diff --git a/机器学习竞赛实战_优胜解决方案/游戏销售数据_特征常用构建方法/.ipynb_checkpoints/游戏销售数据-常用特征构造方法-checkpoint.ipynb b/机器学习竞赛实战_优胜解决方案/游戏销售数据_特征常用构建方法/.ipynb_checkpoints/游戏销售数据-常用特征构造方法-checkpoint.ipynb deleted file mode 100644 index 1dc6681..0000000 --- a/机器学习竞赛实战_优胜解决方案/游戏销售数据_特征常用构建方法/.ipynb_checkpoints/游戏销售数据-常用特征构造方法-checkpoint.ipynb +++ /dev/null @@ -1,2188 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 离散值处理" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import warnings # 忽略普通警告，不打印太多东西\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NamePlatformYearGenrePublisher
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2Mario Kart WiiWii2008.0RacingNintendo
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5TetrisGB1989.0PuzzleNintendo
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" - ], - "text/plain": [ - " Name Platform Year Genre Publisher\n", - "1 Super Mario Bros. NES 1985.0 Platform Nintendo\n", - "2 Mario Kart Wii Wii 2008.0 Racing Nintendo\n", - "3 Wii Sports Resort Wii 2009.0 Sports Nintendo\n", - "4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo\n", - "5 Tetris GB 1989.0 Puzzle Nintendo\n", - "6 New Super Mario Bros. DS 2006.0 Platform Nintendo" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vg_df = pd.read_csv('data/vgsales.csv', encoding='ISO-8859-1')\n", - "vg_df[['Name', 'Platform', 'Year', 'Genre', 'Publisher']].iloc[1:7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "机器无法识别字符串类型数据，需要做处理" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Action', 'Adventure', 'Fighting', 'Misc', 'Platform', 'Puzzle',\n", - " 'Racing', 'Role-Playing', 'Shooter', 'Simulation', 'Sports',\n", - " 'Strategy'], dtype=object)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genres = np.unique(vg_df['Genre'])\n", - "genres # 不同的字符串并不多" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## LabelEncoder" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 'Action',\n", - " 1: 'Adventure',\n", - " 2: 'Fighting',\n", - " 3: 'Misc',\n", - " 4: 'Platform',\n", - " 5: 'Puzzle',\n", - " 6: 'Racing',\n", - " 7: 'Role-Playing',\n", - " 8: 'Shooter',\n", - " 9: 'Simulation',\n", - " 10: 'Sports',\n", - " 11: 'Strategy'}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.preprocessing import LabelEncoder\n", - "\n", - "gle = LabelEncoder() # 实例化\n", - "genre_labels = gle.fit_transform(vg_df['Genre']) # 转换需要离散值的一列\n", - "genre_mappings = {index: label for index, label in enumerate(gle.classes_)}\n", - "genre_mappings # 映射成数值" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NamePlatformYearGenreGenreLabel
1Super Mario Bros.NES1985.0Platform4
2Mario Kart WiiWii2008.0Racing6
3Wii Sports ResortWii2009.0Sports10
4Pokemon Red/Pokemon BlueGB1996.0Role-Playing7
5TetrisGB1989.0Puzzle5
6New Super Mario Bros.DS2006.0Platform4
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" - ], - "text/plain": [ - " Name Platform Year Genre GenreLabel\n", - "1 Super Mario Bros. NES 1985.0 Platform 4\n", - "2 Mario Kart Wii Wii 2008.0 Racing 6\n", - "3 Wii Sports Resort Wii 2009.0 Sports 10\n", - "4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing 7\n", - "5 Tetris GB 1989.0 Puzzle 5\n", - "6 New Super Mario Bros. DS 2006.0 Platform 4" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vg_df['GenreLabel'] = genre_labels # 赋值到一列\n", - "vg_df[['Name', 'Platform', 'Year', 'Genre', 