50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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hrj2REKLLKS+vYsGCLAoLQ0hKOsOyZbOIjOyyV3xaWruDqyc7BwMrgfu11rvb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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bf82050>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ZPOMq+0Vkf5LIhRDCyTnNMrZCCCEuTRK5EEI4OUnkQgjh5CSRCyGE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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c68de10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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AAzj1AiRCCCH6lF/DD5VSbwHvaZr2rFJqHPCypmkTujlMFkQQQoie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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118b9b3d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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H0Q10fHMnF7JmPbz2bhXjCwZuGNXZ3cN7248yoSQPp8U45j7j4X9+Jr55/enc\n98xmFi2eRSBgGlPHOJLfz7GA2z30rN5UFAQdt552vARD1y7HQ3phoozlvOSZkwrJs5n5YHddZJ0j\nFht21uEPaJx54ris/YxLCnMBaGiVdrZjkWELuaqqCyX1cHB688hH2ZAxSEVFKyNZlTgczCYjp8xw\n09LhY/+Rge1av+0oBvRB2dlKsVNvJ9EoQj4mkYKgNNBb2SlKfixjcXGvLx+ZXcrarTWs3VrN9In9\nG1sdru9g75FW5k5x4XKMfH/w0SLHbKIg30JDq2e0TRFiIEKeBoID9FoRxn464pypRRQ7bby7o44v\nLJzRbyjFv97Xc8cXnjpxNMxLKyUFNg7WtBMMavJdHmNI06w0oA3Qa0UY+xgNBs6ZX463J8DaLTVR\nj7V2+nh7ey0lBTbmxWhXm22UFOQSCGo0t3tH2xThGETIU0xjazfPvbkPb08gct9AgyWEzODc+eVY\nLSZWv30Qj9cfuf/5t/bR4w9yyUcnHxceakkoc0fCK2MPEfIU0tTUwm2/eYuX367kxu+9Eml7OlAb\nWyEzcNotXPLRybR39fDCfw4A+vzM/2yuYUJJHufMLx9lC9NDcWhmZ1ObeORjDRHyFLJkyet0+/VZ\nipVHlEiv5shi56hZJgyXi0+fTFmRnX++f4j7ntnEb57djMFg4KqLleNmak5haDG3uUOEfKxxfHwD\n00RlpZOAX294ZDIHIznRkV4r4pFnLFaLiVs+fzIT3Xls29+E0WDg+sVzmTlp7Fc4NjamZpiHKzTR\nSmLkYw/JWkkhFRWtNPfo4m00+5kYyokeaGankFmUuuz8+JqP0NDioSDfijXHNPRGY4Abb1yTkmEe\nLqcI+VhFPPIUsnTpQpyOZgDGTzgUyYmW7ofZg9FgoNRlzxgRBzhwIJ94q2cHG8XnyM3BbDKMGSF/\ncd0BfvHUhyTTLyrbEI88hbhchcyaWcKuqhZmziyJTGrRBhksIQgjzdSp7WzYEF9r3MFG8RkMBgrz\nrbSMkRj5rspm1EMtkTqN4xkR8hRjCXlqPn8wcp8UBI0efcewVVS0snTpwpSPQhvrLFu2CK83vurZ\noXrfuBxW9h5pJTBEa990ImeVCHnKyTHrX+5oIZesldFiJIc9ZwpFRfFXzw412MLlsKJp0NbZM+ot\nCWTtqRcR8hRjMYc88j4FQZJHPnqM5e6KY5Ghet8U9slcGXUhD/0rIUsR8