{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 機械学習" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "import plotly.io as pio\n", "pio.templates\n", "from matplotlib.ticker import StrMethodFormatter\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelBinarizer\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# サンプルデータセット：titanic\n", "df_train = pd.read_csv('data/train.csv')\n", "df_test = pd.read_csv('data/test.csv')\n", "df_gender = pd.read_csv('data/gender_submission.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## データ可視化" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvived
08920
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" ], "text/plain": [ " PassengerId Survived\n", "0 892 0\n", "1 893 1\n", "2 894 0\n", "3 895 0\n", "4 896 1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_gender.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 891 non-null int64 \n", " 1 Survived 891 non-null int64 \n", " 2 Pclass 891 non-null int64 \n", " 3 Name 891 non-null object \n", " 4 Sex 891 non-null object \n", " 5 Age 714 non-null float64\n", " 6 SibSp 891 non-null int64 \n", " 7 Parch 891 non-null int64 \n", " 8 Ticket 891 non-null object \n", " 9 Fare 891 non-null float64\n", " 10 Cabin 204 non-null object \n", " 11 Embarked 889 non-null object \n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.7+ KB\n" ] } ], "source": [ "# カラムの確認\n", "df_train.info()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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vA37WqTUDIOnlkq4ETuLBi3hERMQ0mFL5gWpPRozobZ8InChpW8r8+46jHDcZqhHRqmG+XjLunLuk/wBeS7k4ugql5O8Jtl8j6eHA74ANbd+zlMdfDWxte6mFwzLnHhFtyJz7GEYrP9CpK0MpL/Cj7o5d0ia1JAGStgJWZpTaMhER0Z7Jlh/o2IOyQEe3VwKvk3Q/cDdlJaaMzCNi2mVaZhnLtExMxKB/1I7mDPpzpall9iKWC+lsI8bX08hd0rXA34EHgIW250rajbKc3pMoF0znd+2/BfBVysXXRcAzlnbBFTJyj4iYjKZG7tuPiHj5LfAKSie+WF2g45vAa21fLOlhwP0TaCdiTIP+UTuaM8zPlUlPy9i+AqAGxnR7IXCJ7YvrfomUiUYtTy+giOVVrxmqBk6TdH5NPhrLEwBLOlXSBZIOnNopRkTERPU6cn+u7ZskrQf8RNKVtn81xjGfCzwD+AfwM0nn2/5ZA+cbERE96Gnkbvum+v024ERg6zF2vxH4le3bbf8DOBnYauROkvaVNF/S/EWL7pr4mUdExFL1UltmFjDD9t/rzy8EPjrGQ04FDpS0GnAf8DzgcyN3sj0PmAeJlomIdgzz9ZlepmUeQSkC1tn/W7Z/LOnlwJeAdYGTJF1k+0W2/yrps8B5lLn6k22f1NL5xxAa5giIiF4lQzX6Tjr36NWgP1eSoRoDJZ1txPgyco+I6FNTHrkvpfzAwcA+wJ/qbh+wfbKklShZq3MppQf2t/2LSZ99RMQkDfq0zFimUn4A4HO2PzNi2z4Atp9S4+JPkfQM26Mt0RcxYcP8go3oVRtz7psBp0OJi5d0B2UUf24LbcUQSmcbMb6plh/YT9Ilkg6XtHbddjGwi6QVJG0MPB2Y0+A5R0TEOHrt3J9reyvgxcDb68LXhwKPA7YEbgEOqfseTslSnQ98HjiLMlf/IMlQjYhoz4SjZeqF1AXdc+2SHkNZS3XzUfY/C3iz7cuXdsxEy8REZM49ejXoz5UpLZAtaZakNTo/U8oP/FbS+l27vZxS3x1Jq9X9kPQCSnTNUjv2iIho3lTKD/yvpC0p8/HXAm+p+68HnCppEXAT8NqmTzqGW0bS0athfq4kiSkiBlamZSIiYqD0mqG6FvB1YHPKNMwbgd8BxwKPoUzL/EutCLkX8G+AKFmtb+ssuRfRhEEfjUU0odckpi8AP7b9qlpeYDXgA8DPbH9K0vuB91M69WuA59WO/sWUmu3btHDuMaTS2UaMr5domTWBbYHDAGzfZ/sOYFfgyLrbkcDL6v1n2f5r3X42sFGzpxwREePpZeS+MaU42BGSngqcD+wPPML2LXWfP1KiakZ6E3BKEyca0ZFpmYjx9dK5r0BZA/Udts+R9AXKFMxiti3pQREvkrandO7PbepkIyCdbUQveomWuRG40fY59fbxlM7+1k4iU/1+W+cBkragXIDd1fafRztoyg9ERLSnpzh3SWdQSgj8rpYfmFXv+nPXBdV1bB8o6VGUqpCvs31WLyeROPeIiIkbK8691859S8pIfCXgamBvyqj/OOBRwHWUUMi/SPo68Mq6DeriHmMdP517TETm3KNXg/5cmXLn3rZ07hHRhnTuy1g694iIiUv5gYiIITPp8gO2f1Pvew/wGWBd27dL2g74PiVTFeAE2x9t9rRjmA36R+1ozjA/V6ZSfgBJcyj13a8fsf8Ztndu7jQjImIixu3cu8oPvAFK+QHgvnr354ADKSP1iGmxPI2OIpZXUyk/sCNwk+2L60Ie3Z4l6WLgZuC9ti9r8JwjInoyzAOByZYfOJgymn/hKPtfADza9gJJLwG+Bzx+5E6S9gX2BdDMNZkxY9bIXSIiYpLGDYWU9EjgbNuPqbf/idK5PwX4R91tI8oofWvbfxzx+GuBubZvX1obCYWMiRjmi2QxMYP+XBkrFHLckbvtP0q6QdITbf8O2AG4wPYOnX26O/D6ZnBrLSa2NSXcctT6MhGTkc42Yny9Rsu8Azi6Rsp0yg8szauAt0laCNwN7OHlIVMqImKIJEM1IqJPTWlaJiKiXw36nPtYeio/IGktScdLulLSFZKeJWlLSWdLuqjWZd+67rumpB9KuljSZZLGmsKJiIgWTCVD9TjgI7ZPqSGP/wVsB7wduNz2SyWtC/xO0tE1+SliyoZ5NBbRq0lnqNZl9WbX3dakhEJCqT2zhkpm0+rAX4CFzZ52DLN0thHjm0qG6ruAUyV9hjK98+y6/5eBH1A6+zWA3W0vavi8IyJiDL3MuXcyVA+1/TTgLsoC2W8DDrA9BzgAOKzu/yLgImADYEvgy5JmExER02YqC2S/HjihbvsOsHX9eW9KmV/bvopS+nfTkQfNAtkREe0Zt3Ov5QRukPTEumkH4HLKtMvz6rbnA/9Xf76+7oOkRwBPpCQ+jTzuPNtzbc9NXZmIiGZNZYHsJ1OiaFYA7gH+1fb5kjYAvgGsDwj4lO1vjnX8JDFFRExc1lCNiKE06GGz6dwjIgZQyg/EQBn00VhEE3qp5/5E4NiuTY8FPgQ8DNgVWATcBrzB9s2SNgWOoETUfND2Z8Y7iYzcIyImrrFpGUkzgZuAbYC/2r6zbn8nsJntt0paD3g08LK6Tzr3iFgmBv1T3lide0+Fw7rsAPzB9nWdjr2aRSk7gO3bbJ8H3D/hM42IiEZMdM59D+CYzg1JnwBeB/wN2L7B84pYqkEfjUU0oeeRe60GuQslGxUA2x+s5QeOBvabSMPJUI2IaM9ERu4vpqydeuso9x0NnAx8uNeD2Z4HzIPMucfEZCQdMb6JdO578uApmcfb7pQc2BW4sskTi1iaTMtEr4b5f9dr+YFZlJoxj7X9t7rtu5S6MYuA64C32r5J0iOB+ZRa74uABZRImjtHPTgZuUdEOwZ9IJAM1YiIAZQM1YgYSoM+ch/LuNEykp5YF8HufN0p6V2Sju3adq2ki0Y87lGSFkh6b2tnHxERoxp35G77d5QVlbozVE+0/fnOPpIOocS6d/sscEpTJxrRMcyjsYheTXRaZnGGamdDXQj7XygLdnS2vYyyAlMC2KNx6WwjxjelDNXqn4BbO2GRklYH/g14AZApmWhcRu4R4+u5c+/KUD1oxF0Pin8HDgY+Z3tBGdRHNCudbfRqmJ8rU8pQlbQC8Arg6V37bQO8StJ/AWsBiyTdY/vL3QeTtC+wL4BmrknWUY2Ipg3zp7ye49wlfRs41fYRXdt2Ag6y/bylPOZgYMF4ZX8T5x4RMXFTjnOvGaovAN4y4q7R5uAjIpYLGbkvYxm5R0RMXDJUY6AM82gsJmaYnyvp3KPvLE8voIjlVU+LdUg6QNJlkn4r6RhJq0jaT9JVkizp4V37vq+rLMFvJT0gaZ32foWIiBipl9oyGwLvBOba3hyYSbmQeiawI6Xc72K2P217S9tbUmLif2n7L02feERELF2v0zIrAKtKuh9YDbjZ9oUA4yQqjUxwioiYNsM8hddL4bCbJH2GsljH3cBptk8b73GSVgN2YoJrq0ZENGWYL6j2Mi2zNmUZvY2BDYBZkl7Tw7FfCpy5tCmZLJAdEdGeXi6o7ghcY/tPtu8HTgCe3cPjxkxwsj3P9lzbc1N6ICKiWb3MuV8PPLNOs9xNKfs7f6wHSFoTeB7Qywg/YkKG+aN2RK96mXM/R9LxwAXAQuBCYJ6kdwIHAo8ELpF0su0314e9nDI3n/mWaFw624jxpfxARAysQf+UN1b5gZ6SmCIior9MJUNVkj4h6feSrqjTNNTtX6zZq5dI2qrdXyEiIkYad869K0N1M9t3SzqOEgkjYA6wqe1FktarD3kx8Pj6tQ1waP0e0YhB/6gd0YRJZ6gCHwdebXsRgO3b6r67Ake5TOafLWktSevbvqXhc48hlc42YnzjTsvYvgnoZKjeAvytZqg+Dti9JiKdIunx9SEbAjd0HeLGui0iIqZJL9My3RmqdwDfqRmqKwP32J4r6RXA4UCGVNG6TMtEjG8qGao31p8BTgS2qD/fRJmL79iobnuQlB+IiGjPVDJU7wS2B66hZKP+vu7/A2C/uqD2NpRpnIfMt9ueB8yDxLnHxGQkHb0a5ufKpDNUgVWBoyUdACwAOtmpJwMvAa4C/gHs3cJ5R0TEGJKhGhEDa9Cvz4yVoZrOPSKiT43VuWeB7IgYWIM+ch9LRu4REX1qrJE7tvv2C9g37aW9tDf97Q3y7zYo7fV7Vch9017aS3vLpL1B/t0Gor1+79wjImIU6dwjIgZQv3fu89Je2kt7y6S9Qf7dBqK95SJaJiIimtXvI/eIiBhFOveIiAGUzj0ixiXpOb1si+VH5twjGiTpkcDWgIHzbP9xGZ9SIyRdYHur8bbF8qNvastI+hLlBTMq2+9suL2/j9Pe7Cbb62r3ccCNtu+VtB1lEZSjbN/RUnuPAD4JbGD7xZI2A55l+7AW2npT93ElzQT+3fZHmm6rHn/afrfa3puBDwGnUxaQ/5Kkj9o+vI32apsrA68EHkPX69n2Rxs6/rMoi/OsK+ndXXfNBmY20cY47T8XeLztIyStC6xu+5qW2loX2IeH/i3f2HA7rxjrftsnjHV/r/qmc6csEALwHGAz4Nh6ezfg8qYbs70GgKSPUdaO/V/KC3YvYP2m2+vyXWCupE0o4VHfB75FqZHfhm8ARwAfrLd/T/nbttEB7iDplcCbgHVq279soZ2ObzB9vxvA+4Cn2f4zgKSHAWdRlqBsy/eBvwHnA/e2cPyVgNUpfcUaXdvvBF7VQnuLSfowMBd4IuX/uCLwTUof0IbvA2cAPwUeaKkNgJfW7+tR3jhPr7e3pzxfGuncp612QoM1GM4GVui6vSJwdovtXdzLtgbbu6B+fx/wjvrzhS22d97INoCLWmxvd+B24DrgOS0/V6b7dzsLWKnr9krAWS3/jr9t8/hd7Ty6fl9tOtrr/K8oA6ru/98lbbY3Xb9bbe80YP2u2+sDpzZ1/H68oLo25SNhx+p1W1vukrSXpJmSZkjaC2hz0df7Je0JvB74Ud22Yovt3VVHmAaQ9EzKSLBxkh4P7E/5dHId8Nq6fGNbpu13q64CzpF0cB11ng38XtK7R0xpNOksSU9p6djdNpB0OXAlgKSnSvrvltu8z6XX6/z/ZrXc3o8ktfUJeTRz/OAlSG8FHtXUwftpWqbjU8CFkn5OeVffFji4xfZeDXyhfhk4s25ry97AW4FP2L5G0saUKaG2vJuy7u3jJJ0JrEt7H7d/COxn+6eSVNs+D3hyS+1N5+8G8If61fH9+n2NUfadEkmXUp6PKwB7S7qaMi0jwLa3GOvxk/B54EWUvye2L5a0bcNtjHScpK8Ca0naB3gj8LUW29sf+ICke4H7WfK3bOX6GvAzSacCx9Tbu1OmhBrRl9EyNSJhm3rzHA9IRMJIktamvLtf0nI7K1DmNQX8zvb9LbUz2/adI7Y9wfbvl/aYBtqclt9tlHbXBu5wSy8wSY8e637b1zXc3jm2t5F0oe2n1W0X235qk+10tSdgI2BT4IWU/9+ptn/SRnvLiqSXUwaoAL+yfWJTx+6bkbukkSFXN9TvG0jawPYFLbX7BOBQ4BG2N5e0BbCL7Y+31N4vgF0o/5vzgdsknWm7lY/1NWLlJSyJEHihJGx/toXmVpX0OWBD2zt1olcoFzobN0pUwhMk/Q241PZtDbbzIeA421fW6JVTgC2BhZJebbux0VhHp/OuU02X2f57vT0beBJl2qtJN0h6NmBJK1JGuVc03MZiti3pZNtPAVrt0CVtWv93o4Z1ttW3VBcAf6+fZleTtEbnfzlVfTNyr9MwS2Pbz2+p3V9SLm5+tWvE8lvbm7fU3oW2n1bD6ubY/rCkS1r4mN1p72TgHuBSYFFnu1sIT5R0CjV6xfZT66j6wvoCbpykkyhvHp3nznaUN8yNgY/abmS6S9JlwOa1Q9qXMm23A/AE4EjbWzfRzlLavhDYqvMJQdIMYL4bjj+X9HDK1OSOlFH0acA7bf+lyXZGtHkk8GXb57XVRm1nnu19l9LHtNm37EOp476O7cfVa1L/Y3uHJo7fNyN329vXJ+6zbJ85jU2vZvvc8ilxsYUttreCpPWBf2FJCF+bNmrrjWMUD7d9nKSDAGwvlNRmyNkKwJNs3wqL496Pokzp/YrmrmV0LvxBmZc+xvYDwBX1DaxN6p76sb2opTafYXuvBzUsvRX4nxba6tgG2EvSdZQghlauJ9jet37fvsnj9uDtlIS3c2r7/ydpvaYO3jedOyx+4n4ZeNo0Nnu7SmJRZ2T0Kkrce1s+CpwK/Nr2eZIeC/xfi+2dIumFtk9rsY2O6Y5emdPp2Kvb6ra/SGpy7v1eSZtToh22B97bdV+b0UAAV0t6J2XqEOBfgatbaOf/SbrX9ukAkt4HPJ92O/cXtXjsh5C0CuXv91zKc/QMykj6npaavNf2fZ2BY31Tbmwqpa869+pnNRHmhLYuVo3wdkoy0aaSbgKuoSQytcL2d4DvdN2+mpKB2JazgRPrp6K2IwSmO3rlF5J+xJK/5yvrtlnAHQ22sz9wPOX3+ZxrBmUNq7uwwXZG81bgi8C/UzqGn9HOEnG7UEIF3wfsRLnQuWsL7SzWdV1hPWCVNtuqjgL+Dnyp3n415dPdbi2190tJH6Bci3oB5Y3lh00dvG/m3DtUygLMomSQ3U3L4UqSZtp+oHYIM5q62DFGe6tQMjifTNcT2g2nQHe1dw3lRXppi5EdzwBusP3HOjp5C6WjvRz4UFvztjXi4hWUkRjAXykXxt/eRnvTrV4MP2rkdEmL7a1HCdU7H3hj24MrSbsAhwAbUD51PRq4wnYrobOSLre92XjbGmxvBuW13h0N1FioZ98lMdlew/YM2yvanl1vtxWHCnCNpHnAM4EFLbbT8b/AIykfSX9JCQdr8w3lBkqWY5sv1K8C99Wfn025lvAVSmfb2oo39Xe6mnKN5OWUKZPWIjwkPUzSFyVdIOl8SV+o01CtqPP6j5a0UlttSPq7pDvroOoqykXi3YA7Jd059qOn7GOU193vbW9MuUh9dovtXVCnCgGQtA1Lyp604WDbX7O9m+1XAYdLOrqpg/fdtEwdje0FbGz7Y5LmUFJ4z22pyU2BnSnTM4fVj/nftv3rltrbxPZukna1faSkb1Hm/tpyNWWq4hS6apM0HAo5s2t0vjswz/Z3ge9KuqjBdoDF4at71q/bKfVkNA0XzL5NuVDbmUbbq7a9Y4ttXg2cKekHdGVON/X/c62xtIzcb/vPKpnhM2z/XNLnm25ESxLCVqRk/F5fbz+ampHbkjmSDrL9H/UN+jhKyYVG9F3nDvw3JWTv+ZR39gWUUeAz2mjM9j8of/TjVBJTvkAZUbdVEa9zoe+OepHuj5QCQ225pn6tVL/aMFPSCrYXUkZf3XPCbTwHr6S8Ie5s+yoASQe00M5I69v+WNftj0vaveU2O1mxM2ghE7ZDJdnmdNt/q7fXAraz/b222qS8BlanvGEeLek22in9sXMLx+zFGym/10GUT5Wn2P5cUwfvxzn3C2xvpWnKlKvHfx5lxLkT5WPasXXk2UZbb6bUXtmCEhO+OmVeus2ohFZJ+iAlUep2Su2MrWpM+CaUOPBGq/xJehmwB6V64I8pI+qv14/2rZH0WeBcymAAysXirW2/d+mP6g+SLrK95Yhti1+DDbf1KNvX1+tcd1PeuPYC1gSOdq262ZaRF3BtX9/w8btzEFakTFueSa1W6oaSpvqxcz+HMm97Xu3k1wVOa+NJVtu7lhLxcBzwA9ttFg2bdvXvdyAPvYDbaOJGnctcn/K/uqtuewKlPndb2cWzKBeL96R80jsKOLHpsE8tqf0vllzsh/LpbkGb14Sm8f/3kEQ6SZe6hQQ0dS0CIum7ttuMFutud1ou4GqaEjL7cVrmi8CJwHqSPkEZHf17i+1t4RH1UNqgcaoGNjwH3u1oyrzwzpSwutcDf2q6EdsPuRDmFmvK1OPfRamF/606pbYb8G+U7Mom21mW89LT8v8D5tdPJl+pt99OiZppQ3fG4GNbamM0nQu4P3XJEt8eeE3TjXhJQuZuto8d9wGT1Hcjdyi1IChztwJ+ZrvxCAhJB9r+Ly1lBSg3v/LTh8e63+2tVnS+7ad3j8wknWe7lWsYg0jLsDbJdP3/6qeg/8eSi8M/AT7exifZESP3aVvKT9J823MlXUxZdGVRm1O+nfbaODb04chd0jqUj0zHdG1b0c1X++u8YbQZCrVYW513Dzp/t1sk/TNwM2WVpOjduykXiQ/p2tY9IGilNkk1Lf+/2om/v+njLsVTa5ilKAk+nU/ObZfgna4LuB0/lfReyiev7kinRvI++m7kXufA51BipAWsRYkouRXYx3ajHxUlbdXmyGuU9o4E9nddM7VOJxzSYhLTzpTIkjmUzLzZwEds/6CN9gaRpK2B611LT0t6PSUc8lpKLHObxbVa/f9J+rLt/ST9kNE/we7SRDvLUr2w/whKGGL3BdxHAyc13ad0tTvaWrC23chUVD927l8Djrd9ar39QsoL6QjgC7a3Gevxk2jv55SkouMpUTK/bfL4o7T3kAiEtqISohmSLgB2dKlZsy0lOucdlLK/T6oJKk23uQpljn0TSkXPw2qoadPt3Gl7do0Yewjbba6BOy1q7spBti8dsf0pwCdtv3T0Ry7f+m5aBnim7X06N2yfJukztt+iUku7UfXixyMpVRq/qlIv+1i3VM8dmCFpbdt/hcXTUI3/n1RqkC+NR8Rrx9imNUmrOpIyJXMG8GLKovH7t9DOH2AwOvExPGJkxw5g+1JJj2mz4ZrLshkPjnQ6qolj92Pnfoukf6OMjqC8mG5VqbOxaOkPm7z6cfuLdRR/IPAhoK3O/RDgbEmdWOndgE+00M5oc4mzKLUuHkaJHIjeTHeSFsBmnTBESYdR4uvbsO5YkVwtRnFNp7XGuG/VthqtQRTbUTr3kylv0r+mhOxOWT927q8GPgx8r97urGk6kzK6bpSkJ1HeQF4J/Jly8eM9TbfTYfsoSfNZchHuFbYvb6GdxRf/JK1BGfXtTXnTPGRpj4tRHUOp8Hc7Zc72DFg8l9tWSePFAQQudfFbaoaZlES61hpYDsyXtI9HFO2qCYVthXtCCeN+KmXBmr1V1hv4ZlMH77s59+km6TeUDu87tm9usZ1pmUMd0eY6lEiPvSgf87/QmQ6KiZnuJC2VRU46n75EGWH+g4YjSqYzFHFZqZ3qiZTidp3OfC6lHMfL3dIazZLOtb21pPMp5Qf+Tkma2rSJ4/fdyL2+YN7LkjU/geYz8mpbM4FrbH+h6WOPYuQc6pOAd7XVmKRPU8rhzgOeYns6Kl4OrOlO0rLdVm2jkQZ5xA6Ay4Iuz65JS53lM09yXZikRfNVavR8jfKmsgD4TVMH77uRe00w+B/KH2PxEm0thiudAexg+75xd55aO4tTuVVqnp/b5ohJ0iJKFciFPDjEre1Y4ugjktZpM5QzinrhdrbtS5o6Zt+N3IGFtg8df7fGXEOLJVW7TNccaqeNvqvlH9MvHXu7JHUWkzHlYupQd+4/lPSvlDmy7vrjbT0Jp6WkKkuy8uDBmXkZSUcMIEn/TbnG1sm2f4ukHd3QSmH9OC3TalZXRMR0kHQlJcmts2D8DOAy209q4vh9N3J3yzW5R6qx7aOlXbdZLyQiBt9VlPUNrqu359Rtjei7zl3SapTwvUfZ3lfS44En2v5RS012L7SwCiXevdUQxYgYXF11etYArpB0br29DQ0mo/XjtMyxlEiZ19nevHb2Z3nEKjEtn8O5treervYiYnAsrU5PR1OlHvpu5A48zvbukvaEssapWgwtqYk+HTMoyQ1rttVeRAy2kZ13rVfVeF/cj537fZJWpc6DS3ocXVEzLTifJXPuCyllXN/UYnsRMQQk7Qt8FLiHUhdLlL6mkeCQfuzcP0xZ9HiOpKMpiyC/oelGJD0DuKFzAXdEje7Ga71ExNB5H7C57dvbOHjfzbkDSHoYZa1DAWe38cdZFjW6I2J4SPoxpTDgP1o5fr917pKeA1xk+y5JrwG2ohS8um6ch060ncVrJ0r6CvAn2wfX2xdN5wXciBg8kp5GWWToHB6ckNnI+sz9mIJ+KPAPSU+lhET+gYbqH48ws9Z4gVKju7uIUD9OZ0XE8uWrlH7lbMq1vc5XI/qxk1po25J2Bb5i+zBJbVzgXBY1uiNieKxoe6kLoUxVP07L/JJyQXVvYFvgNuDiTkXFhtua1hrdETE8JH2SEqDxQ1qok9WPnfsjKSsvnWf7DEmPArZrat3BiIjp0HadrH7s3GcB99h+oI6iNwVOsX3/OA+NiBga/XhB9VfAypI2BE4DXgt8Y5meUUREjyQd2PXzbiPu+2RT7fRj564aF/oK4L9t78aSpbEiIpZ3e3T9fNCI+3ZqqpG+7NwlPYuyqPNJdVs//h4RMZy0lJ9Huz1p/dgp7k95tzvR9mWSHgv8fBmfU0REr7yUn0e7PWl9d0E1IqKfSXqAsh6zgFWBTvkBAavYXrGRdvqtc5e0LnAg8GTK4hlAVkaKiOjWj9MyRwNXAhsDH6EkAZy3LE8oImJ5048j9/NtP13SJba3qNvOs/2MZX1uERHLi36sLdNJVrpF0j8DNwPrjLF/RMTQ6cfO/eOS1gTeA3wJmA0csGxPKSJi+dI30zKSVgHeCmwCXAocZnvhsj2riIjlUz917sdSpmTOAF4MXGd7/2V7VhERy6d+6twv7ZT1rYtonGt7q2V8WhERy6V+CoVcXPUx0zEREWPrp5F7J6sLHpzZJUoN5NnL6twiIpY3fdO5R0RE7/ppWiYiInqUzj0iYgClc4+IGEDp3CMiBlA694iIAfT/Adu7B5mIKdCMAAAAAElFTkSuQmCC", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 欠損の確認\n", "sns.heatmap(df_train.isnull(), cbar=False);" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "% of women who survived: 0.7420382165605095\n", "% of