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regonn/kkbox.ipynb

Created November 25, 2017 11:10
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kkbox.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mPrecompiling module DataFrames.\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mRecompiling stale cache file /home/regonn/.julia/lib/v0.6/ScikitLearnBase.ji for module ScikitLearnBase.\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mRecompiling stale cache file /home/regonn/.julia/lib/v0.6/DecisionTree.ji for module DecisionTree.\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mRecompiling stale cache file /home/regonn/.julia/lib/v0.6/Loess.ji for module Loess.\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mRecompiling stale cache file /home/regonn/.julia/lib/v0.6/Gadfly.ji for module Gadfly.\n",
"\u001b[39mWARNING: StatsBase.WeightVec is deprecated, use StatsBase.Weights{#1#S, #2#T, #3#V} where #3#V<:AbstractArray{#2#T, 1} where #2#T<:Real where #1#S<:Real instead.\n",
" likely near /home/regonn/.julia/v0.6/KernelDensity/src/univariate.jl:80\n"
]
}
],
"source": [
"using DataFrames\n",
"using DecisionTree\n",
"using Gadfly"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>msno</th><th>is_churn</th></tr></thead><tbody><tr><th>1</th><td>ugx0CjOMzazClkFzU2xasmDZaoIqOUAZPsH1q0teWCg=</td><td>1</td></tr><tr><th>2</th><td>f/NmvEzHfhINFEYZTR05prUdr+E+3+oewvweYz9cCQE=</td><td>1</td></tr><tr><th>3</th><td>zLo9f73nGGT1p21ltZC3ChiRnAVvgibMyazbCxvWPcg=</td><td>1</td></tr><tr><th>4</th><td>8iF/+8HY8lJKFrTc7iR9ZYGCG2Ecrogbc2Vy5YhsfhQ=</td><td>1</td></tr><tr><th>5</th><td>K6fja4+jmoZ5xG6BypqX80Uw/XKpMgrEMdG2edFOxnA=</td><td>1</td></tr><tr><th>6</th><td>ibIHVYBqxGwrSExE63/omeDD99M5vYB3CN2HzkEY+eM=</td><td>1</td></tr><tr><th>7</th><td>kVmM8X4iBPCOfK/m1l3KR7LjFoxD1EcKYCc76F5IAWw=</td><td>1</td></tr><tr><th>8</th><td>moRTKhKIDvb+C8ZHOgmaF4dXMLk0jOn65d7a8tQ2Eds=</td><td>1</td></tr><tr><th>9</th><td>dW/tPZMDh2Oz/ksduEctJbsz0MXw3kay/1AlZCq3EbI=</td><td>1</td></tr><tr><th>10</th><td>otEcMhAX3mU4gumUSogqgteN3oaNmhdmTkoof2iRYEE=</td><td>1</td></tr><tr><th>11</th><td>t5rqTxCnG7s5VBgEfdkQCezv5KBK7+DMujNibYgylrs=</td><td>1</td></tr><tr><th>12</th><td>dfLS2/Pom6O3iUpo+mf/K1Bc6g+7LFKzoNf+zlJtDoQ=</td><td>1</td></tr><tr><th>13</th><td>a7AtvhlY8KnKZGpiVe+4HhFerqTEgmno3x7Rc7YGwzw=</td><td>1</td></tr><tr><th>14</th><td>F45GsXJIeLvzUJqz/v4R/6CVYPdek7K4Leu+aqbCRo8=</td><td>1</td></tr><tr><th>15</th><td>SJCoxreWp6Cu9WPitvAT6pZ9gChiSR4tWP4lvJGdxSM=</td><td>1</td></tr><tr><th>16</th><td>Oo2RDQixJ0pRWqec4/CksiKwhz0Za0yU2GIQtKa1JFk=</td><td>1</td></tr><tr><th>17</th><td>f91n3lDipDjRtAVNgMGnTsxP0VrzDsfB0Hdth12gqQE=</td><td>1</td></tr><tr><th>18</th><td>/L2095JD4M/BNLTCboucgyuT2i8JIiJ87bOIsxtFvBw=</td><td>1</td></tr><tr><th>19</th><td>1AzXWFlRO6EfMBzfBdk98sBVnjzY7U1G24mVFNdzGNQ=</td><td>1</td></tr><tr><th>20</th><td>WkF/FvlzpBLFoa+Hm3hagOfme2pHLE2y+RJ3eGcLT0k=</td><td>1</td></tr><tr><th>21</th><td>w2O0vchmncaBEQLoC5Pn7qRoSPiJ6X2x5O+woVFDhnY=</td><td>1</td></tr><tr><th>22</th><td>nGmbdyGVU+LCI2SXyYosErJCCYp371hQNP91BpP2d2I=</td><td>1</td></tr><tr><th>23</th><td>SlkPQrh6oIqsdpW+Sl2adYlJNEwWSQsIYycM11dSUQg=</td><td>1</td></tr><tr><th>24</th><td>qpV8BYuYz/Z7LFqEuo2QEMfwWWxdCIQQT4X/XsPXwhc=</td><td>1</td></tr><tr><th>25</th><td>ePx+sYifOKWLbG1fC8Gy2rjlg92Z7+Blxj94zzyC2+g=</td><td>1</td></tr><tr><th>26</th><td>Z9Y6f2hXZpPIvbLgHHgUncs7Tb+qmKgGNSwwVAiMlm0=</td><td>1</td></tr><tr><th>27</th><td>z+DiUWa91dI277IJDsJLTTe61ho3oRnUG/0UXS+y8Mk=</td><td>1</td></tr><tr><th>28</th><td>TotbNxPUifAoL9diWEMXcRnBgKDStI/a/SdXcf5WhfQ=</td><td>1</td></tr><tr><th>29</th><td>NTOh3fAZrLoIyE1lxoRpp2lzEraNnnWbUvOhrmO1bUM=</td><td>1</td></tr><tr><th>30</th><td>wLtPucL8EyK8yUp+F9J1S8Ad/D9CRA8kSB0maalwzjE=</td><td>1</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/plain": [
"970960×2 DataFrames.DataFrame\n",
"│ Row │ msno │ is_churn │\n",
"├────────┼────────────────────────────────────────────────┼──────────┤\n",
"│ 1 │ \"ugx0CjOMzazClkFzU2xasmDZaoIqOUAZPsH1q0teWCg=\" │ 1 │\n",
"│ 2 │ \"f/NmvEzHfhINFEYZTR05prUdr+E+3+oewvweYz9cCQE=\" │ 1 │\n",
"│ 3 │ \"zLo9f73nGGT1p21ltZC3ChiRnAVvgibMyazbCxvWPcg=\" │ 1 │\n",
"│ 4 │ \"8iF/+8HY8lJKFrTc7iR9ZYGCG2Ecrogbc2Vy5YhsfhQ=\" │ 1 │\n",
"│ 5 │ \"K6fja4+jmoZ5xG6BypqX80Uw/XKpMgrEMdG2edFOxnA=\" │ 1 │\n",
"│ 6 │ \"ibIHVYBqxGwrSExE63/omeDD99M5vYB3CN2HzkEY+eM=\" │ 1 │\n",
"│ 7 │ \"kVmM8X4iBPCOfK/m1l3KR7LjFoxD1EcKYCc76F5IAWw=\" │ 1 │\n",
"│ 8 │ \"moRTKhKIDvb+C8ZHOgmaF4dXMLk0jOn65d7a8tQ2Eds=\" │ 1 │\n",
"│ 9 │ \"dW/tPZMDh2Oz/ksduEctJbsz0MXw3kay/1AlZCq3EbI=\" │ 1 │\n",
"│ 10 │ \"otEcMhAX3mU4gumUSogqgteN3oaNmhdmTkoof2iRYEE=\" │ 1 │\n",
"│ 11 │ \"t5rqTxCnG7s5VBgEfdkQCezv5KBK7+DMujNibYgylrs=\" │ 1 │\n",
"⋮\n",
"│ 970949 │ \"SjVZDYaiKgEHpFX1PcFDS94b9CFdaHjg78rfumtm/F4=\" │ 0 │\n",
"│ 970950 │ \"tcqOxdQ5kBbPc2PBgMz7brdDvDwIR24RqhPuUdymYyo=\" │ 0 │\n",
"│ 970951 │ \"dusKsElluiYEfgyc8MPKbyH7MsY6cH9RXqZD3WmxJ7U=\" │ 0 │\n",
"│ 970952 │ \"YAm2MZo94+P66Vi7QQai49CZ1MJQrIt96LtXckRg9Co=\" │ 0 │\n",
"│ 970953 │ \"5NE9tsGhg7HbFNMdPT+3tytaIKG5013bBQG2vQTe+OI=\" │ 0 │\n",
"│ 970954 │ \"eZ3y0lsY2SVZc2h8T3zB454TuBz6oVDMlFsEpEPQclQ=\" │ 0 │\n",
"│ 970955 │ \"/QlhSIWEZelYnwttYJSQL50EJJ2yRV+ThQIVQLvctp8=\" │ 0 │\n",
"│ 970956 │ \"OHnZbu+EVaP+vN7Z+OfT5OMcp90MWFZonmM0o3pb8FY=\" │ 0 │\n",
"│ 970957 │ \"S92bDK//uI6hk3u1vuApro0qJBQOToBozZ7lL1yTC+w=\" │ 0 │\n",
"│ 970958 │ \"eUa3xo16vpAjr43Cjlb6Kjf1NTILYyJIkBayJQdXWnw=\" │ 0 │\n",
"│ 970959 │ \"iZE41tbAQ65rJq60olkJT4BJzuUAYgQdfbEemXe/TTk=\" │ 0 │\n",
"│ 970960 │ \"oECkzJik4wKsbOEVY6UACLbmgM8qymFdb5cJaHrodY8=\" │ 0 │"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = readtable(\"./input/train_v2.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>msno</th><th>city</th><th>bd</th><th>gender</th><th>registered_via</th><th>registration_init_time</th></tr></thead><tbody><tr><th>1</th><td>Rb9UwLQTrxzBVwCB6+bCcSQWZ9JiNLC9dXtM1oEsZA8=</td><td>1</td><td>0</td><td>NA</td><td>11</td><td>20110911</td></tr><tr><th>2</th><td>+tJonkh+O1CA796Fm5X60UMOtB6POHAwPjbTRVl/EuU=</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20110914</td></tr><tr><th>3</th><td>cV358ssn7a0f7jZOwGNWS07wCKVqxyiImJUX6xcIwKw=</td><td>1</td><td>0</td><td>NA</td><td>11</td><td>20110915</td></tr><tr><th>4</th><td>9bzDeJP6sQodK73K5CBlJ6fgIQzPeLnRl0p5B77XP+g=</td><td>1</td><td>0</td><td>NA</td><td>11</td><td>20110915</td></tr><tr><th>5</th><td>WFLY3s7z4EZsieHCt63XrsdtfTEmJ+2PnnKLH5GY4Tk=</td><td>6</td><td>32</td><td>female</td><td>9</td><td>20110915</td></tr><tr><th>6</th><td>yLkV2gbZ4GLFwqTOXLVHz0VGrMYcgBGgKZ3kj9RiYu8=</td><td>4</td><td>30</td><td>male</td><td>9</td><td>20110916</td></tr><tr><th>7</th><td>jNCGK78YkTyId3H3wFavcBLDmz7pfqlvCfUKf4G1Lw4=</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20110916</td></tr><tr><th>8</th><td>WH5Jq4mgtfUFXh2yz+HrcTXKS4Oess4k4W3qKolAeb0=</td><td>5</td><td>34</td><td>male</td><td>9</td><td>20110916</td></tr><tr><th>9</th><td>tKmbR4X5VXjHmxERrckawEMZ4znVy1lAQIR1vV5rdNk=</td><td>5</td><td>19</td><td>male</td><td>9</td><td>20110917</td></tr><tr><th>10</th><td>I0yFvqMoNkM8ZNHb617e1RBzIS/YRKemHO7Wj13EtA0=</td><td>13</td><td>63</td><td>male</td><td>9</td><td>20110918</td></tr><tr><th>11</th><td>OoDwiKZM+ZGr9P3fRivavgOtglTEaNfWJO4KaJcTTts=</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20110918</td></tr><tr><th>12</th><td>dCvvBHlaOAqgkAcv36IZ9twaCyH0Z9om0wEiKn13B3o=</td><td>22</td><td>18</td><td>male</td><td>9</td><td>20110919</td></tr><tr><th>13</th><td>6bra2AiVV8SGlm7R6mp97Ot5