'GenreLabel']].iloc[1:7]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Map\n", - "自己建一个字典" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Action': 0,\n", - " 'Adventure': 1,\n", - " 'Fighting': 2,\n", - " 'Misc': 3,\n", - " 'Platform': 4,\n", - " 'Puzzle': 5,\n", - " 'Racing': 6,\n", - " 'Role-Playing': 7,\n", - " 'Shooter': 8,\n", - " 'Simulation': 9,\n", - " 'Sports': 10,\n", - " 'Strategy': 11}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gen_ord_map = {label:index for index, label in enumerate(gle.classes_)}\n", - "gen_ord_map" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameGenreGenreLabelGenreMap
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" - ], - "text/plain": [ - " Name Genre GenreLabel GenreMap\n", - "1 Super Mario Bros. Platform 4 4\n", - "2 Mario Kart Wii Racing 6 6\n", - "3 Wii Sports Resort Sports 10 10\n", - "4 Pokemon Red/Pokemon Blue Role-Playing 7 7\n", - "5 Tetris Puzzle 5 5\n", - "6 New Super Mario Bros. Platform 4 4" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vg_df['GenreMap'] = vg_df['Genre'].map(gen_ord_map)\n", - "vg_df[['Name', 'Genre', 'GenreLabel', 'GenreMap']].iloc[1:7] # 结果呈现我们设置的map" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## One-Hot Encoder\n", - "对于离散型特征，基于树的方法是不需要使用one-hot编码的，例如随机森林等。基于距离的模型，都是要使用one-hot编码，例如神经网络等。" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0., 0., 0., ..., 0., 1., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]])" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.preprocessing import OneHotEncoder\n", - "# 获取onehot后的结果，将字符串变成多列的0/1值，有则为1，无则为0\n", - "gen_ohe = OneHotEncoder()\n", - "gen_feature_arr = gen_ohe.fit_transform(vg_df[['GenreLabel']]).toarray()\n", - "gen_feature_arr" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameGenre
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" - ], - "text/plain": [ - " Name Genre\n", - "0 Wii Sports Sports\n", - "1 Super Mario Bros. Platform\n", - "2 Mario Kart Wii Racing\n", - "3 Wii Sports Resort Sports\n", - "4 Pokemon Red/Pokemon Blue Role-Playing" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 拿出两列原本的数据，实际场景中是全部数据合并，这里是为了查看方便\n", - "vg_df_2 = vg_df[['Name', 'Genre']]\n", - "vg_df_2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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3Wii Sports ResortSports0.00.00.00.00.00.00.00.00.00.01.00.0
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" - ], - "text/plain": [ - " Name Genre Action Adventure Fighting Misc \\\n", - "0 Wii Sports Sports 0.0 0.0 0.0 0.0 \n", - "1 Super Mario Bros. Platform 0.0 0.0 0.0 0.0 \n", - "2 Mario Kart Wii Racing 0.0 0.0 0.0 0.0 \n", - "3 Wii Sports Resort Sports 0.0 0.0 0.0 0.0 \n", - "4 Pokemon Red/Pokemon Blue Role-Playing 0.0 0.0 0.0 0.0 \n", - "\n", - " Platform Puzzle Racing Role-Playing Shooter Simulation Sports \\\n", - "0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "1 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", - "4 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", - "\n", - " Strategy \n", - "0 0.0 \n", - "1 0.