men who survived: 0.18890814558058924\n" ] } ], "source": [ "# 生存率の確認\n", "women = df_train.loc[df_train['Sex'] == 'female']['Survived']\n", "rate_women = sum(women)/len(women)\n", "men = df_train.loc[df_train['Sex'] == 'male']['Survived']\n", "rate_men = sum(men)/len(men)\n", "\n", "print('% of women who survived:', rate_women)\n", "print('% of men who survived:', rate_men)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "data = [df_train, df_test]\n", "for dataset in data:\n", " mean = df_train['Age'].mean()\n", " std = df_test['Age'].std()\n", " is_null = dataset['Age'].isnull().sum()\n", " # compute random numbers between the mean, std and is_null\n", " rand_age = np.random.randint(mean - std, mean + std, size = is_null)\n", " # fill NaN values in Age column with random values generated\n", " age_slice = dataset['Age'].copy()\n", " age_slice[np.isnan(age_slice)] = rand_age\n", " dataset['Age'] = age_slice\n", " dataset['Age'] = df_train['Age'].astype(int)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "survived = 'survived'\n", "not_survived = 'not survived'\n", "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(16, 6))\n", "women = df_train[df_train['Sex']=='female']\n", "men = df_train[df_train['Sex']=='male']\n", "ax = sns.distplot(women[women['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[0], kde =False, color='green')\n", "ax = sns.distplot(women[women['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[0], kde =False, color='red')\n", "ax.legend()\n", "ax.set_title('Female')\n", "ax = sns.distplot(men[men['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[1], kde = False, color='green')\n", "ax = sns.distplot(men[men['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[1], kde = False, color='red')\n", "ax.legend()\n", "_ = ax.set_title('Male');" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Name=%{x}
Sex=%{y}
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"gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Which Age Survived?" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "PassengerId" }, "type": "log" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Age" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for template in ['plotly']:\n", " fig = px.scatter(df_train,\n", " x='PassengerId', y='Age', color='Survived',\n", " log_x=True, size_max=20,\n", " template=template, title='Which Age Survived?')\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![plot_01](image/newplot_01.png)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(x='Pclass', y='Survived', data=df_train);" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.rc('xtick', labelsize=14) \n", "plt.rc('ytick', labelsize=14)\n", "\n", "plt.figure()\n", "fig = df_train.groupby('Survived')['Pclass'].plot.hist(histtype='bar', alpha=0.8)\n", "plt.legend(('Died','Survived'), fontsize = 12)\n", "plt.xlabel('Pclass', fontsize = 18)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "embarked_mode = df_train['Embarked'].mode()\n", "data = [df_train, df_test]\n", "for dataset in data:\n", " dataset['Embarked'] = dataset['Embarked'].fillna(embarked_mode)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "__init__() got an unexpected keyword argument 'size'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mセル19 を /Users/taichinakabeppu/Desktop/GitHub/documents/src/01_01.ipynb\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m FacetGrid \u001b[39m=\u001b[39m sns\u001b[39m.\u001b[39;49mFacetGrid(df_train, row\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mEmbarked\u001b[39;49m\u001b[39m'\u001b[39;49m, size\u001b[39m=\u001b[39;49m\u001b[39m4.5\u001b[39;49m, aspect\u001b[39m=\u001b[39;49m\u001b[39m1.6\u001b[39;49m)\n\u001b[1;32m 2\u001b[0m FacetGrid\u001b[39m.\u001b[39mmap(sns\u001b[39m.\u001b[39mpointplot, \u001b[39m'\u001b[39m\u001b[39mPclass\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mSurvived\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mSex\u001b[39m\u001b[39m'\u001b[39m, order\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, hue_order\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m )\n\u001b[1;32m 3\u001b[0m FacetGrid\u001b[39m.