jU9FBH2/8JaRk0iaKco=</td><td>4</td><td>34</td><td>female</td><td>9</td><td>20110919</td></tr><tr><th>14</th><td>4De1jAxNRABoyRBDZ82U0yEmzYkqeOugRGVNIf92Xb8=</td><td>4</td><td>28</td><td>female</td><td>9</td><td>20110920</td></tr><tr><th>15</th><td>iOzdu4IHbJxhB5CPpiQsZcGdptCm7OLwWm1Ci6oHLSE=</td><td>12</td><td>29</td><td>female</td><td>9</td><td>20110922</td></tr><tr><th>16</th><td>hZia/3iyvtThD1kv6ozRjRj8T0CAz0+zUFxs3e27wo8=</td><td>1</td><td>0</td><td>NA</td><td>9</td><td>20110922</td></tr><tr><th>17</th><td>QlDflP89J5KChheo2sbtH++SSDY/hUDj54gkgdfE76U=</td><td>1</td><td>0</td><td>NA</td><td>9</td><td>20110922</td></tr><tr><th>18</th><td>fOpj0ApSXk1NVODvC5db5Tvm/1G6exXxgTiCwTqvQn0=</td><td>13</td><td>31</td><td>female</td><td>7</td><td>20110923</td></tr><tr><th>19</th><td>GsPgdrXoV6688/0RFAwjKxQUpBB28JxK/Nx45kEpH60=</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20110924</td></tr><tr><th>20</th><td>QHHKGOY5yLrGdR8WYb0JtdJTP36Ii1QP3QTXxjEpGLk=</td><td>5</td><td>19</td><td>female</td><td>9</td><td>20110925</td></tr><tr><th>21</th><td>SpiCVwYrNY2XIpPpZkHHq4LDTfYyPNxBx7e+klc19H4=</td><td>15</td><td>22</td><td>female</td><td>9</td><td>20110925</td></tr><tr><th>22</th><td>+N2Na8js4BYLhkDXYD94o/5Tn5j1yi9u/+6F8an1MUs=</td><td>4</td><td>23</td><td>male</td><td>9</td><td>20110925</td></tr><tr><th>23</th><td>F6F2FJSHqQ/TLf7fQHJe1nbonEZmrCHtzG87U8VD9rE=</td><td>11</td><td>22</td><td>female</td><td>9</td><td>20110925</td></tr><tr><th>24</th><td>FotY8kXSC6tIyCuVn0NoaXjo0Hd854wuoNMWuwHaLIA=</td><td>5</td><td>29</td><td>male</td><td>9</td><td>20110926</td></tr><tr><th>25</th><td>glvtik6LgKzz/VHI8f7dAAHW4iqU8azER8mEsImDe78=</td><td>9</td><td>21</td><td>male</td><td>9</td><td>20110926</td></tr><tr><th>26</th><td>x+j0IrKnXLIqZawwAtwldq2by7TLMKyohbtSeT5gtDg=</td><td>9</td><td>22</td><td>male</td><td>9</td><td>20110926</td></tr><tr><th>27</th><td>7XXoaLnR+58yBjV/dtpvsvnkbadWYxO3+KkLgaaJ0VM=</td><td>5</td><td>28</td><td>male</td><td>7</td><td>20110927</td></tr><tr><th>28</th><td>GqYHRxlZChiZvB1uzR410wcQzuxqZNZci4AzOTzkAao=</td><td>5</td><td>27</td><td>male</td><td>9</td><td>20110927</td></tr><tr><th>29</th><td>vqz848bjeylg9nGoax8X4qQX2IX8qPwEq+4vbOpgy4c=</td><td>13</td><td>47</td><td>female</td><td>7</td><td>20110928</td></tr><tr><th>30</th><td>ZXtW2HuuVK4aPiO10RhBWT1PmLV+bKqnFlT5bL6YhoQ=</td><td>4</td><td>0</td><td>NA</td><td>9</td><td>20110928</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/plain": [
"6769473×6 DataFrames.DataFrame\n",
"│ Row │ msno │ city │ bd │\n",
"├─────────┼────────────────────────────────────────────────┼──────┼────┤\n",
"│ 1 │ \"Rb9UwLQTrxzBVwCB6+bCcSQWZ9JiNLC9dXtM1oEsZA8=\" │ 1 │ 0 │\n",
"│ 2 │ \"+tJonkh+O1CA796Fm5X60UMOtB6POHAwPjbTRVl/EuU=\" │ 1 │ 0 │\n",
"│ 3 │ \"cV358ssn7a0f7jZOwGNWS07wCKVqxyiImJUX6xcIwKw=\" │ 1 │ 0 │\n",
"│ 4 │ \"9bzDeJP6sQodK73K5CBlJ6fgIQzPeLnRl0p5B77XP+g=\" │ 1 │ 0 │\n",
"│ 5 │ \"WFLY3s7z4EZsieHCt63XrsdtfTEmJ+2PnnKLH5GY4Tk=\" │ 6 │ 32 │\n",
"│ 6 │ \"yLkV2gbZ4GLFwqTOXLVHz0VGrMYcgBGgKZ3kj9RiYu8=\" │ 4 │ 30 │\n",
"│ 7 │ \"jNCGK78YkTyId3H3wFavcBLDmz7pfqlvCfUKf4G1Lw4=\" │ 1 │ 0 │\n",
"│ 8 │ \"WH5Jq4mgtfUFXh2yz+HrcTXKS4Oess4k4W3qKolAeb0=\" │ 5 │ 34 │\n",
"│ 9 │ \"tKmbR4X5VXjHmxERrckawEMZ4znVy1lAQIR1vV5rdNk=\" │ 5 │ 19 │\n",
"│ 10 │ \"I0yFvqMoNkM8ZNHb617e1RBzIS/YRKemHO7Wj13EtA0=\" │ 13 │ 63 │\n",
"│ 11 │ \"OoDwiKZM+ZGr9P3fRivavgOtglTEaNfWJO4KaJcTTts=\" │ 1 │ 0 │\n",
"⋮\n",
"│ 6769462 │ \"P8weYpM9bCHGGvoUFAhJRRDEaehliI7m0eq51ahKxK4=\" │ 1 │ 0 │\n",
"│ 6769463 │ \"y0rwiAeU5goDatsiUDulPdiJpohZ15hxUAkSJHccnvY=\" │ 8 │ 32 │\n",
"│ 6769464 │ \"MwBxLTG0c5B1aJTJNovaqR74v+y/H6yTxQt/yqOxr1c=\" │ 1 │ 0 │\n",
"│ 6769465 │ \"2U0grRyv2DB3Pw3crMBhBfsWWq6VkGcK1IGVqmDmK0w=\" │ 5 │ 23 │\n",
"│ 6769466 │ \"OL6joplCKThE1pZD+AQDnj3kZiC45qzpndlDIRSyPQI=\" │ 1 │ 0 │\n",
"│ 6769467 │ \"O11rAa17ANM6DGMMVb9L2vwCQFv/S6SR3SDB1nGuOAc=\" │ 1 │ 0 │\n",
"│ 6769468 │ \"tyoRqQ72Y2oCcqvdaIxG8T0Hs9DZvuuM8IeTwhxtdBQ=\" │ 6 │ 26 │\n",
"│ 6769469 │ \"VSGkb3hyBRUtb/b1MQUZbvOkktS3vKLnhMHW0CF8eyU=\" │ 1 │ 0 │\n",
"│ 6769470 │ \"nWjH7glPkZ7jOVaCRwwjlpmp0T1hSWdv8hMJxiWCwKc=\" │ 1 │ 0 │\n",
"│ 6769471 │ \"GH+b5+1tlv7ZZXsA8upBzVXMTLyffKcsF7WoU8b5rOI=\" │ 15 │ 26 │\n",
"│ 6769472 │ \"XVlwT3fdCFGKqerEKBzUIjK+jzI6jzSke4cDMVhYyjE=\" │ 1 │ 0 │\n",
"│ 6769473 │ \"isBt+JlgvZRNy6lxVr5vvuJ4lD00ofTaKyJ+uRnGcPg=\" │ 1 │ 0 │\n",
"\n",
"│ Row │ gender │ registered_via │ registration_init_time │\n",
"├─────────┼──────────┼────────────────┼────────────────────────┤\n",
"│ 1 │ NA │ 11 │ 20110911 │\n",
"│ 2 │ NA │ 7 │ 20110914 │\n",
"│ 3 │ NA │ 11 │ 20110915 │\n",
"│ 4 │ NA │ 11 │ 20110915 │\n",
"│ 5 │ \"female\" │ 9 │ 20110915 │\n",
"│ 6 │ \"male\" │ 9 │ 20110916 │\n",
"│ 7 │ NA │ 7 │ 20110916 │\n",
"│ 8 │ \"male\" │ 9 │ 20110916 │\n",
"│ 9 │ \"male\" │ 9 │ 20110917 │\n",
"│ 10 │ \"male\" │ 9 │ 20110918 │\n",
"│ 11 │ NA │ 7 │ 20110918 │\n",
"⋮\n",
"│ 6769462 │ NA │ 4 │ 20151020 │\n",
"│ 6769463 │ \"female\" │ 9 │ 20151020 │\n",
"│ 6769464 │ NA │ 4 │ 20151020 │\n",
"│ 6769465 │ \"female\" │ 4 │ 20151020 │\n",
"│ 6769466 │ NA │ 7 │ 20151020 │\n",
"│ 6769467 │ NA │ 7 │ 20151020 │\n",
"│ 6769468 │ \"female\" │ 7 │ 20151020 │\n",
"│ 6769469 │ NA │ 7 │ 20151020 │\n",
"│ 6769470 │ NA │ 7 │ 20151020 │\n",
"│ 6769471 │ \"female\" │ 4 │ 20151020 │\n",
"│ 6769472 │ NA │ 4 │ 20151020 │\n",
"│ 6769473 │ NA │ 4 │ 20151021 │"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"members = readtable(\"./input/members_v3.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>msno</th><th>is_churn</th><th>city</th><th>bd</th><th>gender</th><th>registered_via</th><th>registration_init_time</th></tr></thead><tbody><tr><th>1</th><td>+++hVY1rZox/33YtvDgmKA2Frg/2qhkz12B9ylCvh8o=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140608</td></tr><tr><th>2</th><td>+++l/EXNMLTijfLBa8p2TUVVVp2aFGSuUI/h7mLmthw=</td><td>0</td><td>15</td><td>26</td><td>male</td><td>9</td><td>20101118</td></tr><tr><th>3</th><td>+++snpr7pmobhLKUgSHTv/mpkqgBT0tQJ0zQj6qKrqc=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140927</td></tr><tr><th>4</th><td>++/9R3sX37CjxbY/AaGvbwr3QkwElKBCtSvVzhCBDOk=</td><td>0</td><td>18</td><td>21</td><td>male</td><td>7</td><td>20160315</td></tr><tr><th>5</th><td>++/UDNo9DLrxT8QVGiDi1OnWfczAdEwThaVyD0fXO50=</td><td>0</td><td>22</td><td>32</td><td>male</td><td>9</td><td>20120122</td></tr><tr><th>6</th><td>++/ZHqwUNa7U21Qz+zqteiXlZapxey86l6eEorrak/g=</td><td>0</td><td>13</td><td>39</td><td>male</td><td>3</td><td>20140421</td></tr><tr><th>7</th><td>++0/NopttBsaAn6qHZA2AWWrDg7Me7UOMs1vsyo4tSI=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160320</td></tr><tr><th>8</th><td>++0BJXY8tpirgIhJR14LDM1pnaRosjD1mdO1mIKxlJA=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>9</td><td>20150807</td></tr><tr><th>9</th><td>++0EzISdtKY48Z0GY62jer/LFQwrNIAbADdtU5xStGY=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>3</td><td>20131205</td></tr><tr><th>10</th><td>++0nOC7BmrUTtcSboRORfg6ZXTajnBDt1f/SEgH6ONo=</td><td>0</td><td>13</td><td>25</td><td>male</td><td>9</td><td>20100203</td></tr><tr><th>11</th><td>++1G0wVY14Lp0VXak1ymLhPUdXPSFJVBnjWwzGxBKJs=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150909</td></tr><tr><th>12</th><td>++1GCIyXZO7834NjDKmcK1lBVLQi9PsN6sOC7wfW+8g=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160705</td></tr><tr><th>13</th><td>++38dVm0EHKCOfND6oEal3xFKtqJYZTZJmnpbAOOZBk=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20151023</td></tr><tr><th>14</th><td>++3A6JMzYJeron30GTcDostfXoAl8rTBuB2M8GeVdNU=