pRjzYnhkQelRH+0kKnv\niTFU75vzR+IGAAAgAElEQVSicC55ezcwyj+KssgZQYQ8xQT8egl3S6uX6677mz4GDFnsHC3GenfF\nTCNSFDQGMlfCP8+CCHnKWfufKnDZwGBk1aov4/M9grm0EIpzefCB9fzyx+ccd4tto8lY766YaRQ5\n9DL9sVDdqYEoeYixseycRbQ06x6L0aT7C2+/3c6OHacC8Pbbn4iU7QtCJlLosACJeeQer591W2s4\n0tCZWmM0MIiSAyLkKaewsO8EFQ0ojsTGNU0W24TMJrzY2ZKAkP/trf089vJOfv2XjQSCwaE3iBMN\nTdadQoiQp5iLLpoW+Xvx4pV87GMBMIQWZTRksU3IaMwmI848S9weua8nwNqteh/3lg4f2w80pc4Y\nWeuMkFSMXFEUM7ACmAJYgJ+pqvpSCu3KWGy23mnry5dfTnNzC9+9dz0aNs48858svVsW24TMxpVv\npaaxE03ThlzAP3i0Ha8vwER3HofrO9lzuJWTT0jNEA4NSSAIk6xHfiXQoKrqOcAlwIOpMym7cLkK\n+fSnZwHw3VvPloVOIeNxOaz4/EG6+gzZGIg9h/VeLZ84bRKgC3uq0DRJ6Q2TbNbKM8Czob+NQE9q\nzMl8YjXwkQo0IZtw9UlBzLPlDPrcsHCfOLUId6GNypQKuSZLnSGS8shVVe1SVbVTURQHuqB/L7Vm\nZRdSgSZkE4nkklc3dJJrNeNyWBlXlEeHp4eu7qE9+XiQ9MNeks4jVxRlEvA34EFVVZ+OZxu325Hs\ny2UEbreDvDxr1G2A3Fzda3G57Bn7HmSq3fEixxc/FeUFAPgxDLrfHn+A2mYPymQXpaVOKsY72bq/\nEb9h8O3ixWw2RlpDZ/vnNxTJLnaWAf8AblJVNe7E6Pr61F1WjTXcbgf19e10dvZ6KeHj7ez0AdDa\n6snI9yB8bNmKHF9imELXmFXVrYPu90h9B8GghrvASn19O/lWvQ/R7gONOK3D7+fe0xOIXO1m++c3\nFMl65HcChcAPFEX5IfpVziWqqo5+udcYJBw3l8iKkA0UxRlaqW/RaypKXXYA3K7c0P2e1BiiSWQl\nTFJCrqrqt4Fvp9iWrCBWH59I90NRciELKIxzdmd9qy7YJQV6Sm6qy/v19MOU7CrjkYKgNBAUj1zI\nInKtZnKtpiGFvCHkkbsLdU/cmaeX97d2+FJihyarnRFEyNNAJP1QvnRCllCYbw21sh2YhmM8cmde\nDgagtTM1Qg6SfhhGhDzFxKoa7m1jm15bBGGkKHJY6ez2Rw1QOZb6lm6sFhP5oawtk9GIw55Dq4RW\nUo4IeRroLQiSb52QHURyyQcQZU3TqG/14C6wRX3vC/KtqfPINTmnwoiQp5xYlZ3ikQvZRW4oTeLa\n69/muuv+RnNzS9TjHZ4evL4AJQW5UfcX5Fno9gXw+gb25ONFemb1IkKeBsQjF7KNf796AIAjRxew\natWX+/XZb2jV4+clhbao+wvyQwuencMPr+hNu4a9m6xAhDwNhD1ymb0sZAv11bpA2/K7iTXUOpwr\n7j7GI4/0M09B5oomeeQRRMhTTKw88qB45EKWUVrUAYDN4SHWUOuqmmYAHvjN7qjQSzgFsS0FcXI9\n+1DOKRAhTwuRGPko2yEIqeJH3zsTgLIJ+1m8eGW/odZr/qmHXrZvWhgVeun1yFOQuSLdDyPI8OU0\n0Nv9cFTNEISUMXF8CWaTgbnznPzgKxf0e9zjt2ADulrt9A29FISLglLmkQ97N1mBeORpoDdrRb51\nQnZgMBgozLcO6FnbHEG8XRYCPWb6hl4KUlndKTHyCOKRpwEZLCFkIy6Hlb1HWgkEg5iMvT5hMKhh\nspmxdnUxf/4LVFS0RUIvDrsu5O1dqfDIhx41d7wgHnmKid00K5y1Il86IXtwOaxoGrR1Rg8Ia+nw\nEghqGHqCoXt6T4pcqwmT0UC7Z/hDxWKda8cr4pGnAckjF7KRcDfDprbuyPg36E093LNzDrs2zWXT\nJg1YyfLll2MwGHDYc1LikYNc5YYRjzzFaDHqzaT7oZCNuEPFPnXH9BcPFwN1teaF7onOM3fYLbR3\npcYjl1NKR4Q8DfR2PxSE7CE8MKKuOVrIa5u7AOhssYfuic