\u001b[39madd_legend()\n", "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'size'" ] } ], "source": [ "FacetGrid = sns.FacetGrid(df_train, row='Embarked', size=4.5, aspect=1.6)\n", "FacetGrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', order=None, hue_order=None )\n", "FacetGrid.add_legend();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot( x='Survived', data=df_train, hue='Embarked', palette='Set1');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data = [df_train, df_test]\n", "for dataset in data:\n", " dataset['relatives'] = dataset['SibSp'] + dataset['Parch']\n", " dataset.loc[dataset['relatives'] > 0, 'travelled_alone'] = 'No'\n", " dataset.loc[dataset['relatives'] == 0, 'travelled_alone'] = 'Yes'\n", "axes = sns.factorplot('relatives','Survived', \n", " data=df_train, aspect = 2.5, );" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedrelativestravelled_alone
0103Braund, Mr. Owen Harrismale2210A/5 211717.2500NaNS1No
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female3810PC 1759971.2833C85C1No
2313Heikkinen, Miss. Lainafemale2600STON/O2. 31012827.9250NaNS0Yes
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female351011380353.1000C123S1No
4503Allen, Mr. William Henrymale35003734508.0500NaNS0Yes
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", "2 Heikkinen, Miss. Laina female 26 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", "4 Allen, Mr. William Henry male 35 0 \n", "\n", " Parch Ticket Fare Cabin Embarked relatives travelled_alone \n", "0 0 A/5 21171 7.2500 NaN S 1 No \n", "1 0 PC 17599 71.2833 C85 C 1 No \n", "2 0 STON/O2. 3101282 7.9250 NaN S 0 Yes \n", "3 0 113803 53.1000 C123 S 1 No \n", "4 0 373450 8.0500 NaN S 0 Yes " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Sex=male
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Assad Alexander", "male", 0, "2625", 8.5167, null, "C", 1, "No" ], [ 805, 1, 3, "Hedman, Mr. Oskar Arvid", "male", 27, "347089", 6.975, null, "S", 0, "Yes" ], [ 806, 0, 3, "Johansson, Mr. Karl Johan", "male", 31, "347063", 7.775, null, "S", 0, "Yes" ], [ 807, 0, 1, "Andrews, Mr. Thomas Jr", "male", 39, "112050", 0, "A36", "S", 0, "Yes" ], [ 809, 0, 2, "Meyer, Mr. August", "male", 39, "248723", 13, null, "S", 0, "Yes" ], [ 811, 0, 3, "Alexander, Mr. William", "male", 26, "3474", 7.8875, null, "S", 0, "Yes" ], [ 812, 0, 3, "Lester, Mr. James", "male", 39, "A/4 48871", 24.15, null, "S", 0, "Yes" ], [ 813, 0, 2, "Slemen, Mr. Richard James", "male", 35, "28206", 10.5, null, "S", 0, "Yes" ], [ 815, 0, 3, "Tomlin, Mr. Ernest Portage", "male", 30, "364499", 8.05, null, "S", 0, "Yes" ], [ 816, 0, 1, "Fry, Mr. Richard", "male", 32, "112058", 0, "B102", "S", 0, "Yes" ], [ 818, 0, 2, "Mallet, Mr. Albert", "male", 31, "S.C./PARIS 2079", 37.0042, null, "C", 2, "No" ], [ 819, 0, 3, "Holm, Mr. John Fredrik Alexander", "male", 43, "C 7075", 6.45, null, "S", 0, "Yes" ], [ 820, 0, 3, "Skoog, Master. 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"rgb(17,17,17)", "lakecolor": "rgb(17,17,17)", "landcolor": "rgb(17,17,17)", "showlakes": true, "showland": true, "subunitcolor": "#506784" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "dark" }, "paper_bgcolor": "rgb(17,17,17)", "plot_bgcolor": "rgb(17,17,17)", "polar": { "angularaxis": { "gridcolor": "#506784", "linecolor": "#506784", "ticks": "" }, "bgcolor": "rgb(17,17,17)", "radialaxis": { "gridcolor": "#506784", "linecolor": "#506784", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "rgb(17,17,17)", "gridcolor": "#506784", "gridwidth": 2, "linecolor": "#506784", "showbackground": true, "ticks": "", "zerolinecolor": "#C8D4E3" }, "yaxis": { "backgroundcolor": "rgb(17,17,17)", "gridcolor": "#506784", "gridwidth": 2, "linecolor": "#506784", "showbackground": true, "ticks": "", "zerolinecolor": "#C8D4E3" }, "zaxis": { "backgroundcolor": "rgb(17,17,17)", "gridcolor": "#506784", "gridwidth": 2, "linecolor": "#506784", "showbackground": true, "ticks": "", "zerolinecolor": "#C8D4E3" } }, "shapedefaults": { "line": { "color": "#f2f5fa" } }, "sliderdefaults": { "bgcolor": "#C8D4E3", "bordercolor": "rgb(17,17,17)", "borderwidth": 1, "tickwidth": 0 }, "ternary": { "aaxis": { "gridcolor": "#506784", "linecolor": "#506784", "ticks": "" }, "baxis": { "gridcolor": "#506784", "linecolor": "#506784", "ticks": "" }, "bgcolor": "rgb(17,17,17)", "caxis": { "gridcolor": "#506784", "linecolor": "#506784", "ticks": "" } }, "title": { "x": 0.05 }, "updatemenudefaults": { "bgcolor": "#506784", "borderwidth": 0 }, "xaxis": { "automargin": true, "gridcolor": "#283442", "linecolor": "#506784", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#283442", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#283442", "linecolor": "#506784", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#283442", "zerolinewidth": 2 } } }, "title": { "text": "Survived or Died?" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "PassengerId" }, "type": "log" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Survived" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for template in ['plotly_dark']:\n", " fig = px.scatter(df_gender,\n", " x='PassengerId', y='Survived', color='Survived',\n", " log_x=True, size_max=20,\n", " template=template, title=\"Survived or Died?\")\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![plot_03](image/newplot_03.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 前処理" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 連続変数 (Numeric)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 連続値のみ取得\n", "def create_numeric(input_df):\n", " use_cols = [\n", " 'Pclass',\n", " 'Age',\n", " 'SibSp',\n", " 'Parch'\n", " ]\n", "\n", " return input_df[use_cols].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### カテゴリ系のカラム" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### CountEncoding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "[1, 1, 1, 2]->[3, 3, 3, 1]\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_count_encoding(input_df):\n", "\n", " use_columns = [\n", " 'Sex',\n", " 'Embarked'\n", " ]\n", "\n", " out_df = pd.DataFrame()\n", " for column in use_columns:\n", " # df_train を基盤とする\n", " vc = df_train[column].value_counts()\n", " out_df[column] = input_df[column].map(vc)\n", "\n", " return out_df.add_prefix('CE_')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### OneHotEncoding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "['a', 'b', 'a'] -> \n", "\n", "[\n", " [1, 0],\n", " [0, 1],\n", " [1, 0]\n", "]\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_one_hot_encoding(input_df):\n", "\n", " use_columns = [\n", " 'Sex',\n", " 'Embarked'\n", " ]\n", "\n", " out_df = pd.DataFrame()\n", " for column in use_columns:\n", "\n", " vc = df_train[column].value_counts()\n", " # vc = vc[vc > 20] # カテゴリ数が多い場合に使用\n", " cat = pd.Categorical(input_df[column], categories=vc.index)\n", " # このタイミングで one-hot 化\n", " out_i = pd.get_dummies(cat)\n", " # column が Catgory 型として認識されているので list にして解除する (こうしないと concat でエラーになる)\n", " out_i.columns = out_i.columns.tolist()\n", " out_i = out_i.add_prefix(f'{column}=')\n", " out_df = pd.concat([out_df, out_i], axis=1)\n", " return out_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 前処理（特徴量エンジニアリング）を関数化" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tqdm import tqdm\n", "\n", "def to_feature(input_df):\n", "\n", " processors = [\n", " create_numeric,\n", " create_count_encoding,\n", " create_one_hot_encoding\n", " ]\n", "\n", " out_df = pd.DataFrame()\n", "\n", " for func in tqdm(processors, total=len(processors)):\n", " _df = func(input_df)\n", "\n", " # データ数が一致しているか確認（ずれている場合　func の実装がおかしいことがわかる）\n", " assert len(_df) == len(input_df), func.__name__\n", " out_df = pd.concat([out_df, _df], axis=1)\n", "\n", " return out_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 3/3 [00:00<00:00, 21.97it/s]\n", "100%|██████████| 3/3 [00:00<00:00, 48.32it/s]\n" ] } ], "source": [ "df_train_feature = to_feature(df_train)\n", "df_test_feature = to_feature(df_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model : Classification" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import lightgbm as lgbm\n", "from sklearn.metrics import roc_auc_score\n", "\n", "def fit_lgbm(X,\n", " y,\n", " cv,\n", " params: dict=None,\n", " verbose: int=500):\n", "\n", " if params is None:\n", " params = {}\n", "\n", " models = []\n", " n_records = len(X)\n", " n_labels = len(np.unique(y))\n", " oof_pred = np.zeros((n_records, n_labels), dtype=np.float32)\n", "\n", " for i, (idx_train, idx_valid) in enumerate(cv):\n", " x_train, y_train = X[idx_train], y[idx_train]\n", " x_valid, y_valid = X[idx_valid], y[idx_valid]\n", "\n", " clf = lgbm.LGBMClassifier(**params)\n", "\n", " clf.fit(x_train, y_train,\n", " eval_set=[(x_valid, y_valid)],\n", " early_stopping_rounds=100,\n", " verbose=verbose)\n", "\n", " pred_i = clf.predict_proba(x_valid)\n", " oof_pred[idx_valid] = pred_i\n", " models.append(clf)\n", " y_pred_label = np.argmax(pred_i, axis=1)\n", " score = roc_auc_score(y_valid, y_pred_label)\n", " print(f'Fold {i} ROC-AUC: {score:.4f}')\n", "\n", " oof_label = np.argmax(oof_pred, axis=1)\n", " score = roc_auc_score(y, oof_label)\n", " print('-' * 50)\n", " print(f'FINISHED | Whole ROC-AUC: {score:.4f}')\n", " return oof_pred, models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = {\n", " 'objective': 'binary',\n", " 'boosting': 'gbdt',\n", " 'metric': 'binary_logloss',\n", " 'learning_rate': .1,\n", " 'reg_lambda': 1.,\n", " 'reg_alpha': .1,\n", " 'max_depth': 5,\n", " 'n_estimators': 10000,\n", " 'colsample_bytree': .5,\n", " 'min_child_samples': 10,\n", " 'subsample_freq': 3,\n", " 'subsample': .9,\n", " 'importance_type': 'gain',\n", " 'random_state': 71,\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Warning] boosting is set=gbdt, boosting_type=gbdt will be ignored. Current value: boosting=gbdt\n", "Training until validation scores don't improve for 100 rounds\n", "Early stopping, best iteration is:\n", "[52]\tvalid_0's binary_logloss: 0.374653\n", "Fold 0 ROC-AUC: 0.7890\n", "[LightGBM] [Warning] boosting is set=gbdt, boosting_type=gbdt will be ignored. Current value: boosting=gbdt\n", "Training until validation scores don't improve for 100 rounds\n", "Early stopping, best iteration is:\n", "[47]\tvalid_0's binary_logloss: 0.453736\n", "Fold 1 ROC-AUC: 0.7874\n", "[LightGBM] [Warning] boosting is set=gbdt, boosting_type=gbdt will be ignored. Current value: boosting=gbdt\n", "Training until validation scores don't improve for 100 rounds\n", "Early stopping, best iteration is:\n", "[68]\tvalid_0's binary_logloss: 0.422482\n", "Fold 2 ROC-AUC: 0.8060\n", "[LightGBM] [Warning] boosting is set=gbdt, boosting_type=gbdt will be ignored. Current value: boosting=gbdt\n", "Training until validation scores don't improve for 100 rounds\n", "Early stopping, best iteration is:\n", "[24]\tvalid_0's binary_logloss: 0.468949\n", "Fold 3 ROC-AUC: 0.7511\n", "[LightGBM] [Warning] boosting is set=gbdt, boosting_type=gbdt will be ignored. Current value: boosting=gbdt\n", "Training until validation scores don't improve for 100 rounds\n", "Early stopping, best iteration is:\n", "[70]\tvalid_0's binary_logloss: 0.409224\n", "Fold 4 ROC-AUC: 0.7927\n", "--------------------------------------------------\n", "FINISHED | Whole ROC-AUC: 0.7847\n" ] } ], "source": [ "from sklearn.model_selection import KFold\n", "\n", "fold = KFold(n_splits=5, shuffle=True, random_state=71)\n", "y = df_train['Survived']\n", "cv = list(fold.split(df_train_feature, y))\n", "\n", "oof, models = fit_lgbm(df_train_feature.values, y, cv, params=params, verbose=500)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def visualize_importance(models, df_train_feature):\n", " \"\"\"lightGBM の model 配列の feature importance を plot する\n", " CVごとのブレを boxen plot として表現.\n", "\n", " args:\n", " models:\n", " List of lightGBM models\n", " feat_train_df:\n", " 学習時に使った DataFrame\n", " \"\"\"\n", " feature_importance_df = pd.DataFrame()\n", " for i, model in enumerate(models):\n", " _df = pd.DataFrame()\n", " _df['feature_importance'] = model.feature_importances_\n", " _df['column'] = df_train_feature.columns\n", " _df['fold'] = i + 1\n", " feature_importance_df = pd.concat([feature_importance_df, _df],\n", " axis=0, ignore_index=True)\n", "\n", " order = feature_importance_df.groupby('column')\\\n", " .sum()[['feature_importance']]\\\n", " .sort_values('feature_importance', ascending=False).index[:50]\n", "\n", " fig, ax = plt.subplots(figsize=(8, max(6, len(order) * .25)))\n", " sns.boxenplot(data=feature_importance_df,\n", " x='feature_importance',\n", " y='column',\n", " order=order,\n", " ax=ax,\n", " palette='viridis',\n", " orient='h')\n", " ax.tick_params(axis='x', rotation=90)\n", " ax.set_title('Importance')\n", " ax.grid()\n", " fig.tight_layout()\n", " return fig, ax" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = visualize_importance(models, df_train_feature)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model : Regression" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import fetch_california_housing\n", "data = fetch_california_housing()\n", "df = pd.DataFrame(data['data'], columns=data['feature_names'])\n", "y = data['target']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(14447, 6193, 14447, 6193)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import train_test_split\n", "df_train, df_test, y_train, y_test = train_test_split(df, y, train_size=0.7, random_state=0)\n", "len(df_train), len(df_test), len(y_train), len(y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import lightgbm as lgbm\n", "from sklearn.metrics import mean_squared_error\n", "\n", "def fit_lgbm(X,\n", " y,\n", " cv,\n", " params: dict=None,\n", " verbose: int=500):\n", "\n", " if params is None:\n", " params = {}\n", "\n", " models = []\n", " oof_pred = np.zeros_like(y, dtype=np.float)\n", "\n", " for i, (idx_train, idx_valid) in enumerate(cv):\n", " x_train, y_train = X[idx_train], y[idx_train]\n", " x_valid, y_valid = X[idx_valid], y[idx_valid]\n", "\n", " clf = lgbm.LGBMRegressor(**params)\n", "\n", " clf.fit(x_train, y_train,\n", " eval_set=[(x_valid, y_valid)],\n", " early_stopping_rounds=100,\n", " verbose=verbose)\n", "\n", " pred_i = clf.predict(x_valid)\n", " oof_pred[idx_valid] = pred_i\n", " models.append(clf)\n", " print(f'Fold {i} RMSE: {mean_squared_error(y_valid, pred_i) ** .5:.4f}')\n", "\n", " score = mean_squared_error(y, oof_pred) ** .5\n", " print('-' * 50)\n", " print('FINISHED | Whole RMSE: {:.4f}'.format(score))\n", " return oof_pred, models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = {\n", " 'objective': 'rmse',\n", " 'learning_rate': .1,\n", " 'reg_lambda': 1.,\n", " 'reg_alpha': .1,\n", " 'max_depth': 5,\n", " 'n_estimators': 10000,\n", " 'colsample_bytree': .5,\n", " 'min_child_samples': 10,\n", " 'subsample_freq': 3,\n", " 'subsample': .9,\n", " 'importance_type': 'gain',\n", " 'random_state': 71,\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training until validation scores don't improve for 100 rounds\n", "[500]\tvalid_0's rmse: 0.463818\n", "[1000]\tvalid_0's rmse: 0.46033\n", "Early stopping, best iteration is:\n", "[984]\tvalid_0's rmse: 0.459897\n", "Fold 0 RMSE: 0.4599\n", "Training until validation scores don't improve for 100 rounds\n", "[500]\tvalid_0's rmse: 0.438333\n", "Early stopping, best iteration is:\n", "[584]\tvalid_0's rmse: 0.437437\n", "Fold 1 RMSE: 0.4374\n", "Training until validation scores don't improve for 100 rounds\n", "Early stopping, best iteration is:\n", "[373]\tvalid_0's rmse: 0.46471\n", "Fold 2 RMSE: 0.4647\n", "Training until validation scores don't improve for 100 rounds\n", "[500]\tvalid_0's rmse: 0.500634\n", "Early stopping, best iteration is:\n", "[498]\tvalid_0's rmse: 0.500504\n", "Fold 3 RMSE: 0.5005\n", "Training until validation scores don't improve for 100 rounds\n", "[500]\tvalid_0's rmse: 0.449492\n", "Early stopping, best iteration is:\n", "[552]\tvalid_0's rmse: 0.449004\n", "Fold 4 RMSE: 0.4490\n", "--------------------------------------------------\n", "FINISHED | Whole RMSE: 0.4628\n" ] } ], "source": [ "from sklearn.model_selection import KFold\n", "\n", "fold = KFold(n_splits=5, shuffle=True, random_state=71)\n", "cv = list(fold.split(df_train, y_train)) # もともとが generator なため明示的に list に変換する\n", "\n", "oof, models = fit_lgbm(df_train.values, y_train, cv, params=params, verbose=500)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = visualize_importance(models, df_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 推論\n", "pred = np.array([model.predict(df_test.values) for model in models])\n", "pred = np.mean(pred, axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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