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150312</td></tr><tr><th>15</th><td>++3Z+W8OPnpbHYfrKwqRKN1bF83XEbxjdYUolhGdHZg=</td><td>0</td><td>5</td><td>35</td><td>female</td><td>7</td><td>20120209</td></tr><tr><th>16</th><td>++3fWHRDC5GWWlovHcrOKWNwZYOjWWkJyeLlL65uv78=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160121</td></tr><tr><th>17</th><td>++3wqX72HmowxFh5MrHvNi5Ce8D3ldo4zaUL932TYbs=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20131016</td></tr><tr><th>18</th><td>++4RuqBw0Ss6bQU4oMxaRlbBPoWzoEiIZaxPM04Y4+U=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140714</td></tr><tr><th>19</th><td>++4cUL0b9CfW8cj0A/wfSxQc4k4fcVtWcLqk2UOdpKs=</td><td>0</td><td>15</td><td>28</td><td>male</td><td>7</td><td>20121009</td></tr><tr><th>20</th><td>++5wYjoMgQHoRuD3GbbvmphZbBBwymzv5Q4l8sywtuU=</td><td>0</td><td>13</td><td>39</td><td>male</td><td>9</td><td>20060218</td></tr><tr><th>21</th><td>++64pHA0OvDq9P9nEAVqSQONHE1ODxEM0KmcazJxKZ8=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20161120</td></tr><tr><th>22</th><td>++6P09mCSJSh+Ft2pvZ0FWTrtcI3v1A7h3/coh8dBKw=</td><td>0</td><td>22</td><td>35</td><td>male</td><td>9</td><td>20100921</td></tr><tr><th>23</th><td>++6xEqu4JANaRY4GjEfEFtLtqOvZvYPyP3uk/PW9Ces=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160501</td></tr><tr><th>24</th><td>++8XaEDast796K/DQcP7sSoRQ8JRnA7X5S+oyXEw62E=</td><td>0</td><td>5</td><td>29</td><td>male</td><td>9</td><td>20071211</td></tr><tr><th>25</th><td>++8dXbkKMJ0rXwUc/m19lTVokEl3c9EfRKWmV6qP9jg=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160321</td></tr><tr><th>26</th><td>++95tJZADNg8U8HKbYdxbbXIRsO6pw1zBK4tHI7BtZo=</td><td>0</td><td>14</td><td>35</td><td>female</td><td>3</td><td>20120603</td></tr><tr><th>27</th><td>++9JUsaZioofS6Fb20Z0z2QOlWNzakO0PRF5GZ75yi4=</td><td>1</td><td>13</td><td>22</td><td>male</td><td>9</td><td>20141210</td></tr><tr><th>28</th><td>++A8p4GrsTnMjI6hAZEtlRsaz6s6O9ddUoH0fmS4s7s=</td><td>0</td><td>5</td><td>43</td><td>female</td><td>9</td><td>20141118</td></tr><tr><th>29</th><td>++BBU1qnsavj2eC5j5G6dTda9ntLhRiz+pidG73im5s=</td><td>0</td><td>17</td><td>26</td><td>female</td><td>9</td><td>20090917</td></tr><tr><th>30</th><td>++BQyzVEMoHru900mTyAKpCsKYjXUGdAXylmmiPsbVY=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20151228</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/plain": [
"860967×7 DataFrames.DataFrame\n",
"│ Row │ msno │ is_churn │ city │\n",
"├────────┼────────────────────────────────────────────────┼──────────┼──────┤\n",
"│ 1 │ \"+++hVY1rZox/33YtvDgmKA2Frg/2qhkz12B9ylCvh8o=\" │ 0 │ 1 │\n",
"│ 2 │ \"+++l/EXNMLTijfLBa8p2TUVVVp2aFGSuUI/h7mLmthw=\" │ 0 │ 15 │\n",
"│ 3 │ \"+++snpr7pmobhLKUgSHTv/mpkqgBT0tQJ0zQj6qKrqc=\" │ 0 │ 1 │\n",
"│ 4 │ \"++/9R3sX37CjxbY/AaGvbwr3QkwElKBCtSvVzhCBDOk=\" │ 0 │ 18 │\n",
"│ 5 │ \"++/UDNo9DLrxT8QVGiDi1OnWfczAdEwThaVyD0fXO50=\" │ 0 │ 22 │\n",
"│ 6 │ \"++/ZHqwUNa7U21Qz+zqteiXlZapxey86l6eEorrak/g=\" │ 0 │ 13 │\n",
"│ 7 │ \"++0/NopttBsaAn6qHZA2AWWrDg7Me7UOMs1vsyo4tSI=\" │ 0 │ 1 │\n",
"│ 8 │ \"++0BJXY8tpirgIhJR14LDM1pnaRosjD1mdO1mIKxlJA=\" │ 0 │ 1 │\n",
"│ 9 │ \"++0EzISdtKY48Z0GY62jer/LFQwrNIAbADdtU5xStGY=\" │ 0 │ 1 │\n",
"│ 10 │ \"++0nOC7BmrUTtcSboRORfg6ZXTajnBDt1f/SEgH6ONo=\" │ 0 │ 13 │\n",
"│ 11 │ \"++1G0wVY14Lp0VXak1ymLhPUdXPSFJVBnjWwzGxBKJs=\" │ 0 │ 1 │\n",
"⋮\n",
"│ 860956 │ \"zzvRDIlZcYclP7ocM+wG1WUPbUHPtMRPKerl/hionLg=\" │ 0 │ 1 │\n",
"│ 860957 │ \"zzwimfmJBJ4SrRw3bYXQzE9UrizlSE10yI7dHPvG5xM=\" │ 0 │ 18 │\n",
"│ 860958 │ \"zzwlIVmSuehTevzOi+WkcC7b3+MBYDiE4qybszTyhII=\" │ 0 │ 13 │\n",
"│ 860959 │ \"zzwsW1MnZalh6V5BwYE+V6hFWyKJYOf+rFd+SWVXxp8=\" │ 0 │ 1 │\n",
"│ 860960 │ \"zzx1qgX8R9EmT8PAn4/KyQ6s7PuLYTfR8kUCABiCW9E=\" │ 0 │ 6 │\n",
"│ 860961 │ \"zzx4hKiyR9XFEGAr7SAjcCPbKJCZ+IqegWL7dPjPwZk=\" │ 0 │ 1 │\n",
"│ 860962 │ \"zzx7wcys2Nha6yP2Skjs+R1/JNAfEuAWUaAp0qsV2Cg=\" │ 1 │ 15 │\n",
"│ 860963 │ \"zzxPS8+wCuE4HE85EDp4WBHgS5l548vfwXJbQq1HZ9I=\" │ 0 │ 22 │\n",
"│ 860964 │ \"zzxZeMFx2fjfKZigMnJa2w0EmloDbm8+8nTf/o/00GY=\" │ 0 │ 4 │\n",
"│ 860965 │ \"zzy7iqSpfcRq7R4hmKKuhI+CJRs79a6pteqEggpiNO0=\" │ 1 │ 5 │\n",
"│ 860966 │ \"zzz1Dc3P9s53HAowRTrm3fNsWju5yeN4YBfNDq7Z99Q=\" │ 0 │ 4 │\n",
"│ 860967 │ \"zzzF1KsGfHH3qI6qiSNSXC35UXmVKMVFdxkp7xmDMc0=\" │ 0 │ 4 │\n",
"\n",
"│ Row │ bd │ gender │ registered_via │ registration_init_time │\n",
"├────────┼────┼──────────┼────────────────┼────────────────────────┤\n",
"│ 1 │ 0 │ NA │ 7 │ 20140608 │\n",
"│ 2 │ 26 │ \"male\" │ 9 │ 20101118 │\n",
"│ 3 │ 0 │ NA │ 7 │ 20140927 │\n",
"│ 4 │ 21 │ \"male\" │ 7 │ 20160315 │\n",
"│ 5 │ 32 │ \"male\" │ 9 │ 20120122 │\n",
"│ 6 │ 39 │ \"male\" │ 3 │ 20140421 │\n",
"│ 7 │ 0 │ NA │ 7 │ 20160320 │\n",
"│ 8 │ 0 │ NA │ 9 │ 20150807 │\n",
"│ 9 │ 0 │ NA │ 3 │ 20131205 │\n",
"│ 10 │ 25 │ \"male\" │ 9 │ 20100203 │\n",
"│ 11 │ 0 │ NA │ 7 │ 20150909 │\n",
"⋮\n",
"│ 860956 │ 0 │ NA │ 7 │ 20170210 │\n",
"│ 860957 │ 43 │ \"female\" │ 7 │ 20161020 │\n",
"│ 860958 │ 26 │ \"male\" │ 4 │ 20160416 │\n",
"│ 860959 │ 0 │ NA │ 7 │ 20151006 │\n",
"│ 860960 │ 31 │ \"male\" │ 9 │ 20141014 │\n",
"│ 860961 │ 0 │ NA │ 7 │ 20110823 │\n",
"│ 860962 │ 30 │ \"female\" │ 7 │ 20100921 │\n",
"│ 860963 │ 28 │ \"female\" │ 9 │ 20070810 │\n",
"│ 860964 │ 24 │ \"male\" │ 7 │ 20120306 │\n",
"│ 860965 │ 31 │ \"male\" │ 3 │ 20150430 │\n",
"│ 860966 │ 48 │ \"female\" │ 9 │ 20140611 │\n",
"│ 860967 │ 26 │ \"male\" │ 3 │ 20170129 │"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_members = join(train, members, on = :msno)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>msno</th><th>is_churn</th></tr></thead><tbody><tr><th>1</th><td>4n+fXlyJvfQnTeKXTWT507Ll4JVYGrOC8LHCfwBmPE4=</td><td>0</td></tr><tr><th>2</th><td>aNmbC1GvFUxQyQUidCVmfbQ0YeCuwkPzEdQ0RwWyeZM=</td><td>0</td></tr><tr><th>3</th><td>rFC9eSG/tMuzpre6cwcMLZHEYM89xY02qcz7HL4//jc=</td><td>0</td></tr><tr><th>4</th><td>WZ59dLyrQcE7ft06MZ5dj40BnlYQY7PHgg/54+HaCSE=</td><td>0</td></tr><tr><th>5</th><td>aky/Iv8hMp1/V/yQHLtaVuEmmAxkB5GuasQZePJ7NU4=</td><td>0</td></tr><tr><th>6</th><td>nu1jZ/flvSaXRg0U9Es+xmo2KlAXq/q+mhcWFStwm9w=</td><td>0</td></tr><tr><th>7</th><td>biDTtgK83fEWXJDaxzD1eh22dion/h3odeIbS7qJMmY=</td><td>0</td></tr><tr><th>8</th><td>k/8uwi/iM9LZmRAIWXLqpZY6ENomXAscwsQsh6PxcTw=</td><td>0</td></tr><tr><th>9</th><td>18rQ/746SjA6nBx325UsyhfsDhu4tK01FXFxHWZjw20=</td><td>0</td></tr><tr><th>10</th><td>2V13OCoWx6vqKr/ZzNmKFrmnC2FtR4SWMz5C5Hi02PY=</td><td>0</td></tr><tr><th>11</th><td>1l/ZwduFxS/q/hZeyssAYH27espkp8Yw6uAnUxfEbTI=</td><td>0</td></tr><tr><th>12</th><td>azfnO16ZeQsbJF6LcqkQhbA3NWiqHYWqaq7AFjsJVaQ=</td><td>0</td></tr><tr><th>13</th><td>RPOzeEr8mSbhj6wrF29+7KciuiNrj7IvkzxJ9rgCTks=</td><td>0</td></tr><tr><th>14</th><td>NAzfjSM2EOyFhV4rIm/RO9pXCbyti6scBfcmV/t+CaU=</td><td>0</td></tr><tr><th>15</th><td>1DCd06ON0rWFHI1bNrY1l/hPW9d80fmmrmroHqpGvNA=</td><td>0</td></tr><tr><th>16</th><td>D9QAV8ZNF8qU96dTBLMzO0sguzlmAIBf4302l0W6jj0=</td><td>0</td></tr><tr><th>17</th><td>5HKzLDUVVbIxWMH9aH67ALAGVPvorE4NvmO5xqO7SMk=</td><td>0</td></tr><tr><th>18</th><td>XwnlNj6nq2MMHe0KoyRRM4ih+RAwj5idHvlS4pTMTbg=</td><td>0</td></tr><tr><th>19</th><td>lNmNPLZZiMwVbRFdBmDYmRdEgo8TWN2Y3m+z5XVl4aQ=</td><td>0</td></tr><tr><th>20</th><td>5NlNga4bsKiYyj3B3I/SihUQH4wb2suNWBcxQMd9oJ8=</td><td>0</td></tr><tr><th>21</th><td>eXWvD4/UBTi6mA9GMYTpY7/buu5xKCyXlu7hFM+ELHU=</td><td>0</td></tr><tr><th>22</th><td>Y1V2qDdKt+5CHqFSvpm82QkPJrwRxkbP5hs7DbtU+tA=</td><td>0</td></tr><tr><th>23</th><td>sLyKNXQo6PGK/CE0a4cUQ9+lVd/oCERRMuCSgZ6KN4Y=</td><td>0</td></tr><tr><th>24</th><td>2TaTUEET5XAOiHPC2qKnKUK2wSqbcbA9YwM4eHTeawE=</td><td>0</td></tr><tr><th>25</th><td>Rrh9afbenme6MuAYudJRn41u9IrLpSi9aQ8p3I34RT0=</td><td>0</td></tr><tr><th>26</th><td>5sjnJjeZd2roOarPgSYK6559VTIhEmeunzJAd2kcuhg=</td><td>0</td></tr><tr><th>27</th><td>FsR4+VSNjAortRRTmx2q9K2q9HtSdQ69PE1FNKKqpKk=</td><td>0</td></tr><tr><th>28</th><td>5LgzR73HtjaKDQzMcUrveEUcvWPCuwAZrizNdCAqRnU=</td><td>0</td></tr><tr><th>29</th><td>3rgFPp+e+xMWuKAw8wgVB4EUYotVuaWYvGuEDyNo9fc=</td><td>0</td></tr><tr><th>30</th><td>QOE4ibjDwKrJvCZze8fKh4yxk7sHEEbffUMTmkUSfhA=</td><td>0</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/plain": [