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 " - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vg_df_ohe = pd.concat([vg_df_2,gen_features],axis=1) # 两个数据合并\n", - "vg_df_ohe.head() # 可以看到Platform列第二行为1，对应着Genre列第二行是Platform字符串" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get Dummy\n", - "更加实用的onehot" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(16598, 13)\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Name Genre Adventure Fighting Misc \\\n", - "0 Wii Sports Sports 0 0 0 \n", - "1 Super Mario Bros. Platform 0 0 0 \n", - "2 Mario Kart Wii Racing 0 0 0 \n", - "3 Wii Sports Resort Sports 0 0 0 \n", - "4 Pokemon Red/Pokemon Blue Role-Playing 0 0 0 \n", - "\n", - " Platform Puzzle Racing Role-Playing Shooter Simulation Sports \\\n", - "0 0 0 0 0 0 0 1 \n", - "1 1 0 0 0 0 0 0 \n", - "2 0 0 1 0 0 0 0 \n", - "3 0 0 0 0 0 0 1 \n", - "4 0 0 0 1 0 0 0 \n", - "\n", - " Strategy \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 " - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gen_dummy_features = pd.get_dummies(vg_df['Genre'],drop_first=True) # drop_first=True删掉全为0的列\n", - "dummy_df = pd.concat([vg_df[['Name', 'Genre']], gen_dummy_features], axis=1)\n", - "print(dummy_df.shape)\n", - "dummy_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可以看到两句话就解决了我们上面那一长串" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(16598, 14)\n" - ] - }, - { - "data": { - "text/html": [ - "
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NameGenreActionAdventureFightingMiscPlatformPuzzleRacingRole-PlayingShooterSimulationSportsStrategy
0Wii SportsSports000000000010
1Super Mario Bros.Platform000010000000
2Mario Kart WiiRacing000000100000
3Wii Sports ResortSports000000000010
4Pokemon Red/Pokemon BlueRole-Playing000000010000
\n", - "
" - ], - "text/plain": [ - " Name Genre Action Adventure Fighting Misc \\\n", - "0 Wii Sports Sports 0 0 0 0 \n", - "1 Super Mario Bros. Platform 0 0 0 0 \n", - "2 Mario Kart Wii Racing 0 0 0 0 \n", - "3 Wii Sports Resort Sports 0 0 0 0 \n", - "4 Pokemon Red/Pokemon Blue Role-Playing 0 0 0 0 \n", - "\n", - " Platform Puzzle Racing Role-Playing Shooter Simulation Sports \\\n", - "0 0 0 0 0 0 0 1 \n", - "1 1 0 0 0 0 0 0 \n", - "2 0 0 1 0 0 0 0 \n", - "3 0 0 0 0 0 0 1 \n", - "4 0 0 0 1 0 0 0 \n", - "\n", - " Strategy \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 " - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gen_dummy_features = pd.get_dummies(vg_df['Genre']) # 和上面相比少了drop_first=True，一般用这种\n", - "dummy_df_true = pd.concat([vg_df[['Name', 'Genre']], gen_dummy_features], axis=1)\n", - "print(dummy_df_true.shape)\n", - "dummy_df_true.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 二值特征化" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
NameYear
0Wii Sports2006.0
1Super Mario Bros.1985.0
2Mario Kart Wii2008.0
3Wii Sports Resort2009.0
4Pokemon Red/Pokemon Blue1996.0
\n", - "
" - ], - "text/plain": [ - " Name Year\n", - "0 Wii Sports 2006.0\n", - "1 Super Mario Bros. 1985.0\n", - "2 Mario Kart Wii 2008.0\n", - "3 Wii Sports Resort 2009.0\n", - "4 Pokemon Red/Pokemon Blue 1996.0" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vg_year_df = vg_df[['Name', 'Year']]\n", - "vg_year_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "我们把2000年以上的归类为1，其它归类为0" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
NameYearYear_tow
0Wii Sports2006.01
1Super Mario Bros.1985.00
2Mario Kart Wii2008.01
3Wii Sports Resort2009.01
4Pokemon Red/Pokemon Blue1996.00
\n", - "
" - ], - "text/plain": [ - " Name Year Year_tow\n", - "0 Wii Sports 2006.0 1\n", - "1 Super Mario Bros. 1985.0 0\n", - "2 Mario Kart Wii 2008.0 1\n", - "3 Wii Sports Resort 2009.0 1\n", - "4 Pokemon Red/Pokemon Blue 1996.0 0" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vg_year_df['Year_tow'] = np.where(vg_year_df['Year'] >= 2000, 1, 0)\n", - "vg_year_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