4zd9pz6PYF6PEPt0xfYuRhJLSSBmT4\nspCNlIYm/tSFhDvM0SZd2D9++j+oKnZELXZC3wXPHoqcyY980xDnKIwIeaoZZLFTdFzIJoqcVkxG\nQz+P/GhjF9YcEw8tWxzTecm3621tdSG39Xs8XjQNDBJTACS0khZksVPIRkxGIyWFudT2EfKgplHX\n3EVZUe6A3/dUpiDKsBYdEfIUE3OwhDTNErKUMlcuHZ4eurr1xcuWdi8+f5BxRfYBt3H08ciHQ1BW\nOyMkFVpRFMUAPATMA7qBr6mquj+VhmUT4pEL2UppaB5nXYuHKeNyqGnS4+VlroGF3BnyyNuG65GL\njkdI1iO/DLCqqnomcCdwX+pMyj4k/VDIVspCnndNoy7gtSEhH1c88h65dD/sJVkhPwt4BUBV1XeB\n01JmUaYTc7FT/1c8ciHbmOjWc8UP1eptbQ/XdwIwflAhT1GMXLofRkhWyJ1Aa5/bfkVRJN4+ANLG\nVshWJpc5AKisbQfgQE0bZpOBie78AbdJpUcuvpFOsumHbYCjz22jqqrBgZ4cxu12DPWUjMbtdmAL\nTQwP3wbIydFzZUtLHeSYk8+bHU2Oh88umxnJ4ysvyaOytp18Zy5H6juYNqGA8eMKBnx+iaZhMhrw\n9ASGZZfBYMBs1v3HbP/8hiJZIV8HXAr8VVGUM4Ct8WxUX9+e5MuNfdxuB/X17Xg8vZeL4eP1+vwA\nNDR0YDZl3oVL+NiyFTm+4TFzUiFvbDzCH1dvwx/QmDbOOeTr5dtzaG7tHpZdwaBGIKBf7Wb75zcU\nyarK84BXUZR1wK+BW5LcT1YTDqmEY+TS/VDIRk6aVgTA6vWVAMyfUTLkNo5cC+2e4cXIZfhyL0l5\n5KqqasANKbYlK+jbWjNcQiyVnUI2c/IJxYwrsnO0qYup4x1MnzhwWCWMw57D4foOevxBcszJX6XK\nKaUjJfojSUjJZfiykM2YjEZu+9IpbNhVx4KZ7riuPMNDmDs8PVEtcBNBk1lvEUTIRxAtpORyCShk\nOy6HlYtOnxT38x2hpIC2Tl/yQo5c5YbJvJW3DCIcZtErieUbJwhhIimIw4qTSx55GBHyNKDPFhxt\nKwRh7NC3lW2yBKWPbQQR8hQTtdjZJ2tF4uOC0EsqhBy50o0gQj6C9IZWNOl8KAh96K3uTD60oknX\nrAgi5CNI2DkPikcuCFGkQshFx3sRIU8xUcOX+3jkouOC0EsqQiuStdKLCPkIEhZ1iZELQjR2mxmT\n0TA8IZc88ggi5KkmarEz/K+kSQlCX4wGA/m5OcNsZStrT2FEyEeQiJAjl4CCcCwOew5tw/XI5bwC\nRMhHGAmtCMJAOOwWPF4//sCQHbAHRNIPdUTIU4wW429JPxSE/sQaMOEPBFm7pYamtu4htxePvBfp\ntTKChEMrkn4oCP0JZ67cdscrVO3Jp6KilUuvOpFV6w+TazXzwLfOxjiIB6RJiX4EEfJUE3Nmp6Qf\nCsKxhD3y9e9eSENVGZs2aQQmPg9WEx6vn+rGzkFHxkkeeS8SWhlBgpESfU08ckE4hrBHbrHroRWz\n1Q/W3lGI+6vbBt1ezz6U8wpEyEeWvt0P5fsmCFEU5utCbsv36LfLmgGYM8UFwKG6jgG3lYHm0Uho\nJcX0rezsXeyU1XVBOJaSglwA5s7biNO/nUnzvGjYOG1WKTsONtPS7h1w2/C5JQ6SjnjkI0k4tCJt\nbAWhHyUFNgDmnjKeV1+9gHkfnQjAvBNKMJsMNA0i5LHWoo5nkvLIFUVxAk8CTiAHuFVV1XdSaVim\nEjv9kEFX3wXheCTXaibPZqaxtRtN09hf04bLYY3819w+cApi+MpX1p50kvXIvwP8S1XV84CvAr9L\nmUVZRG/6oXjkghALd2Eu9S0e6lu7aev0MW28EwCXw0Zrh2/AYiFNPPIokhXy+4BHQn/nAJ7UmJNd\nyGAJQRicSaX5+AMa67fWADCtXBfywnwLGkN3R5QLXZ0hQyuKolwD3EJkJjwa8FVVVT9QFGUcsBK4\neUStzCQkj1wQ4mZymQOo4d8fHAZgasgjzw8NZ+7w9MQczqzJamcUQwq5qqorgBXH3q8oyknAn9Dj\n42vjeTG325GwgZmE2+3AFvoCAriK8nC77