"907471×2 DataFrames.DataFrame\n",
"│ Row │ msno │ is_churn │\n",
"├────────┼────────────────────────────────────────────────┼──────────┤\n",
"│ 1 │ \"4n+fXlyJvfQnTeKXTWT507Ll4JVYGrOC8LHCfwBmPE4=\" │ 0 │\n",
"│ 2 │ \"aNmbC1GvFUxQyQUidCVmfbQ0YeCuwkPzEdQ0RwWyeZM=\" │ 0 │\n",
"│ 3 │ \"rFC9eSG/tMuzpre6cwcMLZHEYM89xY02qcz7HL4//jc=\" │ 0 │\n",
"│ 4 │ \"WZ59dLyrQcE7ft06MZ5dj40BnlYQY7PHgg/54+HaCSE=\" │ 0 │\n",
"│ 5 │ \"aky/Iv8hMp1/V/yQHLtaVuEmmAxkB5GuasQZePJ7NU4=\" │ 0 │\n",
"│ 6 │ \"nu1jZ/flvSaXRg0U9Es+xmo2KlAXq/q+mhcWFStwm9w=\" │ 0 │\n",
"│ 7 │ \"biDTtgK83fEWXJDaxzD1eh22dion/h3odeIbS7qJMmY=\" │ 0 │\n",
"│ 8 │ \"k/8uwi/iM9LZmRAIWXLqpZY6ENomXAscwsQsh6PxcTw=\" │ 0 │\n",
"│ 9 │ \"18rQ/746SjA6nBx325UsyhfsDhu4tK01FXFxHWZjw20=\" │ 0 │\n",
"│ 10 │ \"2V13OCoWx6vqKr/ZzNmKFrmnC2FtR4SWMz5C5Hi02PY=\" │ 0 │\n",
"│ 11 │ \"1l/ZwduFxS/q/hZeyssAYH27espkp8Yw6uAnUxfEbTI=\" │ 0 │\n",
"⋮\n",
"│ 907460 │ \"SZGhUzxFs6/jVf+aVAV6wkIXwC4VOJ945EhHLYjyXqY=\" │ 0 │\n",
"│ 907461 │ \"fgrG5+L9zmTL8wW75FbR3Cts5Z7GAUcfr5cbqrsztAw=\" │ 0 │\n",
"│ 907462 │ \"s2BQi80mz+SH4d+Ld0lCW28ZeyPGnnaBmaD9Mx3D4Ks=\" │ 0 │\n",
"│ 907463 │ \"r+Eb1hNptQDU/Wa0E+Myg/4+K7XqckAvlEU6xY2r9CQ=\" │ 0 │\n",
"│ 907464 │ \"kC0vNHziTsaosr42qwIilPjviq7ToGkmEARWNpvFRqs=\" │ 0 │\n",
"│ 907465 │ \"lmuB1hC6Trpb84bWVC3dvLBJkBtbo7N2OVf0Lwo0c9c=\" │ 0 │\n",
"│ 907466 │ \"puL2A+Pe6eqOM6D+RKnqmdiJPaWrlKYQcCrJXKCXIIM=\" │ 0 │\n",
"│ 907467 │ \"8nS1SbsI9+9vN07m8GyfmsWv/A9H8dUlbIbjbZ2a/Nk=\" │ 0 │\n",
"│ 907468 │ \"KsOpJUA4P2pEaSl0afZhyl1QBaLHXTw33IYuQsnKdxE=\" │ 0 │\n",
"│ 907469 │ \"NIcI2YUj9v2tC/NRtreLz1wxwLczQacJ2Ek9RJKgarI=\" │ 0 │\n",
"│ 907470 │ \"b8MlVUtzUhQrIqKcqVKPENOIUo/5EiZxfExBkkfoBxY=\" │ 0 │\n",
"│ 907471 │ \"YhPGjMES6gPOZMm33BmxwM4hg5I1gbpBM5Be16NXZoI=\" │ 0 │"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = readtable(\"./input/sample_submission_v2.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>msno</th><th>is_churn</th><th>city</th><th>bd</th><th>gender</th><th>registered_via</th><th>registration_init_time</th></tr></thead><tbody><tr><th>1</th><td>+++hVY1rZox/33YtvDgmKA2Frg/2qhkz12B9ylCvh8o=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140608</td></tr><tr><th>2</th><td>+++snpr7pmobhLKUgSHTv/mpkqgBT0tQJ0zQj6qKrqc=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140927</td></tr><tr><th>3</th><td>++/9R3sX37CjxbY/AaGvbwr3QkwElKBCtSvVzhCBDOk=</td><td>0</td><td>18</td><td>21</td><td>male</td><td>7</td><td>20160315</td></tr><tr><th>4</th><td>++0/NopttBsaAn6qHZA2AWWrDg7Me7UOMs1vsyo4tSI=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160320</td></tr><tr><th>5</th><td>++0BJXY8tpirgIhJR14LDM1pnaRosjD1mdO1mIKxlJA=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>9</td><td>20150807</td></tr><tr><th>6</th><td>++0nOC7BmrUTtcSboRORfg6ZXTajnBDt1f/SEgH6ONo=</td><td>0</td><td>13</td><td>25</td><td>male</td><td>9</td><td>20100203</td></tr><tr><th>7</th><td>++1G0wVY14Lp0VXak1ymLhPUdXPSFJVBnjWwzGxBKJs=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150909</td></tr><tr><th>8</th><td>++1GCIyXZO7834NjDKmcK1lBVLQi9PsN6sOC7wfW+8g=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160705</td></tr><tr><th>9</th><td>++38dVm0EHKCOfND6oEal3xFKtqJYZTZJmnpbAOOZBk=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20151023</td></tr><tr><th>10</th><td>++3A6JMzYJeron30GTcDostfXoAl8rTBuB2M8GeVdNU=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150312</td></tr><tr><th>11</th><td>++3Z+W8OPnpbHYfrKwqRKN1bF83XEbxjdYUolhGdHZg=</td><td>0</td><td>5</td><td>35</td><td>female</td><td>7</td><td>20120209</td></tr><tr><th>12</th><td>++3fWHRDC5GWWlovHcrOKWNwZYOjWWkJyeLlL65uv78=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160121</td></tr><tr><th>13</th><td>++3wqX72HmowxFh5MrHvNi5Ce8D3ldo4zaUL932TYbs=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20131016</td></tr><tr><th>14</th><td>++4RuqBw0Ss6bQU4oMxaRlbBPoWzoEiIZaxPM04Y4+U=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140714</td></tr><tr><th>15</th><td>++4cUL0b9CfW8cj0A/wfSxQc4k4fcVtWcLqk2UOdpKs=</td><td>0</td><td>15</td><td>28</td><td>male</td><td>7</td><td>20121009</td></tr><tr><th>16</th><td>++5HPICzApqAq9mYdGB/mdke0MbubM8yUKLOmrPt4xU=</td><td>0</td><td>22</td><td>18</td><td>male</td><td>3</td><td>20140616</td></tr><tr><th>17</th><td>++5nv+2nsvrWM7dOT+ZiWJ5uTZOzQS0NEvqu3jidTjU=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20161201</td></tr><tr><th>18</th><td>++64pHA0OvDq9P9nEAVqSQONHE1ODxEM0KmcazJxKZ8=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20161120</td></tr><tr><th>19</th><td>++6xEqu4JANaRY4GjEfEFtLtqOvZvYPyP3uk/PW9Ces=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160501</td></tr><tr><th>20</th><td>++7IULiyKbNc8jllqhRuyKZjX1J4mPF4tsudFCJfv4k=</td><td>0</td><td>5</td><td>38</td><td>female</td><td>9</td><td>20100127</td></tr><tr><th>21</th><td>++84ebcguKIB0m3x0cb7BVsZIdLWQqBmIZBfmR6Me6I=</td><td>0</td><td>13</td><td>25</td><td>female</td><td>3</td><td>20141006</td></tr><tr><th>22</th><td>++8XaEDast796K/DQcP7sSoRQ8JRnA7X5S+oyXEw62E=</td><td>0</td><td>5</td><td>29</td><td>male</td><td>9</td><td>20071211</td></tr><tr><th>23</th><td>++8dXbkKMJ0rXwUc/m19lTVokEl3c9EfRKWmV6qP9jg=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160321</td></tr><tr><th>24</th><td>++A8p4GrsTnMjI6hAZEtlRsaz6s6O9ddUoH0fmS4s7s=</td><td>0</td><td>5</td><td>43</td><td>female</td><td>9</td><td>20141118</td></tr><tr><th>25</th><td>++BQyzVEMoHru900mTyAKpCsKYjXUGdAXylmmiPsbVY=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20151228</td></tr><tr><th>26</th><td>++BW1PJYU5OZi3n3+IDLiU+d1IL1VE/GLx6p64TDs6U=</td><td>0</td><td>15</td><td>29</td><td>male</td><td>9</td><td>20070609</td></tr><tr><th>27</th><td>++Bks8kE9oclzxZM3hcWs+qzsxuoXFeIE1+7pxKBCQg=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160730</td></tr><tr><th>28</th><td>++CITmZPtQDWOi01uceGb75QJblInDRhjjGM1MmsiSY=</td><td>0</td><td>3</td><td>33</td><td>female</td><td>9</td><td>20061108</td></tr><tr><th>29</th><td>++Ck01c3EF07Ejek2jfXlKut+sEfg+0ry+A5uWeL9vY=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20131229</td></tr><tr><th>30</th><td>++CzVs35NSfU/gjDrGbWLVMweubEiMZsuvd3eTbXs58=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150914</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/plain": [
"795090×7 DataFrames.DataFrame\n",
"│ Row │ msno │ is_churn │ city │\n",
"├────────┼────────────────────────────────────────────────┼──────────┼──────┤\n",
"│ 1 │ \"+++hVY1rZox/33YtvDgmKA2Frg/2qhkz12B9ylCvh8o=\" │ 0 │ 1 │\n",
"│ 2 │ \"+++snpr7pmobhLKUgSHTv/mpkqgBT0tQJ0zQj6qKrqc=\" │ 0 │ 1 │\n",
"│ 3 │ \"++/9R3sX37CjxbY/AaGvbwr3QkwElKBCtSvVzhCBDOk=\" │ 0 │ 18 │\n",
"│ 4 │ \"++0/NopttBsaAn6qHZA2AWWrDg7Me7UOMs1vsyo4tSI=\" │ 0 │ 1 │\n",
"│ 5 │ \"++0BJXY8tpirgIhJR14LDM1pnaRosjD1mdO1mIKxlJA=\" │ 0 │ 1 │\n",
"│ 6 │ \"++0nOC7BmrUTtcSboRORfg6ZXTajnBDt1f/SEgH6ONo=\" │ 0 │ 13 │\n",
"│ 7 │ \"++1G0wVY14Lp0VXak1ymLhPUdXPSFJVBnjWwzGxBKJs=\" │ 0 │ 1 │\n",