NameYearYear_towbn_year
0Wii Sports2006.011.0
1Super Mario Bros.1985.000.0
2Mario Kart Wii2008.011.0
3Wii Sports Resort2009.011.0
4Pokemon Red/Pokemon Blue1996.000.0
\n", - "
" - ], - "text/plain": [ - " Name Year Year_tow bn_year\n", - "0 Wii Sports 2006.0 1 1.0\n", - "1 Super Mario Bros. 1985.0 0 0.0\n", - "2 Mario Kart Wii 2008.0 1 1.0\n", - "3 Wii Sports Resort 2009.0 1 1.0\n", - "4 Pokemon Red/Pokemon Blue 1996.0 0 0.0" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.preprocessing import Binarizer\n", - "# sklearn中的方法\n", - "bn = Binarizer(threshold=2000) # 大于2000我1，小于为0\n", - "vg_year_df['Year']=vg_year_df['Year'].fillna(0) # 数据中有Nan值，需要补0，否则无法二分\n", - "bn_year = bn.transform([vg_year_df['Year']])[0] # 获取转换的值，取第0列\n", - "vg_year_df['bn_year'] = bn_year # 插入数据\n", - "vg_year_df.head() # 结果与手动一致" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 多项式特征\n", - "获得特征的更高维度和互相间关系的项。" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NA_SalesEU_Sales
041.4929.02
129.083.58
215.8512.88
315.7511.01
411.278.89
\n", - "
" - ], - "text/plain": [ - " NA_Sales EU_Sales\n", - "0 41.49 29.02\n", - "1 29.08 3.58\n", - "2 15.85 12.88\n", - "3 15.75 11.01\n", - "4 11.27 8.89" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "polynomial_df = vg_df[['NA_Sales', 'EU_Sales']]\n", - "polynomial_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[4.1490000e+01, 2.9020000e+01, 1.7214201e+03, 1.2040398e+03,\n", - " 8.4216040e+02],\n", - " [2.9080000e+01, 3.5800000e+00, 8.4564640e+02, 1.0410640e+02,\n", - " 1.2816400e+01],\n", - " [1.5850000e+01, 1.2880000e+01, 2.5122250e+02, 2.0414800e+02,\n", - " 1.6589440e+02],\n", - " ...,\n", - " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n", - " 0.0000000e+00],\n", - " [0.0000000e+00, 1.0000000e-02, 0.0000000e+00, 0.0000000e+00,\n", - " 1.0000000e-04],\n", - " [1.0000000e-02, 0.0000000e+00, 1.0000000e-04, 0.0000000e+00,\n", - " 0.0000000e+00]])" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.preprocessing import PolynomialFeatures\n", - "\n", - "# degree二次幂的复杂度\n", - "pf = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)\n", - "res = pf.fit_transform(polynomial_df)\n", - "res" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "以第一行为例：\n", - "
第一列和第二列分别表示原先的第一列和第二列\n", - "
第三列和第五列表示第一列和第二列分别的平方，第四列表示两者的乘积" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
NA_SalesEU_SalesNA_Sales^2NA_Sales*EU_SalesEU_Sales^2
041.4929.021721.42011204.0398842.1604
129.083.58845.6464104.106412.8164
215.8512.88251.2225204.1480165.8944
315.7511.01248.0625173.4075121.2201
411.278.89127.0129100.190379.0321
\n", - "
" - ], - "text/plain": [ - " NA_Sales EU_Sales NA_Sales^2 NA_Sales*EU_Sales EU_Sales^2\n", - "0 41.49 29.02 1721.4201 1204.0398 842.1604\n", - "1 29.08 3.58 845.6464 104.1064 12.8164\n", - "2 15.85 12.88 251.2225 204.1480 165.8944\n", - "3 15.75 11.01 248.0625 173.4075 121.2201\n", - "4 11.27 8.89 127.0129 100.1903 79.0321" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "intr_features = pd.DataFrame(res, columns=['NA_Sales',\n", - " 'EU_Sales',\n", - " 'NA_Sales^2',\n", - " 'NA_Sales*EU_Sales',\n", - " 'EU_Sales^2'])\n", - "intr_features.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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RankNamePlatformYearGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_SalesGenreLabelGenreMap