IABs9mU0cefybbHgxzf6HDa3PE89c/ddHb7ybOZOWP+\nBHLMJhx2XZq++e0NTCzqYNmyRRQVFUa28/UEALBa9OeN1eNLF8kuds4BngGuUFV1a7zb1de3J/Ny\nGYHb7aC+vh2Pp/dSsLGxA4M/QDAYJBgMZuzxh48tW5HjGz2cViOTy/Kpqu3gjLnjaGnuAuDFF3ZB\nSS4HKk9j/b/G4/WuZPnyyyPbhYXc1+MHsl9bhiLZPPJ7ACtwv6IoBqBFVdXLh9jmuEPrWxAkeeSC\n0A+DwcCNl53IgZp2FijuyP0NtTaKSyDH5gMMVFY6o7aLRFbkvAKSFHJVVS9LtSHZSCT9UBrgC8KA\nlLrslLrsUfe5XZ0EyceS6wM0KiqOKdcPnVwSIteRgqBU0zcvKvS3dD8UhMT45k0LAJgweQeLF69k\n6dLzox7XpCIoCinRH0H69iMXHReE+BlfVgTAJxeN59pLL+j3eNhfktNKRzzyFKPFuCF55IKQGHk2\n3cfs8vpjPh4RcjmvABHyEUU8ckFIjtxQWmFXd2whl2Yr0YiQjyDRlZ2jbIwgZBBGowGrRR8wEQup\nB4pGhDzFxFjrlNCKICSB3WqW0EqciJCPIFHDl0fVEkHIPOxW84AeeRiRcR3Rl5QT7ZJrmp4oJZ6D\nICRGrtWMxxuIhCj7oknaShQi5COIhsTyBCFZcq1mgpqGr6d/K9veyk4BRMhHFE3rM1tQlFwQEiI3\nNIg5ZpxcPKQoRMhTTPRipxa5LSX6gpAYduvAueTikUcjQj7C9Mby5CsnCImQGyoKirngGbnSTadF\nYxcR8hQTNbNT65smNSrmCELGEvbIYwm5lANFI0I+wvSGVkTJBSERcgcTcskjj0KEfAQJahrB8CXg\nKNsiCJlGWMhjlelrcl5FIUKeao655hPPQRCSYzCPPIKcVoAI+Yiiab19k0XHBSExBs1akXqgKETI\nU0zfhvcamsTIBSFJBl/slPqMvoiQjyQakRi5uA6CkBiDhlbktIpChHwE0SL/E89BEBJl0MXO8B9y\nWgHDHPWmKMos4B2gVFVVX2pMynD6VXbqd0hlpyAkhs1qwsDgeeQGUXJgGB65oigO4FdAd+rMyS40\nTR+8DOKRC0KiGA0GbFYzXd5A/wclZBnFcEIrvwfuBLpSZEtWcGzFmSalxIKQNHZr7ClB0mslmiFD\nK4qiXAPcQrRGVQF/VlV1q6Io8l4OQN+mWXIJKAiJk2s109Tm7f+AtL6IYkghV1V1BbCi732KouwG\nrlUU5WvAOOBV4Lyh9uV2O5KzMkNwux3YbDmR2wWFdlwuOwC5uTkZffyZbHs8yPGNPRobW6g+3EbA\nYuKmb7zEw8sWUVRUCIAv5Bjl5lqAzDy+VJLUYqeqqjPDfyuKcgC4MJ7t6uvbk3m5jMDtdlBf347H\n0xO5r7m5C2NQb4rv9fZk7PGHjy1bkeMbm1x33YvUGGZSNq2W51/4HD7vX1i+/HIAGhs7Aeju1s+3\nTDy+eInnRyoV6YcaEqqKTZ/2h7LYKQiJUVnppMerX+GaLQEqK500tXXz7o7a3iSCUbRvLDGs9EMA\nVVWnpcKQ7EGL+ksGSwhC4jQ1tVBXt52ioqkA5Fh9TJ7SxncfWg/AtZ+arT9RHCRACoJGFI3eyk7x\nyAUhfpYseZ3q6hvp8e4BYNyEFdx+58cij7d16WUrclrpiJCPJJp0PxSEZKisdAIu/L55ABS7p6KZ\nrJHHw9WeclbpiJCnmOgJQZrkkQtCElRUtAIaPV49+use56GtqzeRoFfI5cSCFMTIhYGJipHLF04Q\n4mbp0oXASuo8TsDAJy+ZzMPLN4A7F4CW9lAdopxWgHjkqSeq14p0PxSEZHC5Clm+/HJ+cvd8AF75\n52HUffMij2/cXAfIaRVGhHxE6VV1Ca0IQuKEe5K3tlux2nsrPL3+UDBBzitAhHxE6ZNGLoMlBCEJ\ncq0mAApcvight+bqhXYSI9cRIU8x2jF/B2WxUxCSJtyTfP6CcYybeDhyv6NQz2CR80pHFjtHkKim\nWfKNE4SEsdt0iQpoRkrK8gkENVo7fHi8wVG2bGwhHnmKCacb6jekja0gDAdrjgmjwUCX109bpw9n\nnoWcHCP+QCi0IucVIEI