"│ 8 │ \"++1GCIyXZO7834NjDKmcK1lBVLQi9PsN6sOC7wfW+8g=\" │ 0 │ 1 │\n",
"│ 9 │ \"++38dVm0EHKCOfND6oEal3xFKtqJYZTZJmnpbAOOZBk=\" │ 0 │ 1 │\n",
"│ 10 │ \"++3A6JMzYJeron30GTcDostfXoAl8rTBuB2M8GeVdNU=\" │ 0 │ 1 │\n",
"│ 11 │ \"++3Z+W8OPnpbHYfrKwqRKN1bF83XEbxjdYUolhGdHZg=\" │ 0 │ 5 │\n",
"⋮\n",
"│ 795079 │ \"zzvRDIlZcYclP7ocM+wG1WUPbUHPtMRPKerl/hionLg=\" │ 0 │ 1 │\n",
"│ 795080 │ \"zzvhQSh5Xs6/9DO+wUTtEbSZRBHjGIpHMOP2kVMrXmk=\" │ 0 │ 1 │\n",
"│ 795081 │ \"zzwX6rVmjqXmY1e49xsRk0f9WcK5gv6kSWV5bQ0qZqk=\" │ 0 │ 1 │\n",
"│ 795082 │ \"zzwimfmJBJ4SrRw3bYXQzE9UrizlSE10yI7dHPvG5xM=\" │ 0 │ 18 │\n",
"│ 795083 │ \"zzwlIVmSuehTevzOi+WkcC7b3+MBYDiE4qybszTyhII=\" │ 0 │ 13 │\n",
"│ 795084 │ \"zzx1qgX8R9EmT8PAn4/KyQ6s7PuLYTfR8kUCABiCW9E=\" │ 0 │ 6 │\n",
"│ 795085 │ \"zzx4hKiyR9XFEGAr7SAjcCPbKJCZ+IqegWL7dPjPwZk=\" │ 0 │ 1 │\n",
"│ 795086 │ \"zzxIjlrMxRzNVDD6XqUDRBqkWn7OJ9mzFmFQlfLtLCE=\" │ 0 │ 1 │\n",
"│ 795087 │ \"zzxZeMFx2fjfKZigMnJa2w0EmloDbm8+8nTf/o/00GY=\" │ 0 │ 4 │\n",
"│ 795088 │ \"zzy7iqSpfcRq7R4hmKKuhI+CJRs79a6pteqEggpiNO0=\" │ 0 │ 5 │\n",
"│ 795089 │ \"zzyHq6TK2+cBkeGFUHvh12Z7UxFZiSM7dOOSllSBPDw=\" │ 0 │ 9 │\n",
"│ 795090 │ \"zzzF1KsGfHH3qI6qiSNSXC35UXmVKMVFdxkp7xmDMc0=\" │ 0 │ 4 │\n",
"\n",
"│ Row │ bd │ gender │ registered_via │ registration_init_time │\n",
"├────────┼────┼──────────┼────────────────┼────────────────────────┤\n",
"│ 1 │ 0 │ NA │ 7 │ 20140608 │\n",
"│ 2 │ 0 │ NA │ 7 │ 20140927 │\n",
"│ 3 │ 21 │ \"male\" │ 7 │ 20160315 │\n",
"│ 4 │ 0 │ NA │ 7 │ 20160320 │\n",
"│ 5 │ 0 │ NA │ 9 │ 20150807 │\n",
"│ 6 │ 25 │ \"male\" │ 9 │ 20100203 │\n",
"│ 7 │ 0 │ NA │ 7 │ 20150909 │\n",
"│ 8 │ 0 │ NA │ 7 │ 20160705 │\n",
"│ 9 │ 0 │ NA │ 7 │ 20151023 │\n",
"│ 10 │ 0 │ NA │ 7 │ 20150312 │\n",
"│ 11 │ 35 │ \"female\" │ 7 │ 20120209 │\n",
"⋮\n",
"│ 795079 │ 0 │ NA │ 7 │ 20170210 │\n",
"│ 795080 │ 0 │ NA │ 7 │ 20160202 │\n",
"│ 795081 │ 0 │ NA │ 4 │ 20170315 │\n",
"│ 795082 │ 43 │ \"female\" │ 7 │ 20161020 │\n",
"│ 795083 │ 26 │ \"male\" │ 4 │ 20160416 │\n",
"│ 795084 │ 31 │ \"male\" │ 9 │ 20141014 │\n",
"│ 795085 │ 0 │ NA │ 7 │ 20110823 │\n",
"│ 795086 │ 0 │ NA │ 4 │ 20170305 │\n",
"│ 795087 │ 24 │ \"male\" │ 7 │ 20120306 │\n",
"│ 795088 │ 31 │ \"male\" │ 3 │ 20150430 │\n",
"│ 795089 │ 0 │ NA │ 7 │ 20130515 │\n",
"│ 795090 │ 26 │ \"male\" │ 3 │ 20170129 │"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_members = join(test, members, on = :msno)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>msno</th><th>is_churn</th><th>city</th><th>bd</th><th>gender</th><th>registered_via</th><th>registration_init_time</th></tr></thead><tbody><tr><th>1</th><td>+++hVY1rZox/33YtvDgmKA2Frg/2qhkz12B9ylCvh8o=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140608</td></tr><tr><th>2</th><td>+++snpr7pmobhLKUgSHTv/mpkqgBT0tQJ0zQj6qKrqc=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140927</td></tr><tr><th>3</th><td>++0/NopttBsaAn6qHZA2AWWrDg7Me7UOMs1vsyo4tSI=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160320</td></tr><tr><th>4</th><td>++0BJXY8tpirgIhJR14LDM1pnaRosjD1mdO1mIKxlJA=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>9</td><td>20150807</td></tr><tr><th>5</th><td>++0EzISdtKY48Z0GY62jer/LFQwrNIAbADdtU5xStGY=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>3</td><td>20131205</td></tr><tr><th>6</th><td>++1G0wVY14Lp0VXak1ymLhPUdXPSFJVBnjWwzGxBKJs=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150909</td></tr><tr><th>7</th><td>++1GCIyXZO7834NjDKmcK1lBVLQi9PsN6sOC7wfW+8g=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160705</td></tr><tr><th>8</th><td>++38dVm0EHKCOfND6oEal3xFKtqJYZTZJmnpbAOOZBk=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20151023</td></tr><tr><th>9</th><td>++3A6JMzYJeron30GTcDostfXoAl8rTBuB2M8GeVdNU=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150312</td></tr><tr><th>10</th><td>++3fWHRDC5GWWlovHcrOKWNwZYOjWWkJyeLlL65uv78=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160121</td></tr><tr><th>11</th><td>++3wqX72HmowxFh5MrHvNi5Ce8D3ldo4zaUL932TYbs=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20131016</td></tr><tr><th>12</th><td>++4RuqBw0Ss6bQU4oMxaRlbBPoWzoEiIZaxPM04Y4+U=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20140714</td></tr><tr><th>13</th><td>++64pHA0OvDq9P9nEAVqSQONHE1ODxEM0KmcazJxKZ8=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20161120</td></tr><tr><th>14</th><td>++6xEqu4JANaRY4GjEfEFtLtqOvZvYPyP3uk/PW9Ces=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160501</td></tr><tr><th>15</th><td>++8dXbkKMJ0rXwUc/m19lTVokEl3c9EfRKWmV6qP9jg=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160321</td></tr><tr><th>16</th><td>++BQyzVEMoHru900mTyAKpCsKYjXUGdAXylmmiPsbVY=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20151228</td></tr><tr><th>17</th><td>++Bks8kE9oclzxZM3hcWs+qzsxuoXFeIE1+7pxKBCQg=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160730</td></tr><tr><th>18</th><td>++Ck01c3EF07Ejek2jfXlKut+sEfg+0ry+A5uWeL9vY=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20131229</td></tr><tr><th>19</th><td>++CzVs35NSfU/gjDrGbWLVMweubEiMZsuvd3eTbXs58=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150914</td></tr><tr><th>20</th><td>++D+fngRaZW1kQC5bvDwcovLYEp6Rn9LY1lxslM0TqM=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20120605</td></tr><tr><th>21</th><td>++EX7OEtJz+hvd7VXRkcZINs+guDwpHq7ahe/o99E1o=</td><td>0</td><td>13</td><td>0</td><td>NA</td><td>4</td><td>20160609</td></tr><tr><th>22</th><td>++F4A2D3J5tCLrRY8t2HU8VpfLu8R8t4czTbbRm5/78=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20151109</td></tr><tr><th>23</th><td>++FHWhbSrK+rHhHRClUmdqGJ1xsnytd3xBOUTO97LWs=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20130429</td></tr><tr><th>24</th><td>++FPL1dXZBXC3Cf6gE0HQiIHg1Pd+DBdK7w52xcUmX0=</td><td>1</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20131016</td></tr><tr><th>25</th><td>++FZyCl52MidGkpw1TehViPEtUMajsPhXsXrzbxWR/w=</td><td>0</td><td>16</td><td>0</td><td>NA</td><td>4</td><td>20161108</td></tr><tr><th>26</th><td>++GAR3JkYXYA10QEapOXAyFu00gIflKFkAgoVDv4i1o=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20170228</td></tr><tr><th>27</th><td>++Ghp/50t+9ckXKaIScS6El99D+qVVeET3TlPOq758g=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20150423</td></tr><tr><th>28</th><td>++GsvnZ4JH4atR+6ObvwrVhDQv9Y1jc7zuTIF4Fv0o8=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20160315</td></tr><tr><th>29</th><td>++HYwmBh/zxrF/wAgCF/TCntq5vjc6TpCEFWTz6AzaI=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20161030</td></tr><tr><th>30</th><td>++IU7zZpfAGO1qdHBDkHlI+6UzjMMpd/JiaY87ykRyo=</td><td>0</td><td>1</td><td>0</td><td>NA</td><td>7</td><td>20110727</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/plain": [
"472062×7 DataFrames.DataFrame\n",
"│ Row │ msno │ is_churn │ city │\n",
"├────────┼────────────────────────────────────────────────┼──────────┼──────┤\n",
"│ 1 │ \"+++hVY1rZox/33YtvDgmKA2Frg/2qhkz12B9ylCvh8o=\" │ 0 │ 1 │\n",
"│ 2 │ \"+++snpr7pmobhLKUgSHTv/mpkqgBT0tQJ0zQj6qKrqc=\" │ 0 │ 1 │\n",
"│ 3 │ \"++0/NopttBsaAn6qHZA2AWWrDg7Me7UOMs1vsyo4tSI=\" │ 0 │ 1 │\n",
"│ 4 │ \"++0BJXY8tpirgIhJR14LDM1pnaRosjD1mdO1mIKxlJA=\" │ 0 │ 1 │\n",
"│ 5 │ \"++0EzISdtKY48Z0GY62jer/LFQwrNIAbADdtU5xStGY=\" │ 0 │ 1 │\n",
"│ 6 │ \"++1G0wVY14Lp0VXak1ymLhPUdXPSFJVBnjWwzGxBKJs=\" │ 0 │ 1 │\n",
"│ 7 │ \"++1GCIyXZO7834NjDKmcK1lBVLQi9PsN6sOC7wfW+8g=\" │ 0 │ 1 │\n",
"│ 8 │ \"++38dVm0EHKCOfND6oEal3xFKtqJYZTZJmnpbAOOZBk=\" │ 0 │ 1 │\n",
"│ 9 │ \"++3A6JMzYJeron30GTcDostfXoAl8rTBuB2M8GeVdNU=\" │ 0 │ 1 │\n",
