From 34dc331c787bc65a199cb54227ac4d01bd87b0bb Mon Sep 17 00:00:00 2001 From: Aurelien Geron Date: Mon, 16 Oct 2017 14:19:08 +0200 Subject: [PATCH] Clarify stratified sampling paragraph in ch02 --- 02_end_to_end_machine_learning_project.ipynb | 539 +++++++++---------- 1 file changed, 267 insertions(+), 272 deletions(-) diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb index 80338b5..57d8f4e 100644 --- a/02_end_to_end_machine_learning_project.ipynb +++ b/02_end_to_end_machine_learning_project.ipynb @@ -35,9 +35,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# To support both python 2 and python 3\n", @@ -80,9 +78,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -106,9 +102,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "fetch_housing_data()" @@ -117,9 +111,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -514,7 +506,7 @@ "data": { "image/png": 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bVh/gqs9NXtbYuX54hlu0eIyMjOB7b3ocw+lx/KZvrsbQOZUlSZIkSQMpyRHA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fqFUd+6rzujuNEMKnlney24FngrcLdBl2uBdVrf9ltbgRuATwBrBl2uPSj/Kczj79Bh3WaqD3sQ\nXxu2bbb/o67jtgBPHHR5F3ubGp0mSZIkSZIkSdKclu2cypIkSZIkSZKkPWdQWZIkSZIkSZLUM4PK\nkiRJkiRJkqSeGVSWJEmSJEmSJPXMoLIkSZIkSZIkqWcGlSVJkiRJkiRJPTOoLEmSJEmSJEnqmUFl\nSZIkSZIkSVLPDCpLkiRJkiRJknpmUFmSJEmSJEmS1DODypIkSZIkSZKknhlUliRJkiRJkiT1zKCy\nJEmSJEmSJKlnBpWlZSTJRJLnzTPvA5NMJrnrYpdLkiRJkiRJo8OgsqRpJdmS5IlTr6vqB1W1sqp+\nNshySZK02JK8P8lr5zhmY5KrFvE9K8mDF+t8kiSNm176Z0mDY1BZkiRJQ6/7ZudiHStJkubP/lla\nvgwqSwPSdqivTPKtJFuTvC/J3dq05yf5bpKbknwmyQEd+SrJnyf5XpIbkrwxyV3atFOSfLjj2LXt\n8Sumef+Dknw+yY3teT6S5F5t2oeABwLntVNevLz7XEkOaMt2U1vW53ec+5Qkn0jywSTbk2xOsqFf\nbSlJ0jhxqilJkuZnuu++kvrDoLI0WMcAvwMcBDwEOCnJ44HXA38I3B+4EvhYV77/CWwAfg14OvDc\nebx32vc5AHgY8EvAKQBV9SzgB8DT2ikv3jBN/o8BV7X5fx/467bsU36vPeZewGeAt8+jjJIkzXSz\n8/fam5Y/adcMeNhMx7b7P5nk2iTbknwpybp5luUv25uxW5Ic07F/7yR/k+QHSX6c5N1J7t6R/rIk\n1yT5UZLndp3z/UneleSzSXYAv5Xknu3N2euTXJnkpI6byHdpX1+Z5Lr2uHu2aVM3gZ+T5IftjesX\nJvn1JJe07fX2jvd+cJIvtu1yQ5KPz6ddJEnLzzD0z2mnp0ryiiTXAu9r9882UOvRSb7WvufXkjy6\nI20iyWuTfKUt53lJ9msHYd3cHr+2PTZJ/rbti29OcmmShy+oUaURYlBZGqy3V9UPq+om4HXAH9ME\nms+qqm9W1W3AK4H/PtVxtU6vqpuq6gfAW9p8e6SqvltVn6uq26rqeuDNwON6yZvkl4DfBF5RVbdW\n1b8Dfwf8ScdhF1XVZ9s5mD8EHLanZZQkCXa/2Ql8Cvgo8P8B9wE+S/Ml9RdmuTH6j8DBwH2BbwIf\nmUdR7gfsDzwAeDZwRpKHtmmn0dwgXg88uD3mVQBJngy8FPjttgzTPfp7NM3fAvsCFwFvA+4JPIim\nf/4T4Dntsce222+16SvZ/ebto9r3+iOavxX+d/u+64A/TDLV578G+Cfg3sAvtu8rSdKchqx/Xg0c\nCBw320CtJKuB84G3AvvRfA8+P8l+Hec7CngWTV9+EPAvNMHq1cC3gVe3xz0JeCxN/3/P9v1unEf5\npZFkUFkarB92/HwlzajfA9qfAaiqSZqO6QFz5NsjSdYk+ViSq5PcDHyY5otyLw4Abqqq7V3l6Czj\ntR0/3wLcLT6KJElaHH8EnN/eHL0D+Bvg7sCjZ8pQVWdV1fb2hu0pwGFTo3v30MntDdkv0nwp/cMk\nAY4D/qK96bsd+GuaL6XQfMl8X1X9R1XtaN+/26er6stV9XPgjjbvK9sybwHeRPMFF5ob0G+uqu+1\nfye8Ejiqq599TXvj95+AHcBHq+q6qroa+GfgV9vj7qD5En5Ae/xF82gTSZJgcP3zz4FXt/3zT5l9\noNbhwH9W1YeqamdVfRS4DHhax/neV1VXVNU2mqD3FVV1YVXtBD7Jrn3ovsAhQKrq21V1zR6WXRpZ\nBpWlwfqljp8fCPyo3Q6c2plkH5o7qFfPkQ+aL4336Ei73yzv/ddAAYdW1SrgmTRTYkypWfL+CFid\nZN+uclw9w/GSJC2m7huwP6e54fqA6Q5OctckpyW5or2RuqVN6vVm6pStbVB4ytSN3fvQ9L/faB/3\n/QlwQbt/qrzdN4S7dabvD+zVdVznzdsDpklbAazp2Pfjjp9/Os3rle3PL6fp//+tfVx5PlNqSZIE\ng+ufr6+qW2cpR+dAre4+FHYfINVTH1pVn6d5UugdwHVJzkiyag/LLo0sg8rSYP2vJL/YPoLzv4GP\n0zwu9Jwk65PsTRP8/Wo7SmnKy5Lcu52G4sVtPoB/Bx6b5IHt3d1XzvLe+wKTwLYkDwBe1pX+Y5pH\nandTVT8EvgK8PsndkjwC+FOa0c6SJPVD583O7huwobnhevU0x0IztcTTaaZ/uCewdirrHpbh3u3N\n3ilTN3ZvoPmSua6q7tVu92wfBQa4ht1vCHfrLPMN3DmCuDPPVP1+NE3aTnb90tuTqrq2qp5fVQcA\nLwDemeTBe3oeSdKyNQz9c/d5Zxuo1d2HwgIGSFXVW6vqkcCv0EyD0f29WhpbBpWlwTqbZh7D7wFX\nAK+tqguBk4H/Q/Ml9CDufHx2yqeBb9AEkc8HzgSoqs/RBJgvadP/YZb3/iuahf62tec4pyv99TQL\nB/4kyUunyf/HNJ3+j4BzaR43unDOGkuSND+dNzs/ARye5AlJ9gI2AbfR3PDsPhaaG6m30YxSugfN\nDdv5+qskv5DkMcDvAp9sR2K9F/jbJPcFSPKAJL/TUd5jk/xKkntw51yM02rXI/gE8Lok+yY5EHgJ\nd968/SjwF0l+OcnKtj4fbx/L3SNJ/iDJL7Yvt9J8Mf/5np5HkrRsDUv/3Gm2gVqfBR6S5OgkK5L8\nEU1AeLbvztNKswjuo9q67gBuxT5Uy4hBZWmwvlZVv9KOaHp2Vd0CUFXvrqqDqmp1Vf1uVV3Vle+z\nVfWgqtqvqja1Xz5p8/6v9nwPrqr3VlWmvmRW1caq+rv2581V9ch2kYT1VfWmqvrFjvN8uqoe2J7r\nb6pqS9e5rmrLtrot67s78p5SVc/seL1LXkmS5uG/bnbSzHv4TJpF5W5oXz+tqm7vPra9MfpBmkdb\nrwa+BfzrPMtwLU3g9Uc0Cwm9sKoua9NeAXwX+Nf2Ed4LgYcCVNU/0iyW9/n2mM/38F5/RvMF9Xs0\nC/edDZzVpp1Fswjul4Dv03yJ/bN51unXga8mmQQ+A7y4qr43z3NJkpafYeifdzHbQK2qupHmpvAm\nmmD2y4Hfraob5vFWq2huKm+lqceNwBsXWn5pVKRqtmlTJfVLki3A8/Z0dG+SAg6uqu/2pWCSJEmS\nJEnSLBypLEmSJEmSJEnqmUFlaUCqau185iBup5FwlLIkSYssyV8mmZxm+8dBl02SpOXK/lkaTk5/\nIUmSJEmSJEnq2YpBF2BP7b///rV27dpFOdeOHTvYZ599FuVcw2Qc62WdRsM41gnGs17Waf6+8Y1v\n3FBV9+nX+ZNMAL8BTC1seXVVPbRNO5pmgZP9gc8Bz62qm9q01cCZwJNoFkZ5ZVWd3XHeGfPOxD53\nfqzreLKu48m6Drd+97nDxD73TqNefhj9Olj+wRv1Olj+wdvTOsyrz62qkdoe+chH1mL5whe+sGjn\nGibjWC/rNBrGsU5V41kv6zR/wNerj/0cMEGziGf3/nXAduCxwErgbOBjHekfBT7epv0PYBuwrpe8\nM232ufNjXceTdR1P1nW49bvPHabNPvdOo17+qtGvg+UfvFGvg+UfvD2tw3z63JEbqSxJ0jJ1DHBe\