+okSX6Ms3ThASxWAwkGs10dLuxecPUpBnIcfUK1sSI9cRIR9BotIPBUFI\nCrvNTEOrPojMabeQY+4j5KLjgAj5CNM3/VC+cYKQDMVOW+TvQke0Ry4OuY4I+Qii9YmRS/dDQUiO\nfFuvTL38wk4Mhj4Okig5IEKeco5Z65Thy4IwTD54pzry97rXL6T2aGfktpxWOiLkI4g0zRKE4dN4\ntLfrYWdLPt5uka1jkTzyEUbyyAVheJTktdHWXI7fm4O304LVEiAsXXJa6chPW4qJbmMrMXJBGC6/\nvOd8Cpqq6djRzOLFTzJrZlGfR+XEAvHIRxRN0yRGLgjDJNwJMcwDz22J/C1nlU5SQq4oihG4D1gA\nWIEfq6r691QalrH0We3UC4JCMfJRMkcQsg3JI+9PsqGVqwCzqqpnA5cB01NnUhah9YZaxCMXhNRg\nNklE+FiSDa1cDGxTFGV16PY3U2RPVqEhWSuCkGqiPXI5sSAOIVcU5RrgFqLX8eoBj6qqlyqKcg7w\nOHDuiFiYYfRf7NT/li+cIKQGc1SvFQHiEHJVVVcAK/repyjKn4HVocffUhRlZjwv5nY7krExLfh8\nPj75yU/y2muvJb0Pt9uB1dr7ljoctkg/ZYfDOuDxezwebr31Vtra2rBYLPz85z+ntLSUt99+m/vv\nv5+cnByKiopYunQpVqs15j5GmrH82aUCOb7MocDRW7L/5JMHefUv21i2bBFFRYWjaNXokmxoZS2w\nCHheUZR5QGU8G9XXtyf5ciOP1+slGEzeRrfbQX19O97QdG+A1jYPPov+Fnd1+gbc9zPP/JmpU2dw\n9dVfY82a1TzwwEPcfPOt/OhHP+Z3v3uUwsJCHnnkd/zhDyv57Ge/kJR9wyF8bNmKHF9m4fP1nmOH\nDyu88d5MvN6VUZkt2UQ8P8LJCvlyYJmiKG+Hbv9PkvuJ8Mxre9mwq264u4ni9FmlXLFw4HVYj8fD\n//7v92lvb2fChImR+/ft28v99/8KAKezgLvu+iF2ex6//vUvUNWdFBUVUVNTzS9+8VtWrHiE1tYW\nPJ5O7rnnPjav/SuH9u5A04JsG/clFnz0bLxtR3n8wZWsclij9hfmiiu+FIml19YeJT9f/+AeeOAR\nCgt1LyMQ8GOxjI43Lghjib4xcj24YqCy0jla5owJkhJyVVV9wLUptiXtvPDCc0ybNp3rrruBHTu2\n8eGHHwCwdOnPuOuuH1FRMYXVq1fx5JNPMGfOXNraWvn97x+npaWFL33p/0X2s2DBR7jppq/z0kv/\noLO1gUln3kAw4Oe1V5Yz+8RTqd3yV75+0+1ccckZkf19/es3RtliMBj41rduYP/+ffzmN78DoKio\nGIA333yNjRs/4LrrorcRhOORvt0Pdf9Ho6KibdTsGQuMmYKgKxZOH9R7HgkOHarkzDPPBmDOnBMx\nm/VBr5WVB/j1r38OgN/vZ+LESVRWHuTEE08GoLCwkIqKKZH9TJ5cAcD+/Xtpqj2Iv+ERQMNpDdLU\neBRfRx0vPfMIa9c8HtlfLO6/fxlVVQe57bZv8/TTLwDwzDN/4o03XuPXv36QnJyckXgbBCGj6OuR\nl5fvYMEVH/CTn5w/ihaNPmNGyEeDKVOmsW3bFs466xx2796F3x8AYPLkKXz/+3dTWlrG1q2baWpq\nxGKx8Morf+fzn/8ibW1tHDrUuyxgNBoj25VOno1p8iI0TWMymykqGY8lv5TPf/nbfOb8eZH99WXl\nyscpLS3l4osXYbPlYjLpPyhPPPEYe/ao/Pa3D2GxWNL0rgjC2MZs6s1Vufba6Xzl0ydm1RpAMhzX\nQn7ZZZ/lpz/9ETfddB2TJ1dgsege76233sFPfvJDAoEARqORO+74ARMnTuLtt9dxww3XUlRUhNVq\nw2yOfvvOOuscVjzzCofWLyMY8DH5rPOwWG2UnnQZz/7xt7zyrCmyv75ceuln+OlPf8zq1avQNI27\n7voxzc1NPP74oyjKbG699ZsYDAYWLryQyy77bJreHUEYm0TlkY+iHWMJg5a+UWRaJv9qVlUdZM+e\n3VxwwUW0tbVy1VVf4LnnVkfEPJwZ8MBzW9i4pwGAqy5WsJiNPPbyTr66aBZnn1w+moeQNNmW9XAs\ncnyZxXs7a3l41XYArjh/OlddOjerju9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"text/plain": [