"│ 10 │ \"++3fWHRDC5GWWlovHcrOKWNwZYOjWWkJyeLlL65uv78=\" │ 0 │ 1 │\n",
"│ 11 │ \"++3wqX72HmowxFh5MrHvNi5Ce8D3ldo4zaUL932TYbs=\" │ 0 │ 1 │\n",
"⋮\n",
"│ 472051 │ \"zzpF6hy2lJv8GjT/eZOGSCmg/kQ5FtuIuI+EUaAx7YU=\" │ 0 │ 1 │\n",
"│ 472052 │ \"zzpTXKXmUXusfh8lgKDrPLEdQyNOvxl4a+PE0CwQIQU=\" │ 0 │ 1 │\n",
"│ 472053 │ \"zzppRL+pLMgQctdB5f9etlPMPQfOBATIah7MyNfOUIs=\" │ 0 │ 1 │\n",
"│ 472054 │ \"zzqhrI0gqPwQnjjWX4PC16TgaLSUwQaejWWeJjclfW8=\" │ 0 │ 1 │\n",
"│ 472055 │ \"zzqv/DjiJJFlfDEeaja2Cq7cvVWz++pwIaJ6SAM42sw=\" │ 0 │ 1 │\n",
"│ 472056 │ \"zzsIerQnY70zKAgAD1GSB89LFnfIxXwXO+FBJEyMbDo=\" │ 0 │ 1 │\n",
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"│ 472059 │ \"zzvB5JRK4WBwtKJoNtnNF34r5dUAuenPpR9xrxdfMSI=\" │ 0 │ 1 │\n",
"│ 472060 │ \"zzvRDIlZcYclP7ocM+wG1WUPbUHPtMRPKerl/hionLg=\" │ 0 │ 1 │\n",
"│ 472061 │ \"zzwsW1MnZalh6V5BwYE+V6hFWyKJYOf+rFd+SWVXxp8=\" │ 0 │ 1 │\n",
"│ 472062 │ \"zzx4hKiyR9XFEGAr7SAjcCPbKJCZ+IqegWL7dPjPwZk=\" │ 0 │ 1 │\n",
"\n",
"│ Row │ bd │ gender │ registered_via │ registration_init_time │\n",
"├────────┼────┼────────┼────────────────┼────────────────────────┤\n",
"│ 1 │ 0 │ NA │ 7 │ 20140608 │\n",
"│ 2 │ 0 │ NA │ 7 │ 20140927 │\n",
"│ 3 │ 0 │ NA │ 7 │ 20160320 │\n",
"│ 4 │ 0 │ NA │ 9 │ 20150807 │\n",
"│ 5 │ 0 │ NA │ 3 │ 20131205 │\n",
"│ 6 │ 0 │ NA │ 7 │ 20150909 │\n",
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"│ 10 │ 0 │ NA │ 7 │ 20160121 │\n",
"│ 11 │ 0 │ NA │ 7 │ 20131016 │\n",
"⋮\n",
"│ 472051 │ 0 │ NA │ 7 │ 20130123 │\n",
"│ 472052 │ 0 │ NA │ 7 │ 20131210 │\n",
"│ 472053 │ 0 │ NA │ 7 │ 20121221 │\n",
"│ 472054 │ 0 │ NA │ 7 │ 20130609 │\n",
"│ 472055 │ 0 │ NA │ 7 │ 20131227 │\n",
"│ 472056 │ 0 │ NA │ 7 │ 20161022 │\n",
"│ 472057 │ 0 │ NA │ 7 │ 20160825 │\n",
"│ 472058 │ 0 │ NA │ 7 │ 20130412 │\n",
"│ 472059 │ 0 │ NA │ 7 │ 20130805 │\n",
"│ 472060 │ 0 │ NA │ 7 │ 20170210 │\n",
"│ 472061 │ 0 │ NA │ 7 │ 20151006 │\n",
"│ 472062 │ 0 │ NA │ 7 │ 20110823 │"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"na_gender_members = train_members[isna.(train_members[:gender]),:]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dict{Union{DataArrays.NAtype, Int64},Int64} with 2 entries:\n",
" 0 => 440950\n",
" 1 => 31112"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"countmap(na_gender_members[:is_churn])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33m!(B::BitArray) is deprecated, use .!(B) instead.\u001b[39m\n",
"Stacktrace:\n",
" [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
" [2] \u001b[1m!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::BitArray{1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
" [3] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/home/regonn/.julia/v0.6/Compat/src/Compat.jl:478\u001b[22m\u001b[22m\n",
" [4] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/home/regonn/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
" [5] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/home/regonn/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
" [6] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
"while loading In[31], in expression starting on line 1\n"
]
},
{
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],
"text/plain": [
"388905×7 DataFrames.DataFrame\n",
"│ Row │ msno │ is_churn │ city │\n",
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"│ 1 │ \"+++l/EXNMLTijfLBa8p2TUVVVp2aFGSuUI/h7mLmthw=\" │ 0 │ 15 │\n",
"│ 2 │ \"++/9R3sX37CjxbY/AaGvbwr3QkwElKBCtSvVzhCBDOk=\" │ 0 │ 18 │\n",
"│ 3 │ \"++/UDNo9DLrxT8QVGiDi1OnWfczAdEwThaVyD0fXO50=\" │ 0 │ 22 │\n",
"│ 4 │ \"++/ZHqwUNa7U21Qz+zqteiXlZapxey86l6eEorrak/g=\" │ 0 │ 13 │\n",
"│ 5 │ \"++0nOC7BmrUTtcSboRORfg6ZXTajnBDt1f/SEgH6ONo=\" │ 0 │ 13 │\n",
"│ 6 │ \"++3Z+W8OPnpbHYfrKwqRKN1bF83XEbxjdYUolhGdHZg=\" │ 0 │ 5 │\n",
"│ 7 │ \"++4cUL0b9CfW8cj0A/wfSxQc4k4fcVtWcLqk2UOdpKs=\" │ 0 │ 15 │\n",
"│ 8 │ \"++5wYjoMgQHoRuD3GbbvmphZbBBwymzv5Q4l8sywtuU=\" │ 0 │ 13 │\n",
"│ 9 │ \"++6P09mCSJSh+Ft2pvZ0FWTrtcI3v1A7h3/coh8dBKw=\" │ 0 │ 22 │\n",
"│ 10 │ \"++8XaEDast796K/DQcP7sSoRQ8JRnA7X5S+oyXEw62E=\" │ 0 │ 5 │\n",
"│ 11 │ \"++95tJZADNg8U8HKbYdxbbXIRsO6pw1zBK4tHI7BtZo=\" │ 0 │ 14 │\n",
"⋮\n",
"│ 388894 │ \"zztg1DNR0a6n+9+CXd8WAfNZHsl5VvyYC2ngeXlwpkc=\" │ 1 │ 22 │\n",
"│ 388895 │ \"zzuGxQfF86Zsx5nqF1Yh+oJpEIfdjFKKYpTqFZMdxzk=\" │ 0 │ 22 │\n",
"│ 388896 │ \"zzv/igs0VigNppe7WR8qgz1h6aXFFzQLu5paAswIbcQ=\" │ 0 │ 14 │\n",
"│ 388897 │ \"zzwimfmJBJ4SrRw3bYXQzE9UrizlSE10yI7dHPvG5xM=\" │ 0 │ 18 │\n",
"│ 388898 │ \"zzwlIVmSuehTevzOi+WkcC7b3+MBYDiE4qybszTyhII=\" │ 0 │ 13 │\n",
"│ 388899 │ \"zzx1qgX8R9EmT8PAn4/KyQ6s7PuLYTfR8kUCABiCW9E=\" │ 0 │ 6 │\n",
"│ 388900 │ \"zzx7wcys2Nha6yP2Skjs+R1/JNAfEuAWUaAp0qsV2Cg=\" │ 1 │ 15 │\n",
"│ 388901 │ \"zzxPS8+wCuE4HE85EDp4WBHgS5l548vfwXJbQq1HZ9I=\" │ 0 │ 22 │\n",
"│ 388902 │ \"zzxZeMFx2fjfKZigMnJa2w0EmloDbm8+8nTf/o/00GY=\" │ 0 │ 4 │\n",
"│ 388903 │ \"zzy7iqSpfcRq7R4hmKKuhI+CJRs79a6pteqEggpiNO0=\" │ 1 │ 5 │\n",
"│ 388904 │ \"zzz1Dc3P9s53HAowRTrm3fNsWju5yeN4YBfNDq7Z99Q=\" │ 0 │ 4 │\n",
"│ 388905 │ \"zzzF1KsGfHH3qI6qiSNSXC35UXmVKMVFdxkp7xmDMc0=\" │ 0 │ 4 │\n",
"\n",
"│ Row │ bd │ gender │ registered_via │ registration_init_time │\n",
"├────────┼────┼──────────┼────────────────┼────────────────────────┤\n",
"│ 1 │ 26 │ \"male\" │ 9 │ 20101118 │\n",
"│ 2 │ 21 │ \"male\" │ 7 │ 20160315 │\n",
"│ 3 │ 32 │ \"male\" │ 9 │ 20120122 │\n",
"│ 4 │ 39 │ \"male\" │ 3 │ 20140421 │\n",
"│ 5 │ 25 │ \"male\" │ 9 │ 20100203 │\n",
"│ 6 │ 35 │ \"female\" │ 7 │ 20120209 │\n",
"│ 7 │ 28 │ \"male\" │ 7 │ 20121009 │\n",
"│ 8 │ 39 │ \"male\" │ 9 │ 20060218 │\n",
"│ 9 │ 35 │ \"male\" │ 9 │ 20100921 │\n",
"│ 10 │ 29 │ \"male\" │ 9 │ 20071211 │\n",
"│ 11 │ 35 │ \"female\" │ 3 │ 20120603 │\n",
"⋮\n",
"│ 388894 │ 18 │ \"male\" │ 3 │ 20150831 │\n",
"│ 388895 │ 30 │ \"male\" │ 9 │ 20051216 │\n",
"│ 388896 │ 33 │ \"male\" │ 9 │ 20060721 │\n",
"│ 388897 │ 43 │ \"female\" │ 7 │ 20161020 │\n",
"│ 388898 │ 26 │ \"male\" │ 4 │ 20160416 │\n",
"│ 388899 │ 31 │ \"male\" │ 9 │ 20141014 │\n",
"│ 388900 │ 30 │ \"female\" │ 7 │ 20100921 │\n",
"│ 388901 │ 28 │ \"female\" │ 9 │ 20070810 │\n",
"│ 388902 │ 24 │ \"male\" │ 7 │ 20120306 │\n",
"│ 388903 │ 31 │ \"male\" │ 3 │ 20150430 │\n",
"│ 388904 │ 48 │ \"female\" │ 9 │ 20140611 │\n",
"│ 388905 │ 26 │ \"male\" │ 3 │ 20170129 │"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filled_gender_members = train_members[!isna.(train_members[:gender]),:]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dict{Union{DataArrays.NAtype, Int64},Int64} with 2 entries:\n",
" 0 => 338569\n",
" 1 => 50336"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"countmap(filled_gender_members[:is_churn])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"NULL\""
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_members[train_members[:gender].==\"NA\", :gender] = \"NULL\""
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
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" // Browser globals (glob is window)\n",
" // Snap adds itself to window\n",
" glob.Gadfly = factory(glob.Snap);\n",
" }\n",
"}(this, function (Snap) {\n",
"\n",
"var Gadfly = {};\n",
"\n",
"// Get an x/y coordinate value in pixels\n",
"var xPX = function(fig, x) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
"};\n",
"\n",
"var yPX = function(fig, y) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
"};\n",
"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
" // Traverse upwards from a snap element to find and return the first\n",
" // note with the \"plotroot\" class.\n",