nVX0JIMnJwLeT7Av8HDgSeHhVTQIXJfkM8CzgxNnyVtX2AdRFkiRJkjTCXKhPkqTh8/okNyT5cpKN\n7b51wMVTB1TVFcDtwEPabWdVXd5xjovbPHPllSRJkiRpjzhSWZKk4fIK4Fs0Qd+jgPOSrKeZtmJb\n17HbgH2BnwE3z5DGHHl3keQ44DiANWvWMDExMd967GJycnLRzjXsrOt4sq7jybpKkiTNj0FlSZKG\nSFV9tePlB5L8MfBUYBJY1XX4Kpq5kn8+Sxpz5O1+/zOAMwA2bNhQGzdu3PNKTGNiYoLFOtews67j\nybqOJ+sqSZI0P05/IUnScCsgwGbgsKmdSR4E7A1c3m4rkhzcke+wNg9z5JUkSZIkaY8YVJYkaUgk\nuVeS30lytyQrkhwDPBa4APgI8LQkj0myD3AqcE5Vba+qHcA5wKlJ9knym8DTgQ+1p54x71LXUZIk\nSZI0+pz+QpKk4bEX8FrgEJp5ki8DjphagC/JC2kCxPsBFwLP6cj7IuAs4DrgRuD4qtoMUFWb58gr\nSZIkSVLPDCpLkjQkqup64NdnST8bOHuGtJuAI+aTV5IkSZKkPeH0F5IkSZIkSZKknhlUliRJkiRJ\nkiT1zKCyJEmSJEmSJKlnBpUlSZIkSZIkST0zqCxJkiRJkiRJ6tmKQRdgmK098fxZ07ecdvgSlUSS\npOXL/liSpKVhnytJ6pUjlSVJkiRJkiRJPTOoLEmSJEmSJEnqmUFlSZIkSZIkSVLPDCpLkiRJkiRJ\nknpmUFmSJEmSJEmS1DODypIkSZIkSZKknhlUliRJkiRJkiT1zKCyJEmSJEmSJKlnBpUlSZIkSZIk\nST0zqCxJkiRJkiRJ6plBZUmSJEmSJElSzwwqS5IkSZIkSZJ6ZlBZkiRJkiRJktQzg8qSJEmSJEmS\npJ4ZVJYkSZIkSZIk9cygsiRJkiRJkiSpZwaVJUmSJEmSJEk9M6gsSZIkSZIkSeqZQWVJkiRJkiRJ\nUs8MKkuSJEmSJEmSemZQWZIkSZIkSZLUM4PKkiRJkiRJkqSeGVSWJEmSJEmSJPXMoLIkSZIkSZIk\nqWcGlSVJkiRJkiRJPTOoLEmSJEmSJEnqmUFlSZIkSZIkSVLPDCpLkiRJkiRJknrWU1A5ydokn02y\nNcm1Sd6eZEWbtj7JN5Lc0v67viNfkpye5MZ2Oz1JOtJnzCtJkiRJkiRJGj69jlR+J3AdcH9gPfA4\n4EVJfgH4NPBh4N7AB4BPt/sBjgOOAA4DHgE8DXgBQA95JUmSJEmSJElDpteg8i8Dn6iqW6vqWuAC\nYB2wEVgBvKWqbquqtwIBHt/mezbwpqq6qqquBt4EHNumzZVXkiRJkqS+SDKR5NYkk+32nY60o5Nc\nmWRHkk8lWd2RtjrJuW3alUmO7jrvjHklSRoXK3o87i3AUUkmaEYVPwU4mSawfElVVcexl7T7pwLP\nF3ekXdzuo4e8/yXJcTSjnlmzZg0TExM9Fnt2k5OTs55r06E7Z82/WOVYbHPVaxRZp9EwjnWC8ayX\ndZIkSQLghKr6u84dSdYB7wEOB74JnEHz9O5R7SHvAG4H1tA8yXt+kouranMPeSVJGgu9BpW/RBPU\nvRm4K81UFZ8CTgK2dR27Ddi3/XllV/o2YGU7r3J3Wnfe/1JVZ9B0xmzYsKE2btzYY7FnNzExwWzn\nOvbE82fNv+WYxSnHYpurXqPIOo2GcawTjGe9rJMkSdKMjgHOq6ovASQ5Gfh2kn2BnwNHAg+vqkng\noiSfAZ4FnDhb3qraPoC6SJLUF3MGlZPchWbk8BnAo2mCwWcBpwPXAKu6sqwCpjrLya70VcBkVVWS\n7rTuvJIkSZIk9dPrk5wGfAf431U1QfP07FemDqiqK5LcDjyEJqi8s6ou7zjHxTTrDjFH3m90vvGg\nnsidzTA8rTsOT5+Neh0s/+CNeh0s/+AtRR16Gam8Gngg8Paqug24Lcn7gNcCLwE2JUnHNBaPoHkc\nCGAzzSJ9/9a+PqzdN5U2W15JkiRJkvrlFcC3aKayOAo4L8l6Zn+q9mc0T/BOl8YceXcxqCdyZzMM\nT+uOw9Nno14Hyz94o14Hyz94S1GHORfqq6obgO8DxydZkeReNAvwXQJM0HSqf55k7yQntNk+3/77\nQeAlSR6Q5ABgE/D+Nm2uvJIkSZIk9UVVfbWqtrcLx38A+DLwVHZ/4hbufKp2ridufSJXkrQszBlU\nbj0DeDJwPfBd4A7gL6rqduAI4E+AnwDPBY5o90OzQMF5wKXAfwDnt/voIa8kSZIkSUulgHDnE7cA\nJHkQ/P/t3X+wZnddJ/j3J2kqZNLp2UTgSq1lusQwrZ1UYtFTzDiLXMQfOKxC0c5uJFq0bGhNCmtn\nicX2VgXoSWSNizpVq8LaVDIRSQCZShCNldpiiouLP6aMPwLVS0+crGkEEwzadv0bE4IAACAASURB\nVPp2SELwu3885+rTD33vc273vff5cV+vqlP9POd7Puf5fu55nuec8+nznG8uSvJwN+2oqiuH4kZ/\nkbtaLADMjV4D9bXW/izJ4iptf5rkZau0tSRv76Z1xQIAAMBm6H6B+/Ikn0ryXJL/Mcl3Jfmfkzwv\nyR9U1SuS/EmSW5PcuzLQXlXdm+TWqrohybVJXpfB+ENJcvdasQAwL3oVlQEAAGCOPC+DcYL2ZHBb\nxmMZ/HL24SSpqp/MoED8DUk+keTHh2JvymDw+r9O8jdJbmytHU2S1trRMbEAMBcUlQEAANhWWmtP\nJPnna7Tfk+SeVdr+NoNbOa47FgDmRd97KgMAAAAAgKIyAAAAAAD9uf0FADDTdh+6f832R29/7Rb1\nBAAAYHtwpTIAAAAAAL0pKgMAAAAA0JuiMgAAAAAAvSkqA8AUqqorq+rpqvrg0Lw3VtXxqjpdVR+r\nqsuH2i6vqvu6tuNV9caR9a0aCwAAAOuhqAwA0+lXkvzRypOq2pvkV5P8WJKFJE8lee/I8s92bdcn\neV8X0ycWAAAAetsx6Q4AAGeqquuS/F2S30/yrd3s65P8Vmvtd7tl3pHkc1V1aZK/T7I/yVWtteUk\nn66qj2dQRD60Vmxr7dQWpgYAAMAcUFQGgClSVbuS3Jrku5PcMNS0N4Mic5KktfZIVT2b5KUZFJWf\na609PLT8Q0le2SP2j0de/2CSg0mysLCQpaWlDclreXn5nNd189XPnddrb1QOfZ1PrrNGrvNJrvNp\nO+UKAGw+RWUAmC63JbmjtfaFqhqevzPJyZFlTya5NMnXkjy5Stu42DO01o4kOZIk+/bta4uLi+vP\n4CyWlpZyrus6cOj+83rtR68/t9c9V+eT66yR63yS63zaTrmytt3nuV8FgERRGQCmRlVdm+R7knzH\nWZqXk+wambcryakMrlRerW1cLAAAAKyLojIATI/FJLuTfL67Snlnkgur6tuTPJDkmpUFq+pbklyU\n5OEMiso7qurK1tqfd4tck+Ro9/joGrEAAACwLorKADA9jiT58NDzn86gyHxjkhcl+YOqekWSP8ng\nvsv3rgy0V1X3Jrm1qm5Icm2S1yX5zm49d68VCwAAAOtxwaQ7AAAMtNaeaq09vjJlcNuKp1trT7TW\njib5yQwKxH+dwf2QbxoKvynJxV3bh5Lc2MWkRywAAAD05kplAJhSrbXDI8/vSXLPKsv+bZLXr7Gu\nVWMBAABgPVypDAAAAABAb4rKAAAAAAD0pqgMAAAAAEBvisoAAAAAAPSmqAwAAAAAQG+KygAAAAAA\n9KaoDAAAAABAb4rKAAAAAAD0pqgMAAAAAEBvisoAAAAAAPSmqAwAAAAAQG+KygAAAAAA9KaoDAAA\nAABAb4rKAAAAAAD0pqgMAAAAAEBvisoAAAAAAPSmqAwAAAAAQG+KygAAAAAA9KaoDAAAAABAb4rK\nAAAAAAD0pqgMAAAAAEBvisoAAAAAAPSmqAwAAAAAQG+KygAAAAAA9KaoDAAAAABAb4rKAAAAAAD0\npqgMAADAtlVVV1bV01X1waF5b6yq41V1uqo+VlWXD7VdXlX3dW3Hq+qNI+tbNRYA5oWiMgAAANvZ\nryT5o5UnVbU3ya8m+bEkC0meSvLekeWf7dquT/K+LqZPLADMhR2T7gAAAABMQlVdl+Tvkvx+km/t\nZl+f5Ldaa7/bLfOOJJ+rqkuT/H2S/Umuaq0tJ/l0VX08gyLyobViW2untjA1ANhUisoAAABsO1W1\nK8mtSb47yQ1DTXszKDInSVprj1TVs0lemkFR+bnW2sNDyz+U5JU9Yv945PUPJjmYJAsLC1laWtqQ\nvJaXl9dc181XP3fO696oPq5lXP9nwaznoP+TN+s56P/kbUUOisoAAABsR7cluaO19oWqGp6/M8nJ\nkWVPJrk0ydeSPLlK27jYM7TWjiQ5kiT79u1ri4uL68/gLJaWlrLWug4cuv+c1/3o9auvd6OM6/8s\nmPUc9H/yZj0H/Z+8rchBURkAAIBtpaquTfI9Sb7jLM3LSXaNzNuV5FQGVyqv1jYuFgDmhqIyAAAA\n281ikt1JPt9dpbwzyYVV9e1JHkhyzcqCVfUtSS5K8nAGReUdVXVla+3Pu0WuSXK0e3x0jVgAmBuK\nygAAAGw3R5J8eOj5T2dQZL4xyYuS/EFVvSLJn2Rw3+V7Vwbaq6p7k9xaVTckuTbJ65J8Z7eeu9eK\nBYB5ccGkOwAAAABbqbX2VGvt8ZUpg9tWPN1ae6K1djTJT2ZQIP7rDO6HfNNQ+E1JLu7aPpTkxi4m\nPWIBYC64UhkAAIBtrbV2eOT5PUnuWWXZv03y+jXWtWosAMwLVyoDAAAAANBb76JyVV1XVZ+rqtNV\n9Uh3j6hU1aur6lhVPVVVn6yqK4ZiLqqqO6vqyap6vKreNrLOVWMBAAAAAJg+vYrKVfW9SX4uyY9n\ncE+o70ry/1XVC5Lcm+QdSS5P8mCSjwyFHk5yZZIrkrwqydur6jXdOsfFAgAAAAAwZfpeqfzvktza\nWvvD1trft9a+2Fr7YpI3JDnaWvtoa+3pDIrI11TVni7uTUlua62daK19Lsn7kxzo2sbFAgAAAAAw\nZcYWlavqwiT7krywqv5rVX2hqn65qi5OsjfJQyvLttZOJ3kkyd6quizJi4fbu8d7u8erxp5fSgAA\nAAAAbJYdPZZZSPK8JD+c5BVJvprkN5PckmRnkidGlj+ZwS0ydg49H23LmNgzVNXBJAeTZGFhIUtL\nSz26Pd7y8vKa67r56ufWjN+ofmy0cXnNIjnNhnnMKZnPvOQEAAAAnKs+ReWvdP/+UmvtsSSpql/M\noKj8u0l2jSy/K8mpJMtDz58eaUvXvlrsGVprR5IcSZJ9+/a1xcXFHt0eb2lpKWut68Ch+9eMf/T6\njenHRhuX1yyS02yYx5yS+cxLTgAAAMC5Gnv7i9baiSRfSNKGZ3f/Hk1yzcrMqrokyUsyuFfyiSSP\nDbd3j4+Oi113FgAAAAAAbIm+A/X9hyQ/VVUv6u6V/L8k+e0k9yW5qqr2V9Xzk7wzyWdaa8e6uA8k\nuaWqLusG4HtLkru6tnGxAAAAAABMmb5F5duS/FGSh5N8LsmfJnl3a+2JJPuTvDvJiSQvT3LdUNy7\nMhh873iSTyV5T2vtgSTpEQsAAAAAwJTpc0/ltNa+muSmbhpt+0SSPavEPZPkzd10tvZVYwEAAIDp\nsXvcuEO3v3aLegLApPW9UhkAAAAAABSVAQAAAADoT1EZAAAAAIDeFJUBAAAAAOhNURkApkhVfbCq\nHquqJ6vq4aq6Yajt1VV1rKqeqqpPVtUVQ20XVdWdXdzjVfW2kfWuGgsAAADroagMANPlZ5Psbq3t\nSvJDSX6mql5WVS9Icm+SdyS5PMmDST4yFHc4yZVJrkjyqiRvr6rXJEmPWAAAAOhtx6Q7AAD8o9ba\n0eGn3fSSJC9LcrS19tEkqarDSb5cVXtaa8eSvCnJgdbaiSQnqur9SQ4keSDJG8bEAgAAQG+KygAw\nZarqvRkUhC9O8qdJfifJu5M8tLJMa+10VT2SZG9VfSnJi4fbu8ev7x7vXS02yRlF5ao6mORgkiws\nLGRpaWlDclpeXj7ndd189XPn9doblUNf55PrrJHrfJLrfNpOuQIAm09RGQCmTGvtpqr6qST/Msli\nkmeS7EzyxMiiJ5Nc2rWtPB9ty5jY0dc+kuRIkuzbt68tLi6eaxpnWFpayrmu68Ch+8/rtR+9/txe\n91ydT66zRq7zSa7zaTvlCgBsPvdUBoAp1Fr7Wmvt00m+KcmNSZaT7BpZbFeSU11bRtpX2jImFgAA\nANZFURkAptuODO6pfDTJNSszq+qSlfndfZQfG27vHq/cn3nV2E3tOQAAAHNJURkApkRVvaiqrquq\nnVV1YVV9f5IfSfKfktyX5Kqq2l9Vz0/yziSfGRpo7wNJbqmqy6pqT5K3JLmraxsXCwAAAL0pKgPA\n9GgZ3OriC0lOJPn5JP+2tfbx1toTSfZnMGDfiSQvT3LdUOy7kjyS5HiSTyV5T2vtgSTpEQsAAAC9\nGagPAKZEV/x95Rrtn0iyZ5W2Z5K8uZvWFQsAAADr4UplAAAAAAB6U1QGAAAAAKA3RWUAAAAAAHpT\nVAYAAAAAoDdFZQAAAAAAelNUBgAAAACgN0VlAAAAAAB62zHpDgAA7D50/6S7AAAAQE+uVAYAAAAA\noDdFZQAAAAAAelNUBgAAAACgN0VlAAAAAAB6U1QGAAAAAKC3HZPuAADAZtp96P5V2x69/bVb2BMA\nAID54EplAAAAAAB6U1QGAAAAAKA3RWUAAAAAAHpTVAYAAGDbqaoPVtVjVfVkVT1cVTcMtb26qo5V\n1VNV9cmqumKo7aKqurOLe7yq3jay3lVjAWBeKCoDAACwHf1skt2ttV1JfijJz1TVy6rqBUnuTfKO\nJJcneTDJR4biDie5MskVSV6V5O1V9Zok6RELAHNhx6Q7AAAAAFuttXZ0+Gk3vSTJy5Icba19NEmq\n6nCSL1fVntbasSRvSnKgtXYiyYmqen+SA0keSPKGMbEAMBcUlQEAANiWquq9GRSEL07yp0l+J8m7\nkzy0skxr7XRVPZJkb1V9KcmLh9u7x6/vHu9dLTbJGUXlqjqY5GCSLCwsZGlpaUNyWl5eXnNdN1/9\n3Ia8ztlsRA7j+j8LZj0H/Z+8Wc9B/ydvK3JQVAYAAGBbaq3dVFU/leRfJllM8kySnUmeGFn0ZJJL\nu7aV56NtGRM7+tpHkhxJkn379rXFxcVzTeMMS0tLWWtdBw7dvyGvczaPXr/66/Y1rv+zYNZz0P/J\nm/Uc9H/ytiIH91QGAABg22qtfa219ukk35TkxiTLSXaNLLYryamuLSPtK20ZEwsAc0NRGQAAAAa/\n5H1JkqNJrlmZWVWXrMzv7qP82HB793jl/syrxm5qzwFgiykqAwAAsK1U1Yuq6rqq2llVF1bV9yf5\nkST/Kcl9Sa6qqv1V9fwk70zymaGB9j6Q5Jaquqyq9iR5S5K7urZxsQAwFxSVAQAA2G5aBre6+EKS\nE0l+Psm/ba19vLX2RJL9GQzYdyLJy5NcNxT7riSPJDme5FNJ3tNaeyBJesQCwFwwUB8AAADbSlf8\nfeUa7Z9IsmeVtmeSvLmb1hULAPPClcoAAAAAAPSmqAwAAAAAQG+KygAAAAAA9KaoDAAAAABAb4rK\nAAAAAAD0pqgMAAAAAEBvisoAAAAAAPS2Y9IdmGW7D92/atujt792C3sCAAAAALA1XKkMAAAAAEBv\nisoAAAAAAPSmqAwAAAAAQG+KygAAAAAA9LatB+r77BdP5sAag+0BAAAAAHAmVyoDAAAAANCbojIA\nAAAAAL2tq6hcVVdW1dNV9cGheW+squNVdbqqPlZVlw+1XV5V93Vtx6vqjSPrWzUWAAAAAIDps94r\nlX8lyR+tPKmqvUl+NcmPJVlI8lSS944s/2zXdn2S93UxfWIBAAAAAJgyvQfqq6rrkvxdkt9P8q3d\n7OuT/FZr7Xe7Zd6R5HNVdWmSv0+yP8lVrbXlJJ+uqo9nUEQ+tFZsa+3UhmQHAAAAAMCG6lVUrqpd\nSW5N8t1Jbhhq2ptBkTlJ0lp7pKqeTfLSDIrKz7XWHh5a/qEkr+wR+8cjr38wycEkWVhYyNLSUp9u\nj7VwcXLz1c9tyLpGbVQfz8Xy8vJEX38zyGk2zGNOyXzmJScAAADgXPW9Uvm2JHe01r5QVcPzdyY5\nObLsySSXJvlakidXaRsXe4bW2pEkR5Jk3759bXFxsWe31/ZLd/9mfuGzvS/WXpdHr1/clPX2sbS0\nlI36G00LOc2Gecwpmc+85AQAAACcq7EV1aq6Nsn3JPmOszQvJ9k1Mm9XklMZXKm8Wtu4WAAAAAAA\nplCfy3QXk+xO8vnuKuWdSS6sqm9P8kCSa1YWrKpvSXJRkoczKCrvqKorW2t/3i1yTZKj3eOja8QC\nAAAAADCF+hSVjyT58NDzn86gyHxjkhcl+YOqekWSP8ngvsv3rgy0V1X3Jrm1qm5Icm2S1yX5zm49\nd68VCwAAAADA9Llg3AKttadaa4+vTBnctuLp1toTrbWjSX4ygwLxX2dwP+SbhsJvSnJx1/ahJDd2\nMekRCwAAAADAlBlbVB7VWjvcWvvRoef3tNa+ubV2SWvtda21vx1q+9vW2uu7tm9urd0zsq5VYwFg\nu6mqi6rqjqo6XlWnqurPquoHhtpfXVXHquqpqvpkVV0xEntnVT1ZVY9X1dtG1r1qLAAAAKzHuovK\nAMCm2ZHkL5O8Msk/TXJLkt+oqt1V9YIk9yZ5R5LLkzyY5CNDsYeTXJnkiiSvSvL2qnpNkvSIBQAA\ngN763FMZANgCrbXTGRSHV/x2Vf1Fkpcl+YYkR1trH02Sqjqc5MtVtae1dizJm5IcaK2dSHKiqt6f\n5EAGg+q+YUwsAAAA9KaoDABTqqoWkrw0ydEMBsh9aKWttXa6qh5JsreqvpTkxcPt3ePXd4/3rhab\n5IyiclUdTHIwSRYWFrK0tLQhuSwvL6+5rpuvfm5DXme9Niq/YeNynSdynU9ynU/bKVcAYPMpKgPA\nFKqq52UwmO2vtdaOVdXOJE+MLHYyg4Fudw49H21L175a7Blaa0eSHEmSffv2tcXFxfPI4h8tLS1l\nrXUdOHT/hrzOej16/eKGr3NcrvNErvNJrvNpO+UKAGw+91QGgClTVRck+fUkzyZ5azd7OcmukUV3\nJTnVtWWkfaVtXCwAAACsi6IyAEyRqqokdyRZSLK/tfbVrulokmuGlrskyUsyuFfyiSSPDbd3j4+O\ni92kNAAAAJhjisoAMF3el+Tbkvxga+0rQ/PvS3JVVe2vqucneWeSzwwNtPeBJLdU1WVVtSfJW5Lc\n1TMWAAAAelNUBoApUVVXJPmJJNcmebyqlrvp+tbaE0n2J3l3khNJXp7kuqHwdyV