01Wii SportsWii2006.0SportsNintendo41.4929.023.778.4682.741010
12Super Mario Bros.NES1985.0PlatformNintendo29.083.586.810.7740.2444
23Mario Kart WiiWii2008.0RacingNintendo15.8512.883.793.3135.8266
34Wii Sports ResortWii2009.0SportsNintendo15.7511.013.282.9633.001010
45Pokemon Red/Pokemon BlueGB1996.0Role-PlayingNintendo11.278.8910.221.0031.3777
\n", - "
" - ], - "text/plain": [ - " Rank Name Platform Year Genre Publisher \\\n", - "0 1 Wii Sports Wii 2006.0 Sports Nintendo \n", - "1 2 Super Mario Bros. NES 1985.0 Platform Nintendo \n", - "2 3 Mario Kart Wii Wii 2008.0 Racing Nintendo \n", - "3 4 Wii Sports Resort Wii 2009.0 Sports Nintendo \n", - "4 5 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo \n", - "\n", - " NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales GenreLabel \\\n", - "0 41.49 29.02 3.77 8.46 82.74 10 \n", - "1 29.08 3.58 6.81 0.77 40.24 4 \n", - "2 15.85 12.88 3.79 3.31 35.82 6 \n", - "3 15.75 11.01 3.28 2.96 33.00 10 \n", - "4 11.27 8.89 10.22 1.00 31.37 7 \n", - "\n", - " GenreMap \n", - "0 10 \n", - "1 4 \n", - "2 6 \n", - "3 10 \n", - "4 7 " - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vg_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Binning 特征\n", - "一般用来处理年龄" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
NameYear
0Wii Sports2006.0
1Super Mario Bros.1985.0
2Mario Kart Wii2008.0
3Wii Sports Resort2009.0
4Pokemon Red/Pokemon Blue1996.0
\n", - "
" - ], - "text/plain": [ - " Name Year\n", - "0 Wii Sports 2006.0\n", - "1 Super Mario Bros. 1985.0\n", - "2 Mario Kart Wii 2008.0\n", - "3 Wii Sports Resort 2009.0\n", - "4 Pokemon Red/Pokemon Blue 1996.0" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bin_df = vg_df[['Name','Year']] # 假设GenreLabel是年龄\n", - "bin_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Frequency')" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import scipy.stats as spstats\n", - "\n", - "fig, ax = plt.subplots()\n", - "bin_df['Year'].hist(color='#A9C5D3')\n", - "ax.set_title('Developer Global_Sales Hostogram', fontsize=12)\n", - "ax.set_xlabel('Global_Sales', fontsize=12)\n", - "ax.set_ylabel('Frequency', fontsize=12)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "这样区间就出来了，我们可以分成多个区间，如1980-1985是一个区间，1986-1990是一个区间" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameYearYear_bin
0Wii Sports2006.05
1Super Mario Bros.1985.01
2Mario Kart Wii2008.06
3Wii Sports Resort2009.06
4Pokemon Red/Pokemon Blue1996.03
5Tetris1989.02
6New Super Mario Bros.2006.05
7Wii Play2006.05
8New Super Mario Bros. Wii2009.06
9Duck Hunt1984.00
\n", - "
" - ], - "text/plain": [ - " Name Year Year_bin\n", - "0 Wii Sports 2006.0 5\n", - "1 Super Mario Bros. 1985.0 1\n", - "2 Mario Kart Wii 2008.0 6\n", - "3 Wii Sports Resort 2009.0 6\n", - "4 Pokemon Red/Pokemon Blue 1996.0 3\n", - "5 Tetris 1989.0 2\n", - "6 New Super Mario Bros. 2006.0 5\n", - "7 Wii Play 2006.0 5\n", - "8 New Super Mario Bros. Wii 2009.0 6\n", - "9 Duck Hunt 1984.0 0" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gle = LabelEncoder() # 实例化\n", - "bin_df['Year_bin'] = pd.cut(bin_df['Year'], 9) # 切分成9组\n", - "bin_df['Year_bin'] = bin_df['Year_bin'].astype(str) # 转换类型为字符串\n", - "bin_year = gle.fit_transform(bin_df['Year_bin']) # 利用LabelEncoder方法变成1-9的数值\n", - "bin_df['Year_bin'] = bin_year # 赋值到新的列\n", - "bin_df.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}