" Element.prototype.plotroot = function () {\n",
" var element = this;\n",
" while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.svgroot = function () {\n",
" var element = this;\n",
" while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.plotbounds = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x0: bbox.x,\n",
" x1: bbox.x + bbox.width,\n",
" y0: bbox.y,\n",
" y1: bbox.y + bbox.height\n",
" };\n",
" };\n",
"\n",
" Element.prototype.plotcenter = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x: bbox.x + bbox.width / 2,\n",
" y: bbox.y + bbox.height / 2\n",
" };\n",
" };\n",
"\n",
" // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
" // does everything right.\n",
" // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
" var events = [\"mouseenter\", \"mouseleave\"];\n",
"\n",
" for (i in events) {\n",
" (function (event_name) {\n",
" var event_name = events[i];\n",
" Element.prototype[event_name] = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var fn2 = function (event) {\n",
" if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
" return;\n",
" }\n",
"\n",
" var reltg = event.relatedTarget ? event.relatedTarget :\n",
" event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
" while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
"\n",
" if (reltg != this.node) {\n",
" return fn.apply(this, event);\n",
" }\n",
" };\n",
"\n",
" if (event_name == \"mouseenter\") {\n",
" this.mouseover(fn2, scope);\n",
" } else {\n",
" this.mouseout(fn2, scope);\n",
" }\n",
" }\n",
" return this;\n",
" };\n",
" })(events[i]);\n",
" }\n",
"\n",
"\n",
" Element.prototype.mousewheel = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var el = this;\n",
" var fn2 = function (event) {\n",
" fn.apply(el, [event]);\n",
" };\n",
" }\n",
"\n",
" this.node.addEventListener(\n",
" /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
" fn2);\n",
"\n",
" return this;\n",
" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
" // through eve.\n",
" Element.prototype.attribute = function(key, val) {\n",
" if (val === undefined) {\n",
" return this.node.getAttribute(key);\n",
" } else {\n",
" this.node.setAttribute(key, val);\n",
" return this;\n",
" }\n",
" };\n",
"\n",
" Element.prototype.init_gadfly = function() {\n",
" this.mouseenter(Gadfly.plot_mouseover)\n",
" .mouseleave(Gadfly.plot_mouseout)\n",
" .dblclick(Gadfly.plot_dblclick)\n",
" .mousewheel(Gadfly.guide_background_scroll)\n",
" .drag(Gadfly.guide_background_drag_onmove,\n",
" Gadfly.guide_background_drag_onstart,\n",
" Gadfly.guide_background_drag_onend);\n",
" this.mouseenter(function (event) {\n",
" init_pan_zoom(this.plotroot());\n",
" });\n",
" return this;\n",
" };\n",
"});\n",
"\n",
"\n",
"// When the plot is moused over, emphasize the grid lines.\n",
"Gadfly.plot_mouseover = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" var keyboard_zoom = function(event) {\n",
" if (event.which == 187) { // plus\n",
" increase_zoom_by_position(root, 0.1, true);\n",
" } else if (event.which == 189) { // minus\n",
" increase_zoom_by_position(root, -0.1, true);\n",
" }\n",
" };\n",
" root.data(\"keyboard_zoom\", keyboard_zoom);\n",
" window.addEventListener(\"keyup\", keyboard_zoom);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" xgridlines.data(\"unfocused_strokedash\",\n",
" xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
"\n",
" // emphasize grid lines\n",
" var destcolor = root.data(\"focused_xgrid_color\");\n",
" xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"focused_ygrid_color\");\n",
" ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // reveal zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 1.0}, 250);\n",
"};\n",
"\n",
"// Reset pan and zoom on double click\n",
"Gadfly.plot_dblclick = function(event) {\n",
" set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
"};\n",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
" root.data(\"keyboard_zoom\", undefined);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // hide zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 0.0}, 250);\n",
"};\n",
"\n",
"\n",
"var set_geometry_transform = function(root, tx, ty, scale) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
"\n",
" var old_scale = root.data(\"scale\");\n",
"\n",
" var xscale = xscalable ? scale : 1.0,\n",
" yscale = yscalable ? scale : 1.0;\n",
"\n",
" tx = xscalable ? tx : 0.0;\n",
" ty = yscalable ? ty : 0.0;\n",
"\n",
" var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
"\n",
" root.selectAll(\".geometry, image\")\n",
" .forEach(function (element, i) {\n",
" element.transform(t);\n",
" });\n",
"\n",
" bounds = root.plotbounds();\n",
"\n",
" if (yscalable) {\n",
" var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
" root.selectAll(\".xfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(xfixed_t);\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .transform(xfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(yfixed_t);\n",
" });\n",
"\n",
" root.select(\".xlabels\")\n",
" .transform(yfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" var xgridlines = root.select(\".xgridlines\");\n",
" var ygridlines = root.select(\".ygridlines\");\n",
" var xlabels = root.select(\".xlabels\");\n",
" var ylabels = root.select(\".ylabels\");\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" if (xlabels) {\n",
" xlabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
" }\n",
"\n",
" if (ylabels) {\n",
" ylabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
" }\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" if (xgridlines) {\n",
" xgridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (ygridlines) {\n",
" ygridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// drag actions, i.e. zooming and panning\n",
"var pan_action = {\n",
" start: function(root, x, y, event) {\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" root.data(\"tx0\", root.data(\"tx\"));\n",
" root.data(\"ty0\", root.data(\"ty\"));\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var dx0 = root.data(\"dx\"),\n",
" dy0 = root.data(\"dy\");\n",
"\n",
" root.data(\"dx\", dx);\n",
" root.data(\"dy\", dy);\n",
"\n",
" dx = dx - dx0;\n",
" dy = dy - dy0;\n",
"\n",
" var tx = tx0 + dx,\n",
" ty = ty0 + dy;\n",
"\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
" },\n",
" end: function(root, event) {\n",
"\n",
" },\n",
" cancel: function(root) {\n",
" set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
" }\n",
"};\n",
"\n",
"var zoom_box;\n",
"var zoom_action = {\n",
" start: function(root, x, y, event) {\n",
" var bounds = root.plotbounds();\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
" var ratio = width / height;\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" x = xscalable ? x / px_per_mm : bounds.x0;\n",
" y = yscalable ? y / px_per_mm : bounds.y0;\n",
" var w = xscalable ? 0 : width;\n",
" var h = yscalable ? 0 : height;\n",
" zoom_box = root.rect(x, y, w, h).attr({\n",
" \"fill\": \"#000\",\n",
" \"opacity\": 0.25\n",
" });\n",
" zoom_box.data(\"ratio\", ratio);\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" var bounds = root.plotbounds();\n",
" if (yscalable) {\n",
" y /= px_per_mm;\n",
" y = Math.max(bounds.y0, y);\n",
" y = Math.min(bounds.y1, y);\n",
" } else {\n",
" y = bounds.y1;\n",
" }\n",
" if (xscalable) {\n",
" x /= px_per_mm;\n",
" x = Math.max(bounds.x0, x);\n",
" x = Math.min(bounds.x1, x);\n",
" } else {\n",