5JMnxJJ9K8p7W\n2gNJ0iMWAAAAejNQHwBMidba8SS1RvsnkuxZpe2ZJG/upnXFAgAAwHq4UhkAAAAAgN4UlQEAAAAA\n6E1RGQAAAACA3hSVAQAAAADoTVEZAAAAAIDeFJUBAAAAAOhNURkAAAAAgN4UlQEAAAAA6E1RGQAA\ngG2lqi6qqjuq6nhVnaqqP6uqHxhqf3VVHauqp6rqk1V1xUjsnVX1ZFU9XlVvG1n3qrEAMC92TLoD\nAAAAsMV2JPnLJK9M8vkk/zrJb1TV1UmWk9yb5IYkv5XktiQfSfIvutjDSa5MckWSb0zyyar6f1tr\nD1TVC8bEzrXdh+5fte3R21+7hT0BYLMpKgMAALCttNZOZ1AcXvHbVfUXSV6W5BuSHG2tfTRJqupw\nki9X1Z7W2rEkb0pyoLV2IsmJqnp/kgNJHkjyhjGxADAXFJUBgG1rrSuqEldVAWwXVbWQ5KVJjia5\nMclDK22ttdNV9UiSvVX1pSQvHm7vHr++e7x3tdgkZxSVq+pgkoNJsrCwkKWlpQ3JZXl5ec113Xz1\ncxvyOuvVN79x/Z8Fs56D/k/erOeg/5O3FTkoKgMAALBtVdXzktyd5Ndaa8eqameSJ0YWO5nk0iQ7\nh56PtqVrXy32DK21I0mOJMm+ffva4uLieWTxj5aWlrLWug6M+Q/VzfLo9Yu9lhvX/1kw6zno/+TN\neg76P3lbkYOB+gAAANiWquqCJL+e5Nkkb+1mLyfZNbLoriSnuraMtK+0jYsFgLmhqAwAAMC2U1WV\n5I4kC0n2t9a+2jUdTXLN0HKXJHlJBvdKPpHkseH27vHRcbGblAYATISiMgAAANvR+5J8W5IfbK19\nZWj+fUmuqqr9VfX8JO9M8pmhgfY+kOSWqrqsqvYkeUuSu3rGAsBcUFQGAABgW6mqK5L8RJJrkzxe\nVcvddH1r7Ykk+5O8O8mJJC9Pct1Q+LuSPJLkeJJPJXlPa+2BJOkRCwBzwUB9AAAAbCutteNJao32\nTyTZs0rbM0ne3E3rigWAeeFKZQAAAAAAelNUBgAAAACgN0VlAAAAAAB6U1QGAAAAAKA3RWUAAAAA\nAHpTVAYAAAAAoDdFZQAAAAAAelNUBgAAAACgN0VlAAAAAAB6U1QGAAAAAKA3RWUAAAAAAHpTVAYA\nAAAAoDdFZQAAAAAAelNUBgAAAACgN0VlAAAAAAB6U1QGAAAAAKA3RWUAAAAAAHpTVAYAAAAAoDdF\nZQAAAAAAelNUBgAAAACgtx2T7gAAAACwMT77xZM5cOj+SXcDgDnnSmUAAAAAAHpTVAYAAAAAoDdF\nZQAAAAAAelNUBgAAAACgN0VlAAAAAAB6U1QGAAAAAKA3RWUAAAAAAHpTVAYAAAAAoLexReWquqiq\n7qiq41V1qqr+rKp+YKj91VV1rKqeqqpPVtUVI7F3VtWTVfV4Vb1tZN2rxgIAAAAAMH36XKm8I8lf\nJnllkn+a5JYkv1FVu6vqBUnuTfKOJJcneTDJR4ZiDye5MskVSV6V5O1V9Zok6RELAAAAAMCU2TFu\ngdba6QyKwyt+u6r+IsnLknxDkqOttY8mSVUdTvLlqtrTWjuW5E1JDrTWTiQ5UVXvT3IgyQNJ3jAm\nFgAAAACAKTO2qDyqqhaSvDTJ0SQ3Jnlopa21drqqHkmyt6q+lOTFw+3d49d3j/euFpvkjKJyVR1M\ncjBJFhYWsrS0tN5un9XCxcnNVz+3IesatVF9PBfLy8sTff3NIKfZMI85JfOZl5wAAACAc7WuonJV\nPS/J3Ul+rbV2rKp2JnliZLGTSS5NsnPo+WhbuvbVYs/QWjuS5EiS7Nu3ry0uLq6n26v6pbt/M7/w\n2XXX1Xt59PrFTVlvH0tLS9mov9G0kNNsmMeckvnMS07TqaremsEveq5O8qHW2oGhtlcn+ZUk35zk\nP2fwS6DjXdtFSd6X5IeTPJXk/2it/WKfWNa2+9D9q7Y9evtrt7AnAAAA06PPPZWTJFV1QZJfT/Js\nkrd2s5eT7BpZdFeSU11bRtpX2sbFAsB29FdJfibJncMzjWEAAADANOlVVK6qSnJHkoUk+1trX+2a\njia5Zmi5S5K8JIN7JZ9I8thwe/f46LjYc8oEAGZca+3e1trHkvzNSNM/jEPQWns6gyLyNVW1p2t/\nU5LbWmsnWmufS7IyhkGfWAAAAFiXvlcqvy/JtyX5wdbaV4bm35fkqqraX1XPT/LOJJ8ZGmjvA0lu\nqarLupPXtyS5q2csADDwdeMQJFkZw+CynH0Mg73jYje5zwAAAMypsTcUrqorkvxEkmeSPD64aDlJ\n8hOttburan+SX07ywQzu03jdUPi7MihIH0/ylSQ/11p7IElaa0+MiQUABrZkDINk8wbHHTeQ4mYN\nnLuZVstnOw0aKdf5JNf5tJ1yBQA239iicjeQT63R/okkZ/0JbWvtmSRv7qZ1xQIA/6DvGAZPj7SN\ni/06mzU47riBFA+sMSDetFptUN55GDSyL7nOJ7nOp+2UKwCw+XoP1AcATIwxDAAAAJgaY69UBgC2\nRlXtyGDffGGSC7sxB57LYByC93S3jbo/q49h8GAGg+q+JcmPd23jYgEANt3uMb9KevT2125RTwDY\nCIrKADA9bslgPIIVP5rk37XWDs/6GAaf/eLJmbzFBQAAAF/P7S8AYEq01g631mpkOty1faK1tqe1\ndnFrbbG19uhQ3DOttTe31na11hZaa784st5VYwFgO6qqt1bVg1X1TFXdNdL26qo6VlVPVdUnu8Hr\nV9ouqqo7q+rJqnq8qt7WNxYA5omiMgAAANvNXyX5mSR3Ds+sqhckuTfJO5JcnuTBJB8ZWuRwkiuT\nXJHkVUneXlWv6RkLAHNDURkAAIBtpbV2b2vtY0n+ZqTpDRkMhPvR1trTGRSRr6mqPV37m5Lc1lo7\n0Vr7XJL3JznQMxYA5oaiMgAAAAzsTfLQypPW2ukkjyTZW1WXJXnxcHv3eO+42E3uMwBsOQP1bRIj\n2wIAAMycnUmeGJl3MsmlXdvK89G2cbFfp6oOJjmYJAsLC1laWjrnTg9buDi5+ernNmRdW2kl/+Xl\n5Q37W0zKrOeg/5M36zno/+RtRQ6KygAAADCwnGTXyLxdSU51bSvPnx5pGxf7dVprR5IcSZJ9+/a1\nxcXF8+n3P/ilu38zv/DZ2TvVf/T6xSSD4vJG/S0mZdZz0P/Jm/Uc9H/ytiIHt78AAACAgaNJrll5\nUlWXJHlJBvdKPpHkseH27vHRcbGb3GcA2HKKygAAAGwrVbWjqp6f5MIkF1bV86tqR5L7klxVVfu7\n9ncm+Uxr7VgX+oEkt1TVZd0AfG9JclfXNi4WAOaGojIAAADbzS1JvpLkUJIf7R7f0lp7Isn+JO9O\nciLJy5NcNxT3rgwG3zue5FNJ3tNaeyBJesQCwNyYvRstAQAAwHlorR1OcniVtk8k2bNK2zNJ3txN\n64oFgHniSmUAAAAAAHpTVAYAAAAAoDdFZQAAAAAAelNUBgAAAACgN0VlAAAAAAB6U1QGAAAAAKA3\nRWUAAAAAAHpTVAYAAAAAoLcdk+4AAMAs2n3o/rPOv/nq53Lg0P159PbXbnGPAAAAtoYrlQEAAAAA\n6E1RGQAAAACA3hSVAQAAAADoTVEZAAAAAIDeFJUBAAAAAOhNURkAAAAAgN52TLoDAAAAwPa2+9D9\nSZKbr34uB7rHKx69/bWT6BIAa3ClMgAAAAAAvSkqAwAAAADQm6IyAAAAAAC9uacyAMAm2D1yP8hR\n7g8JAADMKlcqAwAAAADQm6IyAAAAAAC9uf3FhKz1k1g/hwUAAAAAppUrlQEAAAAA6E1RGQAAAACA\n3tz+AgAAAJhaa90+MnELSYBJUFQGAJgA4ysAAACzyu0vAAAAAADoTVEZAAAAAIDeFJUBAAAAAOhN\nURkAAAAAgN4M1AcAAADMrLUGv00MgAuwGRSVAQCmjJNjAABgmrn9BQAAAAAAvSkqAwAAAADQm9tf\nTCE/eQUAAAAAppWiMgDAjFnrP6D95zMAALDZFJUBAACAueU/YwE2nqIypAkALQAAC5xJREFUAMAc\ncRstAABgsykqAwAAANuS/4wFODeKyjNo3E7vrtdcskU9AQBmjZNnAADgfCkqAwAAAJzFuP+MPZub\nr34uBw7d7z9qgbmmqAwAwD8wmBEAADDOxIvKVXV5kjuSfF+SLyf531pr90y2V7Pts188mQOrnBA6\nGQTYvuxzOV+jBeeVK7FWOM4AsL/lH7nlFDDPJl5UTvIrSZ5NspDk2iT3V9VDrbWjk+3WfDqXn+70\nZYcIMPXsc9lUm3mccT4cowBbzP6WXs5nv2nfBkzaRIvKVXVJkv1JrmqtLSf5dFV9PMmPJTk0yb6x\nfpt5ImnwQYDzY5/LdrYRxyijV2VP2vkUEwz6DJvH/patspnn39O2z1uxWfs+BXo4N9Vam9yLV31H\nkt9rrf2ToXk/neSVrbUfHJp3MMnB7uk/S/JfNqgLL8jg50jzZh7zktNsmMeckvnMS07n7orW2gu3\n4HU2lH3ulpLrfJLrfJLrdJu5fW7f/W033z737Ga9/8ns56D/kzfrOej/5K03h3Xvcyd9+4udSZ4c\nmXcyyaXDM1prR5Ic2egXr6oHW2v7Nnq9kzaPeclpNsxjTsl85iWnbck+d4vIdT7JdT7JlU3Qa3+b\n2OeuZtb7n8x+Dvo/ebOeg/5P3lbkcMFmrryH5SS7RubtSnJqAn0BgHlmnwsAm8/+FoBtYdJF5YeT\n7KiqK4fmXZPEAAYAsLHscwFg89nfArAtTLSo3Fo7neTeJLdW1SVV9a+SvC7Jr29RFzb8p0ZTYh7z\nktNsmMeckvnMS07bjH3ulpLrfJLrfJIrG2oK9rfJ7G/rWe9/Mvs56P/kzXoO+j95m57DRAfqS5Kq\nujzJnUm+N8nfJDnUWrtnop0CgDlknwsAm8/+FoDtYOJFZQAAAAAAZsek76kMAAAAAMAMUVQGAAAA\nAKC3bVlUrqrLq+q+qjpdVcer6o2T7tNqqmqpqp6uquVu+i9DbW/s+n+6qj7W3btrpW3NHNeK3eD+\nv7WqHqyqZ6rqrpG2V1fVsap6qqo+WVVXDLVdVFV3VtWTVfV4Vb1to2I3M6+q2l1VbWh7LVfVO6Y9\nr27dd3TviVNV9WdV9QMb0a9Jbqu18prVbdWt/4NV9Vi3/oer6oaN6Nc05jTL24mBmqF97qiawD5s\nUtb6vuza5y3fLf8enbSqurIGx5QfHJo39ceS61Ezfty8XlV1XVV9ruvXI1X1im7+XL6HGW/ce3lC\nfZqpz2XNwfnrajnUDBxX1xycl66Vwyxsg25dM32+uVr/Z+XvP7TOLTt2Gxe7qtbatpuSfCjJR5Ls\nTPLfJTmZZO+k+7VKX5eS3HCW+XuTnEryXV0e9yT5cJ8cx8VucP/fkOT1Sd6X5K6h+S/o+vRvkjw/\nyXuS/OFQ+88m+X+SXJbk25I8nuQ15xu7BXntTtKS7FglbirzSnJJksNd/y9I8t9375Hds7ytxuQ1\nk9tq6DN8Ufd4T7f+l834tlotp5ndTqZ/+DvPzD73LH3f8n3YBHOdyH5ggvlu+ffopKck/3fX9w8O\n/Q2m/lhynTkuZYaPm9eZ6/cmOZ7kX3Sf2f+2m+b2PWzq9b6Yun3urH0uMwfnr2vksDtTflydOTgv\nHZPD1G+Doc/YzJ5vrtH/mfj7D61zy47d1opds4/nk+AsThl8wJ9N8tKheb+e5PZJ922V/i7l7Dvh\n/z3JPUPPX9Lldem4HNeK3cQ8fiZn7tAOJvn9ke3ylSR7uud/leT7htpvW3nDn0/sFuQ17ktqJvLq\n1v+ZJPvnZVudJa+52FZJ/lmSx5L8D/OyrUZymovttF2nzNg+d408tmwfNk1TtmA/MA3TVn2PTjjH\n65L8RgYnuSsnJjN1LNkzz6XMwXFzz1x/P8n/dJb5c/keNvV6T0zlPndWP5eZg/PXs+SwOzN4XJ05\nOC/NDJ+DZsbPNzOj55bZwmO3cbFrTdvx9hcvTfJca+3hoXkPZVC1n1Y/W1Vfrqrfq6rFbt7eDPqd\nJGmtPZLuTZDxOa4Vu1VG+3A6ySNJ9lbVZUlePNyetfu/ntitcryqvlBV/6GqXpAks5RXVS1k8H44\nep79mpqckq/La8VMbquqem9VPZXkWAY7yd85z35Na04rZnI7MZP73D425bO2yX1el63YD2xm//vY\nyu/RTUxjrKraleTWJKM/w5z1Y8nVzONx8xmq6sIk+5K8sKr+a7d//OWqujhz+B6mt2ne587D53Km\nj7NHzMxx9Tycl87qOeisn2/O8rnlBI7dznn/sR2LyjuTPDky72QG1flp9L8m+ZYMfs52JMlvVdVL\nMsjj5MiyK3mMy3Gt2K0yrv8Zae/b/3Gxm+3LSf55kisy+HnFpUnu7tpmIq+qel4Gff611tqx8+zX\nVOSUnDWvmd5WrbWbunW+Ism9SZ45z35Na04zvZ2YuX1uX5v1WZsKW7gfmKgt/h6dpNuS3NFa+8LI\n/Fk/ljybeT1uHrWQ5HlJfjiD9++1Sb4jy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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -532,9 +524,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# to make this notebook's output identical at every run\n", @@ -544,9 +534,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -581,9 +569,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import hashlib\n", @@ -600,9 +586,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# This version supports both Python 2 and Python 3, instead of just Python 3.\n", @@ -613,9 +597,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "housing_with_id = housing.reset_index() # adds an `index` column\n", @@ -625,9 +607,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n", @@ -789,9 +769,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -947,7 +925,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -958,7 +936,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -972,9 +950,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Divide by 1.5 to limit the number of income categories\n", @@ -1016,7 +992,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -1027,7 +1003,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1041,9 +1017,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import StratifiedShuffleSplit\n", @@ -1058,6 +1032,31 @@ "cell_type": "code", "execution_count": 25, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.0 0.350533\n", + "2.0 0.318798\n", + "4.0 0.176357\n", + "5.0 0.114583\n", + "1.0 0.039729\n", + "Name: income_cat, dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { @@ -1070,7 +1069,7 @@ "Name: income_cat, dtype: float64" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1081,10 +1080,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ "def income_cat_proportions(data):\n", @@ -1103,7 +1100,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1188,7 +1185,7 @@ "5.0 0.114438 0.109496 0.114583 -4.318374 0.127011" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1199,10 +1196,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "for set_ in (strat_train_set, strat_test_set):\n", @@ -1218,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": { "collapsed": true }, @@ -1229,7 +1224,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1257,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1292,7 +1287,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1324,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1370,7 +1365,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": { "collapsed": true }, @@ -1381,7 +1376,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1399,7 +1394,7 @@ "Name: median_house_value, dtype: float64" ] }, - "execution_count": 35, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1410,7 +1405,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1443,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1473,7 +1468,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": { "collapsed": true }, @@ -1493,7 +1488,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1514,7 +1509,7 @@ "Name: median_house_value, dtype: float64" ] }, - "execution_count": 39, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1526,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1549,7 +1544,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1754,7 +1749,7 @@ "max 1243.333333 " ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1772,7 +1767,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -1784,7 +1779,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1900,7 +1895,7 @@ "19252 3468.0 1405.0 3.1662 <1H OCEAN " ] }, - "execution_count": 43, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1912,7 +1907,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1958,7 +1953,7 @@ "Index: []" ] }, - "execution_count": 44, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1969,7 +1964,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -2079,7 +2074,7 @@ "19252 1405.0 3.1662 <1H OCEAN " ] }, - "execution_count": 45, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -2090,7 +2085,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -2206,7 +2201,7 @@ "19252 3468.0 1405.0 3.1662 <1H OCEAN " ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -2219,7 +2214,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": { "collapsed": true }, @@ -2239,7 +2234,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": { "collapsed": true }, @@ -2250,7 +2245,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -2259,41 +2254,13 @@ "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)" ] }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "imputer.fit(housing_num)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ -118.51 , 34.26 , 29. , 2119.5 , 433. ,\n", - " 1164. , 408. , 3.5409])" - ] - }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "imputer.statistics_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check that this is the same as manually computing the median of each attribute:" + "imputer.fit(housing_num)" ] }, { @@ -2313,6 +2280,34 @@ "output_type": "execute_result" } ], + "source": [ + "imputer.statistics_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check that this is the same as manually computing the median of each attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ -118.51 , 34.26 , 29. , 2119.5 , 433. ,\n", + " 1164. , 408. , 3.5409])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "housing_num.median().values" ] @@ -2326,7 +2321,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": { "collapsed": true }, @@ -2337,7 +2332,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": { "collapsed": true }, @@ -2349,7 +2344,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2459,7 +2454,7 @@ "19252 3468.0 1405.0 3.1662 " ] }, - "execution_count": 54, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -2470,7 +2465,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2479,7 +2474,7 @@ "'median'" ] }, - "execution_count": 55, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -2490,7 +2485,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -2600,7 +2595,7 @@ "4 4459.0 1463.0 3.0347 " ] }, - "execution_count": 56, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -2619,7 +2614,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -2638,7 +2633,7 @@ "Name: ocean_proximity, dtype: object" ] }, - "execution_count": 57, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -2657,7 +2652,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -2666,7 +2661,7 @@ "array([0, 0, 1, 2, 0, 2, 0, 2, 0, 0])" ] }, - "execution_count": 58, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -2678,7 +2673,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2687,7 +2682,7 @@ "Index(['<1H OCEAN', 'NEAR OCEAN', 'INLAND', 'NEAR BAY', 'ISLAND'], dtype='object')" ] }, - "execution_count": 59, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -2712,7 +2707,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -2722,7 +2717,7 @@ "\twith 16512 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 60, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2744,7 +2739,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -2759,7 +2754,7 @@ " [ 0., 0., 0., 1., 0.]])" ] }, - "execution_count": 61, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -2777,7 +2772,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 63, "metadata": { "collapsed": true }, @@ -2980,7 +2975,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -2990,7 +2985,7 @@ "\twith 16512 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 63, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -3011,39 +3006,6 @@ "The default encoding is one-hot, and it returns a sparse array. You can use `toarray()` to get a dense array:" ] }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 0., 0., 0., 0.],\n", - " [ 1., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 1.],\n", - " ..., \n", - " [ 0., 1., 0., 0., 0.],\n", - " [ 1., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 1., 0.]])" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housing_cat_1hot.toarray()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can specify the encoding to be `\"onehot-dense\"` to get a dense matrix rather than a sparse matrix:" - ] - }, { "cell_type": "code", "execution_count": 65, @@ -3067,9 +3029,14 @@ } ], "source": [ - "cat_encoder = CategoricalEncoder(encoding=\"onehot-dense\")\n", - "housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n", - "housing_cat_1hot" + "housing_cat_1hot.toarray()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can specify the encoding to be `\"onehot-dense\"` to get a dense matrix rather than a sparse matrix:" ] }, { @@ -3080,7 +3047,13 @@ { "data": { "text/plain": [ - "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'], dtype=object)]" + "array([[ 1., 0., 0., 0., 0.],\n", + " [ 1., 0., 0., 0., 0.],\n", + " [ 0., 0., 0., 0., 1.],\n", + " ..., \n", + " [ 0., 1., 0., 0., 0.],\n", + " [ 1., 0., 0., 0., 0.],\n", + " [ 0., 0., 0., 1., 0.]])" ] }, "execution_count": 66, @@ -3088,6 +3061,28 @@ "output_type": "execute_result" } ], + "source": [ + "cat_encoder = CategoricalEncoder(encoding=\"onehot-dense\")\n", + "housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n", + "housing_cat_1hot" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'], dtype=object)]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cat_encoder.categories_" ] @@ -3101,7 +3096,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 68, "metadata": { "collapsed": true }, @@ -3133,7 +3128,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -3268,7 +3263,7 @@ "4 3.04785 " ] }, - "execution_count": 68, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -3287,7 +3282,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": { "collapsed": true }, @@ -3307,7 +3302,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -3328,7 +3323,7 @@ " -0.09586294, 0.10180567]])" ] }, - "execution_count": 70, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -3346,7 +3341,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 72, "metadata": { "collapsed": true }, @@ -3374,7 +3369,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -3396,7 +3391,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "metadata": { "collapsed": true }, @@ -3412,7 +3407,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -3433,7 +3428,7 @@ " 1. , 0. ]])" ] }, - "execution_count": 74, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -3445,7 +3440,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -3454,7 +3449,7 @@ "(16512, 16)" ] }, - "execution_count": 75, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -3472,7 +3467,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -3481,7 +3476,7 @@ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, - "execution_count": 76, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -3495,7 +3490,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -3525,7 +3520,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -3542,7 +3537,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -3570,7 +3565,7 @@ " 0. ]])" ] }, - "execution_count": 79, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -3581,7 +3576,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -3590,7 +3585,7 @@ "68628.198198489219" ] }, - "execution_count": 80, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -3606,7 +3601,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -3615,7 +3610,7 @@ "49439.895990018973" ] }, - "execution_count": 81, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -3629,7 +3624,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -3642,7 +3637,7 @@ " presort=False, random_state=42, splitter='best')" ] }, - "execution_count": 82, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -3656,7 +3651,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -3665,7 +3660,7 @@ "0.0" ] }, - "execution_count": 83, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -3686,7 +3681,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 85, "metadata": { "collapsed": true }, @@ -3701,7 +3696,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -3727,7 +3722,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -3751,7 +3746,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -3765,7 +3760,7 @@ " oob_score=False, random_state=42, verbose=0, warm_start=False)" ] }, - "execution_count": 87, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -3779,7 +3774,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -3788,7 +3783,7 @@ "21941.911027380233" ] }, - "execution_count": 88, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -3802,7 +3797,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -3828,7 +3823,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -3845,7 +3840,7 @@ "dtype: float64" ] }, - "execution_count": 90, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -3857,7 +3852,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -3866,7 +3861,7 @@ "111094.6308539982" ] }, - "execution_count": 91, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -3884,7 +3879,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -3903,7 +3898,7 @@ " scoring='neg_mean_squared_error', verbose=0)" ] }, - "execution_count": 92, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -3934,7 +3929,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -3943,7 +3938,7 @@ "{'max_features': 8, 'n_estimators': 30}" ] }, - "execution_count": 93, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -3954,7 +3949,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -3968,7 +3963,7 @@ " verbose=0, warm_start=False)" ] }, - "execution_count": 94, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -3986,7 +3981,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -4022,7 +4017,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -4651,7 +4646,7 @@ "[18 rows x 23 columns]" ] }, - "execution_count": 96, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -4662,7 +4657,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -4682,7 +4677,7 @@ " verbose=0)" ] }, - "execution_count": 97, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -4704,7 +4699,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -4732,7 +4727,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -4746,7 +4741,7 @@ " 2.85647464e-03])" ] }, - "execution_count": 99, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -4758,7 +4753,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -4782,7 +4777,7 @@ " (6.0280386727365991e-05, 'ISLAND')]" ] }, - "execution_count": 100, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -4797,7 +4792,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 102, "metadata": { "collapsed": true }, @@ -4817,7 +4812,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -4826,7 +4821,7 @@ "47766.003966433083" ] }, - "execution_count": 102, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -4851,7 +4846,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -4861,7 +4856,7 @@ " 59218.98886849, 189747.55849879])" ] }, - "execution_count": 103, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -4885,7 +4880,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 105, "metadata": { "collapsed": true }, @@ -4896,7 +4891,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 106, "metadata": { "collapsed": true }, @@ -4917,7 +4912,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -4976,7 +4971,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -5549,7 +5544,7 @@ " scoring='neg_mean_squared_error', verbose=2)" ] }, - "execution_count": 107, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -5577,7 +5572,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -5586,7 +5581,7 @@ "70363.903139641669" ] }, - "execution_count": 108, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -5606,7 +5601,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -5615,7 +5610,7 @@ "{'C': 30000.0, 'kernel': 'linear'}" ] }, - "execution_count": 109, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -5647,7 +5642,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -6221,7 +6216,7 @@ " verbose=2)" ] }, - "execution_count": 110, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -6256,7 +6251,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -6265,7 +6260,7 @@ "54767.99053704409" ] }, - "execution_count": 111, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -6285,7 +6280,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -6294,7 +6289,7 @@ "{'C': 157055.10989448498, 'gamma': 0.26497040005002437, 'kernel': 'rbf'}" ] }, - "execution_count": 112, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -6319,7 +6314,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -6355,7 +6350,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -6405,7 +6400,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 116, "metadata": { "collapsed": true }, @@ -6443,7 +6438,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 117, "metadata": { "collapsed": true }, @@ -6461,7 +6456,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -6470,7 +6465,7 @@ "array([ 0, 1, 7, 9, 12])" ] }, - "execution_count": 117, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -6482,7 +6477,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -6492,7 +6487,7 @@ " dtype='