"<matplotlib.figure.Figure at 0x11dd2b990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# data for plot model prediction\n",
"x_plot = x_list.reshape(-1, 1)\n",
"\n",
"plot_degree = [0, 1, 2, 4 , 32]\n",
"\n",
"# plot model\n",
"for degree in plot_degree:\n",
"\n",
" plt.scatter(X_train, y_train, label=\"training point\")\n",
" model = model_dict[str(degree)] \n",
" y_plot = model.predict(x_plot)\n",
" plt.plot(x_plot, y_plot, label=\"degree {0}\".format(degree))\n",
" plt.ylim(ymax = y_train.max(), ymin = y_train.min())\n",
" plt.legend(loc='lower left')\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## check MSE for each degree"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"degree_list = list()\n",
"lbl_list = list()\n",
"mse_list = list()\n",
"\n",
"# cal mse for each degree and train / cv\n",
"for degree in degree_trial_list:\n",
" \n",
" model = model_dict[str(degree)]\n",
" \n",
" # cv mse\n",
" mse_list.append(mean_squared_error(y_cv, model.predict(X_cv)))\n",
" lbl_list.append('cv')\n",
" degree_list.append(degree)\n",
" \n",
" # train mse\n",
" mse_list.append(mean_squared_error(y_train, model.predict(X_train)))\n",
" lbl_list.append('train')\n",
" degree_list.append(degree)\n",
"\n",
"# convert as DataFrame\n",
"df_mse = pd.DataFrame({\"degree\": degree_list, \"data_type\": lbl_list, \"mse\": mse_list})"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0, 20)]"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OF300nptfzr7WA0Cg3LOnyUNurmXcd8YRCATjx6Ez3cHXhbmp5OcPLqOcXZoZ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\n/4aNs1k0M5v5ZZkcPd05ZBpyyexsLo9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Pq3ppd3Sws2lPVKFWUWl2tFHZejBmP+ttjZzq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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c08e490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot\n",
"g = sns.pointplot(x='degree', y='mse', hue='data_type', data=df_mse)\n",
"g.set(ylim=(0, 20))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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