" x = bounds.x1;\n",
" }\n",
"\n",
" dx = x - zoom_box.attr(\"x\");\n",
" dy = y - zoom_box.attr(\"y\");\n",
" if (xscalable && yscalable) {\n",
" var ratio = zoom_box.data(\"ratio\");\n",
" var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
" var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
" dx = width * dx / Math.abs(dx);\n",
" dy = height * dy / Math.abs(dy);\n",
" }\n",
" var xoffset = 0,\n",
" yoffset = 0;\n",
" if (dx < 0) {\n",
" xoffset = dx;\n",
" dx = -1 * dx;\n",
" }\n",
" if (dy < 0) {\n",
" yoffset = dy;\n",
" dy = -1 * dy;\n",
" }\n",
" if (isNaN(dy)) {\n",
" dy = 0.0;\n",
" }\n",
" if (isNaN(dx)) {\n",
" dx = 0.0;\n",
" }\n",
" zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
" zoom_box.attr(\"width\", dx);\n",
" zoom_box.attr(\"height\", dy);\n",
" },\n",
" end: function(root, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var zoom_bounds = zoom_box.getBBox();\n",
" if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
" return;\n",
" }\n",
" var plot_bounds = root.plotbounds();\n",
" var zoom_factor = 1.0;\n",
" if (yscalable) {\n",
" zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
" } else {\n",
" zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
" }\n",
" var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
" ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
" zoom_box.remove();\n",
" },\n",
" cancel: function(root) {\n",
" zoom_box.remove();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
" var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
" var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
" var drag_action = zoomable ? zoom_action :\n",
" panable ? pan_action :\n",
" undefined;\n",
" root.data(\"drag_action\", drag_action);\n",
" if (drag_action) {\n",
" var cancel_drag_action = function(event) {\n",
" if (event.which == 27) { // esc key\n",
" drag_action.cancel(root);\n",
" root.data(\"drag_action\", undefined);\n",
" }\n",
" };\n",
" window.addEventListener(\"keyup\", cancel_drag_action);\n",
" root.data(\"cancel_drag_action\", cancel_drag_action);\n",
" drag_action.start(root, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.update(root, dx, dy, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
" window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
" root.data(\"cancel_drag_action\", undefined);\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.end(root, event);\n",
" }\n",
" root.data(\"drag_action\", undefined);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
" event.preventDefault();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseover = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseout = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomout_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
"// The constants a, b, and c are solved using the constraint that the function\n",
"// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
"var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return a * Math.exp(b * position) + c;\n",
"}\n",
"\n",
"// inverse of scale_from_slider_position\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return 1 / b * Math.log((scale - c) / a);\n",
"}\n",
"\n",
"var increase_zoom_by_position = function(root, delta_position, animate) {\n",
" var scale = root.data(\"scale\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
" position += delta_position;\n",
" scale = scale_from_slider_position(position, min_scale, max_scale);\n",
" set_zoom(root, scale, animate);\n",
"}\n",
"\n",
"var set_zoom = function(root, scale, animate) {\n",
" var min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"scale\");\n",
" var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
" if (animate) {\n",
" Snap.animate(\n",
" old_scale,\n",
" new_scale,\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
" } else {\n",
" update_plot_scale(root, new_scale);\n",
" }\n",
"}\n",
"\n",
"\n",
"var update_plot_scale = function(root, new_scale) {\n",
" var trans = scale_centered_translation(root, new_scale);\n",
" set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
"\n",
" root.selectAll(\".zoomslider_thumb\")\n",
" .forEach(function (element, i) {\n",
" var min_pos = element.data(\"min_pos\"),\n",
" max_pos = element.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
" element.transform(new Snap.Matrix().translate(\n",
" Math.max(min_pos, Math.min(\n",
" max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
" });\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var min_pos = this.data(\"min_pos\"),\n",
" max_pos = this.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"old_scale\");\n",
"\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
" dx / (max_pos - min_pos);\n",
"\n",
" // compute the new scale\n",
" var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
" var root = this.plotroot();\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly) {\n",
" var fig = Snap(\"#img-f62bfa1a\");\n",
"fig.select(\"#img-f62bfa1a-4\")\n",
" .drag(function() {}, function() {}, function() {});\n",
"fig.select(\"#img-f62bfa1a-6\")\n",
" .data(\"color_class\", \"color_0\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-f62bfa1a-7\")\n",
" .data(\"color_class\", \"color_1\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-f62bfa1a-9\")\n",
" .data(\"color_class\", \"color_0\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-f62bfa1a-10\")\n",
" .data(\"color_class\", \"color_1\")\n",
".click(Gadfly.colorkey_swatch_click)\n",
";\n",
"fig.select(\"#img-f62bfa1a-14\")\n",
" .init_gadfly();\n",
"fig.select(\"#img-f62bfa1a-17\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-17\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-147\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-147\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-160\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-160\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-160\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#img-f62bfa1a-164\")\n",
" .data(\"max_pos\", 106.77)\n",
";\n",
"fig.select(\"#img-f62bfa1a-164\")\n",
" .data(\"min_pos\", 89.77)\n",
";\n",
"fig.select(\"#img-f62bfa1a-164\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#img-f62bfa1a-166\")\n",
" .data(\"max_pos\", 106.77)\n",
";\n",
"fig.select(\"#img-f62bfa1a-166\")\n",
" .data(\"min_pos\", 89.77)\n",
";\n",
"fig.select(\"#img-f62bfa1a-166\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-166\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-166\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
";\n",
"fig.select(\"#img-f62bfa1a-168\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-168\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#img-f62bfa1a-168\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"text/plain": [
"Plot(...)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot(train_members, x=\"gender\", y=\"is_churn\", color=\"is_churn\", Geom.histogram(position=:stack), Scale.color_discrete_manual(\"red\",\"green\"))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dict{Union{DataArrays.NAtype, Int64},Int64} with 2 entries:\n",
" 0 => 779519\n",
" 1 => 81448"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"countmap(train_members[:is_churn])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.6.0",
"language": "julia",
"name": "julia-0.6"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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